Delaware Journal of
Volume 12 | Issue 4
October 2026
Public Health The official publication of the Delaware Academy of Medicine and Public Health
Chronic Diesase and Poverty
www.delamed.org
Delaware Academy of Medicine & Public Health – OFFICERS – Stephen C. Eppes, M.D. President Jeffrey M. Cole, D.D.S., M.B.A. President Elect Ann Painter, M.S.N., R.N. Treasurer Megan L. Werner, M.D., M.P.H. Secretary Lynn C. Jones, L.F.A.C.H.E. Immediate Past President Katherine Smith, M.D., M.P.H. Executive Director – DIRECTORS – David M. Bercaw, M.D. Peggy M. Geisler, M.A. Jennifer A. Horney, Ph.D., M.P.H., C.P.H. Eric T. Johnson, M.D. Erin M. Kavanaugh, M.D. Joseph Kelly, D.D.S. Omar A. Khan, M.D., M.H.S. Daniel J. Meara, M.D., D.M.D. Jonathan M. Miller, M.D. John P. Piper, M.D. S. John Swanson, M.D. Charmaine Wright, M.D., M.S.H.P. – EMERITUS – Barry S. Kayne, D.D.S. Joseph F. Kestner, Jr., M.D. Brian W. Little, MD, Ph.D. – ADVISORY COUNCIL – Omar Khan, M.D., M.H.S. Peggy M. Geisler, M.A. Co-Chairs
Delaware Journal of
October 2026
Public Health Volume 12 | Issue 4
The official publication of the Delaware Academy of Medicine and Public Health
3 | In This Issue: Chronic Disease and Poverty
Omar A. Khan, M.D., M.H.S.; Katherine Smith, M.D., M.P.H.
4 | From the Guest Editors
Gina Crist, M.S.; Benjamin Golden, M.D..
79 | Building a Sustainable Teen Wellness Curriculum in a Faith-Based Community Setting: A Six-Year Program Evaluation in Wilmington, Delaware Sangeetha D. Nathaniel, M.B.B.S., Ph.D.; Michelle Innasimuthu; Subramanian Gnanaguruparan, M.D.
6 | Obesity and Poverty in Delaware: The Need for Community Level Interventions
84 | A Review of the USDA’s Expanded Food and Nutrition Education Program (EFNEP)
12 | Chronic Disease in Delaware: A Legislative Perspective
86 | Meeting People Where They Are: A Harm Reductionist’s Perspective on Medical Mistrust
16 | Public Data as Public Health Infrastructure
88 | The Household as Dialysis Infrastructure: Family Caregiving, Poverty, and In-Center Hemodialysis in Delaware
Dorothy Dillard, Ph.D.; Gwendoline B. Angalet, Ph.D.
Jordan Myrick, B.S.; Alexandra Wynn, Ph.D.; Yendelela Cuffee, Ph.D., M.P.H.; Jennifer A. Horney, Ph.D., M.P.H. David D. Chen, M.D., M.P.H.; Allison Karpyn, Ph.D.; Dorothy Dillard, Ph.D.; Michael B. Knapp, Ph.D.; Alexandra Wynn, Ph.D.; Janice Barlow, M.P.A.; Rita M. Landgraf, B.S.; Emily Kauffman, M.P.H.; Gwendoline B. Angalet, Ph.D.
24 | From Availability to Meaningful Access: Poverty, Disability, and Public Parks as Infrastructure for Chronic Disease Prevention Obrusnikova, Ph.D., C.A.P.E.; Cora J. Firkin, Ph.D.; Richard R. Suminski, M.P.H., Ph.D.
30 | Factors Associated with Chronic Disease Mortality: Decomposing Mortality Disparities Across County Income Groups Alek Lee; Amy L. Gootee-Ash, Ph.D.; Cynthia Newton, Ph.D, M.P.A.
38 | Achieving HbA1c Reduction in Diabetes Management: A Retrospective Comparison of Glucose Monitoring Methods
Eshita Sharmin, M.D., M.P.H.; Cameron Golden, Pharm.D., B.C.A..C.P; Ejaz Mahmood, M.D.
Diane Oliver, M.P.H., R.D.N., L.D.N.
Emerson Trimble, D.O., M.P.H.
Avi Kapoor
96 | From Awareness to Action: Fall Prevention in Practice
Felicia Cruz, M.S.N., A.P.R.N., A.G.C.N.S.-B.C., MEDSURG-B.C., C.P.A.F.H.; Lisa Fellin, B.S.N., R.N.; L. Margaret Gulledge, O.T.D., O.T.R./L.; Jola Jimoh, D.P.T., M.Sc.
100 | Neighborhood-Level Non-Medical Factors Alone Poorly Predict Proxy PASC Phenotype: Limited Performance of Logistic Regression and XGBoost Models Amirah Ellis-Gilliam, M.P.H.; Felix Rivera-Mariani, Ph.D., F.A.A.A.A.I.; Chuming Chen, Ph.D.; Zughi Zhang, Ph.D.; Cathy Wu, Ph.D.; Jennifer Horney, Ph.D.
109 | On-Ramps and Off -Ramps: Situating Food Is Medicine Within a Continuumof Prevention and Community Care Allison Karpyn, Ph.D.; McKenna Halverson, Ph.D.; Gina Crist, M.S., C.H.E.S.
Katherine Smith, M.D., M.P.H. Executive Director
44 | Global Health Matters Newsletter – July/August 2026
114 | Tackling the Lack of Literacy Regarding OTC Medications in Adolescents
– COUNCIL MEMBERS – Alfred Bacon, M.D. Gerard Gallucci, M.D., M.S.H. Allison Karpyn, Ph.D. Laura Lessard, Ph.D. Melissa K. Melby, Ph.D. Joyce Robert, M.D. William Swiatek, M.A., A.I.C.P.
68 | Evaluation of a Contingency Management Intervention To Treat Substance Use Disorders in a Family Medicine Residency Program: A Retrospective Observational Review Study of Patient Outcomes and Experiences
122 | C ancer Incidence, Healthcare Access, and Support Resource Distribution in Delaware: A Statewide Epidemiological Analysis, 2018–2022
Delaware Journal of Public Health Katherine Smith, M.D., M.P.H. Publisher Omar Khan, M.D., M.H.S. Editor-in-Chief Gina Crist, M.S. Benjamin Golden, M.D. Guest Editors
Suzanne Fields Image Director
ISSN 2639-6378
Kimberly D. Williams, Dr.P.H., M.P.H.; Deanna Gorgei, D.O.; Kendall McDowell, B.S.N., R.N., P.M.H.-B.C.; Jordan Jacobs, D.O.; James M. LaRosa, M.D.
Anika Bhat; Katherine Smith, M.D., M.P.H.
Ryan Stephens, B.S.; Katherine Smith, M.D., M.P.H.
132 | Resources 133 | Index of Advertisers 134 | Delaware Journal of Public Health Submission Guidelines
The Delaware Journal of Public Health (DJPH), first published in 2015, is the official journal of the Delaware Academy of Medicine and Public Health (Academy). Submissions: Contributions of original unpublished research, social science analysis, scholarly essays, critical commentaries, departments, and letters to the editor are welcome. Questions? Contact managingeditor@djph.org . Advertising: Please contact ksmith@delamed.org for other advertising opportunities. Ask about special exhibit packages and sponsorships. Acceptance of advertising by the Journal does not imply endorsement of products. Copyright © 2026 by the Delaware Academy of Medicine and Public Health. Opinions expressed by authors of articles summarized, quoted, or published in full within the DJPH represent only the opinions of those authors and do not necessarily reflect the official policy of the Academy, the DJPH, or the institution with which the authors are affiliated. Any report, article, or paper prepared by employees of the U.S. government as part of their official duties is, under Copyright Act, a “work of United States Government” for which copyright protection under Title 17 of the U.S. Code is not available. However, the journal format is copyrighted and pages are not be photocopied, except in limited quantities, or posted online, without permission of the Academy/DPHA. Copying done for other than personal or internal reference use-such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale- without the expressed permission of the Academy/DPHA is prohibited. Requests for special permission should be sent to managingeditor@djph.org .
IN T H IS ISSU E Chronic Disease and Poverty According to the Delaware Division of Public Health (DPH), chronic disease costs the State of Delaware billions of dollars every year in health care costs.1 In 2020, chronic diseases were responsible for seven of the top ten leading causes of death in the state, and cardiovascular disease and cancer accounted for nearly half of all deaths statewide. According to the same report, certain populations bear a disproportionate burden of chronic disease; they are often more common, diagnosed later, and result in worse health outcomes in people of color, those with lower incomes, and those living in areas without access to care or resources.1 Delawareans of low-income, with limited access to high-quality education and employment opportunities, healthy food options, and/or access to care have historically faced greater barriers to maintaining healthy lifestyles and preventative care. The articles within this issue of the Delaware Journal of Public Health highlight those stories. Within this issue, Guest Editors Benjamin Golden, MD and Gina Crist, MS have compiled research articles on the intersection of obesity and poverty, factors associated with chronic disease mortality and county income groups, improving diabetes management, and how the household can be utilized as infrastructure for dialysis patients, among others. They have also included commentaries and discussions on the 2026 legislative wins regarding chronic disease in Delaware, how public health data can be used as public health infrastructure, the use of public parks as infrastructure for chronic disease prevention, and how we can build sustainable wellness curricula in community settings. The value of a supportive community can be judged best by how it takes care of its most vulnerable community members. We thank the authors for helping all of us do so through science, evidence, and ethics. As always, we invite you to let us know what you think of the content, and submit your own work at https://djph.org .
REFERENCES 1. Delaware Division of Public Health. (2024, Jun). The burden of chronic disease in Delaware 2024. Delaware Department of Health and Human Services. https://www-test.dhss.delaware.gov/wp-content/uploads/sites/12/dph/pdf/BurdenOfChronicDiseaseInDelaware2024Final.pdf
Omar A. Khan, M.D., M.H.S. Editor-in-Chief, Delaware Journal of Public Health
Doi: 10.32481/djph.2026.10.01
Katherine Smith, M.D., M.P.H. Publisher, Delaware Journal of Public Health
3
F rom the Guest Editors Gina Crist, M.S. Department of Behavioral Health and Nutrition Cooperative Extension, University of Delaware Benjamin Golden, M.D. Emergency Medicine & Family Medicine, ChristianaCare Health System
Chronic disease and poverty are often linked in the literature as variables leading to health care costs, and the need for more upstream approaches to manage disease while addressing vital conditions. However, it is also important to consider each of these issues as independent drivers of health outcomes and as root conditions that shape community needs, public health policies, and programs. As guest editors on this issue, we bring a clinical and community perspective to the intersection of vital conditions and clinical care. We know the importance of understanding how practitioners across public health can continue to understand the interactions between social drivers and health outcomes. While the connections between the two are well studied, continuing to understand how these two operate individually and together can better inform how the field invests and partners within and across sectors to impact the way in which people are able to achieve their most optimal health potential.
COMMUNITY PERSPECTIVE As a community health practitioner, the articles in this issue continue to build the evidence that health is a combination of social drivers and individual behaviors. In the field, we note that the Vital Conditions Framework showcases this perspective, and it is when we work within this framework that we ultimately achieve the most impactful changes within individuals and among the systems in which they interact. Food access continues to be a clear driver of chronic disease prevention and management, and yet, as the articles in this issue show, the opportunity to address many of the policies and community factors that impact our most vulnerable Delawareans. There are many opportunities to engage cross-sector collaboration that identifies communities at greatest risk, and ensures that economic limitations do not impact the likelihood of accessing and consuming nutritious foods. This holds true when looking at chronic disease data affirming individuals in under-resourced communities, and those communities living paycheck to paycheck, and the ALICE population are all impacted by community and clinical linkages that impact health care related to chronic disease management - whether that is transportation challenges, insurance access, housing issues, or balancing financial needs.
4 Delaware Journal of Public Health - October 2026
CLINICAL PERSPECTIVE All clinicians receive extensive training in the effects of chronic, unmanaged medical conditions. The effects of conditions like uncontrolled hypertension, diabetes, smoking, and substance use are all well documented in the medical literature and can often be seen visibly in the patients that we treat. We have developed increasingly sophisticated guidelines, technologies, and medications to treat these illnesses to prevent the ravages that they can bring, yet a disparity remains. Those patients that live in poverty bear the greatest burden of disease. This juxtaposition forces an uncomfortable question with regards to poverty: are we attempting to treat chronic disease while also ignoring one of its most powerful causes? As the articles of this issue suggest, there is a clear understanding that technologic and food access can help with improvement in chronic disease. Even having meaningful access to a public park can be helpful in assuaging some of the effects of chronic disease. Poverty is not just a social construct that affects chronic disease, it is a biological exposure that produces compounding physiological effects over time. As the effects of this exposure deepen, the trust in healthcare is eroded. Ready access to high-quality data allows us to study this phenomenon and mitigate its most harmful effects, though infrastructure for this data must be maintained and supported for research on this topic to occur. As healthcare continues to evolve, it is imperative for clinicians to understand a fundamental fact that our colleagues in public health have known for decades: the most important intervention for a patient may not be the medicine. What the articles in this issue note—as they relate to community needs and interventions to address poverty and chronic disease— is that there is no one-size fits all. There will not be one single solution to an issue this large; however, working together and addressing everything across the continuum, we are continuing to address needs that change environments and systems, and impact an individual’s ability to make healthier choices.
Doi: 10.32481/djph.2026.10.02
HIGHLIGHTS FROM HIGHLIGHTS FROM
The The
October 2026 October 2026 The Nation’s Health headlines
NATION’S HEALTH NATION’S HEALTH A P U B L I C AT I O N O F T H E A M E R I C A N P U B L I C H E A LT H A S S O C I AT I O N
A P U B L I C AT I O N O F T H E A M E R I C A N P U B L I C H E A LT H A S S O C I AT I O N
Online-only news from The Nation’s Health newspaper The Nation’s Health headlines Online-only news from The Nation’s Health newspaper US aging boom outpacing nation’s readiness Teddi Nicolaus US aging boom outpacing nation’s readiness TeddiofNicolaus Lack US regulations on food marketing puts kids at risk
Sophia Meador Lack of US regulations on food marketing puts kids at risk Sophia change Meadorfueling increase in infections from fungal diseases Climate
Mark Barna Climate change fueling increase in infections from fungal diseases Markcommunity Barna Faith partners with scientists to track urban heat in Northern Virginia
Sonali Singh Faith community partners with scientists to track urban heat in Northern Virginia Sonali Singh grow, states split on how to handle kratom sales As poisonings Sophia Meador As poisonings grow, states split on how to handle kratom sales Sophia Meador How voting can be beneficial for your health
How voting canwith be community beneficial for healthjustice APHA partners onyour disability Natalie McGill APHA partners with community on disability justice Natalie McGill APHA 2026 attendees can support immigrant rights through donation campaign
Natalie McGill APHA 2026 attendees can support immigrant rights through donation campaign Natalie McGill APHA Press book opens door to skills needed for fundraising Mark Barna APHA Press book opens door to skills needed for fundraising Mark 2026 Barnain San Antonio offers path to educational growth APHA Sophia Meador APHA 2026 in San Antonio offers path to educational growth Sophia Meador Newsmakers: October 2026 Sophia Meador Newsmakers: October 2026 Sophia Meador Many other articles available when you purchase access Visit https://www.thenationshealth.org/user Many other articles available when you purchase access Visit https://www.thenationshealth.org/user
5
Obesity and Poverty in Delaware: The Need for Community Level Interventions Dorothy Dillard, Ph.D. HER Center CED Core, Delaware State University Gwendoline B. Angalet, Ph.D. GBA Consulting and Community Health Advocacy Mobilization Group
ABSTRACT Objective. The study aimed to examine the community characteristics of vulnerable communities where obesity is one of the top three chronic conditions with the greatest disparity compared to the Delaware statewide obesity rate to demonstrate the need for community specific education and interventions. Methods. The study is a secondary analysis using a subset of the CHAMG research project dataset and includes 47 vulnerable communities. The dataset included official data reported between 2019 and 2022 and abstracted from My Healthy Communities Delaware data portal and the IP3 data portal. Results. The study communities differed notably from Delaware’s general population and from each other. The average poverty rate was greater (21% compared 11%) and the average obesity rate was greater (41% compared to 34%). The minority population in the study communities ranged from 6% to 93% Black and from 2% to 70% Hispanic. The average age ranged from 27 years old to 54 years old. The poverty rate ranged from 4% to 50%. Eleven (23%) of the vulnerable communities with obesity disparity were rural. Depression and heart disease ranked in the top three most disparate chronic conditions in at least half of the study communities and HPB in 36% of the study communities. Fewer community assets were identified in the study communities, including fewer medical professionals, lower rates of HBP management, and lower English proficiency. Conclusions. Vulnerable communities with high rates of obesity differ from Delaware’s general population. They have fewer community assets and greater prevalence of obesity-related chronic conditions. There is notable variation across the study communities, underscoring the need for community specific education and interventions. Public Health Implications. Obesity is a key multi-solver health condition. Because obesity is correlated with other chronic conditions, mental health diagnoses, and well-being indicators, community specific obesity education and interventions will likely decrease obesity rates and improve other health and well-being conditions.
INTRODUCTION In 2013, the AMA designated obesity as a chronic disease. Many hoped this designation would positively impact access to care, advances in treatment, and reduction in stigma.1 Despite the AMA designation, rates of obesity continue to rise. It has more than doubled since 1990, from 19% to 43%.2,3 Worldwide, 1 in 8 persons is obese, representing 16% of adults. In the US, 40% of adults are obese with adults between 40 and 59 years old having the highest prevalence at 46%. Obesity is slightly more prevalent among women (41%) than among men (39%). Almost one tenth of American adults are severely obese, with a higher prevalence among women (13%) compared to men (7%). Although the prevalence of obesity in the US has not changed significantly since its AMA chronic condition designation, severe obesity has increased by about 2%.4 Race and ethnic differences are also notable. In 2022, non-Hispanic Black females have the highest prevalence of obesity, at 57%. The prevalence of obesity among Hispanic females is about 10% lower at 49%. Hispanic males, non-Hispanic White males and females, and non-Hispanic Black males had similar prevalence, about 42%.3 Delaware ranks 34th in adult obesity, meaning that only 16 states have higher obesity rates. About 38% of adult Delawareans are obese, a 10% increase over the previous decade. The prevalence of obesity among adult men and women is similar. However, differences by race and ethnicity 6 Delaware Journal of Public Health - October 2026
are evident with 49% of Black adults but only 35% of White and Hispanic adults being obese. About 45% of Delawareans between 45 and 54 years old are obese. The rate of prevalence of obesity is lowest among 18- to 24-year-olds at 29%.5 In the past decade, many advances have been made in addressing obesity. Research demonstrates that altering eating habits reduces weight and improves other health indicators.6,7 Physical activity has been proven to be a core element of weight management and reduction.8 Multi-prong approaches where diet, physical activity and other interventions are administered in combination, frequently referred to as intensive lifestyle interventions (ILI), have been shown to be the most effective behavioral interventions.9 Clinical research on bariatric surgery has the longest track record, showing a 25% reduction in total body weight sustained long-term.10 The most recent development, anti-obesity medications (AOM), has demonstrated indisputable clinical effectiveness for weight loss, diabetes management, and other chronic health conditions.11 Despite evidence of effectiveness, intervention uptake as well as long term weight loss continue to be issues. Participation in weight loss and management programs is very low, about 5%, and less than 1% of Americans eligible for bariatric surgery receive it.1 An extensive review of weight loss clinical trials and found that about eight weeks after a weight loss intervention, many participants began to gain weight and some experienced regaining all the weight lost.12 Doi: 10.32481/djph.2026.10.03
Several barriers to access and long-term success have been identified, including physician bias as well as weight stigma, cost, and insurance benefits.1,13 Most concerning, however, is the cumulative effect these and other barriers have on limiting the availability of obesity interventions for those who need it most. The correlation between poverty and obesity is well established.14,15 According to the U.S. Department of Health and Human Services, 40% of Americans with incomes less than $25,000 are obese compared to 28% of those with incomes of $150,000 or greater.16 In general, as income increases the prevalence of obesity decreases. This trend is evident in Delaware as well. Among Delawareans with an annual income less than $25,000, 41% are obese; whereas only 34% of Delawareans with an annual income of $150,000 or more are obese.5 In this article we examine the obesity disparity using data collected through a recent needs assessment of chronic conditions in Delaware’s vulnerable communities. We present the rates of obesity as well as the prevalence of related chronic conditions. We also examine the neighborhood assets necessary to address obesity and address the obesity disparity in the context of the 5 As of access and make suggestions for action.17 Finally, we present obesity as a multi-solver issue. We discuss the potential of a multipronged approach across the socioecological framework focusing on community specific obesity education and intervention to improve health and wellbeing outcomes in Delaware’s most vulnerable communities.
METHODS In 2023, the Community Health Advocacy Mobilization Group (CHAMG), a coalition of over 40 community organizations promoting health equity through advocacy and community engagement, led a mixed methods research project to promote health equity through advocacy for data-driven policies and practice changes. CHAMG partnered with Delaware Health and Social Services Division of Public Health Bureau of Health Equity and the Delaware State University Interdisciplinary Health Research Center Community Engagement and Dissemination Core. The original research project aimed to demonstrate how data at the census tract level and dynamic, continuous community engagement could drive the development of solutions, grounded in social determinants and community assets, that were relevant to vulnerable communities with the greatest need of community appropriate and acceptable resources and services.18 The CHAMG research project focused on vulnerable communities, frequently referred to underserved and/or marginalized communities. It used the Social Vulnerability Index (SVI) to select the communities of interest for the research project. The CDC adopted the SVI to identify communities’ level of need. The index is based on 16 social factors, including poverty, and scores 0.0 to 1.0, with 0.0 representing the lowest vulnerability and 1.0 representing the highest. Of Delaware’s 218 census tracts, 52 (24%) had scores of .75 or higher on the SVI. This group of communities comprised the sample for the CHAMG report. The chronic condition data examined in the CHAMG study was abstracted from the Delaware Division of Public Health’s My Healthy Community (MHC) data portal. The MHC data portal provided census tract level data, including SVI scores, demographic and population health indicators as well as
environmental and social determinants of health data.18 The MHC data was a robust, easily searchable, free data source accessible by the public. The MHC portal was sunset at the end of 2025, making the CHAMG research project a vital source of data while DPH transitions to another publicly accessible data portal. The nine chronic health conditions included in the CHAMG study were arthritis, cancer, chronic obstructive pulmonary disease (COPD), depression, diabetes, heart disease, high blood pressure, high cholesterol, and obesity. These chronic health conditions were selected due to their significance established in the health literature. The disparity for each health condition was quantified by calculating the difference between state prevalence and census tract prevalence. Health conditions for which prevalence was higher in the census tract than in the state were identified and ranked in descending order according to the magnitude of the difference. The three conditions exhibiting the greatest disparity were designated as the greatest disparity, second greatest disparity, and third greatest disparity. Every census tract demonstrated at least one chronic health condition with a disparity, although some tracts were assigned only one or two ranked conditions. In instances where two or more health conditions had identical differences between census tract prevalence and state prevalence, they were assigned the same rank. For two of the census tracts, the prevalence for the nine chronic health conditions was not available because the census tract population was too small to report health data publicly.18 The CHANG report also used IP3 source of community assets information. The IP3 ASSESS platform, a digital data tool developed by the Institute for People, Place, and Possibility (IP3), is designed to assist organizations in examining population health, equity, and community requirements. The platform employs strategic frameworks such as the Seven Vital Conditions to convert raw data into practical insights for community development. The CHAMG research project found that the most common chronic condition with the greatest disparity was obesity. Obesity was ranked as one of the top three chronic conditions with the greatest disparity in 47 (94%) of Delaware’s vulnerable communities. It was the chronic condition with the greatest disparity in 39 (78%) of the communities. These findings alone justify a closer examination of obesity in Delaware’s communities most impacted by obesity. Coupling the disparity in obesity prevalence and the disparity in co-occurring chronic conditions, such as heart disease, diabetes, and depression, the need to focus on obesity becomes imperative. This study is a secondary analysis of the chronic condition dataset created and used in the CHAMG research project. The primary aim is to examine the community characteristics of vulnerable communities where obesity is one of the top three chronic conditions with the greatest disparity compared to the Delaware statewide obesity rate. A subset of the CHAMG research project data, including 47 communities, was curated to examine the vulnerable communities where the prevalence of obesity was ranked as one of the top three chronic conditions with the greatest disparity compared to the state prevalence. The dataset included community demographics, community assets, health literacy, and chronic condition rates for obesity, chronic obstructive pulmonary disease, diabetes, depression, heart disease, and high blood pressure. 7
RESULTS As expected, the obesity and poverty rates of the vulnerable community in this study were higher than the state rates. Obesity rates for the study communities ranged from 34% to 52% with an average of 41% compared to Delaware’s rate of 34%. On average, the poverty rate at 21% was twice that of the state average of 11%. The vulnerable communities included in the study differed from the state’s general population by race/ethnicity and age characteristics (Table 1). The race/ethnicity composition of the study communities was 42% Black, 47% White and 16% Hispanic. Comparatively, Delaware’s population is 22% Black, 67% White and 9% Hispanic. The vulnerable communities included in this study, on average, were home to more minorities than the Delaware’s overall population with almost twice as many Black and Hispanic residents and notably fewer White residents. The average age was younger than the state average, 36 years old compared to 41 years old. The differences between study communities are important to note. The poverty rate ranged from 4% to 50%, underscoring that poverty alone does not define community vulnerability. The minority population in the study communities ranged from 6% to 93% Black and from 2% to 70% Hispanic. The average age ranged from 27 years old to 54 years old. Eleven (23%) of the vulnerable communities with obesity disparity are rural. Not surprisingly, all are in Kent and Sussex Counties. The rural communities included in the sample include five (83%) of Kent County’s six vulnerable communities and six (55%) of the eleven Sussex County vulnerable communities.
Table 2 presents the number of communities in the study sample which also had notably high rates of other chronic conditions associated with obesity, including COPD, depression, diabetes, heart disease and high blood pressure. Half of the study communities also had high rates of depression and heart disease that ranked in the top three most disparate chronic conditions. In about one third of the study communities, high blood pressure (HPB) was also one of the top three chronic conditions with higher rates of prevalence than the state rate. In about one quarter of the study communities, chronic obstructive pulmonary disease (COPD) and diabetes were one of the top three chronic conditions with higher rates of prevalence than the state rate. Understanding community assets, or the lack thereof, are important in developing community level interventions. For the 47 vulnerable communities included in this study, many of them lacked adequate structural assets key to health and wellbeing. The community asset data was abstracted from the IP3 data portal. Table 3 defines the six community assets using the IP3 definition and presents the state prevalence of each. The state prevalence indicates the percentage of census tracts with adequate community resources. Inadequate data was available for three additional essential community assets: exercise opportunities, food insecurity, and mental health professionals per 100,000 residents. Table 4 shows the number of study communities with fewer assets than the state prevalence by county. Five (11%) of the study communities had lower food access than the state with similar differences across the counties. Almost half (47%) of the study communities had lower rates of primary care visits
Table 1. Delaware Demographic Characteristics Compared to Study Communities Demographic Characteristics Demographic Characteristics
Delaware
Vulnerable Communities with Obesity Disparity
Black
22%
42%
White
67%
47%
Hispanic
9%
16%
Median Age
41 years old
36 years old
Race/Ethnicity
Poverty Rate
11%
21%
Obesity Rate
34%
41%
Table 2. Study Communities and Other Obesity Related Chronic Conditions Obesity Ranking
Number of Vulnerable Communities by Obesity Ranking
Number of Study Communities with Other Obesity Related Chronic Conditions Ranked in Top Three Greatest Disparity COPD
Depression
Diabetes
Heart Disease
HPB
1
39 (83%)
---
7 (15%)
---
---
1 (2%)
2
5 (11%)
3 (6%)
15 (32%)
2 (4%)
6 (13%)
15 (32%)
3
3 (6%)
8 (17%)
3 (6%)
10 (21%)
18 (38%)
1 (2%)
TOTAL
47 (100%)
11 (23%)
25 (53%)
12 (26%)
24 (51%)
17 (36%)
8 Delaware Journal of Public Health - October 2026
Table 3. Delaware Prevalence of Community Assets Community Asset
Definition
State Prevalence
Low Food Access
Percentage of population with low food access, defined as living beyond 1 mile (urban) or 10 miles (rural) of supermarket
42%
Recent Primary Care Visit
Percentage of adults aged 18 years and older who report having been to a doctor for a routine checkup in the past year
75%
Medical Professionals per 1,000 Residents
Number of health diagnosing and treating practitioners per 1,000 population
21.4
HBP Management
Percentage of adults aged 18 years and older with high blood pressure who report taking medicine for high blood pressure
83%
Parks
Percentage of the population living within 0.5 miles of a park
63%
Limited English
Percentage of the population aged 5 years and older who speak English less than “very well”
5%
Table 4. Number and Percentage of Study Communities Lacking Adequate Community Assets County (number of study communities) New Castle (30)
Low Food Access
Recent Primary Medical Professionals Care Visit per 1,000 residents
HBP Management
Parks
Limited English
4 (13%)
18 (60%)
26 (87%)
30 (100%)
---
20 (67%)
Kent (6)
---
1 (17%)
4 (67%)
5 (83%)
6 (100%)
2 (33%)
Sussex (11)
1 (9%)
1 (9%)
9 (82%)
11 (100%)
11 (100%)
10 (91%)
Study Total (47)
5 (11%)
20 (43%)
39 (83%)
46 (98%)
17 (36%)
32 (68%)
compared to the state. This disparity was most evident in the New Castle County vulnerable communities. Most (83%) of the study communities had fewer medical professionals than the state rate with the greatest number of communities in New Castle and Sussex Counties. Across all the study communities, the number of communities with adults aged 18 years and older with high blood pressure who reported taking medicine for high blood pressure was much lower than the state rate. All of the study communities in New Castle and Sussex Counties and 83% of the Kent County study communities had a lower rate of HBP medication compliance than the state rate. About one third of the study communities had a lower prevalence of access to parks than the state. However, there is a clear geographical difference in access to parks. None of the New Castle County vulnerable communities but all of the Kent and Sussex County vulnerable communities had fewer accessible parks compared to the state park accessibility rate. Overall, about two thirds (68%) of study communities had lower rates of English proficiency than the state with notable variation by county. Ten (91%) of the Kent County study communities compared to two thirds of the New Castle County and one third of the Kent County study communities had higher rates of limited English proficiency.
DISCUSSION The secondary analysis of the CHAMG research project dataset demonstrates its viability in examining specific chronic conditions in Delaware’s most vulnerable communities. Our findings clearly show that vulnerable communities with high rates of obesity differ from Delaware’s general population by race/ethnicity, age, and income characteristics. They also differ from the state in terms of community assets. Within the sample, notable differences in community characteristics and assets were also evident, underscoring the need for community specific education and interventions.
As noted in the CHAMG report, creating a census tract level profile allows for community specific interventions.18 Penchansky and Thomas offer a framework to create community specific education and intervention efforts.17 They define access beyond costs and location. The five As of access are defined as: 1) Access - how much a service costs and the ability and willingness of the community member to pay; 2) Availability - the existence of the services in the community to meet the needs of the community members; 3) Accessibility - how easily the community member can get to the service; 4) Accommodation – how well the service meets the preferences and constraints of the community member; and 5) Acceptability - the extent to which the community member and the provider of the service are comfortable and can relate to each other. These elements can be used as a checklist to ensure that education and interventions are community specific. Community level responses to obesity or any other health conditions cannot be developed using only official data.19,20 Without input from community members, critical information about the five As, particularly acceptability, will be limited. The community culture, preferences, understanding of obesity as a chronic condition, and readiness for change will be limited and most likely weaken the effectiveness of the intervention. To this end, CHAMG in partnership with the DSU IHER Center CED Core will use the information produced by this study to engage with residents of vulnerable communities most greatly impacted by obesity to gain better insight in their understanding of obesity as a chronic condition and their suggestions for acceptable community level education and interventions. Lowering obesity rates in Delaware is not solely a community level intervention. As established in the American Diabetes Association (ADA)’s Obesity Association Standards of Care in Overweight and Obesity, it requires changing the conversation and approach in the doctor’s office, in the workplace, in the community, and in the legislature.21 Research has shown that 9
physician bias against overweight and obese patients worsens stigma and reduces intervention uptake.13,22 Education of health care providers needs to include how they view obesity and how they empower their patients with culturally and linguistically appropriate guidance to adopt practices to promote healthy lifestyles.21 The conversation and culture in the workplace need to shift to support employees with obesity to reduce their health risks and have information to make healthier choices, increasing their productivity and overall well-being.21 The community, including grassroots and community-based organizations, neighborhood groups, and coalitions, need to advocate for and implement community-driven education and placebased approaches. Structural investments designed to increase community assets need to be made.22 Policy changes must include community members to ensure that, like educational materials and interventions, are appropriate for and acceptable to the individuals and communities they are designed to help.23,24
PUBLIC HEALTH IMPLICATIONS The findings from this study have two significant public health implications. First, it demonstrated that using community level data to examine obesity in the context of community characteristics, community assets, and other chronic conditions produces community specific profiles that are essential for identifying data-driven responses. However, quantitative data must be coupled with community voice and input to create truly community defined and acceptable interventions. Second, because obesity is correlated with other chronic conditions, mental health diagnoses, and well-being indicators, it is a key multi-solver health condition. If community specific education and interventions are developed, resourced, and administered, it is likely that not only will obesity rates decrease but additional health and well-being concerns will also be alleviated. Addressing multiple, correlated health conditions simultaneously reduces the potential for mixed or contradictory messages and random acts with limited impact. It allows for coordinated and mutually beneficial interventions, such as strengthening community assets that are known to improve multiple health conditions. And it will reduce healthcare costs and increase sustainable improvement over time. Dr. Dillard may be contacted at ddillard@desu.edu.
FINANCIAL DISCLOSURE This study was supported by NIH grant U54MD15959.
REFERENCES 1. Schumacher, L. M., Ard, J., & Sarwer, D. B. (2023). Promise and unrealized potential: 10 years of the American Medical Association classifying obesity as a disease. Frontiers in Public Health, 11, 1205880. 2. Mlynarska, E., Bojdo, K., Bulicz, A., Frankenstein, H., Gąsior, M., Kustosik, N., Rysz, J., & Franczyk, B. (2025). Obesity as a multifactorial chronic disease: Molecular mechanisms, systemic impact, and emerging digital interventions. Current Issues in Molecular Biology, 47(10), 787. Advance online publication. 3. DeCleene, N. K., Kahn, E., Yuan, C. W., Gakidou, E., Mokdad, A. H., Murray, C. J. L., Johnson, C. O., & Roth, G. A. (2026). US State-Level Prevalence of Adult Obesity by Race and Ethnicity From 1990 to 2022 and Forecasted to 2035. Journal of the American Medical Association, 335(11), 975–985. 4. Emmerich, S. D., Fryar, C. D., Stierman, B., & Ogden, C. L. (2024). Obesity and severe obesity prevalence in adults: United States, August 2021–August 2023. NCHS Data Brief, no 508. National Center for Health Statistics., https://doi.org/10.15620/cdc/159281
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5. Delaware Department of Health and Social Services, Division of Public Health. (2022). Delaware Behavioral Risk Factor Survey (BRFS), 2022. State of Delaware. 6. Gill, H., Lankatillake, C., Zafiris, E., & Pillidge, C. (2026). A multifaceted lifestyle program for weight loss in overweight adults: Evidence from a randomised clinical trial. European Journal of Nutrition, 65(1), 22. 7. Kim, J. Y. (2021). Optimal Diet Strategies for Weight Loss and Weight Loss Maintenance. Journal of Obesity & Metabolic Syndrome, 30(1), 20–31. 8. Raiman, L., Amarnani, R., Abdur-Rahman, M., Marshall, A., & Mani-Babu, S. (2023). The role of physical activity in obesity: Let’s actively manage obesity. Clinical Medicine (London, England), 23(4), 311–317. 9. Finn, E. B., Whang, C., Hong, P. H., Costa, S. A., Callahan, E. A., & Huang, T. T. (2023). Strategies to improve the implementation of intensive lifestyle interventions for obesity. Frontiers in Public Health, 11, 1202545. 10. Perdomo, C. M., Cohen, R. V., Sumithran, P., Clément, K., & Frühbeck, G. (2023). Contemporary medical, device, and surgical therapies for obesity in adults. Lancet, 401(10382), 1116–1130. 11. Grandl, G., Novikoff, A., Liu, X., & Müller, T. D. (2024). Recent achievements and future directions of anti-obesity medications. The Lancet Regional Health. Europe, 47, 101100. Advance online publication. 12. Machado, A. M., Guimarães, N. S., Bocardi, V. B., da Silva, T. P. R., Carmo, A. S. D., Menezes, M. C., & Duarte, C. K. (2022). Understanding weight regain after a nutritional weight loss intervention: Systematic review and meta-analysis. Clinical Nutrition ESPEN, 49, 138–153. 13. Antoun, J., Kfoury, C., Chamaa, Y., Saade, Y., El Tannir, S. (2025). Acceptance, knowledge, and attitude of family physicians towards obesity as a chronic disease: a multinational cross-sectional study. International Journal of Obesity (2005), 49(4), 682–687. https://doi.org/10.1038/s41366-024-01693-2 14. Inoue, K., Seeman, T. E., Nianogo, R., & Okubo, Y. (2023). The effect of poverty on the relationship between household education levels and obesity in U.S. children and adolescents: An observational study. Lancet Regional Health. Americas, 25, 100565. Advance online publication. 15. Fan, J. X., Wen, M., & Li, K. (2019). Associations between obesity and neighborhood socioeconomic status: Variations by gender and family income status. SSM Population Health, 10, 100529. Advance online publication. 16. U.S. Department of Health and Human Services. (2024). Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. 17. Penchansky, R., & Thomas, J. W. (1981). The concept of access: Definition and relationship to consumer satisfaction. Medical Care, 19(2), 127–140. 18. Angalet, G., Dillard, D., Smith, H., Billie, M., & the Department of Health and Social Services Division of Public Health. (2025). Promoting Health Equity Through Advocacy for Data-Driven Policies and Practice Changes, September 2025. Available at: https://www.desu.edu/sites/flagship/files/final_chamg_report_dph_ approved_10312025-compressed.pdf 19. Michener, J. L., Williams, A., Oto-Kent, D., & Aguilar-Gaxiola, S. A. (2025). Community engagement: A foundation for health equity and resilience. American Journal of Public Health, 115(S2), S104–S109. 20. Akintobi, T. H., Bailey, R. E., II, & Michener, J. L. (2025). Harnessing the Power of Community Engagement for Population Health. Preventing Chronic Disease, 22, E25. 21. Bannuru, R. R. (2025). Weight stigma and bias: Standards of care in overweight and obesity—2025. BMJ Open Diabetes Research & Care, 13(Suppl 1), e004962. 22. Chae, K., German, J., Kendrick, K., Tackett, S., O’Rourke, P., Gudzune, K. A., & Laudenslager, M. (2024). What are Internal medicine residents’ attitudes toward obesity as a disease, people living with obesity, and obesity treatment? Obesity Science & Practice, 10(2), e748. Advance online publication. 23. Robeznieks, A. (2023). Health inequities can be addressed with systems-focused policies. American Medical Association. https://www.ama-assn.org/print/pdf/node/96396 24. Douglas, J. A., Subica, A. M., Franks, L., Johnson, G., Leon, C., Villanueva, S., & Grills, C. T. (2020). Using participatory mapping to diagnose upstream determinants of health and prescribe downstream policy-based interventions. Preventing Chronic Disease, 17, E138.
11
Chronic Disease in Delaware: A Legislative Perspective Jordan Myrick, B.S.; Alexandra Wynn, Ph.D.; Yendelela Cuffee, Ph.D., M.P.H. and Jennifer A. Horney, Ph.D., M.P.H. University of Delaware
ABSTRACT The Delaware State Health Improvement Plan (SHIP), a multi-year health promotion plan spanning 2025 to 2028, aims to improve health outcomes in Delaware by setting and working toward specific goals, objectives, and strategies. A key priority of the SHIP is preventing chronic diseases, such as cancer, heart disease, and diabetes, by promoting healthy lifestyles and statewide access to preventive care. To support organizations in Delaware in designing and implementing strategies for chronic disease prevention and management, the SHIP team conducted a 2024-2026 legislative scan. Reviewing legislative activities is an essential step in developing public health programming informed by state policy and designed to address state priorities for health and wellness. For the legislative scan, the SHIP team reviewed legislation proposed by the 153rd Delaware General Assembly (2024-2026) on the Delaware General Assembly website and analyzed its alignment with the Vital Conditions for Health and Well-Being Framework. While chronic disease was a focus for Delaware legislators between 2024 and 2026, legislation related to chronic disease primarily targeted cancer and diabetes, despite the high prevalence of cardiovascular disease across Delaware. Additionally, legislation related to chronic diseases consisted primarily of acts intended to raise awareness, such as health condition awareness days, rather than action-oriented acts that directly addressed disease risk factors. This commentary summarizes 2024-2026 legislation related to chronic disease and recommends that members of the 154th Delaware General Assembly place greater emphasis on cardiovascular disease, with a focus on risk factor reduction and the prioritization of action-oriented legislation. Chronic disease prevention is one of five health challenges identified by the Delaware State Health Improvement Plan (SHIP) as a priority for the state. The SHIP calls for comprehensive healthcare and lifestyle interventions to mitigate risk factors for chronic conditions, including cancer, heart disease, and diabetes.1 These interventions include highlighting the importance of physical activity, healthy dietary habits, and smoking cessation, as well as advocating for routine health visits and widespread access to preventive screenings.1 Individual- and communityfocused programs can provide targeted strategies for chronic disease prevention and align with the SHIP’s goals. However, state policy initiatives can promote systemic changes that reduce the burden of chronic disease for all Delaware residents. Thus, it is essential for policymakers to regard chronic disease prevention and management as a legislative priority and introduce policies that improve health outcomes across the state. This report, based on the 2024-2026 legislative scan created by the SHIP team, highlights legislation that aligns with the SHIP’s efforts to reduce chronic disease in Delaware and offers recommendations for policymakers to foster statewide health and well-being through future action.
THE LEGISLATIVE SCAN The legislative scan was developed by the Delaware SHIP team in collaboration with the University of Delaware (UD) Partnership for Healthy Communities, the UD Department of Epidemiology, and the Delaware Division of Public Health. To create the scan, nearly 2,000 acts of legislation introduced by the 153rd Delaware General Assembly (present) were examined using the Delaware General Assembly website to assess their alignment with the Vital Conditions for Health and Well-Being framework. The Vital Conditions framework presents seven domains as essential for 12 Delaware Journal of Public Health - October 2026
positive public health outcomes and population-level well-being: Basic Needs for Health and Safety; Humane Housing; Meaningful Work and Wealth; Lifelong Learning; Reliable Transportation; Thriving Natural World; and Belonging and Civic Muscle.2 The current legislative scan features over 800 bills, resolutions, substitutes, and amendments organized into seven sheets, each representing a Vital Condition domain. Acts were included in the scan if the provisions outlined in their synopses related to one or more of the Vital Conditions. The legislative scan was developed as an online resource for advocates and community organizations seeking to create programming informed by state health policy and to assess political progress towards organizational goals related to health promotion.
LEGISLATION ON CHRONIC DISEASE Chronic disease as a priority health outcome is one theme included in the legislative scan. A considerable proportion of the bills and resolutions in the document include policies to prevent chronic conditions or to increase public awareness of them. These acts are primarily concentrated in the Basic Needs for Health and Safety domain, though several include provisions that overlap with other Vital Conditions. Acts related to cancer form a sizable portion of legislation related to chronic disease, but other conditions, like diabetes and human immunodeficiency virus (HIV), also appear in some acts. Several of the acts included in the scan highlight conditions that are relatively uncommon in both Delaware and the United States, such as myasthenia gravis and IgA nephropathy (IgAN). Despite heart disease being specifically identified by the SHIP as a chronic disease priority, legislation related to cardiovascular disease, or associated risk factors like hypertension, high cholesterol, and physical inactivity, is limited. Doi: 10.32481/djph.2026.10.04
CANCER
Cancer Legislation
The most common cancers in Delaware are breast cancer, prostate cancer, lung and bronchus cancer, and colorectal cancer.3 From 2018 to 2022, these four cancers collectively accounted for over half of all cancer diagnoses in the state.3 Of these, female breast cancer (17%) and prostate cancer (16%) were the most common diagnoses.3 Despite accounting for only 14% of new cancer diagnoses in Delaware from 2018 to 2022, lung and bronchus cancer contributed to over one-fourth (about 27%) of new cancer deaths, while female breast cancer and prostate cancer accounted for only 8% and 6% of cancer deaths, respectively.3 In 2018-2022, Delaware ranked 15th among all states for the highest cancer mortality rate, an improvement from the 1990s, when the state ranked second.3 Existing preventive screening and early detection efforts, particularly for female breast cancer and colorectal cancer, have likely contributed to these measurable declines in cancer incidence and mortality. Results from the 2024 Delaware Behavioral Risk Factor Survey (BRFS) indicate that 78% of Delaware females ages 40 and older received a mammogram sometime within the previous two years, placing Delaware fourth in the nation for highest mammogram participation.3 Additionally, over three-fourths (76%) of Delaware adults ages 45 to 74 reported meeting national recommendations for colorectal cancer screenings.3 The preventive screening rates for other forms of cancer do not meet recommended levels. According to BRFS data, only 38% of men ages 50 to 69 in Delaware reported discussing the potential benefits of a prostate-specific antigen (PSA) test with their primary care provider.3 Of these men, 70% reported receiving a PSA test sometime in the last two years.3 Additionally, BRFS data suggest that only 21% of Delaware adults eligible for lung cancer screening—about 14% of Delaware adults ages 50 to 80 are potentially eligible—have received a CT scan as recommended by the American Cancer Society.3 Though Delaware has made progress in reducing cancer mortality and promoting early detection, the need for screenings and other prevention strategies persists, particularly for lung and bronchus and other cancers that contribute the most to state cancer mortality rates.
State legislation related to cancer reduction can be divided into two categories: awareness and action. Awareness legislation highlights specific cancers, risk factors, or demographics affected by cancer by designating months, weeks, or days of awareness. These designations do not guarantee activities related to cancer prevention or awareness, but they may supplement the efforts of organizations seeking to promote education or solidarity. Action legislation is a tangible approach to prevention that addresses risk factors or proposes strategies to reduce the occurrence of cancer. The majority of cancer-related legislation introduced by the 153rd General Assembly focuses on awareness. Table 1 provides a sample of both awareness and action legislation related to cancer.
DIABETES In 2023 and 2024, 13.3% of Delaware adults had been diagnosed with diabetes.4,5 The prevalence of diabetes in Delaware has increased since 2004; in that year, only 7.0% of adults in the state had been diagnosed with the condition.4 Certain demographic groups experience a disproportionate burden of diabetes. In Delaware, adults ages 55 and older, non-Hispanic Black adults, and residents of Kent County are more likely to develop diabetes compared to younger adults, adults of other racial and ethnic groups, and residents of New Castle or Sussex Counties.5 Adults with pre-existing risk factors such as obesity and hypertension are more likely to develop diabetes during their lifetime.5 Obesity can increase an individual’s risk of developing type 2 diabetes sixfold.5 Hypertension is also associated with an elevated risk of diabetes, and it often presents in tandem with other risk factors like obesity, high cholesterol, and a sedentary lifestyle.5 As more Delawareans have been diagnosed with diabetes over time, the prevalence of these known risk factors has increased concurrently. In 1992, the prevalence of obesity in the state was 13.0%; in 2023, it was 35.7%.5 In Kent County, where over 40% of adults have obesity, the prevalence of diabetes was 15.6% in 2023, higher than the state diabetes prevalence rate
Table 1. A Sample of Awareness and Action Legislation Related to Cancer, 2024-2026 Awareness Legislation Act
Status
Description
Vital Condition
HCR 114
Passed 4/16/26
Designates Adolescent and Young Adult Cancer Awareness Month
Basic Needs for Health and Safety
SCR 150, SCR 22
Passed 3/19/26, Passed 3/19/25
Designates Colorectal Cancer Awareness Month
Basic Needs for Health and Safety
SCR 146
Passed 3/12/26
Designates World Cholangiocarcinoma Day
Basic Needs for Health and Safety
SCR 195, SCR 75
Passed 5/21/26, Passed 5/22/25
Designates Prostate Cancer Awareness Month
Basic Needs for Health and Safety
Action Legislation Act
Status
Description
Vital Condition
HB 356
Lieu/Substituted 5/7/26
Prohibits the sale of firefighting foam with intentionally added per- and polyfluoroalkyl substances (PFAS) chemicals
Basic Needs for Health and Safety, Meaningful Work, and Wealth
HB 215
Lieu/Substituted 4/17/26
Taxes tobacco products
Basic Needs for Health and Safety
Signed 9/19/25
Requires the Division of Public Health to create a website to track PFAS in local drinking water and notify residents if levels of PFAS exceed established limits
Basic Needs for Health and Safety, Thriving Natural World
SB 72
Note: HCR=House Concurrent Resolution; SCR=Senate Concurrent Resolution; HB=House Bill; SB=Senate Bill 13
(13.3%).5 In 2023, 37.8% of Delaware adults had been diagnosed with hypertension; in 2003, only 27.7% of adults had been diagnosed.4,5 The rising rates of diabetes and modifiable risk factors associated with the condition signal a need for proactive state policies that emphasize prevention and behavior change.
Diabetes Legislation Legislation related to diabetes focuses primarily on education, awareness, and health promotion. SCR 176 (Passed 5/5/26) acknowledges April 25th, 2026, as Diabetic Retinopathy Screening Day in Delaware and encourages residents to receive regular eye exams and retinopathy screenings.6 HB 305 (Passed 6/30/26) and HB 163 (Lieu/Substituted 6/5/25) both call for and outline observational studies on diabetes care in Delaware.7,8 HB 305 seeks “to change standard healthcare from current reactive ‘sick care’ to proactive ‘well care’” by working with Delawareans with diabetes and their healthcare providers to reduce care costs and improve outcomes.7 HB 163 is similar to HB 305, but it requires the observational study to include individuals with prediabetes.8 SB 160 (Signed 7/21/25) and SB 146 (Out of Committee 5/21/25) focus on diabetes education.9,10 Both acts call for adding “informational materials” on type 1 diabetes to the Department of Health and Social Services (DHSS) website and require local school administrators to make these materials available to guardians of students in their districts.9,10
OTHER CHRONIC CONDITIONS Beyond cancer and diabetes, additional chronic conditions are highlighted in legislation from 2024 to 2026. For example, several acts address the increasing incidence of HIV/AIDS. HIV diagnoses in Delaware increased by 26% from 2020 to 2025.11 Currently, more than 4,200 people in Delaware live with HIV, according to AIDS Delaware.12 Certain populations of individuals living with HIV experience a greater burden of disease than others: in Delaware, 73.0% of people with HIV are male, 54.4% are Black, 82.5% are between the ages of 20 and 49, and about 60% reside in New Castle County.12 Other chronic diseases featured in legislation from the 153rd General Assembly include lupus, Parkinson’s disease, and epilepsy. Table 2 provides a sample of legislation related to HIV/AIDS and other chronic diseases.
RECOMMENDATIONS The legislative scan and this commentary provide critical insights into Delaware legislators’ priorities regarding chronic disease and highlight opportunities for policymakers to advance state prevention efforts. The following recommendations were developed for the 154th General Assembly, which is set to succeed the 153rd General Assembly in the 2027 legislative session:
Place greater emphasis on cardiovascular disease in legislation on chronic disease prevention. Cancer and cardiovascular disease are the first and second leading causes of death, respectively, in Delaware.13 Yet, the considerable amount of cancer-related legislation compared to the lack of legislation related to cardiovascular disease suggests the two chronic conditions are weighed unequally by state policymakers, even as the prevalence of risk factors for cardiovascular disease, such as high cholesterol and obesity, continues to rise. Rather than reducing the volume of cancer-related legislation, policymakers should expand legislation to include cardiovascular disease screening, prevention, and treatment.
Introduce more legislation that targets risk factors for chronic disease and encourages lifestyle changes. The SHIP highlights many risk factors that can contribute to chronic disease, including physical inactivity, poor nutrition, obesity, hypertension, and high cholesterol.1 To target these modifiable risk factors, policymakers should consider enacting legislation that creates environments, systems, and social conditions conducive to healthy behaviors. The Vital Conditions for Health and Well-Being framework outlines specific sectors policymakers can address to achieve such legislation. For example, introducing Thriving Natural World policies that create more walkable green spaces and healthier ecosystems may promote physical activity and decrease sedentary behaviors.2
Prioritize action legislation over awareness legislation. Awareness-focused legislation can create opportunities to increase Delawareans’ knowledge of chronic conditions, a crucial first step for prevention. Additionally, awareness legislation can bolster the efforts of community and advocacy groups to support individuals in need. However, a greater focus on introducing legislation that produces substantial, outcomes-based change may
Table 2. A Sample of Legislation Related to Other Chronic Conditions, 2024-2026 Act
Status
Description
Vital Condition
HB 200
Lieu/Substituted 1/8/26
Requires insurance coverage for PrEP and PEP treatment
Basic Needs for Health and Safety
HCR 47
Passed 5/14/25
Recognizes higher HIV infection rates in Delaware
Basic Needs for Health and Safety
SCR 184, SCR 66
Passed 5/13/26, Passed 5/8/25
Designates Tardive Dyskinesia Awareness Week
Basic Needs for Health and Safety
SCR 194, SCR 104
Passed 5/21/26, Passed 6/26/25
Designates Myasthenia Gravis Awareness Month
Basic Needs for Health and Safety
SCR 161, SCR 33
Passed 3/26/26, Passed 4/9/25
Designates Epilepsy Awareness Day
Basic Needs for Health and Safety
SCR 153, SCR 34
Passed 3/26/26, Passed 3/25/25
Designates Parkinson’s Disease Awareness Month
Basic Needs for Health and Safety
SCR 193, SR 11
Passed 5/19/26, Passed 5/22/25
Designates Lupus Awareness Month
Basic Needs for Health and Safety
Note: HCR=House Concurrent Resolution; SCR=Senate Concurrent Resolution; HB=House Bill; SB=Senate Bill; SR=Senate Resolution 14 Delaware Journal of Public Health - October 2026
lead to measurable effects on Delaware’s chronic disease rates. The prioritization of action-oriented legislation can also place policymakers alongside community organizations at the forefront of efforts to reduce the burden of chronic disease.
CONCLUSION Legislation proposed and passed by Delaware’s 153rd General Assembly includes provisions to reduce and bring awareness to a variety of chronic diseases, including both common conditions, like cancer and diabetes, and rarer conditions, like cystic fibrosis and lupus. The Delaware SHIP team’s scan of the Assembly’s legislation highlights opportunities for state policymakers to engage more in chronic disease prevention efforts. Although common in Delaware, cardiovascular diseases receive less legislative attention than cancer and diabetes, and their risk factors are rarely addressed through proposed bills and resolutions. Additionally, while awareness is an important component of prevention, there is a critical need for practical strategies to reduce the burden of disease. Thus, policymakers should consider elevating legislation that promotes sustainable change. By focusing on action-oriented legislation that targets the risk factors and chronic diseases that most affect Delawareans, legislators can help achieve the SHIP’s chronic disease reduction goals and improve the health status of all residents. The authors may be contacted at jsmyrick@udel.edu.
REFERENCES 1. Division of Public Health. (2025a). Delaware state health improvement plan 20252028. Delaware Department of Health and Social Services. https://img1.wsimg.com/blobby/go/7298608e-3c37-4361-b78a-b30fff51587e/ Final%20State%20Health%20Improvement%20Plan_09032025.pdf
2. The Rippel Foundation. (n.d.). Vital conditions for health and well-being. https://rippel.org/vital-conditions/ 3. Division of Public Health. (2026). Cancer incidence and mortality in Delaware, 2018-2022. Delaware Department of Health and Social Services. https://publichealthalerts.delaware.gov/wp-content/blogs.dir/203/files/ sites/203/2026/04/2018-2022-IM-Report_FINAL.pdf 4. Centers for Disease Control and Prevention. (2024). BRFSS prevalence & trends data. National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. https://www.cdc.gov/brfss/brfssprevalence/index.html 5. Division of Public Health. (2025b). The impact of diabetes in Delaware. Delaware Department of Health and Social Services. https://dhr.delaware.gov/benefits/facts-figures/documents/impact-of-diabetesreport-2025.pdf 6. SCR 176, 153rd Gen. Assemb., Sess. (Del. 2026). https://legis.delaware.gov/BillDetail?LegislationId=143157 7. HB 305, 153rd Gen. Assemb., Sess. (Del. 2026). https://legis.delaware.gov/BillDetail?LegislationId=142889 8. HB 163, 153rd Gen. Assemb., Sess. (Del. 2025). https://legis.delaware.gov/BillDetail?LegislationId=142293 9. SB 160, 153rd Gen. Assemb., Sess. (Del. 2025). https://legis.delaware.gov/BillDetail?LegislationId=142370 10. SB 146, 153rd Gen. Assemb., Sess. (Del. 2025). https://legis.delaware.gov/BillDetail?LegislationId=142292 11. Abdallah, S. (2025). HIV prevention in Delaware: A local and global public crisis. Delaware Journal of Public Health, 11(1), 22. 12. AIDS Delaware. (2026). 2026 June surveillance report. https://aidsdelaware.org/wp-content/uploads/2026/07/june-2026.pdf 13. National Center for Health Statistics. (2026, April). Delaware: Statistics by state. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/state-stats/states/de.html
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15
Public Data as Public Health Infrastructure David D. Chen, M.D., M.P.H. ChristianaCare Health System Allison Karpyn, Ph.D. Department of Human Development and Family Sciences, University of Delaware Dorothy Dillard, Ph.D. IHER Center CED Core, Delaware State University Michael B. Knapp, Ph.D. Sedoha, LLC Alexandra Wynn, Ph.D. Department of Epidemiology, University of Delaware
Janice Barlow, M.P.A. KIDS COUNT in Delaware, Center for Community Research & Service, University of Delaware Rita M. Landgraf, B.S. Adjunct Faculty, University of Delaware; Secretary, Delaware Department of Health and Social Services (2009-2017) Emily Kauffman, M.P.H. United Way of Delaware Gwendoline B. Angalet, Ph.D. GBA Consulting and Community Health Advocacy Mobilization Group
ABSTRACT Public health efforts addressing chronic disease and poverty require information that describes local conditions, preserves historical comparisons, and can be interpreted by the communities expected to act on it. Delaware’s My Healthy Community (MHC) platform brought health and social data together within a shared interface. Its 2025 decommissioning and transition to distributed sources offer a case for examining what community access to public data requires. We argue that access depends not on publication alone but on sustained stewardship and interpretive capacity. Adapting Penchansky and Thomas’s five dimensions of access (availability, accessibility, accommodation, affordability, and acceptability), we use Delaware examples, including a dated, topic-by-topic inventory, to show how access can erode even when data remain technically public. Substitute measures may not answer the original question, historical releases may become difficult to retrieve, and distributed sources require documentation and assistance. Free data also carry costs in staff time and expertise, and unexplained changes can weaken trust. Artificial intelligence makes analysis easier to produce without making it easier to verify, increasing the importance of these supports. We propose maintaining historical releases, documenting changes, and funding the partnerships and interpretive support required for responsible community use.
INTRODUCTION Efforts to reduce chronic disease depend on understanding the conditions in which people live, including whether they can afford housing, obtain nutritious food, and receive care when it is needed. These circumstances influence both the development of disease and the resources available to manage it, though their effects may accumulate across years and remain poorly characterized in clinical records.1 Patients who frequently require emergency medical care for diabetes, for example, may do so as a direct consequence of difficulty obtaining medications, unstable living circumstances, limited access to affordable nutrition, or limited outpatient care. Responding to those needs requires information that extends beyond a singular healthcare encounter, and observable patterns in the prevalence of disease can highlight disparities and opportunities for intervention. Delaware’s My Healthy Community (MHC) platform brought health and social data together to describe these relationships locally, reflecting a collaboration between public health informatics and epidemiology.2 In late 2025, the Delaware Department of Health and Social Services (DHSS) decommissioned MHC, intending to transition source data to the Delaware Open Data Portal while the next phase was planned.3,4 A statewide resource supports a common understanding of the incidence, prevalence, contextual factors, and growth or contraction of health issues requiring attention. When that source becomes difficult to retrieve and future updates are uncertain, disruption of this data commons draws resources away from deciding how to respond, and limits participation to those with the capacity and skills to find the data elsewhere. 16 Delaware Journal of Public Health - October 2026
Using MHC’s decommissioning as a case, we propose that community access to public health data depends not on publication alone but on sustained stewardship and interpretive capacity. Our concern is community accessibility, meaning whether residents and communitybased organizations can obtain, interpret, and continue using relevant data. We first describe MHC and adapt the five dimensions of access to public health data. We then examine each dimension in turn, drawing on Delaware examples from chronic disease and poverty measurement, State Health Improvement Plan (SHIP) implementation, and community data partnerships. We close by considering artificial intelligence (AI) and outlining what data transitions must preserve under each dimension.
MY HEALTHY COMMUNITY AND THE FIVE DIMENSIONS OF ACCESS A Common Basis for Understanding and Action MHC integrated health outcomes, health care utilization, environmental conditions, and social circumstances within a common data interface. Users ranging from seasoned scientists to everyday residents could examine topics across Delaware geographies, including units smaller than the county.2 By bringing these measures together, MHC provided a basis for discussing how conditions overlapped within communities, while keeping definitions and source documentation close to the displayed findings. Doi: 10.32481/djph.2026.10.05
This arrangement also supported collaboration. Organizations working on housing, food access, or chronic disease could begin with a common measure and identify where their questions differed, without each independently reconstructing the source, denominator, or geographic boundaries.
What the Transition Changed Our May 2026 comparison of MHC’s former content with the Open Data Portal identified gaps involving substance use, hospital discharge data, integrated Social Vulnerability Index displays, and Equity Counts presentations.5 The comparison identified seventeen categories missing outright. We distinguished source datasets from dashboards and narrative reports, because losing an interface differs from losing the underlying records. The comparison did not cover every standalone agency publication or reconstruct every former holding. Its contribution is to identify specific discontinuities in access rather than to estimate the total amount of Delaware data that remain publicly available.
Adapting the ‘Five As’ to Public Data In 1981, Penchansky and Thomas described access to health care as the fit between people and the system serving them, distinguishing availability, accessibility, accommodation, affordability, and acceptability.6 We adapt these five dimensions to public health data as a conceptual framework to organize and analyze the impact of MHC discontinuation on access to a common data set. (1) Availability concerns whether an appropriate indicator exists for the population, geography, and period of interest. (2) Accessibility describes whether people can locate and retrieve it through the digital resources available to them, with reproducible methods, and whether the data persist over time. (3) Accommodation concerns how publication is organized around users’ needs, including understandable documentation, ease of interpretation, usable formats, language, and technical assistance. (4) Affordability includes the staff time, expertise, and software required to prepare and interpret the data even when the download itself is free. (5) Acceptability concerns whether communities regard the source, categories, and proposed uses as credible and appropriate, including whether their knowledge informs interpretation. These dimensions specify what a transition must preserve to meet the needs of a diverse user base, and each can erode even when data remain technically public. A file may remain available but through migration becomes difficult to locate. A dashboard may be easy to navigate for a single condition but lack coherence with other variables and datasets. An organization may retrieve both and still lack the staff time to reconcile discrepancies. Community accessibility therefore depends on the relationship between the resource and its intended users, including the support necessary to use it responsibly.
1. AVAILABILITY AND APPROPRIATE MEASURES When a familiar source disappears, a similar measure is often substituted. Availability depends not on whether some indicator exists, but on whether the indicator answers the original question for the intended population, geography, and period. As an example, the loss of block-group resolution examined in the next section affects both dimensions as it changes the geography at which a measure is available as well as the effort required to retrieve it.
Substitute Measures are Not Equivalent Poverty can be described through income, assets, material hardship, or eligibility for assistance, each capturing different circumstances. When individual socioeconomic information is incomplete, investigators frequently link health records to area-based measures, which vary substantially in variables, geography, and intended application.7 The Index of Concentration at the Extremes, for example, characterizes concentrations of privilege and deprivation, including racialized economic segregation,8 while the CDC/ATSDR Social Vulnerability Index (SVI) was designed to support preparedness and response to hazards and emergencies.9 These measures cannot be assumed to be interchangeable. Rollings and colleagues found that Area Deprivation Index (ADI) and SVI rankings had only moderate correlation, with fewer than half of census tracts differing by no more than one decile.10 Substituting an available index may therefore change which communities are classified as most disadvantaged, and none of these area-level measures establishes the income, resources, or experience of every resident. Outcome substitution creates a related problem. Replacing observed diabetes-related emergency visits with modeled diabetes prevalence changes the question from health care utilization to estimated disease burden. CDC PLACES provides useful small-area estimates, but its models draw on survey and census information, including a county poverty measure,11 and so associations between modeled outcomes and socioeconomic indices may partly reflect shared model inputs. Geography can also interrupt comparisons. Postal ZIP codes describe mail delivery, while Census ZIP Code Tabulation Areas provide approximations for statistical analysis,12 and community service areas may cross several of these boundaries. Changes in boundaries, population composition, or denominators can interrupt comparisons even when the measures themselves remain unchanged, and small populations produce estimates that are unstable or cannot be released. Federal policy changes may further complicate this estimation process. The Department of Commerce’s June 2026 disclosure-avoidance policy establishes coarsening (grouping or rounding information) as the preferred approach for covered statistical products, and the Census Bureau acknowledges potential reductions in detail for small areas and population groups.13 Because the policy applies prospectively and implementation varies by program, Delaware users should examine each release for changes in categories or resolution rather than assume that a new estimate preserves the detail of its predecessor. 17
Some Needed Measures Were Never Available Availability limits predated MHC’s transition. Many chronic diseases develop through exposures operating across years, and a profile of a neighborhood at a single point in time cannot reconstruct earlier exposures or an individual’s residential history.1 Repeated observations of a neighborhood also differ from following the same residents over time, particularly where displacement or migration, often driven by economic forces, changes the population being described. In 2022–2023, United Way of Delaware’s Equity Counts Partnership Initiative provided data consultation to four community-based organizations, helping them use local data to assess community needs, guide programming, and support grant applications.14 This work revealed concrete gaps in the available data. ZIP-code-level adverse childhood experience data available at that time came from the 2015 Delaware School Survey, with gaps where reliable estimates were unavailable, and organizations sought measures of eviction, foreclosure, and social cohesion that existing sources could not adequately provide. Restoring an interface alone would not resolve the need for timely, sufficiently detailed data.
2. ACCESSIBILITY AND PERSISTENCE OVER TIME Preserving the Ability to Examine Change Historical releases permit examination of gradual change, while retained definitions help distinguish population trends from changes in measurement. The 2025 KIDS COUNT in Delaware FOCUS book, Strength in Numbers, shows how incremental changes become visible across thirty years of measurement and partnership, and documents difficulty comparing racial and ethnic categories across changing collection practices.15 The FAIR principles identify findability, accessibility, interoperability, and reusability as foundations for scientific data stewardship, with metadata and provenance supporting both human and automated use.16 The National Institutes of Health (NIH) updated 2026 data management and sharing guidance to explicitly encourage practices consistent with FAIR,17 although the NIH requirement does not govern every state public health dataset. For community data systems, these principles support retaining the exact releases, definitions, denominators, and transformations used in earlier work. A current file cannot Figure 1: My Healthy Community Data Architecture
18 Delaware Journal of Public Health - October 2026
reproduce an earlier analysis if the accompanying definitions or geographic assignments have disappeared.
Discontinuities Require Active Stewardship Preservation of data is not a passive process, as data discontinuities can arise in myriad ways, including ended collections, interrupted updates, altered definitions, and withdrawal of an interface. These events require different responses, but each can prevent users from continuing an analysis that was previously feasible. The relevant question is whether the original measure, its interpretation, and a comparable subsequent observation can still be recovered. MHC’s distinguishing feature was the resolution at which it published (Figure 1). The platform presented health indicators at geographic levels reaching the census block group, an area of as few as 600 residents, and it was one of few systems nationally able to publish health data at that resolution.2 Small-area publication is ordinarily limited by disclosure risk, because a single case within a small population can identify a person. MHC addressed that risk in software rather than by withholding whole geographic levels. Any frequency based on fewer than eleven individuals was suppressed at display time and the data automatically aggregated over a longer period, a larger geographic unit, or the attribute itself until the threshold was met, a (k, p)-anonymity scheme with k = 11 and p = 0.18 That allowed publication at the census block group, a geography of as few as 600 people, wherever the threshold was met, and its developers identified the algorithm as among the platform’s most important innovations.2,18 The method was certified under the expert determination provision of the HIPAA Privacy Rule, 45 CFR 164.514(b)(1), which permits release when a qualified expert determines that the risk of identifying an individual is very small.2 A privacy methodology of this kind is not a limit on small-area release but its precondition, and it is a capability separate from the data itself. Consequently, this resolution has not carried over. Green River’s 2024 case study reported nearly 1,900 indicators from more than 30 datasets by July 2021.19 Our May 2026 inventory documented 53 source datasets.5 As of May 30, 2026, the only current subcounty health content is communicable disease surveillance at the census tract. Table 1 provides a comparison by topic area of holdings previously reported in MHC which currently can no longer be retrieved or reproduced at the equivalent geographic specificity from the Open Data Portal.
Table 1. My Healthy Community Content and Delaware Open Data Portal Status, by Topic Area, May 2026 Topic area
Published Geography
Status on the Open Data Portal, May 2026
Substance Use
ZIP
Not found
Chronic Disease and Hospital Utilization
Census Tract
Not found
Mental Health
Census Tract
Not found
Sexual Violence
County
Not found
Social Vulnerability Index
Census Tract
Not found
Equity Counts Data Center
Census Tract
Not found
Youth Health Behavior
State
Not found
Prescription Monitoring
ZIP
Not found
Emergency Department and Syndromic Surveillance
ZIP
Not found
Community Characteristics
Census Block Group
Not found below the county
Communicable Disease
Census Block Group
Census tract, headline indicators only
Maternal and Child Health
Census Tract
County only, 2009-2016
Vital Statistics and Mortality
Census Tract
County only, 2009–2016 (cancer mortality: state only, 2008–2012)
Cancer
Census Tract
State only, 2008-2012
Air Quality
Census Tract
Facility or state emissions only
Footnote: Portal status reflects a search of the portal’s Health category and relevant Environment entries conducted May 30, 2026, with the vital statistics, cancer, and communicable-disease holdings re-verified September 4, 2026. “Not found” means that no matching dataset was identified in that search. MHC geographies are the finest published geographies documented in the May 2026 inventory. Tract-level chronic disease measures include ECDC small-area estimates; hospital discharge measures were published at ZIP level. Sub-county portal values appear on three communicable-disease datasets only, where geography is recorded as a text label without federal geographic identifiers, and so joining them to census-tract data requires a name-based crosswalk.
Currently no portal dataset reaches the census block group, and as no publicly documented suppression methodology accompanies the portal, the resolution that distinguished the MHC platform is unlikely to be replicated. Discontinuities in national dataset availability illustrate how fragility to digital data permanence can occur at scale. Jacobs and colleagues identified in October 2025 unexplained pauses in 38 of 82 CDC databases previously updated at least monthly, and recommended visible update status, reasons for interruptions, and anticipated resumption.20 The Federal Data Terminations Tracker and Data Rescue Project similarly distinguish loss of collections, data elements, and tools.21,22 Continuity does not maintain itself but requires active stewardship.
Updating SHIP Objectives The 2025–2028 SHIP was informed by the 2022–2023 State Health Assessment (SHA), which was undertaken by the University of Delaware Partnership for Healthy Communities in partnership with the Delaware Department of Health and Social Services Division of Public Health.23,24 This work drew on MHC for several baseline measures. Its cancer-mortality objective, for example, specifies an age-adjusted baseline of 164.8 deaths per 100,000 for 2016–2018 and a target of 148.3 by 2028, citing MHC as the source.24 Updating such an objective requires comparable definitions, time periods, and adjustment methods, together with a clear account of how the original baseline was produced. Loss of the MHC interface and incomplete transfer to the Open Data Portal have consequently made it difficult or impossible to update some objectives through their originally specified
sources. The July 2026 Community Data Resource Guide developed by the SHIP team and KIDS COUNT in Delaware identifies alternative publications, portals, selected archives, and contacts for requesting data.25 This work can support recovery, although each alternative must be reconciled with the objective’s original measure.
3. ACCOMMODATION OF USERS’ NEEDS Integration and Documentation MHC’s integrated design reduced the work of combining sources and understanding their definitions, and distributed sources shift that work to users. The Open Data Portal provides a general repository across Delaware agencies, while other platforms organize data around particular populations and purposes. Healthy Communities Delaware uses IP3 ASSESS to support assessment through vital-conditions frameworks, geographic comparisons, and shareable visualizations, but does not cover the scope or share the sources of MHC.26 KIDS COUNT illustrates why documentation matters. Its national indicators draw on federal sources for comparisons across states, while independently operated state organizations contribute locally sourced measures, so similarly named national and state indicators may contain different data.27 A common website does not ensure uniform content, geographic detail, or historical coverage. Nescott, Barlow, and PerezRivera’s description of Delaware poverty alongside employment, housing, food, and other basic needs demonstrates the interpretive work state partners contribute in determining which measures describe local circumstances and how their limitations should be communicated.28 19
Technical Assistance and Research Literacy United Way of Delaware’s Equity Counts Partnership Initiative illustrates the assistance community use requires. Supported by Highmark Blue Cross Blue Shield Delaware, the initiative worked with four organizations (Children and Families First, Cornerstone West, Network Connect, and Wilmington Alliance) from October 2022 through July 2023 on questions ranging from neighborhood population and wealth change to adverse childhood experiences and workforce development.13 A data specialist met weekly or biweekly with designated representatives to refine questions, locate information, and prepare findings. Support extended beyond finding data in MHC. The initiative also helped organizations assess alternative sources, interpret uncertainty, and distinguish population conditions from outcomes attributable to their programs. Participant feedback also informed the addition of state, county, and census-tract views to the Equity Counts Data Center in July 2023. Feedback was favorable, though the initiative’s small size and descriptive evaluation limit conclusions about lasting organizational independence or improved health outcomes. Goodman and Sanders Thompson’s Public Health Research Methods for Partnerships and Practice provides a methodological resource for community engagement in building capacity to understand data, research, and public health, and their related Community Research Fellows Training program combined research education with community participation.29,30 A similar Delaware implementation could pair research literacy with recurring technical assistance and shared documentation. The relevant capacity includes recognizing when a question cannot be answered by the available data and explaining that limit to partners and funders. SHIP provides a structure for sustaining this support across organizations. The University of Delaware’s Partnership for Healthy Communities has supported sustained, reciprocal partnerships aligned with SHIP, and the current plan is implemented through a Partnership Coalition spanning CBOs, nonprofits, government, health systems, education, transportation, and housing.31,32 The coalition includes 162 individual members representing 83 distinct organizations, as of August 2026.
4. AFFORDABILITY AND THE COST OF FREE DATA Open data support independent analysis and allow users to construct questions beyond those anticipated by a dashboard. However, publication leaves substantial work involving discovery, preparation, linkage, and interpretation, described by Kucharczyk as the “last-mile” public data problem.33 Martin and colleagues’ study of community-based organizations (CBOs) identified constraints in funding, staffing, skills, and data systems, alongside population data that did not adequately describe local communities.34 These costs are concrete. In addition to the data specialist’s time, Equity Counts participants committed approximately two to four hours per month to meetings and a similar amount outside meetings, and progress had to be documented to survive changes in participating staff.14 For CBOs, these costs affect decisions about initiating and 20 Delaware Journal of Public Health - October 2026
sustaining services. Population data help establish need in grant applications, identify service areas, and describe the conditions against which programs operate. When an update is delayed or no longer accessible, organizations may be unable to refresh those accounts even as funders expect current evidence. Missing data do not demonstrate that need has diminished. Nevertheless, smaller communities may become harder to represent in funding decisions precisely because their populations cannot support stable, publicly reportable estimates.
5. ACCEPTABILITY, CREDIBILITY, AND COMMUNITY KNOWLEDGE Shared Definitions and Community Knowledge A shared dataset cannot resolve every disagreement about priorities or causation, but explicit definitions and jointly interpreted findings can reduce repeated disputes about what the numbers represent, allowing more attention to the actions partners can take and the changes they will assess together. Community knowledge also shapes what the numbers mean. Delaware’s Community Health Advocacy Mobilization Group (CHAMG), working with the Division of Public Health and Delaware State University, examined chronic health conditions and community assets in 52 census tracts with high social vulnerability, selected using tract-level SVI published in MHC. Its 2025 report combined these profiles with resident conversations to develop recommendations concerning care, food access, transportation, and other conditions affecting health.35 As described in Table 1, tract-level SVI is not available on the portal, and so the next cycle of this work cannot reproduce its tract selection from the portal alone.5 The SHA’s community conversations in all three counties provided complementary detail, with New Castle County participants describing transportation constraints, difficulty finding information about services, and forms that made assistance difficult to obtain.23 These accounts help explain how an apparently available resource can remain inaccessible and identify questions for quantitative assessment, without establishing causal effects or implying that participants’ experiences represent every resident.
Trust and Misinformation Public confidence depends on whether people can understand changing evidence and evaluate the information presented to them. In 2020, Lee and colleagues described conflicting COVID-19 messages and the need to work through trusted churches, barbershops, and salons in Delaware.36 Troisi placed the growth of vaccine misinformation and disinformation within a longer history of hesitancy, identifying declining trust during and after COVID-19 as a continuing public health concern.37 Credible information travels through established relationships. Unexplained data interruptions complicate this work. When an agency cannot show why a number changed, where a prior release went, or when an update is expected, community partners have less capacity to distinguish a legitimate revision from an unsupported claim, which may weaken confidence in both the data and their interpretation. Transparent corrections, retained source material, and accessible explanations give partners a basis for addressing misinformation while acknowledging uncertainty and legitimate criticism. Accountability depends on the same transparency. As an example,
SHIP coalition members need to know whether an apparent lack of progress on an objective reflects worsening health, an unchanged estimate, or an unavailable update. Recording that distinction preserves accountability without treating missing information as evidence of either success or failure.
RESPONSIBLE AI AND VERIFICATION CAPACITY The increasing accessibility of AI tools makes community data capacity more consequential. An organization may now produce a map or summary without staff who can determine whether the system substituted a national estimate, combined incompatible geographies, or interpreted an area-level association as an individual risk. AI lowers the cost of producing analyses but not the cost of verifying them. This increases the need for informed review, particularly when findings support service decisions or funding requests. The National Institute of Standards and Technology’s AI Risk Management Framework provides a basis for governing and evaluating these uses.38 As for human work, AI-assisted work should retain exact source releases, executable calculations, and documented transformations, with review by people who understand epidemiology and the affected community. Training should include checking denominators, recognizing suppression and uncertainty, and identifying unsupported causal claims. Unavailable measures should be identified explicitly, any substitution justified and disclosed, and privacy protections maintained when public sources are combined.
CONCLUSION AND RECOMMENDATIONS FOR DATA TRANSITIONS Public data function as public health infrastructure whose value depends on maintenance, responsible ownership, and the people equipped to use it. MHC’s decommissioning shows that access can erode along each dimension even when data remain technically public. Replacement systems should therefore be assessed against community tasks, such as updating a neighborhood profile or following a specific objective, using the five dimensions of access to identify what must be preserved and what additional support is needed. 1.
Availability. When a measure is discontinued or replaced, identify the loss explicitly and document how any substitute differs in definition, geography, and population.
2.
Accessibility. Accompany changes to collections and interfaces with advance notice, a named responsible source, visible update status, and a plan for maintaining historical releases with their definitions and denominators.
3.
Accommodation. Keep documentation close to the data and sustain technical assistance and research-literacy training for community partners.
4.
5.
Affordability. Provide recurring funding that covers both data maintenance and the staff time required for CBOs to interpret and use the data. Acceptability. Explain revisions and interruptions transparently and include community partners in interpreting findings.
For communities addressing chronic disease and poverty, accessible data and documented measures allow earlier questions to be revisited as conditions change, while sustained partnerships provide the interpretation needed to guide subsequent action. This article includes recommendations about the importance of persistence and reproducibility/verification of data. The authors have used Zenodo as document repositories for two data pieces they feel are important to include: Knapp’s Indicator inventory and Open Data Portal Comparison, and Kauffman’s Equity Counts Partnership Project report https://doi.org/10.5281/zenodo.22841341. Dr. Chen may be contacted at dchen@christianacare.org.
DISCLOSURE STATEMENT MBK founded Green River, the firm that designed and built the My Healthy Community platform under contract to DHSS. He transferred ownership of Green River to its employees in November 2024 and retains a financial interest in the firm through seller financing from that transition. He is the principal of Sedoha, LLC, which conducted the inventory comparison (Knapp, 2026) without compensation. Sedoha, LLC has no current contract relating to the platform or its successor. This work was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number U54GM104941 (PI: Hicks) and the State of Delaware.
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19. Green River—Case Study—State of Delaware. My Healthy Community. (n.d.). Retrieved September 19, 2026, from https://greenriver.com/mhc/ 20. Jacobs, J. W., Booth, G. S., Brewer, N. T., & Freilich, J. (2026). Unexplained Pauses in Centers for Disease Control and Prevention Surveillance: Erosion of the Public Evidence Base for Health Policy. Annals of Internal Medicine, 179(3), 425–429. 21. Federal Data Terminations Tracker. (2026, August 17). Dataindex.Us . https://dataindex.us/terminations-tracker 22. Understanding Federal Data Loss and Future Directions. (2026, August 18). Data Rescue Project. https://www.datarescueproject.org/data-loss-report/
34. Martin, M. A., Echeverria, J., Wilkins, D., Navalpakkam, P., Battalio, S., Jacobs, J., Holcomb, J., Hartstein, M., Vu, M., & Spring, B. (2025). How Community-Based Organizations View and Use Data: Practices, Challenges, and Opportunities. Journal of Health Care for the Poor and Underserved, 36(2), 458–479. 35. Angalet, G., Dillard, D., Smith, H., & Billie, M. (2025). Promoting health equity through advocacy for data-driven policies and practice changes. Delaware Department of Health and Social Services, Division of Public Health. https://img1.wsimg.com/blobby/go/7298608e-3c37-4361-b78a-b30fff51587e/ downloads/36c75dd9-a29c-4181-89d2-e9ca423c7011/10-31%20FINAL%20 CHAMG%20Report_DPH%20approved_10312025.pdf 36. Lee, M., Ortiz, J., & Washington, J. (2020). Addressing COVID-19 Health Disparities Through Community Engagement. Delaware Journal of Public Health, 6(2), 88–90.
23. Delaware Department of Health and Social Services & Delaware Division of Public Health. (2023). Delaware State Health Assessment (SHA) 2022-2023 [Report]. Delaware Department of Health and Social Services. https://delawareship.org/
37. Troisi, C. (2025). Measles is Just a Harbinger. Delaware Journal of Public Health, 11(1), 50–52.
24. Delaware Division of Public Health & University of Delaware Partnership for Healthy Communities. (2025). Delaware State Health Improvement Plan (SHIP) 2025-2028. Delaware Department of Health and Social Services. https://delawareship.org/
38. Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1; p. NIST AI 100-1). National Institute of Standards and Technology (U.S.).
22 Delaware Journal of Public Health - October 2026
Saturday, 14, and the entire month of November 2026
Delaware Organizations who have endorsed the NLCSD campaign: American Cancer Society www.cancer.org/ 1-800-227-2345 American Lung Association www.lung.org 1-800-586-4872 Bayhealth www.bayhealth.org 302-744-7060 (Kent County) 302-430-5707 (Sussex County) Beebe Healthcare 302-645-3278 www.beebehealthcare.org ChristianaCare 302-623-0552 www.christianacare.org/lung-screenings Delaware Cancer Consortium, Early Detection & Prevention Committee www.healthydelaware.org Delaware Division of Public Health, Bureau of Cancer Prevention and Control Screening for Life https://www.dhss.delaware.gov/dph 302-744-1040 Delaware Health Equity Coalition www.delawarehealthequitycoalition.org 302-394-6060 Delaware Imaging Network www.delawareimagingnetwork.com 877-990-2121 TidalHealth 410-543-7012 www.tidalhealth.org
Dear Medical Provider, The American Cancer Society National Lung Cancer Roundtable (ACS NLCRT), American College of Radiology® (ACR®), Radiology Health Equity Coalition (RHEC), and Go2 For Lung Cancer have partnered once again for the annual National Lung Cancer Screening Day (“National LCS Day”). This year’s National Lung Cancer Screening Day is on Saturday, November 14, 2026. In Delaware, we will be encouraging lung screening during the entire month of November. The goal of this event is to reduce disparities, promote access to care, and save lives. Lung cancer is one of the deadliest forms of cancer and is estimated to claim the lives of over 124,990 Americans this year. Did you know that even though survival rates can be improved significantly by screening, only a small percentage of eligible individuals get screened for lung cancer. In Delaware in 2024, only 20.8% of those that are eligible reported having a CT scan for lung cancer screening within the past 12 months. Most cases continue to be diagnosed at distant stage, which is harder to treat and decreases survival. In 2018-2022, 44% of new lung cancer cases were diagnosed at the distant stage. Our purpose for this focus in November is to increase screening access for people who cannot take time off during their weekly schedules to get screened for the early detection of lung cancer. Primary care providers are the backbone of successful lung screening programs. Join us by supporting National Lung Cancer Screening Day on November 14 and all month long, by referring your eligible patients for screening. Show them you care about their health. Refer them to a nearby screening center today. We appreciate your support this November. Together, we can make a difference and create a nation where lung cancer is screened for, detected early, treated effectively, not stigmatized, and conquered! Let’s work together to save lives! Find a screening site near you: Lung Cancer Screening Locator
For more information visit the National Lung Cancer Roundtable and the National Lung Cancer Screening Day to register for free resources! 23
From Availability to Meaningful Access: Poverty, Disability, and Public Parks as Infrastructure for Chronic Disease Prevention Iva Obrusnikova, Ph.D., C.A.P.E. Department of Health Behavior and Nutrition Sciences, University of Delaware Cora J. Firkin, Ph.D. Health Science Department, Lincoln University of Pennsylvania Richard R. Suminski, M.P.H., Ph.D. Department of Health Behavior and Nutrition Sciences, University of Delaware
ABSTRACT Public parks are widely available, relatively low-cost resources for physical activity and chronic disease prevention, but their availability does not necessarily translate into meaningful access. A park may be nearby and open to the public, yet remain difficult for some residents to reach, navigate, or use given their abilities, needs, and available resources. This distinction may be particularly consequential when poverty and disability intersect, because limited financial and transportation resources can reduce the alternatives available when a particular recreation setting does not meet an individual’s needs. This analytic essay examines the distinction between park availability and meaningful access within the context of chronic disease prevention, with particular attention to the intersection of poverty and disability. Evidence from Delaware shows variation in transportation access, pathways, trails, restrooms, wayfinding, and communication across community parks, as well as gaps in the information residents may need to determine whether available recreation opportunities are appropriate for them. The Delaware experience also illustrates opportunities for collaboration among parks and recreation, public health, transportation and planning, academic researchers, people with disabilities, community organizations, and other partners. Such collaboration can help address connections between parks and surrounding communities, identify user-relevant characteristics that can be feasibly documented and communicated, incorporate community perspectives, and apply public health data to planning and evaluation. Meaningful access does not require every park or trail to meet every need. It does require attention to whether residents can reach and use available recreation environments and have sufficient information to identify opportunities appropriate to their abilities, needs, and preferences.
INTRODUCTION: CHRONIC DISEASE, POVERTY, AND DISABILITY Chronic disease remains a major public health concern in the United States. In 2023, 76.4% of U.S. adults (more than 194 million people) reported at least one of 12 selected chronic conditions, and more than half (51.4%) reported multiple chronic conditions.1 Regular physical activity plays an important role in chronic disease prevention,2 yet participation varies considerably by income. In 2024, only 35.4% of U.S. adults living below the federal poverty level met the federal aerobic physical activity guidelines for leisure time, compared with 56.5% of those with family incomes at or above 400% of the federal poverty level.3 Disability is another important and often overlapping dimension of chronic disease disparities. In 2022, more than one in four U.S. adults (28.7%, or approximately 70 million people) reported having a disability.4 An analysis of 2022 Behavioral Risk Factor Surveillance System data found that 80% of adults with disabilities reported at least one chronic health condition, with arthritis, depression, and diabetes the most prevalent.5 Participation in physical activity also varies substantially by disability status. In 2024, only 22.4% of adults with disabilities met federal aerobic physical activity guidelines for leisure time, compared with 49.8% of adults without disabilities.3 24 Delaware Journal of Public Health - October 2026
Disability frequently co-occurs with socioeconomic disadvantage. Socioeconomic disadvantage reflects limited access to the economic and social resources and opportunities that shape health outcomes, including income, education, housing, and health care.6 In 2025, the employment-population ratio among adults ages 16 to 64 with disabilities was 38.1%, compared with 74.8% among those without disabilities.7 Poverty rates also differ substantially by disability status. In 2022, 24.9% of adults with disabilities aged 18 to 64 lived in poverty, compared with 10.1% of adults without disabilities in the same age range.8 Disabilityrelated needs can place additional demands on household resources. Households with a working-age adult with a work limitation have been estimated to require approximately 29% more income to achieve the same standard of living as comparable households without a member with a disability.9 Transportation access also varies by disability status. In 2022, 14.4% of adults with disabilities reported lacking reliable transportation for daily living in the previous 12 months, compared with 4.9% of adults without disabilities.10 These population-level differences do not suggest that people with disabilities are uniformly low-income or economically disadvantaged. Rather, they underscore the overlap between disability and socioeconomic disadvantage and illustrate how economic constraints can intersect with other barriers, such as transportation, to constrain access to physical activity and other health-promoting opportunities. Doi: 10.32481/djph.2026.10.06
Opportunities for physical activity depend not only on individual resources but also on the environments where people live and engage in recreation. Built-environment characteristics, such as trail connectivity, can influence physical activity by shaping opportunities for people to be active.11 Public parks are particularly relevant in this context because they are publicly supported community resources that offer opportunities for physical activity and recreation without the recurring membership costs of many other settings. However, their presence does not necessarily mean they are usable by all residents. Understanding the role of parks in chronic disease prevention therefore requires considering not only whether these resources are available, but also whether residents can meaningfully access and use them. This essay examines these issues at the intersection of poverty, disability, public park access, and chronic disease prevention, with particular attention to opportunities for collaboration in Delaware.
PUBLIC PARKS AS COMMUNITY INFRASTRUCTURE FOR CHRONIC DISEASE PREVENTION Public parks, trails, and greenways are common settings for physical activity, and the Community Preventive Services Task Force (CPSTF) found sufficient evidence that improving this infrastructure, along with efforts to engage communities, raise awareness, expand programming, or improve access, can increase physical activity and use of these spaces.12 In a study of predominantly low-income, minority communities, public parks were the most commonly reported setting for exercise, and meeting friends and socializing were also common park activities.13 Parks and green spaces may also contribute to health through other pathways. A systematic review found that naturebased recreation was associated with improved mental well-being and reduced anxiety, depression, and stress,14 while a metaanalysis of observational studies found that greater exposure to green space was associated with a lower risk of depression.15 Yet the potential health benefits of these environments depend in part on whether people can access and use them. Together, these findings underscore the relevance of public parks to physical, social, and mental health. Public park availability may be particularly important for people with limited financial resources, for whom affordable alternatives for physical activity may be more limited. A systematic review found stronger protective associations between public green space and physical health among socioeconomically disadvantaged populations and suggested that public park availability may be especially important for people with fewer affordable private recreation options.16 Consistent with this broader evidence, a recent study of renovated parks serving low-income communities in New York City found that frequent park use was associated with higher physical activity, with a stronger association among adults with annual household incomes below $25,000.17 Unlike many private recreation facilities, public parks generally do not require recurring membership fees.18 However, being free and geographically available does not necessarily ensure that parks are used, particularly in socioeconomically disadvantaged communities.19 Transportation availability, along with environmental and accessibility barriers, can further shape whether a nearby park is genuinely usable, particularly for people with disabilities.20,21
Public parks are part of the community infrastructure that supports population health. An American Heart Association science advisory identified access to parks and recreational facilities as a component of evidence-based built-environment approaches to increasing physical activity, alongside pedestrian and bicycle infrastructure, and emphasized the importance of cross-sector collaboration among public health, transportation, planning, and parks and recreation.11 A recent international position statement on active outdoor play similarly called for coordinated action across these sectors to promote equitable access to active outdoor play.22 Economic evidence further supports the value of investments in parks and related infrastructure. A systematic review found that the estimated societal benefits of park, trail, and greenway infrastructure interventions generally exceeded their costs.18 Recent evidence also suggests an association between investments in park programming and population health outcomes. Among residents of 10 Southern California cities, each additional $30 per capita spent on park programming was associated with a lower prevalence of several chronic conditions, including stroke, heart failure, atrial fibrillation, coronary heart disease, type 2 diabetes, and obesity.23 Nonetheless, the presence of a public park in a community creates opportunities for recreation but does not, by itself, ensure meaningful access. Park characteristics can influence whether people use available opportunities. A multi-city study found that higher overall park quality, reflecting access, facilities, amenities, aesthetics, and the surrounding neighborhood, was associated with a greater likelihood of park use.24 For public parks to realize their potential as community resources for health promotion, attention must therefore extend beyond their presence and geographic distribution to the conditions that enable people to reach, navigate, evaluate, and use them.
FROM AVAILABILITY TO MEANINGFUL ACCESS Whether a public park serves as a usable community resource depends on more than its presence or proximity to residents. Availability and accessibility are related yet conceptually distinct dimensions of park use. Availability generally refers to a park’s existence and proximity, whereas accessibility concerns barriers that may affect its use.25 This distinction aligns with longitudinal evidence among older adults showing that the presence of a neighborhood walking trail was not reliably associated with exercise behavior on its own; associations depended in part on residents’ awareness and use of the trail.26 Physical accessibility and overall usability shape whether people can use available park environments,27,28 while access to information may influence whether residents can identify recreation opportunities appropriate to their needs.21 These dimensions are related but not interchangeable. The World Health Organization’s International Classification of Functioning, Disability and Health conceptualizes functioning and disability as the result of interactions between individual and environmental factors, helping explain why the same recreational environment may present different opportunities and barriers for different people.29 A nearby park may therefore be readily usable by one resident while difficult for another to access or use, depending on functional abilities and needs, environmental characteristics, 25
and available information. Park presence or proximity alone may thus provide an incomplete picture of meaningful access. Figure 1 illustrates this distinction between park availability and meaningful access and shows how access to alternative recreation opportunities may depend on available financial, transportation, and support resources. Figure 1. Meaningful Access to Public Parks and the Role of Alternative Recreation Resources
Note. Public park availability alone does not ensure meaningful access. Reaching the park, using it, and having adequate information beforehand determine whether an available park functions as a usable recreation resource in relation to an individual’s abilities, needs, and preferences. When meaningful access to a given park is limited, the ability to use alternative recreation opportunities may depend on financial, transportation, and support resources. One dimension of access concerns whether residents can reach a park. This includes transportation availability, pedestrian connections, accessible parking, drop-off locations, and safe, navigable entrances.20,21,28,30 Federal accessibility standards address many elements of pedestrian access. The 2023 Public Rightsof-Way Accessibility Guidelines set accessibility requirements for pedestrian facilities in the public right-of-way, including sidewalks, crosswalks, curb ramps, transit stops, and onstreet parking.31 Responsibility for these features may fall to transportation agencies, municipalities, planning agencies, and parks and recreation agencies. For parks to support regular physical activity, residents need practical and reliable ways to reach them on a recurring basis.20 A park with accessible internal facilities may still be difficult to use if a resident cannot safely or reliably reach its entrance. Meaningful park access, therefore, depends in part on connections between the park and the surrounding community. Once inside a park, route and amenity characteristics can affect whether residents can move through and use the space. Relevant features include path surfaces and widths, slope, route continuity, seating and rest opportunities, restrooms, and connections among park amenities. Research on park accessibility has documented how characteristics such as route surfaces, widths, and slopes can affect park use by people with disabilities.28,32 These characteristics need not be understood as deficiencies in themselves. A natural-surface trail, for example, is not necessarily poorly designed simply because it cannot accommodate every user; rather, its characteristics may make it appropriate for some users and difficult for others. This illustrates why usability cannot be reduced to a simple distinction between “accessible” and “inaccessible”; instead, it reflects the interaction between environmental characteristics and individual abilities and needs. 26 Delaware Journal of Public Health - October 2026
People may need information before arriving at a park to determine whether the available recreational opportunities are appropriate for them. Research on wayfinding among people with disabilities found that most participants sought information about unfamiliar environments in advance, yet accessibility information was often unavailable, vague, or outdated.33 Relevant information can include route distance, surface, and grade; accessible parking and restrooms; seating and rest opportunities; and potential barriers.21 Because route preferences and needs can differ both across and within disability groups, the information needed to evaluate a recreational environment may also vary.33 A general designation such as “accessible” may therefore provide less information than some users need to determine whether a particular park or route is appropriate for them. Providing more detailed information about recreational environments may complement improvements in physical accessibility. These dimensions point to a broader concept of meaningful access. From this perspective, meaningful access does not require every feature of every park to accommodate every user. Rather, it requires attention to whether residents can reach available recreation opportunities, whether those environments can be used in relation to their abilities and needs, and whether sufficient information is available to evaluate those opportunities in advance. Recent scholarship likewise emphasizes that disability inclusion in public open spaces requires attention not only to minimum accessibility standards but also to usability, communication, and other aspects of functional inclusion.34 For parks and public health practice, this perspective shifts the question from whether a park is present or technically accessible to whether residents have the environmental conditions and information needed to identify and use recreational opportunities appropriate for them.
POVERTY, DISABILITY, AND THE LIMITS OF SUBSTITUTION Socioeconomic disadvantage can constrain both the resources available for health-promoting behaviors and access to protective community assets such as green space.16 As illustrated in Figure 1, when meaningful access to a particular park is limited, the ability to use an alternative recreation opportunity may depend on available financial, transportation, and support resources. A person with greater financial and transportation resources, for example, may have more options, such as using another park, joining a private fitness facility, or purchasing equipment for home use. For people with disabilities, built-environment and transportation barriers can further constrain access to these alternatives.35 When disability and economic constraints intersect, alternatives requiring additional travel, fees, or equipment may therefore be less feasible. Economic disparities are also evident within public park settings. In a nationally representative study of neighborhood parks, use was lower in higher-poverty than in lower-poverty neighborhoods. Parks in higher-poverty neighborhoods had fewer supervised activities and less on-site marketing, both of which were associated with park use.36 These findings suggest that geographic presence alone does not account for observed differences in use. Programming and outreach may influence whether available parks are used,36 while access to useful information may help residents identify opportunities that meet their needs.21
Disability can also place additional demands on the resources needed to pursue physical activity. Some individuals may need accessible transportation, assistive equipment, specialized services, and personal support, which can add to household expenses.37 Transportation is particularly important because people with disabilities report barriers related to availability, accessibility, affordability, and the need for advance planning.20 Disability-related expenses can also reduce the financial resources available for other needs. U.S. households with a working-age adult with a work limitation have been estimated to require approximately 29% more income to achieve the same standard of living as comparable households without a member with a disability.9 A more recent national study found that workingage adults with disabilities living below the federal poverty level spent less out of pocket on disability-related goods and services but devoted a substantially greater share of household income to those expenses.37 For people facing both disability-related needs and limited financial resources, these additional demands may further narrow the range of feasible physical activity options. Public parks may therefore be particularly important as relatively low-cost recreation resources if residents can use them and have sufficient information to determine whether available opportunities meet their needs.
DELAWARE AS A CONTEXT FOR ADVANCING MEANINGFUL PARK ACCESS Delaware’s network of state, county, and municipal parks provides important settings for physical activity and recreation across the state’s three counties. Meaningful access to these resources depends on more than the presence of parks and can involve factors that extend beyond the responsibility of parks and recreation agencies alone. Delaware provides a useful context for examining how these issues intersect and how cross-sector collaboration can strengthen the role of public parks in chronic disease prevention. Delaware research shows that characteristics relevant to meaningful park access vary across community recreation environments. An initial audit of 10 non-fee-based community parks with playgrounds in New Castle County documented strengths and barriers related to transportation access, pathways, parking, playgrounds, and other accessibility features.38 A subsequent study extended the assessment statewide to 50 public, non-fee-based community parks with playgrounds and examined pedestrian access, parking, pathways, restrooms, trails, wayfinding, and communication features.21 For example, primary pathways were generally well maintained, but only 30% of parks had a public transit stop near the entrance.21 Improvements within a park, however, cannot fully address access if residents lack a practical means of reaching it. Features such as curb ramp completeness and pedestrian crossing signals also fell short of federal pedestrian right-of-way standards in several areas.21,31 Accessibility features also varied across restrooms, trails, wayfinding, and communication.21 These studies show that Delaware’s community recreation environments differ in characteristics that may affect how residents reach and use public parks. The Delaware studies also highlight an information and communication gap. The initial New Castle County study found limited availability of accessible health-promotion materials,38
and the statewide audit found a similar pattern. Electronic plain-text information was available at 8% of parks, large-print information at 12%, and pictograms at 14%; only 6% included visual representations of people with disabilities.21 These findings are particularly relevant because people with disabilities often seek information about unfamiliar environments in advance and may encounter accessibility information that is unavailable, vague, or outdated.33 Improving how existing park characteristics are documented and communicated may therefore complement efforts to improve physical accessibility and provide residents with information they can use to evaluate recreation opportunities in relation to their own needs and abilities. The link between access and chronic disease prevention is also evident in earlier Delaware research. Adults with disabilities had significantly higher rates of cardiovascular disease and obesity and lower levels of physical activity than adults without disabilities.39 In the same study, an assessment of Delaware health-promotion programs identified transportation, physical accessibility, communication, staff training, and other barriers to participation. Nearly three-quarters of programs could not determine how many people with disabilities they served.39 The range of barriers identified across Delaware parks and healthpromotion programs suggests that no single organization or agency can address meaningful access.
OPPORTUNITIES FOR CROSS-SECTOR COLLABORATION One opportunity for cross-sector collaboration is to identify a core set of user-relevant characteristics that can be consistently documented and communicated. These might include route surface, distance, grade, and the locations of accessible parking, restrooms, and rest areas. Parks and recreation professionals bring knowledge of park facilities, operations, and community use, while public health and academic partners can contribute expertise in population health, disability, assessment, and evaluation. Prior work developing standardized, disabilityinclusive community health assessment tools shows that a core set of items can be identified through structured collaboration among researchers, disability community members, and practitioners.27 A similar approach could identify information that is useful to residents while remaining feasible for parks agencies to collect and maintain. Community input is also important because navigation needs and preferences vary across and within disability groups. Guidance for equitable park planning emphasizes community engagement,12 and recent scholarship on disability inclusion in public spaces calls for attention beyond minimum accessibility standards to participatory, community-informed design.34 People with disabilities, older adults, caregivers, and residents of communities experiencing socioeconomic disadvantage may identify information needs that are not apparent from technical standards or agency data alone. Because meaningful park access extends beyond the park entrance, transportation and planning agencies can contribute expertise on how parks connect with surrounding communities and whether available transportation options make repeated park visits feasible. In Delaware, collaboration among parks and recreation agencies, municipalities, transportation and planning agencies, and public health could help address access to and within parks as related components of meaningful access.11 27
Public health information on chronic disease burden, disability, socioeconomic conditions, transportation access, and physical activity can provide another useful input to park planning and decision-making, complementing operational, environmental, and community considerations when setting priorities. Strategic use of data can help identify inequities and barriers to park access and use and evaluate progress over time.12 In Delaware, collaboration among parks and recreation agencies, public health professionals, transportation and planning agencies, academic researchers, disability organizations, municipalities, healthcare partners, and community members could support more informed decisions about priorities for improving meaningful access.
CONCLUSION Public parks are an important component of community infrastructure for chronic disease prevention, particularly for residents with limited alternatives for physical activity. However, their public availability should not be equated with meaningful access. For people with disabilities, particularly those who also face economic constraints, access to these resources can depend on transportation, the characteristics of the physical environment, and access to information needed to determine whether available opportunities are usable. Meaningful access does not require every park or trail to meet every need. Rather, it requires attention to whether residents can reach available recreation opportunities, the characteristics that affect their use once there, and the information available to determine whether those opportunities meet their needs. Delaware has an opportunity to build on its existing park infrastructure through collaboration among parks and recreation, public health, transportation and planning, people with disabilities and community organizations, academic partners, and other sectors. Improving meaningful access to existing recreation resources can extend the public health value of these investments and enable more residents to identify and use physical activity opportunities appropriate to their abilities, needs, and preferences. Dr. Obrusnikova may be contacted at obrusnik@udel.edu .
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28 Delaware Journal of Public Health - October 2026
9. Morris, Z. A., McGarity, S. V., Goodman, N., & Zaidi, A. (2022). The extra costs associated with living with a disability in the United States. Journal of Disability Policy Studies, 33(3), 158–167. 10. Centers for Disease Control and Prevention. (2024). QuickStats: Percentage of adults aged ≥18 years who lacked reliable transportation for daily living in the past 12 months, by disability status and age group—National Health Interview Survey, United States, 2022. MMWR. Morbidity and Mortality Weekly Report, 73, 152. 11. Omura, J. D., Carlson, S. A., Brown, D. R., Hopkins, D. P., Kraus, W. E., Staffileno, B. A., Thomas, R. J., Lobelo, F., Fulton, J. E., & the American Heart Association Physical Activity Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular and Stroke Nursing; and Council on Clinical Cardiology. (2020). Built environment approaches to increase physical activity: A science advisory from the American Heart Association. Circulation, 142(11), e160–e166. 12. Community Preventive Services Task Force. (2021). Physical activity: Park, trail, and greenway infrastructure interventions to increase physical activity (CPSTF Finding and Rationale Statement). https://doi.org/10.15620/cdc/168568 13. Cohen, D. A., McKenzie, T. L., Sehgal, A., Williamson, S., Golinelli, D., & Lurie, N. (2007). Contribution of public parks to physical activity. American Journal of Public Health, 97(3), 509–514. 14. Lackey, N. Q., Tysor, D. A., McNay, G. D., Joyner, L., Baker, K. H., & Hodge, C. (2021). Mental health benefits of nature-based recreation: A systematic review. Annals of Leisure Research, 24(3), 379–393. 15. Liu, Z., Chen, X., Cui, H., Ma, Y., Gao, N., Li, X., Meng, X., Lin, H., Abudou, H., Guo, L., & Liu, Q. (2023). Green space exposure on depression and anxiety outcomes: A meta-analysis. Environmental Research, 231, 116303. 16. Rigolon, A., Browning, M. H. E. M., McAnirlin, O., & Yoon, H. V. (2021). Green space and health equity: A systematic review on the potential of green space to reduce health disparities. International Journal of Environmental Research and Public Health, 18(5), 2563. 17. Olivera León, L. D., Thompson, R. L., Wyka, K. E., & Huang, T. T. K. (2025). Associations of park use with physical activity in renovated parks serving lowincome communities in New York City: Insights from the community parks initiative. Journal of Racial and Ethnic Health Disparities, 12(5), 2871–2881. 18. Jacob, V., Reynolds, J. A., Chattopadhyay, S. K., Hopkins, D. P., Brown, D. R., Devlin, H. M., Barrett, A., Berrigan, D., Crespo, C. J., Heath, G. W., Brownson, R. C., Cuellar, A. E., Clymer, J. M., & Chriqui, J. F. (2024). Parks, trails, and greenways for physical activity: A Community Guide systematic economic review. American Journal of Preventive Medicine, 66(6), 1089–1099. 19. Won, Y., Kim, J. E., Kim, Y., & Corley, E. A. (2026). Urban neighborhoods with greater access but less use of nearby parks: Case of Los Angeles, California. Environment and Behavior, 58(1), 32–60. 20. Remillard, E. T., Campbell, M. L., Koon, L. M., & Rogers, W. A. (2022). Transportation challenges for persons aging with mobility disability: Qualitative insights and policy implications. Disability and Health Journal, 15(1S), 101209. 21. Obrusnikova, I., Firkin, C. J., Pennington, R., Dixon, I., & Bilbrough C. (2026). Statewide assessment of public park accessibility and usability and playground safety. International Journal of Environmental Research and Public Health, 23(1), 139. 22. Lee, E.-Y., de Lannoy, L., Kim, Y.-B., Rathod, A., James, M. E., Lopes, O., Nasrallah, B., Thankarajah, A., Adjei-Boadi, D., de Barros, M. I. A., Duncan, S., Miller, R. M., Mygind, L., Vanderloo, L. M., Wang, P.-Y., Tremblay, M. S., Jerbine, A., Amero, A., McGarty, A. M., . . . Carson, V., & the AOP10 Steering Committee Group. (2025). 2025 Position statement on active outdoor play. The International Journal of Behavioral Nutrition and Physical Activity, 22(1), 117. 23. Cohen, D. A., Liu, I. A., Chen, A., Teichrow, D., & Shen, E. (2025). Per capita spending on parks and associations with cardiovascular disease and other health outcomes. Journal of Urban Health : Bulletin of the New York Academy of Medicine, 102(4), 848–856. 24. Kaczynski, A. T., Wende, M., Hughey, M., Stowe, E., Schipperijn, J., Hipp, A., & Javad Koohsari, M. (2023). Association of composite park quality with park use in four diverse cities. Preventive Medicine Reports, 35, 102381. 25. Błaszczyk, M., Suchocka, M., Wojnowska-Heciak, M., & Muszyńska, M. (2020). Quality of urban parks in the perception of city residents with mobility difficulties. PeerJ, 8, e10570. 26. Harada, K., Masumoto, K., & Okada, S. (2024). Walking trail access, exercise behavior, and going out-of-home among older adults: Examining longitudinal associations and mediators. Archives of Gerontology and Geriatrics, 126, 105534.
27. Eisenberg, Y., Rimmer, J. H., Mehta, T., & Fox, M. H. (2015). Development of a community health inclusion index: An evaluation tool for improving inclusion of people with disabilities in community health initiatives. BMC Public Health, 15(1), 1050. 28. Perry, M. A., Devan, H., Fitzgerald, H., Han, K., Liu, L. T., & Rouse, J. (2018). Accessibility and usability of parks and playgrounds. Disability and Health Journal, 11(2), 221–229. 29. World Health Organization. (2001). International classification of functioning, disability and health (ICF). 30. Wojnowska-Heciak, M., Suchocka, M., Błaszczyk, M., & Muszyńska, M. (2022). Urban parks as perceived by city residents with mobility difficulties: A qualitative study with in-depth interviews. International Journal of Environmental Research and Public Health, 19(4), 2018. 31. U.S. Access Board. (2023). Accessibility guidelines for pedestrian facilities in the public right-of-way. Federal Register, 88(151), 53604–53685. 32. Lepoglavec, K., Papeš, O., Lovrić, V., Raspudić, A., & Nevečerel, H. (2023). Accessibility of urban forests and parks for people with disabilities in wheelchairs, considering the surface and longitudinal slope of the trails. Sustainability (Basel), 15(10), 7741. 33. Gupta, M., Abdolrahmani, A., Edwards, E., Cortez, M., Tumang, A., Majali, Y., Lazaga, M., Tarra, S., Patil, P., Kuber, R., & Branham, S. M. (2020). Towards more universal wayfinding technologies: Navigation preferences across disabilities. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
34. Gupta, A., Yadav, M., & Nayak, B. K. (2025). A systematic literature review on inclusive public open spaces: Accessibility standards and universal design principles. Urban Science (Basel, Switzerland), 9(6), 181. 35. Eisenberg, Y., Vanderbom, K. A., & Vasudevan, V. (2017). Does the built environment moderate the relationship between having a disability and lower levels of physical activity? A systematic review. Preventive Medicine, 95S, S75– S84. 36. Cohen, D. A., Han, B., Nagel, C. J., Harnik, P., McKenzie, T. L., Evenson, K. R., Marsh, T., Williamson, S., Vaughan, C., & Katta, S. (2016). The first national study of neighborhood parks: Implications for physical activity. American Journal of Preventive Medicine, 51(4), 419–426. 37. Morris, Z. A., Rennane, S., Goodman, N., Mont, D., Ballan, M., Davis, A., McGarity, S., Ellison, B., & Weldeslassie, G. (2026). The disability squeeze: Out-of-pocket expenses and unmet needs for disability-related goods and services in the U.S. Disability and Health Journal, 19(1), 101930. 38. Firkin, C. J., Rechner, L., & Obrusnikova, I. (2024). Paving the way to active living for people with disabilities: Evaluating park and playground accessibility and usability in Delaware. Delaware Journal of Public Health, 10(1), 74–83. 39. Papas, M. A., Stolz, N., Orsega-Smith, E., Sparling, E., & Freedman, B. (2018). The importance of inclusion for cardiovascular health promotion programs in Delaware. Health Promotion Practice, 19(2), 256–266.
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Factors Associated with Chronic Disease Mortality: Decomposing Mortality Disparities Across County Income Groups Alek Lee The Charter School of Wilmington Amy L. Gootee-Ash, Ph.D. Delaware State University Cynthia Newton, Ph.D, M.P.A. Delaware State University
ABSTRACT Objective. To decompose the contribution of social determinants of health to chronic-disease mortality disparities between low, medium, and high-income counties. Methods. Mortality data for mainland US counties was obtained from the CDC WONDER database using ICD 10 codes C00-C97 (cancer), I00-I99 (heart disease), J40-J47 (chronic lower respiratory disease), and E10-E14 (diabetes) for adults over the age of 35 from 2016 through 2020. Counties were divided into three groups by income quartile (low-income, Q1; middle-income; Q2-3; high-income; Q4) and differences in county demographics and social determinants of health (SDOH) were examined. Random forest analysis was conducted as an exploratory study to assess the contribution of these social determinants of health to chronic disease mortality. Oaxaca-Blinder decomposition was used to quantify the contributions of SDOH to county-group mortality differences using the poorest quartile of counties as a reference group. Results. Average chronic disease mortality was found to be 37% higher in low-income counties compared to their high-income counterparts. The percentage of mortality disparity explained by socioeconomic, behavioral, demographic, and environmental factors was higher than 90% between these groups. Smoking, physical inactivity, and food insecurity were the top contributors to mortality differences, followed by poor quality of care. Conclusions & Public Policy Implications. The findings of this study suggest that health risk behaviors and social determinants of health account for a substantial proportion of income-mediated differences in chronic disease mortality across U.S. counties. Because state and federal funding is often allocated at the county level, the results of this study may be useful for policymakers as they develop and evaluate plans to address chronic disease mortality in socioeconomically disadvantaged areas.
INTRODUCTION Chronic diseases are responsible for seven out of ten deaths in the United States each year,1 affecting more than half of all adults nationwide.2 Although many chronic diseases are preventable through healthy lifestyle choices and proper medical care, they collectively drive the vast majority of healthcare expenditures in the United States.1 Social determinants of health (SDOH), including behavioral, socioeconomic, and environmental factors, contribute widely to the incidence of cancer, diabetes, and various chronic cardiovascular and respiratory disorders.3 There exist wide geographic disparities in mortality across the U.S., especially in the lowest income counties.4 Several social determinants of health, including transportation barriers, provider availability, and inadequate education, contribute to negative health outcomes along with individual risk factors such as obesity, smoking, and inactivity.5,6 Income is consistently associated with chronic disease mortality, and evidence suggests several mechanisms for this relationship.7,8 Poverty may affect health through deprivation and decreased social connection, 30 Delaware Journal of Public Health - October 2026
and higher income may improve health outcomes through stable housing, healthcare, and education, among other factors.8 Because state and federal government funding is often allocated at the county level, identifying areas with high chronic disease mortality has direct policy implications.9 Frequently investigated SDOH such as education, unemployment, and food insecurity have been shown to correlate with income and mortality at the individual and population levels.10,11 The relative contributions of social determinants of health to income-mediated chronic disease disparity remain to be quantified at the county level, and it is unclear which SDOH most strongly drive this difference. Analyzing the factors that mediate income-driven inequalities in chronic disease mortality may suggest targetable interventions for community health outcomes. In this context, we studied the association between chronic disease death rates and county-level income. The goal of this study was to decompose the mortality differences between low, medium, and high-income counties using a wide array of social determinants of health. Doi: 10.32481/djph.2026.10.07
METHODS Data Collection We obtained county-level age-adjusted mortality figures for adults over the age of 35 using ICD 10 codes C00-C97 (malignant neoplasms), I00-I99 (circulatory diseases), J40-J47 (chronic lower respiratory diseases), and E10-E14 (diabetes) from the CDC’s Wide-Ranging Online Data for Epidemiologic Research (WONDER) database for the years from 2016 through 2020 in the mainland U.S. The study excluded counties in Alaska and Hawaii due to limited data availability. Thirteen mainland counties were removed from the primary analysis due to data suppression, but a sensitivity analysis revealed no significant change in results after imputing values for these counties using Bayesian spatial modeling.12 The chronic disease mortality of each county was linked to median household income using data from the 2016 Census Bureau Small Area Income and Poverty Estimates. Various economic, demographic, clinical, behavioral, and environmental characteristics that have been shown to impact mortality were identified in the literature. Racial demographics were collected from the 2018 American Community Survey, and all other data was sourced from the 2018 Robert Wood Johnson County Health Rankings and CDC Social Vulnerability Index data. Unemployment was defined using the percent of civilians of more than 16 years of age not currently working but in search of work. Covariates included the following county-level variables: income (median household income, income inequality), education (percent of population over 25 years of age with no high schoolequivalent diploma), family stress (percent of children in singleparent households), clinical care (primary care providers per 100,000, percent of adults uninsured, preventable Medicare hospitalizations per capita), behavioral factors (percent of population smoking, obese, or inactive), environmental factors (food insecurity, average air particulate matter, percent of population facing housing difficulties), demographic factors (race, sex, percent of rural residents), and access to transportation (percent of population without a personal vehicle). Data for Medicare hospitalizations were missing for 225 counties, and a sensitivity analysis was conducted using estimates from multiple imputation. Poverty, English language proficiency, and exercise
access were considered as covariates but ultimately excluded to reduce multicollinearity. Variance inflation factor (VIF) values were below four for all variables with the exception of high school education (5.17).
Statistical Analysis We stratified counties into low, medium, and high-income groups. A Kruskal-Wallis test was used to determine differences in mortality and social determinants of health between counties. As an exploratory analysis, we used the random forest (RF) algorithm to determine the relative predictive importance of county-level measures for chronic disease mortality, both including and excluding income as a covariate. While random forest algorithms have been shown to explain more variation than linear regression as measured by R-squared, they provide less transparency about the calculations made to support model predictions.13 RFs indicate the relative strength of association between covariates and the dependent variable, but they do not indicate the direction of correlation.13 After conducting a Moran’s I test to measure spatial autocorrelation, a sensitivity analysis was conducted using spatial random forest algorithms with no significant change in results. To decompose the difference in mortality between low, middle, and high-income counties, we conducted the Oaxaca-Blinder decomposition,14,15 which quantifies the contributions of individual community-level factors to the observed mortality differences between groups. The Oaxaca-Blinder decomposition conducts stratified linear regressions to separate disparities in the dependent variable into explained and unexplained differences. Explained differences represent disparity due to differences in covariates, while unexplained differences absorb excluded coefficients and differences in how counties translate community characteristics to mortality. Low-income counties served as the baseline for comparison (low vs. medium income, low vs. high income). Racial demographics were coded using quartiles due to high skewness in the data. A twofold decomposition was conducted for each comparison using pooled regression coefficients as the reference coefficient structure. The explained component was calculated from differences in mean county characteristics between income groups, evaluated using the pooled reference coefficients.
Figure 1. Income and Chronic Disease Mortality by County (Mainland US)
31
The percentage contribution of each variable to the explained component was calculated accordingly. Standard errors and 95% confidence intervals for the decomposition coefficients were obtained by bootstrapping over 10,000 replications. All analyses were conducted using R version 4.6.1.
RESULTS County Group Characteristics There was a significant difference in age-adjusted chronic disease mortality between low, medium, and high-income counties (Figure 1). The lowest quartile of counties by income experienced 17% greater mortality than medium-income counties and 37% greater mortality than high-income counties. Significant differences were observed across county groups in demographics, social determinants of health, and individual health behaviors (Table 1). On average, low-income counties experienced greater unemployment, income inequality, family stress, and housing difficulties. Individuals in these counties were on average less educated than their high-income counterparts and more likely to smoke, be inactive, or live in areas with greater air pollution. Quality and access to clinical care were lower in the poorest counties.
Random Forest Analysis Random forest analysis was conducted using age-adjusted chronic disease mortality for adults as the dependent variable. The relative importance of 20 community measures is shown both with (Figure 2a) and without (Figure 2b) median household income as a covariate. Income ranked third behind smoking and inactivity as the most important factor in the initial analysis. After removing income as a covariate, inactivity, smoking, food insecurity, obesity, low-quality care, and high school education ranked as the six most important factors for chronic disease mortality.
Oaxaca-Blinder Decomposition Oaxaca-Blinder decomposition revealed that differences in ageadjusted mortality between county income groups were largely attributable to disparities in social determinants of health. Using the pooled decomposition, 271.7 deaths per 100,000 (92.0%) of the total gap in mortality between the lowest and highest income counties (295.3 deaths per 100,000) was explained by measurable socioeconomic, demographic, behavioral, and environmental factors. Similarly, disparities in SDOH accounted for 140.4 deaths per 100,000 out of the total difference of 158 deaths per 100,000 between the lowest- and middle-income counties. The main contributors to the explained difference between counties by income were smoking, food insecurity, inactivity,
Table 1. Additional Data Gathered Q1 (Lowest Income) Mean (SD)
Q1 Median
Middle Income Mean (SD)
Middle Income Median
Q4 (Highest Income) Mean (SD)
Q4 Median
p-value
Sig.
36.0 (3.8)
36.7
47.8 (4.0)
47.6
66.5 (12.0)
62.1
< 0.001
***
Age Adjusted Mortality Rate
1089.7 (189.6)
1090.4
931.7 (146.6)
928.3
794.4 (138.5)
794.1
< 0.001
***
Air Pollution
8.9 (1.4)
9.3
8.9 (1.6)
9.2
9.2 (1.7)
9.3
< 0.001
***
Food Insecurity (%)
18.0 (4.7)
17.3
13.7 (3.0)
13.5
11.0 (2.6)
11.0
< 0.001
***
0.470 (0.035)
0.467
0.441 (0.031)
0.439
0.431 (0.035)
0.428
< 0.001
***
High School (%)
22.9 (6.3)
22.1
14.8 (3.9)
14.6
10.0 (3.2)
9.8
< 0.001
***
Housing Problems (%)
15.5 (4.4)
15.0
13.8 (4.1)
13.5
14.3 (4.7)
13.5
< 0.001
***
Low Quality Care
74.9 (29.0)
69.0
58.5 (19.2)
56.0
47.7 (15.2)
46.0
< 0.001
***
Income (thousands)
Gini Index
No Vehicle (%)
8.1 (3.9)
7.5
5.8 (2.7)
5.6
5.1 (4.4)
4.4
< 0.001
***
PCP/100k
41.0 (28.9)
37.5
52.3 (35.5)
47.9
64.2 (37.1)
59.5
< 0.001
***
Percent Black
17.4 (21.1)
5.0
6.5 (10.4)
1.8
5.7 (8.2)
2.0
< 0.001
***
Percent Female
49.9 (3.0)
50.6
49.9 (2.0)
50.2
50.1 (1.5)
50.3
< 0.001
***
Percent Hispanic
9.9 (18.0)
3.0
8.5 (12.0)
3.8
10.2 (11.2)
6.0
< 0.001
***
Percent Inactive
30.5 (4.7)
30.5
27.0 (4.2)
27.0
22.9 (4.5)
23.3
< 0.001
***
Percent Obese
33.8 (4.5)
34.0
31.7 (3.6)
32.0
28.7 (4.6)
29.3
< 0.001
***
Percent Other (Race)
5.3 (11.7)
2.3
4.1 (4.5)
2.8
5.3 (4.8)
3.7
< 0.001
***
Percent Smokers
21.1 (3.5)
21.1
17.6 (2.7)
17.2
15.1 (2.5)
15.0
< 0.001
***
Percent Uninsured
18.1 (6.1)
17.7
13.8 (5.6)
13.2
10.9 (5.0)
9.7
< 0.001
***
Percent White
67.4 (24.7)
67.8
80.9 (16.5)
87.0
78.8 (16.3)
84.0
< 0.001
***
Rurality (%)
71.7 (25.9)
73.3
59.6 (30.2)
59.5
42.1 (31.8)
35.7
< 0.001
***
Single Parent Households (%)
40.3 (11.8)
38.9
31.8 (8.4)
31.8
26.6 (6.8)
26.7
< 0.001
***
Unemployment (%)
6.6 (2.0)
6.3
5.0 (1.6)
4.9
4.2 (1.1)
4.1
< 0.001
***
32 Delaware Journal of Public Health - October 2026
low-quality care, and educational attainment, the same top factors implicated by random forest analysis. The aggregate of these factors accounted for more than 90% of the explained difference between low and high-income counties as well as low and medium-income counties. Physical inactivity, smoking, food insecurity, low-quality care, and high school education were responsible for 24.2%, 22.7%, 20.7%, 14.5%, and 8.8% of the explained difference between high and low-income counties, respectively (Figure 3a). Although high school education accounted for an estimated 8.8% of the explained difference, its 95% confidence intervals encompassed zero for the comparison between the lowest and highest income counties, indicating that this result was not statistically significant. However, the 95% confidence intervals for the comparison between low and middleincome counties did not cross zero (Figure 3b). Smoking, food insecurity, physical inactivity, low-quality care, and high school education were responsible for 27.6%, 24.7%, 18.3%, 14.8%, and 11.6% of the explained difference between middle and lowincome counties, respectively. The Oaxaca-Blinder decomposition
also identified several community factors associated with smaller mortality differences between income groups, holding all other variables constant. These included income inequality (Gini index), rurality, and the percent of residents facing housing difficulties.
DISCUSSION We found substantial disparities in age-adjusted chronic disease mortality between U.S. counties and identified community factors that may account for more than 90% of the observed difference in mortality between low-, medium-, and high-income counties. We identified food insecurity, smoking, physical inactivity, lowquality care, and high school education as the largest contributors to this disparity, suggesting possible targets for intervention in socioeconomically disadvantaged areas. The findings of this study corroborate prior research that analyzes the effect of SDOH on chronic disease disparities. Quality of care ranked among the strongest individual
Figure 2a. Random Forest Analysis with Income as a Covariate
Figure 2b. Random Forest Analysis without Income as a Covariate
33
Figure 3a. Oaxaca-Blinder Decomposition Explained Mortality Difference Between Lowest and Highest Income Countries, with 95% Confidence Intervals
Figure 3b. Oaxaca-Blinder Decomposition Explained Mortality Difference Between Lowest and Middle-Income Countries, with 95% Confidence Intervals
contributors to income-mediated mortality differences, supporting prior research implicating it as a strong predictor of community health.16 Low quality care may contribute to chronic disease incidence and mortality through poor diagnoses, inadequate disease management, delayed treatment, and limited access to preventive services.17 These barriers can allow chronic conditions to reach advanced disease states unchecked, emphasizing the continued need for healthcare investment in socioeconomically disadvantaged areas. Health behaviors like smoking and physical inactivity contributed to the largest proportion of the explained mortality difference between counties. Prior studies have indicated that health risk behaviors are the factors that most strongly predict health outcomes,18 and epidemiological research links smoking, obesity, and physical inactivity to a wide variety of chronic diseases. Physical inactivity in particular has been linked to heart disease and diabetes,19 and there exists strong evidence for a direct causal link between smoking and respiratory diseases as well as cancer.20 34 Delaware Journal of Public Health - October 2026
This analysis also suggested a potential role for high school graduation in explaining mortality differences, supporting prior research that demonstrates an association between educational attainment and positive health outcomes. Existing research suggests that education may mediate chronic disease mortality by increasing health literacy and improving access to healthcare,21 and its close link with socioeconomic conditions may also influence individual-level health risks, financial stability, and access to community resources.22 While education’s contribution to the Oaxaca-Blinder’s explained component was relatively large, its 95% confidence interval encompassed zero in the low versus high-income comparison despite excluding zero when comparing low and middle-income counties, indicating different levels of statistical significance. As such, this result should be interpreted with more caution than other SDOH, such as food insecurity or quality of care. Education ranked highly among all community factors in the random forest analysis, but its high collinearity with other covariates (VIF = 5.17) may have limited the precision with which the Oaxaca-Blinder decomposition estimated its relative importance.
In addition to these well-established factors, both random forest analysis and Oaxaca-Blinder decomposition identified food insecurity as an important factor associated with differences in chronic disease mortality across income groups. A growing body of evidence continues to implicate food insecurity in chronic diseases such as cancer, heart disease, and diabetes.23 Poor nutritional intake and an excess diet of unhealthy calorie-dense foods may contribute to obesity, diabetes, cardiovascular diseases, and a wide variety of cancers.24, 25 While air pollution ranked highly in a random forest analysis of SDOH and chronic disease mortality, its relative significance decreased dramatically in the results from Oaxaca-Blinder decomposition. Although air pollution operates alongside socioeconomic factors in shaping health outcomes, this result may reflect a more complex distribution of SDOH across the United States. For example, urban counties often have higher rates of air pollution than rural counties,26 but their higher average income levels and improved access to healthcare may serve to offset some of this risk.27
Oaxaca Blinder decomposition is limited by its dependence on linear regression.
Public Health Implications While the findings of this study contribute to a strong body of evidence that supports targeting health risk behaviors at the community level, there remains a pressing need to address systemic factors such as access to education and high-quality care in socioeconomically disadvantaged areas. Because state and federal level funding is often allocated at the county level, it is important to analyze the factors associated with county-level disparities in chronic disease mortality. The longterm burden of chronic disease on the healthcare system creates a clear need for effective interventions to address mortality in health programs that are often funded on limited local budgets. The various social determinants of health identified in this study may be useful for policymakers as they develop and evaluate plans to address chronic disease mortality in lower income counties. The authors may be contacted at akaleklee@gmail.com.
While differences in social determinants of health successfully accounted for more than 90% of the mortality disparity in this study, the unexplained component may reflect complex relationships between factors, systemic differences between county groups, and excluded coefficients such as local and statelevel healthcare policy.
REFERENCES
The findings of this study also suggest that differences in income inequality, housing difficulties, and rurality may diminish the disparities in chronic disease mortality between income groups, holding all other covariates constant. This result likely reflects the complex relationship between socioeconomic conditions and population health. For example, rural counties can experience lower levels of unhealthy environmental exposures or stronger social cohesion that offsets the increased mortality commonly associated with rural residence.28 The findings of this study may corroborate prior research suggesting that social determinants of health more than account for the mortality disparity between rural and urban counties.29 Similarly, measures of income inequality and housing difficulty may fail to fully align with county income categories or capture more granular neighborhood-level factors. These results highlight the complex relationship between social determinants of health and mortality and should be interpreted within the broader socioeconomic and geographic context of individual communities.
3. Hyder, F., Pervaiz, H., Ashraf, A., Perveen, A., & Pervaiz, H. (2026). The impact of social determinants of health on chronic diseases: A narrative review. Journal of Community Hospital Internal Medicine Perspectives, 16(2), 30–37. https://scholarlycommons.gbmc.org/jchimp/vol16/iss2/6/
Limitations This study has several limitations. Using population data leaves research prone to ecological fallacies due to a lack of individuallevel detail. Community factors may also vary substantially within a county, but data availability did not support analysis at a more granular level. As such, the findings of this study should be applied to population-level health programs rather than individual interventions. While we attempted to account for demographic factors such as sex and race, it was impossible to account for all covariates. The unexplained difference from the Oaxaca Blinder decomposition may reflect important factors such as state and county-level policies (e.g., Medicaid expansion, no smoking laws) and other community factors. Results from random forest analysis should be used to measure relative importance in predicting variance, not causality. Finally, the
1. Raghupathi, W., & Raghupathi, V. (2018). An empirical study of chronic diseases in the United States: A visual analytics approach to public health. International Journal of Environmental Research and Public Health, 15(3), 431. 2. Boersma, P., Black, L. I., & Ward, B. W. (2020). Prevalence of multiple chronic conditions among US adults, 2018. Preventing Chronic Disease, 17, E106.
4. Cheng, E. R., & Kindig, D. A. (2012). Disparities in premature mortality between high- and low-income US counties. Preventing Chronic Disease, 9, E75. 5. Vrtikapa, K., Hoque Urmy, F., & Hoque, F. (2025). Social determinants of health: The impact of this overlooked vital sign. Journal of Brown Hospital Medicine, 4(3), 138072. Advance online publication. 6. Marmot, M. (2005). Social determinants of health inequalities. Lancet, 365(9464), 1099–1104. 7. Adler, N. E., & Newman, K. (2002). Socioeconomic disparities in health: Pathways and policies. Health Affairs (Project Hope), 21(2), 60–76. 8. Marmot, M. (2002). The influence of income on health: Views of an epidemiologist. Health Affairs (Project Hope), 21(2), 31–46. 9. Chan, M. P. L., Weinhold, R. S., Thomas, R., Gohlke, J. M., & Portier, C. J. (2015). Environmental predictors of US county mortality patterns on a national basis. PLoS One, 10(12), e0137832. 10. Bundy, J. D., Mills, K. T., He, H., LaVeist, T. A., Ferdinand, K. C., Chen, J., & He, J. (2023). Social determinants of health and premature death among adults in the USA from 1999 to 2018: A national cohort study. The Lancet. Public Health, 8(6), e422–e431. 11. Vo, A., Tao, Y., Li, Y., & Albarrak, A. (2023). The association between social determinants of health and population health outcomes: Ecological analysis. JMIR Public Health and Surveillance, 9, e44070. 12. Quick, H. (2019). Estimating County-Level Mortality Rates Using Highly Censored Data From CDC WONDER. Preventing Chronic Disease, 16, E76. Advance online publication. 13. Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3–29. 14. Oaxaca, R. (1973). Male–female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709. 15. Blinder, A. S. (1973). Wage discrimination: Reduced form and structural estimates. The Journal of Human Resources, 8(4), 436–455. 16. Ingram, R. C., Scutchfield, F. D., Charnigo, R., & Riddell, M. C. (2012). Local public health system performance and community health outcomes. American Journal of Preventive Medicine, 42(3), 214–220.
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17. Kumah, A. (2025). Poor quality care in healthcare settings: An overlooked epidemic. Frontiers in Public Health, 13, 1504172.
24. Gundersen, C., & Ziliak, J. P. (2015). Food insecurity and health outcomes. Health Affairs (Project Hope), 34(11), 1830–1839.
18. Schroeder, S. A. (2007). Shattuck Lecture. We can do better—Improving the health of the American people. The New England Journal of Medicine, 357(12), 1221–1228.
25. Mozaffarian, D. (2016). Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: A comprehensive review. Circulation, 133(2), 187–225.
19. Cleven, L., Krell-Roesch, J., Nigg, C. R., & Woll, A. (2020). The association between physical activity with incident obesity, coronary heart disease, diabetes and hypertension in adults: A systematic review of longitudinal studies published after 2012. BMC Public Health, 20, 726. 20. U.S. Department of Health and Human Services. (2014). The health consequences of smoking—50 years of progress: A report of the Surgeon General. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. 21. Cutler, D. M., & Lleras-Muney, A. (2010). Understanding differences in health behaviors by education. Journal of Health Economics, 29(1), 1–28. 22. Ross, C. E., & Mirowsky, J. (2010). Why education is the key to socioeconomic differentials in health. In C. E. Bird, P. Conrad, A. M. Fremont, & S. Timmermans (Eds.), Handbook of medical sociology (pp. 33–51). Vanderbilt University Press.
26. Strosnider, H., Kennedy, C., Monti, M., & Yip, F. (2017). Rural and Urban Differences in Air Quality, 2008-2012, and Community Drinking Water Quality, 2010-2015 - United States. MMWR. Surveillance Summaries, 66(13), 1–10. 27. Restrepo, B. J., Dobis, E. A., Low, S. A., & Thomas, K. L. (2026). Rural health disadvantages in the United States: Evidence from nationally representative data. Economics and Human Biology, 62, 101623. 28. Yang, T. C., Jensen, L., & Haran, M. (2011). Social capital and human mortality: Explaining the rural paradox with county-level mortality data. Rural Sociology, 76(3), 347–374. 29. Spencer, J. C., Wheeler, S. B., Rotter, J. S., & Holmes, G. M. (2018). Decomposing mortality disparities in urban and rural U.S. counties. Health Services Research, 53(6), 4310–4331.
23. O’Connor, J. M., Sedghi, T., Dhodapkar, M., Kane, M. J., & Gross, C. P. (2018). Factors associated with cancer disparities among low-, medium-, and high-income US counties. JAMA Network Open, 1(6), e183146.
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37
Achieving HbA1c Reduction in Diabetes Management: A Retrospective Comparison of Glucose Monitoring Methods Eshita Sharmin, M.D., M.P.H. Bayhealth Family Medicine Cameron Golden, Pharm.D., B.C.A..C.P. Bayhealth Endocrinology Ejaz Mahmood, M.D. Bayhealth Endocrinology
ABSTRACT Objective. To evaluate the effectiveness of continuous glucose monitoring (CGM) compared to point-of-care (POC) glucose testing in reducing HbA1c among adults with diabetes. Methods. This single-center, retrospective cohort study included adult patients with type 1 or type 2 diabetes attending Bayhealth Sussex Endocrinology Clinic between January 1, 2023 and December 31, 2024. Controls were POC testers, while the intervention group was CGM users. Data was extracted from Epic electronic medical records via SlicerDicer and chart review. Exclusion criteria included pregnancy, terminal illnesses (i.e. end-stage renal disease, cancer), and medications affecting CGM accuracy (i.e. hydroxyurea, ascorbic acid). The primary endpoint was change in HbA1c from baseline to 3- and 6-month follow-ups. Secondary endpoints included estimated average glucose (eAG) and CGM metrics (time sensor active, time in range (TIR), time above range (TAR)). Results. Of 90 patients, 63 used CGM and 27 were POC testers. Baseline characteristics were similar between the groups, though CGM patients had higher comorbidity burden and insulin use. CGM users achieved significantly greater HbA1c reduction than POC testers (-1.8% vs. -1%; p < 0.001). Improvements were most pronounced among patients with baseline HbA1c ≥9% (time sensor active of –2 with p = 0.227, TIR 13.4 with p = 0.43, high TAR –8.7 with p = 0.344, very high TAR 1.9 with p = 0.455) but significant among baseline HbA1c 7-9% (time sensor active of -1.5, TIR –3.6, high TAR 1.9, very high TAR -1, with p < 0.001 for all metrics). CGM compliance maintained high (81.9% at 3 months vs. 75.4% at 6 months, p = 0.021) with notable improvement of time-in-range (TIR) (1.8, p = 0.002). Conclusion. CGM use significantly improves glycemic outcomes for patients at-risk of developing HbA1c >9%, which supports broader CGM access for all patients with diabetes.
INTRODUCTION Diabetes is a chronic metabolic condition characterized by persistent hyperglycemia, that, if left untreated, leads to lifethreatening complications such as cardiovascular disease, kidney dysfunction, vision impairments, and neuropathy.1 Type 2 diabetes mellitus (T2DM) is the leading cause of diabetes worldwide and a major driver of mortality in the United States.1-3 T2DM arises from insulin resistance and is often influenced by genetic predisposition, elevated body mass index (BMI), and lifestyle factors (i.e. poor diet control and physical inactivity).1 Management modalities for diabetes target lifestyle modification, pharmacotherapy, and blood glucose level monitoring.2 Hemoglobin A1c (HbA1c) is a biomarker that reflects average blood glucose levels over 2-3 months, while point-of-care (POC) glucose testing provides instantaneous blood glucose readings.2 Continuous Glucose Monitoring (CGM) has emerged as a transformative approach to glycemic control given its capabilities for reporting real-time blood glucose data, identifying trends, and providing customizable 38 Delaware Journal of Public Health - October 2026
alerts to prevent adverse health outcomes from hyper- and hypoglycemia.4 Through subcutaneous sensors that transmit to smart devices, CGM empowers patients to make informed decisions about insulin dosing, dietary choices, and overall lifestyle modifications while viewing their live glucose data.3 Unlike HbA1c, CGM is unaffected by conditions that alter red blood cell turnover, such as anemia, chronic kidney disease, and blood loss or transfusion.5 Although studies about CGM show improvement in selfmanagement and glycemic awareness, access remains limited.4,6 Most insurers cover CGM only for insulin-dependent patients, excluding many patients with T2DM or gestational diabetes who might benefit from CGM.1,2 High out-of-pocket costs for CGM components (i.e. receiver/smart device, sensor replacements) further restrict access for socioeconomically disadvantaged populations.7 This study aims to evaluate CGM’s effectiveness compared with POC testing in reducing HbA1c levels across a diverse endocrinology clinic population. Doi: 10.32481/djph.2026.10.08
METHODS Study Design
Figure 1. Flow Chart Demonstrating Patient Selection and Inclusion/ Exclusion Criteria on Epic SlicerDicer Software
We conducted a single-center, retrospective cohort analysis of patients followed for routine diabetes care at our institution’s endocrinology clinic from January 1, 2023 to December 31, 2024. The project was formally reviewed and approved by our institution’s Institutional Review Board (IORG #008401) under protocol reference #BH-139. Patients using POC fingerstick testing served as controls, while CGM users formed the intervention group. Guided by the study aim, this analysis sought to answer each of the following research questions: 1. Among adults with type 1 or type 2 diabetes receiving routine endocrinology care, is CGM use associated with greater improvement in HbA1c form baseline to 3- and 6-month follow-up compared with POC fingerstick glucose monitoring? 2. Does baseline HbA1c severity modify the observed change in glycemic control among CGM users, particularly when comparing patients with less severely uncontrolled diabetes (HbA1c 7-9%) to those with very severely uncontrolled diabetes (HbA1c ≥ 9%)? 3. Among CGM users with less severely and very severely uncontrolled diabetes, how do CGM-derived measures of glycemic control (i.e. time sensor active, time-in-range (TIR), and time-above-range (TAR)) differ between follow-up visits?
Study Population Eligible patients were ≥18 years old and diagnosed with type 1 or type 2 diabetes. Pregnancy-related diagnoses were excluded. Inclusion required at least one HbA1c result and three consecutive endocrinology visits during the study period. Both insulin and non-insulin users were included. Other factors excluding patients include inconsistent 3-6 month follow-up, poor HbA1c testing adherence, unclear CGM initiation date or CGM initiation prior to establishing care with the endocrinology clinic, significant comorbidities (end-stage renal disease, active chemotherapy, massive blood transfusion, uncontrolled anemia), >3 hospitalizations within one year, or medications interfering with CGM readability (e.g. hydroxyurea, high-dose acetaminophen, ascorbic acid).8-11 Patients with inconsistent adherence to home fingerstick glucose monitoring were included in the study. Average daily fingersticks were reviewed in relation to estimated average glucose (eAG). A flow chart of the patient selection process and list of the eligibility criteria can be found in Figure 1.
Data Collection Patient clinical data and downloaded CGM metrics were recorded onto Epic electronic medical record (EMR) during routine diabetes follow-up visits. Epic SlicerDicer identified 567 potentially eligible patients. Chart reviews were conducted on a randomized subset (n = 190). Baseline demographic data such as age, sex, race, body mass index (BMI), and smoking status were obtained through Epic SlicerDicer. Chart review refined eligibility and captured variables not extractable via SlicerDicer (i.e. physical activity, diet control, medication adherence, financial barriers).
For POC patients, the first three consecutive visits (3–6 months apart) were analyzed. Estimated average glucose (eAG) was derived from HbA1c using the American Diabetes Association (ADA) eAG Conversion Calculator.12 For CGM patients, device data (Freestyle Libre 2/3 (Abbott Diabetes Care Inc.; Abbott Park, IL) or Dexcom G6/G7 (Dexcom, Inc.; San Diego, CA)) were collected at follow-ups. CGM metrics analyzed include time sensor active, TIR, and TAR with categorization per International Consensus Criteria.13,14 Time-below-range was not analyzed given it was documented as 0% for all patients.
Study Endpoints and Assessments The primary endpoint was the average change in HbA1c from baseline to 3- and 6-month follow-ups. Secondary endpoints included CGM metrics and eAG. Stratification by baseline HbA1c severity was employed: Group A (7-9%; less severely uncontrolled diabetes) and Group B (9-14%; very severely uncontrolled diabetes).15,16 CGM compliance was categorized as low (50-74%), mostly compliant (75-89%), and compliant (>90%) based on sensor usage. Comorbidity conditions was grouped as “Low” (0-3), “Intermediate” (4-6), and “High” (≥7).
Statistical Analysis Analyses were performed in SPSS with two-tailed significance set at p < 0.05. Linear regression assessed HbA1c changes relative to eAG. Non-parametric Friedman’s Two-way Analysis of Variance (ANOVA) by Ranks test compared HbA1c changes between visits. Descriptive statistics summarized baseline characteristics such as age, sex, race, BMI, number of chronic conditions associated with diabetes, number of glycemic control medications, insulin use status, frequency of daily fingersticks, CGM type, and risk factors for diabetes outcomes. 39
Table 1. Descriptive Analysis of Baseline Characteristics Overall (n = 90)
Fingerstick (Control) (n = 27)
CGM Users (n = 63)
65.5 ± 12.1
66.2 ± 10.7
65.2 ± 12.8
Male
49 (54.4)
16 (59.3)
33 (52.4)
Female
41 (45.6)
11 (40.7)
30 (47.6)
White or Caucasian
62 (68.9)
17 (63.0)
45 (71.4)
Black or African American
25 (27.8)
8 (29.6)
17 (27.0)
1 (1.1)
--
1 (1.6)
Variables Age in years, mean ± SD Sex, n (%)
Race, n (%)
Asian
2 (2.2)
2 (7.4)
--
Body Mass Index, mean ± SD
Other
33.1 ± 7.8
33.7 ± 9.8
32.8 ± 6.9
*# of Comorbid Conditions, mean ± SD
4.8 ± 1.9
4.3 ± 1.6
5.0 ± 2.0
By Range, n (%) Low (0-3)
24 (26.7)
9 (33.3)
15 (23.8)
Intermediate (4-6)
49 (54.4)
15 (55.6)
34 (54.0)
High (7+)
17 (18.9)
3 (11.1)
14 (22.2)
2.6 ± 1.3
2.0 ± 1.0
2.9 ± 1.3
No change
--
13 (48.1)
29 (46)
Reduced by 1 med
--
2 (7.4)
11 (17.5)
Reduced by >2 meds
--
--
2 (3.2)
Increased by 1 med
--
9 (33.3)
16 (25.4)
Increased by >2 meds
--
3 (11.1)
5 (7.9)
Medications Of Initial Diabetes Medications, mean ± SD Change in Meds from Baseline to 3rd Visit, n (%)
Insulin Use, n (%) Yes
60 (66.7)
11 (40.7)
49 (77.8)
No
30 (33.3)
16 (59.3)
14 (22.2)
Baseline Visit
--
1.5 ± 0.9
1.5 ± 1.3
2nd Visit
--
1.4 ± 1.1
--
3 Visit
--
1.7 ± 1.1
--
Dexcom G6
--
--
7 (11.1)
Dexcom G7
--
--
12 (19.0)
Freestyle Libre 2
--
--
25 (39.7)
Freestyle Libre 3
--
--
19 (30.2)
§Frequency of Daily Fingersticks, mean ± SD
rd
CGM Type, n (%)
Risk Factors Minimal Exercise
47 (52.2)
16 (59.3)
31 (49.2)
Poor Diet Control
31 (34.4)
11 (40.7)
20 (31.7)
Medication Non-Compliance
18 (20)
3 (1.1)
15 (23.8_
Affordability of Meds
3 (3.3)
1 (3.7)
2 (3.2)
1 (1.1)
1 (3.7)
--
Smoking Status Heavy Smoker Every Day
5 (5.6)
2 (7.4)
3 (4.8)
Former
41 (45.6)
15 (55.6)
26 (41.3)
Never
43 (47.8)
9 (33.3)
34 (54.0)
*Number of comorbid conditions is further stratified into ranges of low (0-3 conditions), medium (4-6 conditions), and high (7+ conditions). §Missing values: Frequency of daily fingersticks for control group (3rd visit, n = 3). 40 Delaware Journal of Public Health - October 2026
Adjusted p-values were calculated based on the descriptive variables. Secondary analysis compared CGM metrics between visits among Group A and Group B using adjusted linear regression modeling.
RESULTS Ninety patients were included in the study following EMR chart reviews: 63 CGM users and 27 POC testers. Three patients had T1DM, while the remainder had T2DM.
Baseline Characteristics Groups were similar in age, sex, and BMI (Table 1). The population was predominantly male (54.4%) and Caucasian (68.9%). Over half (54.4%) had “Intermediate” comorbidity burden, with CGM patients more likely to have “High” burden (22.2% vs. 11.1%). CGM patients used 1.5 times more diabetes medications at baseline but experienced greater medication reductions by the third visit (20.7% vs. 7.4%). Conversely, POC patients had higher rates of medication escalation (44.4% vs. 33.3%). 2/3 of patients were Insulin users, with higher usage in CGM patients (77.8% vs. 40.7%). CGM devices used were Freestyle Libre (69.9%) and Dexcom (30.1%). Minimal exercise (52.2%) and poor diet control (34.4%) were the most frequently reported lifestyle factors.
Primary Endpoint Analysis The average time between visits was variable (baseline to 2nd HbA1c: 3.9 months for POC, 4.5 months for CGM; vs. 2nd to 3rd HbA1c: 4.7 months for POC, 3.8 months for CGM). Across all patients, eAG improved by 29.6 mg/dL (p <0.001), with Group B improving fourfold compared to Group A (-92.2 vs. -20.1 mg/dL; p <0.001) (Table 2). HbA1c reduction was significant for CGM users (p < 0.001) but not for POC testers (p = 0.081). CGM patients achieved an average HbA1c reduction of nearly double compared to POC (-1.8% vs. -1%). Stratification showed larger improvements among Group B patients in both groups (CGM: -4.1%; POC: –3.2%) compared with Group A (CGM: -0.4%; POC: –0.7%).
CGM Metrics Sensor wear time averaged 84%, with 59% of patients ≥90% compliant by the 3rd visit (Table 3). TIR improved by 1.8% (p = 0.002) between 2nd and 3rd visits, driven mainly by Group A. Group B showed a nonsignificant 13.4% increase in TIR (p = 0.43). TAR decreased in the “High” range (-2.1%, p = 0.010) but increased in the “Very High” range (2.7%, p = 0.012) from 2nd to 3rd visits. Group A shifted significantly from “Very High” (-1%, p <0.001) to “High” (1.9%, p <0.001), while Group B showed non-significant variability (“High”: -8.7%, p = 0.344; “Very High”: 1.9%, p = 0.455).
DISCUSSION CGM patients presented with greater disease complexity, higher insulin use, more medications at baseline, and higher rates of medication nonadherence (23.8% vs. 1%), yet achieved superior HbA1c outcomes compared to POC testers. Real-time feedback likely enabled healthier lifestyle decisions, reducing the average medication burden [3,17-18]. In contrast, POC testers had more limited insight into their daily glucose trends, which likely contributed to their need for medication escalation. CGM data integrity is validated given compliance with sensor wear was high. “Compliant” and “Low compliance” patients both increased, showing a dichotomy in behavior over time. The increase in “Compliant” CGM patients is likely associated with the reduction of diabetes medications by the third visit. Despite higher medication nonadherence reported among CGM users, they achieved better glycemic outcomes, thus underscoring CGM’s behavioral impact.17,18 HbA1c improvements were strongest in patients with very poorly controlled diabetes (Group B), though statistical significance was limited by variability from risk factors (i.e. poor lifestyle habits, inconsistent CGM usage).19,20 Redistribution of hyperglycemia from “Very High” to “High” TAR categories in Group A suggests meaningful risk reduction even without dramatic overall glycemic change. It reinforces the value of CGM for populations in which insulin is often not clinically indicated, whereas Group B’s non-significant findings likely reflect continued compliance issues and reinforces the need for targeted lifestyle interventions in this population.
Table 2. Change in HbA1c Between Control (fingerstick) and Intervention (CGM) Groups from Baseline to 3- and 6-Month Visits Baseline to 2nd visit
2nd to 3rd visit
Baseline to 3rd visit
p-value
*Change in eAG vs. HbA1c for Fingerstick Patients, mean ± SD
-18.9 ± 51.9
-10.6 ± 33.3
-29.6 ± 58.9
<0.001
Control Group
†Initial HbA1c 7-9%
-12.6 ± 15.2
-7.5 ± 17.1
-20.1 ± 23.1
Initial HbA1c ≥9%
-67.1 ± 68
-25.1 ± 56.1
-92.2 ± 64.9
Change in HbA1c for fingerstick patients, mean ± SD
-0.7 ± 1.8
-0.4 ± 1.2
-1.0 ± 2.1
Initial HbA1c 7-9%
-0.4 ± 0.5
-0.3 ± 0.6
-0.7 ± 0.8
Initial HbA1c ≥9%
-2.3 ± 2.3
-0.9 ± 2.0
-3.2 ± 2.3
Change in HbA1c for CGM patients, mean ± SD
-1.3 ± 1.7
-0.5 ± 1.8
-1.8 ± 2.5
Initial HbA1c 7-9%
-0.5 ± 0.8
0.1 ± 1.3
-0.4 ± 1.2
Initial HbA1c ≥9%
-2.5 ± 1.9
-1.6 ± 1.6
-4.1 ± 1.6
0.081
CGM Users <0.001
*Estimated average glucose (eAG) was calculated using American Diabetes Association formula: eAG = 28.7 x HbA1c – 46.7. †Sample sizes for HbA1c stratification are as follows: HbA1c 7-9% (fingerstick, n = 10 and CGM, n = 30) vs. HbA1c ≥9% (fingerstick, n = 8 and CGM, n = 27). 41
Study Limitations
Public Health Implications
Study limitations include reliance on retrospective EMR review, missing HbA1c values, and inconsistent visit intervals. The cohort was predominantly Caucasian, which limited assessment of racial disparities in glycemic management. Limiting the cohort to endocrinology patients likely introduced selection bias toward more severe, complex cases than in primary care. Endocrinology patients have greater access to CGM samples and a larger volume of insulin users who qualify for CGM coverage, which is reflected in the POC group being smaller.21 The POC group was captured at initial endocrinology visits before treatment stabilization, whereas CGM users were well established with endocrinology prior to CGM initiation. Nonadherence to diabetes management overtime among the CGM group assists with neutralizing the effects of this limitation.20 Documentation inconsistencies combining “Very High” and “High” TAR may have skewed TAR analysis. Additionally, acetaminophen use could not be excluded or accurately quantified due to non-prescription acquisition and variable reporting, potentially affecting CGM accuracy.8-11
This study serves as a basis for future efforts in diabetes management, healthcare policy, and research. Larger studies should assess long-term outcomes of CGM use compared to POC testing, patient satisfaction, and how CGM use affects daily behavioral patterns. According to the American Academy of Family Physicians, at least 90% of patients in primary care are affected by diabetes.22 As patients seeking diabetes care outside of endocrinology clinics continues to grow, evaluating clinician familiarity with CGM technology, improving medical education to integrate CGM, and assessing CGM adoption and outcomes in primary care is essential.21 Addressing social determinants of health (SDOH) (i.e. low health literacy, limited insurance coverage, financial barriers) is essential for equitable CGM access. Expanding access to diabetes self-management education and support (DSMES) programs, supporting community health worker (CHW) initiatives, and broadening insurance coverage for diabetes resources (i.e. medications, CGM devices, supplies) could play a pivotal role in enhancing diabetes care for underserved populations.2,23,24 Costeffectiveness analyses comparing CGM to costs from diabetes-
Table 3. CGM Data Interpretation with Relation to Change in HbA1c (n = 63) Glycemic Control
Overall
CGM Indicators
2nd visit
3rd visit
*Time sensor active, mean ± SD
86.1 ± 13.5
84.4 ± 15.1
Compliant ≥90%, n(%)
34 (55.7)
36 (59)
Mostly compliant 75-89%, n(%)
16 (26.2)
10 (16.4)
Low compliance 50-74%, n(%)
11 (18.0)
15 (24.6)
Time in range, mean ± SD
60.7 ± 24.4
“High”
Average change p-value p-value (2nd to 3rd visit) (unadjusted) (adjusted) -1.7 ± 4.1
0.575
0.021
62.2 ± 25.7
1.8 ± 38.3
<0.001
0.002
27.8 ± 16.7
25.8 ± 18.4
-2.1 ± 24.3
0.044
0.010
“Very High”
7.6 ± 12.3
9.3 ± 13.5
2.7 ± 10
0.037
0.012
-1.5 ± 4.5
0.398
<0.001
<0.001
†Time above range, mean ± SD
Group ALess Severely Uncontrolled HbA1c 7-9%
Group BVery Severely Uncontrolled HbA1c ≥9%
Time sensor active, mean ± SD
87.3 ± 12.1
85.9 ± 14
Compliant ≥90%, n(%)
18 (62.1)
19 (65.5)
Mostly compliant 75-89%, n(%)
6 (20.7)
4 (13.8)
Low compliance 50-74%, n(%)
5 (17.2)
6 (20.7)
Time in range, mean ± SD
62.4 ± 22.3
58.2 ± 28
-3.6 ± 40.5
<0.001
Time above range, mean ± SD “High”
27.8 ± 15.7
30 ± 20
1.9 ± 27.2
0.055
<0.001
“Very High”
10.7 ± 15.3
9.6 ± 14.6
-1 ± 11.7
0.179
<0.001
Time sensor active, mean ± SD
85.9 ± 14.8
83.9 ± 16.4
-2 ± 3.8
0.48
0.227
Compliant ≥90%, n(%)
14 (53.8)
15 (57.7)
Mostly compliant 75-89%, n(%)
7 (26.9)
4 (15.4)
Low compliance 50-74%, n(%)
5 (19.2)
7 (26.9)
Time in range, mean ± SD
53.4 ± 25.5
66.7 ± 22.4
13.4 ± 35.2
0.361
0.43
“High”
31.3 ± 17.6
22.5 ± 16.2
-8.7 ± 21.4
0.099
0.344
“Very High”
5 ± 8.4
6.9 ± 10.9
1.9 ± 4
0.967
0.455
Time above range, mean ± SD
*All time variable averages are reported as percentages. †Time above range (TAR) percentages have been aggregated into qualitative Y/N variables by goal ranges per International Consensus Criteria. The “High” category (BG range 180-250 mg/dL) has a goal of <25%. The “Very High” category (BG >250 mg/dL) has a goal of <5%. §Missing values: Time sensor active (n = 2) in both 2nd and 3rd visit groups, so frequencies are calculated out of n = 61 for overall, n = 29 for HbA1c 7-9%, and n = 26 for HbA1c ≥9%; all other indicators for 3rd visit n = 1 missing, so frequencies are calculated out of n = 62. 42 Delaware Journal of Public Health - October 2026
related hospitalizations and complications (i.e. amputations, kidney failure, hyper/hypoglycemia episodes) could further justify broader coverage.6 Policy reforms are urgently needed to expand eligibility beyond insulin users.24 The American Diabetes Association (ADA) has highlighted expanding CGM access as a priority for their current diabetes advocacy agenda.25 By addressing these key areas for future diabetes research and advocacy, we can improve glycemic control, reduce disparities in access, and develop equitable, sustainable policies.
CONCLUSION CGM use leads to greater improvements in glycemic control compared to POC glucose monitoring as demonstrated by a significant reduction in HbA1c. By enabling real-time feedback, CGM enhances self-management, reduces medication burden, and improves glycemic outcomes. Expanding access to CGM for all patients with diabetes, regardless of insulin use, is essential for optimizing diabetes care. Dr. Sharmin may be contacted at Eshita.sharmin@alumni.emory.edu.
REFERENCES 1. ElSayed, N. A., McCoy, R. G., Aleppo, G., Balapattabi, K., Beverly, E. A., Briggs Early, K., Bruemmer, D., Ebekozien, O., Echouffo-Tcheugui, J. B., Ekhlaspour, L., Gaglia, J. L., Garg, R., Khunti, K., Lal, R., Lingvay, I., Matfin, G., Pandya, N., Pekas, E. J., Pilla, S. J., …, Bannuru, R. R., & the American Diabetes Association Professional Practice Committee. (2025). 2. Diagnosis and classification of diabetes: Standards of care in diabetes—2025. Diabetes Care, 48(Suppl 1), S27–S49. 2. American Diabetes Association. (2022). Standards of medical care in diabetes—2022 abridged for primary care providers. Clinical Diabetes : a Publication of the American Diabetes Association, 40(1), 10–38. 3. Centers for Disease Control and Prevention, National Center for Health Statistics. (2024). National Vital Statistics System: Provisional mortality on CDC WONDER online database [Data set]. Centers for Disease Control and Prevention. Retrieved April 14, 2024, from http://wonder.cdc.gov/mcd-icd10-provisional.html 4. Galindo, R. J., & Aleppo, G. (2020). Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology. Diabetes Research and Clinical Practice, 170, 108502. Advance online publication. 5. Doucet, J., Gourdy, P., Meyer, L., Benabdelmoumene, N., & Bourdel-Marchasson, I. (2023). Management of glucose-lowering therapy in older adults with type 2 diabetes: Challenges and opportunities. Clinical Interventions in Aging, 18, 1687–1703. 6. Glennie, J. L., Berard, L., & Levrat-Guillen, F. (2022). Sensor-based technology: Bringing value to people with diabetes and the healthcare system in an evolving world. ClinicoEconomics and Outcomes Research, 14, 75–90. 7. Klupa, T., Czupryniak, L., Dzida, G., Fichna, P., Jarosz-Chobot, P., Gumprecht, J., Mysliwiec, M., Szadkowska, A., Bomba-Opon, D., Czajkowski, K., Malecki, M. T., & Zozulinska-Ziolkiewicz, D. A. (2023). Expanding the role of continuous glucose monitoring in modern diabetes care beyond type 1 disease. Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders, 14(8), 1241–1266.
12. American Diabetes Association. (n.d.). EAG/A1C conversion calculator. Retrieved February 3, 2025, from https://professional.diabetes.org/glucose_calc 13. Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., Cobelli, C., Dassau, E., DeVries, J. H., Donaghue, K. C., Dovc, K., Doyle, F. J., III, Garg, S., Grunberger, G., Heller, S., . . . Phillip, M. (2019). Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the International Consensus on Time in Range. Diabetes Care, 42(8), 1593–1603. 14. Association of Diabetes Care & Education Specialists. (2024). Core glycemic metrics explained: TIR, TBR, TAR, GMI, CV, and mean glucose for diabetes care. Retrieved February 3, 2025, from https://www.adces.org/education/danatech/glucose-monitoring/continuousglucose-monitors-(cgm)/glycemic-metrics--an-overview 15. Venkatraman, S., Echouffo-Tcheugui, J. B., Selvin, E., & Fang, M. (2022). Trends and disparities in glycemic control and severe hyperglycemia among US adults with diabetes using insulin, 1988–2020. JAMA Network Open, 5(12), e2247656. Advance online publication. 16. ElSayed, N. A., McCoy, R. G., Aleppo, G., Balapattabi, K., Beverly, E. A., Briggs Early, K., Bruemmer, D., Echouffo-Tcheugui, J. B., Ekhlaspour, L., Garg, R., Khunti, K., Lal, R., Lingvay, I., Matfin, G., Pandya, N., Pekas, E. J., Pilla, S. J., Polsky, S., Segal, A. R., …, Bannuru, R. R., & the American Diabetes Association Professional Practice Committee. (2025). 6. Glycemic goals and hypoglycemia: Standards of care in diabetes—2025. Diabetes Care, 48(Suppl 1), S128–S145. 17. Rhyu, J., Lambrechts, S., Han, M. A., & Freeby, M. J. (2022). Utilizing point-of-care A1c to impact outcomes - can we make it happen in primary care? Current Opinion in Endocrinology, Diabetes, and Obesity, 29(1), 29–33. 18. Simonson, G. D., Bergenstal, R. M., Johnson, M. L., Davidson, J. L., & Martens, T. W. (2021). Effect of professional CGM (pCGM) on glucose management in type 2 diabetes patients in primary care. Journal of Diabetes Science and Technology, 15(3), 539–545. 19. Baxi, S., Lakin, J., Lyles, C. R., Berkowitz, S., Horton, C., & Sarkar, U. (2013). Points for improvement: Performance measurement for glycemic control in diabetes patients in a safety-net population. Joint Commission Journal on Quality and Patient Safety, 39(3), 109–113. 20. Edelman, S. V., & Polonsky, W. H. (2017). Type 2 diabetes in the real world: The elusive nature of glycemic control. Diabetes Care, 40(11), 1425–1432. 21. Mayberry, L. S., Guy, C., Hendrickson, C. D., McCoy, A. B., & Elasy, T. (2023). Rates and correlates of uptake of continuous glucose monitors among adults with type 2 diabetes in primary care and endocrinology settings. Journal of General Internal Medicine, 38(11), 2546–2552. 22. Shrivastav, M., Gibson, W., Jr., Shrivastav, R., Elzea, K., Khambatta, C., Sonawane, R., Sierra, J. A., & Vigersky, R. (2018). Type 2 diabetes management in primary care: The role of retrospective, professional continuous glucose monitoring. Diabetes Spectrum, 31(3), 279–287. 23. Evans, J., Ha, H., & White, P. T. (2025). Evaluating the effectiveness of community health worker interventions on glycaemic control in type 2 diabetes mellitus: A systematic review and meta-analysis. BMJ Open, 15(7), e096651. Advance online publication. 24. American Diabetes Association. (2020). Improving diabetes care and promoting health equity: A call to action. Diabetes Care, 43(7), 1411–1424. 25. American Diabetes Association. (2024). Continuous glucose monitors. Retrieved April 14, 2024, from https://diabetes.org/advocacy/cgm-continuous-glucose-monitors
8. Basu, A., Veettil, S., Dyer, R., Peyser, T., & Basu, R. (2016). Direct evidence of acetaminophen interference with subcutaneous glucose sensing in humans: A pilot study. Diabetes Technology & Therapeutics, 18(Suppl 2), S243–S247. 9. Calhoun, P., Johnson, T. K., Hughes, J., Price, D., & Balo, A. K. (2018). Resistance to acetaminophen interference in a novel continuous glucose monitoring system. Journal of Diabetes Science and Technology, 12(2), 393–396. 10. Dexcom. (2025). Dexcom G7 user guide. Dexcom, Inc. https://dexcompdf.s3.us-west-2.amazonaws.com/en-us/G7-CGM-Users-Guide.pdf 11. Abbott. (2024). FreeStyle Libre 2 flash glucose monitoring system: User’s manual. Abbott Diabetes Care Inc. https://www.freestyle.abbott/content/dam/adc/webu/canada-english/pdf-guides/ System_User_Manual_EN.pdf
43
FOCUS Can antiretroviral therapy transform the lives of the estimated 40.9 million people living with HIV?
PROFILE John R. Koethe, MD, studied nutritional factors contributing to excess mortality among people with HIV
Q&A Benjamin Chi, MD, led a research portfolio in Lusaka, Zambia, that included HIV care and treatment
NIH UPDATE National Institute on Drug Abuse 2026 International Forum highlights illicit drugs in the Americas
N A T I O N A L I N S T I T U T E S O F H E A LT H • D E P A R T M E N T O F H E A LT H A N D H U M A N S E R V I C E S
Global Health Matters F O G A R T Y I N T E R N AT I O N A L C E N T E R
Courtesy of the National Institute of Allergy and Infectious Diseases
44 Delaware Journal of Public Health - October 2026
JU LY/AUGUST 2026 I I SSU E 4
Researcher working on ART
UNDERSTANDING HOW FLU SPREADS PHIRST team members prepare for community testing
EVERY YEAR, SEASONAL FLU
measurable biomarker that indicates
virus without ever feeling ill—that
SICKENS YOUNG AND OLD IN
whether or not someone is protected
would never come to light in a study
COMMUNITIES ACROSS THE
against a disease. Most often it is
based on doctor’s visits. In fact, in
GLOBE. Meanwhile scientists have
measured in the blood, for example,
previous PHIRST studies of the flu,
struggled to answer a deceptively
antibody levels. For many illnesses, a
researchers found that asymptom-
simple question: What factors de-
certain level of antibodies would mean
atic infections and transmission are
termine whether a person gets
that an individual is protected against
much more common than previously
infected? A new study models the
that sickness. This study, then, mod-
thought.
risk of flu infection using data from
eled the risk of influenza infections in
the Prospective Household cohort
South African households based on
people across 327 households in
study of Influenza, Respiratory
high-resolution PCR testing (a type
rural and urban South Africa for three
Syncytial virus, and other respira-
of genetic test that can identify infec-
flu seasons between 2016 and 2018.
tory pathogens community burden
tions) and a pre-flu-season serology
Specifically, they zeroed in on four flu
and Transmission dynamics in
test (serum is the liquid component of
types: two strains of influenza A (H1N1
South Africa (PHIRST).
blood) to measure each participant’s
and H3N2) and two lineages of influ-
Here, the researchers tracked 1,518
The study, published in Nature
existing antibody level. Specifically,
enza B (Victoria and Yamagata). Three
Communications, is representative
the study serology testing “measured
major findings emerged.
of the long and productive collab-
antibody responses to the main influ-
First, they found that both house-
oration between Fogarty and the
enza surface antigen (hemagglutinin),”
hold and community exposure were
PHIRST team.
notes Viboud.
powerful, independent predictors of
UNIQUE STUDY DESIGN
Courtesy of NIAID
Most flu research relies on peo-
who got sick. So, if an individual was
“This work touches on correlates
ple visiting a doctor once they feel
actively shedding a lot of virus, the
of protection, which is an active area
sick, yet the PHIRST studies take a
risk of others in the household becom-
of flu research,” says study co-author
very unusual, proactive approach. The
ing infected jumped substantially—by
Cécile Viboud, PhD, Acting Director
PHIRST study design is the genius
roughly 65% to 95%, depending on
of Fogarty’s Division of International
of Dr. Cheryl Cohen’s team at the
the strain. (Shedding refers to when
Epidemiology and Population Studies.
National Institute for Communicable
an infected person releases the virus
Diseases in South Africa. To under-
into the environment through sneez-
stand how respiratory infections
ing, coughing, or even speaking.)
spread, Cohen’s team set up cohorts
Separately, how much flu was circulat-
of people who get twice weekly PCR
ing in the wider community, including
testing—whether or not they are
at schools and workplaces, also drove
showing symptoms—for influenza and
up individual risk.
A correlate of protection is a
RSV. This approach captures asymp-
Second, pre-existing immunity
tomatic infections—cases where an
offered some, but not total, protec-
individual carries and spreads the
tion. People with higher antibody
Photo’s courtesy of the National Institute for Communicable Diseases, South Africa
Insights from a Fogarty collaboration
influenza virus 2
GLOBAL HEALTH MATTERS
45
Global Health Matters levels prior to the start of flu season were 50% to 70% less likely to be infected with H1N1, H3N2,
Fogarty International Center
or B/Victoria; however, the antibody threshold that protected against these strains did not protect against B/Yamagata. When
PHIRST team members swabs a member of the cohort
WHY THIS MATTERS
the researchers analyzed antibody
These findings have practical
levels on a continuous scale, some
implications. Because young chil-
protective effect did appear, sug-
dren are both more susceptible
gesting a standard threshold may
and more contagious for longer
not be the right benchmark for
stretches, they likely play an out-
every strain.
sized role in spreading flu within
The third finding was most strik-
families and communities. The study
ing: age influenced household
also suggests that current blood
dynamics a lot. Children, especially
tests used to gauge flu protection
those under 5, were more likely to
may be incomplete, particularly for
get infected and, when infected,
children and for certain strains like
shed the virus for several days lon-
H3N2. This points to a need for bet-
ger than adults over 40. This held
ter tools to measure who is actually
true even for children with seem-
protected against flu, beyond the
ingly protective antibody levels.
standard antibody test.
The researchers suggest that the
“Refining our understanding
standard blood test doesn’t cap-
of influenza transmission is essen-
ture everything relevant about a
tial for identifying key risk factors
child’s immune defenses. They
for infection, characterizing pro-
speculate that other immune
tective immunity, and informing
responses may play a bigger pro-
prevention strategies to reduce dis-
tective role in children than previ-
ease burden,” stated the authors.
ously believed.
By combining symptom-blind test-
National Institutes of Health Department of Health and Human Services
July/August 2026 Volume 26, Issue 04 ISSN: 1938-5935 Editor-in-Chief Susan Scutti Advisory Editor Carl W. Dieffenbach Digital Analyst Merrijoy Vicente Graphic Designer Carla Conway
CONNECT WITH US
The Fogarty International Center is dedicated to advancing the mission
ing with sophisticated statistical
of the National Institutes of Health
modeling, this research contributes
by supporting and facilitating global
to the understanding of how flu
health research conducted by U.S. and
moves through real households.
international investigators, building partnerships between health research institutions in the United States and abroad, and training the next gener-
Research from Cécile Viboud, PhD, Acting Director of Fogarty’s Division of International Epidemiology and Population Studies
ation of scientists to address global health needs.
fic.nih.gov July/August 2026
46 Delaware Journal of Public Health - October 2026
3
profile
John R. Koethe MD, MSCI Fogarty Fellow 2008-2009 U.S. institution: Vanderbilt University Medical Center Foreign institution Center for Infectious Diseases Research, Zambia Research topic The nutritional factors contributing to excess mortality among people with HIV during the early years of antiretroviral therapy scale-up in Zambia Current affiliation Professor, Division of Infectious Diseases, Vanderbilt University Medical Center, and Director, Tennessee Center for AIDS Research
a world-class health system.” Zambia The Centre for Infectious Disease Research in Zambia
Nutrition and weight gain in early HIV treatment outcomes
was establishing new clinics and expanding access to antiretroviral therapy (ART) to about 10,000 new patients each month. Koethe saw a rare opportunity “to have an impact and also be part of one of the largest
John R. Koethe, MD, chose the field of
After completing his education,
health initiatives in recent history.”
infectious diseases because it offers
he felt a hankering to return to Africa
For his Fogarty project, he pro-
“breadth and possibilities” and oppor-
to study HIV. “For the first time,
posed a study at the intersection of
tunities for “patient-facing research.” In
the Fogarty Fellows and Scholars
malnutrition and HIV, which involved
particular, the HIV epidemic captured
Program [now called LAUNCH] was
delivering food aid through Lusaka’s
his interest. He’d seen how HIV infec-
offering post-residency fellowships,”
HIV clinics, while looking at health out-
tions and related deaths crested in the
says Koethe. He applied and won a
comes in patients who’d initiated ART.
United States during 1995, and a few
spot in the transformative program.
In 2008, Koethe flew from the United
years later when he began medical
Research challenges
States to Lusaka. Unfortunately, when
school, “U.S. deaths were no longer
“Zambia was one of the epicenters
he arrived, he found that his proposed
going up… and new drugs were being
of the HIV epidemic in sub-Saha-
Fogarty project had been canceled
developed.” During his residency, he
ran Africa. HIV prevalence in Lusaka
due to a lack of funds.
worked with HIV patients in Uganda
[Zambia’s capital] was over 15%
at the Johns Hopkins Infectious
and rising,” says Koethe. The coun-
sis of the factors driving early mortality
Diseases Institute, which provided an
try had few resources, yet it was
in HIV patients (after they began
introduction to the field of infectious
“extremely stable” and “had a great
ART) using Zambia’s National HIV
diseases in an international setting.
deal of leadership interest in building
Database. This idea was based on
Koethe quickly pivoted to an analy-
4 GLOBAL HEALTH MATTERS 4GLOBAL GLOBAL HEALTH MATTERS HEALTH MATTERS
47
what he’d seen: half of patients with
are valuable to the general popula-
a body mass index of less than 16
tion, particularly the aging population
(reflecting severe malnutrition) either
with high rates of overweight, obe-
died or were lost to follow-up (pre-
sity, and frailty. Working abroad in
sumably dead) at 12 weeks after they
different contexts sheds more light
started treatment.
on a research topic, says Koethe. In Zambia, for example, diets and activity
His analysis of nearly 28,000 adults beginning treatment found the great-
Lusaka street corner, circa 2008.
levels are different than in the United
est improvement among people
different research groups “got the
States; people also encounter dissimi-
with a BMI of less than 16. Within this
field interested in obesity and HIV and
lar environmental pollutants. Studying
same group, those who failed to gain
weight gain” while also opening a dis-
the same topic in two or three dif-
weight six months post-ART showed a
cussion on the real problem of weight
ferent countries helps researchers
nearly 10 times increased risk of dying
gain during treatment: “It’s where that
understand whether outcomes are
compared to those who’d gained 10
weight is deposited that matters.”
due to a disease process and its direct
When an individual acquires HIV,
effects on physiology and biology…
all BMI categories, individuals with a
there’s a period of muscle loss and
or due to health habits or the envi-
weight gain of at least 5 kilograms (11
perturbations in the balance between
ronment. “If it’s an environmental or
pounds) after six months of treatment
lean muscle and adipose tissue (fat),
dietary problem, then you modify
had better outcomes than those who
says Koethe. This happens whether
that. But if it’s a physiologic process,
didn’t gain weight.
that person exhibits clinical signs of
common to everyone across differ-
Fellowship benefits
wasting (or is visibly malnourished)
ent countries, that requires deeper
Koethe’s Fogarty project resulted in
or not and “begins at the moment of
investigation to really understand the
the publication of a number of peer-re-
HIV acquisition and may take different
biological pathways.”
viewed papers and provided him with
courses resulting in different pheno-
Currently, the National Institute
“tremendous mentors,” who helped
types.” Some people begin ART and
of Allergy and Infectious Diseases is
him crystallize his area of interest.
“go back to exactly the body com-
supporting Koethe’s work across a
Arguably, the most important out-
position they started with—they’re
range of HIV topic areas through the
come of his project was that it evolved
rare.” By contrast other people’s bod-
Tennessee Center for AIDS Research
into a 1,800-participant clinical trial in
ies collect fat or lipids around the
(CFAR), a program that goes back
Zambia (“NUSTART”) funded by the
heart, the liver, the muscles, and
almost four decades. Koethe says,
European Union.
the viscera. “After a short period,
“HIV was the great global pandemic
they may develop signs of cardio-
of the late 20th century, and it’s going
interested in the obesity epidemic
vascular disease, diabetes, or other
to remain one of the great pandemics
among individuals with HIV in the
complications.”
of the 21st century. We’ve had tremen-
kilograms (22 pounds) or more. In
Returning home, Koethe “became
United States.” Initially, he looked at
Koethe theorizes that “there’s a
dous success over the last 40 years,
large databases—building on ana-
very short window” where a modula-
but this requires continued invest-
lytical skills he developed during his
tion in diet, activity level, and rate
ment, continued commitment, and
Fogarty year—to understand the pre-
of weight gain might help a person
continued attention.”
dictors of ART-related weight gain. He
avoid an adverse phenotype after
found that individuals starting inte-
beginning ART.
grase inhibitor-based ART gained
A goal in sight
more weight than individuals taking
Koethe believes that “overseas
the older classes of HIV drugs. “We
work is critical to improving the health
were one of the first teams to show
of Americans—and not just Americans
this,” he says. Similar reports from
with HIV.” A lot of the lessons learned
Koethe served during the rapid scaleup of antiretroviral therapy in Zambia. July/August 2026
48 Delaware Journal of Public Health - October 2026
5
FOCUS I HIV
Illustration courtesy of the National Institute of Allergy and Infectious Diseases
HIV infec ting a human cell.
A n estimated 40.9 million people globa lly are living with human immunodef iciency virus (HIV ). HIV, f irst identif ied in the early 1980s, damages immune cells and harms the body’s abilit y to clear infections and diseases. Early treatments were for t he infect ions acqu ired by people living w it h HI V a nd these medications helped to prolong their lives in the advanced stage of HIV (acquired immunodef iciency syndrome or A IDS).
FOCUS 49
Remarkable scientific progress over
A recent addition to the ART arse-
Many believe lenacapavir is a true
four decades has led to the develop-
nal is lenacapavir, developed by
“game changer,” says Jared Baeten,
ment of effective antiretroviral therapy
Gilead Sciences, Inc. over nearly two
MD, PhD, Gilead Sciences. “A twice-
(ART). ART has transformed HIV, once
decades based on early-stage science
yearly option has the potential to
considered a death sentence, into a
conducted by academic investigators
change how people experience HIV
manageable chronic condition. When
funded by NIH. It is approved by regu-
prevention.” What feels “paradigm-
taken as prescribed, ART prevents
latory authorities in the United States
shifting” to Baeten, though, is not the
people living with HIV from passing
and other countries for the treatment
“unprecedented science” but “the
their infection onto others and allows
of HIV (combined with other drugs)
opportunity to expand choice and
people, if diagnosed and treated
and for the prevention of HIV (pre-ex-
rethink how prevention can be deliv-
early, to live a normal lifespan. It’s
posure prophylaxis or PrEP). As PrEP,
ered for people and communities that
important to note that taking the med-
lenacapavir is delivered as a twice
have not always been well served by
ication reliably is critical.
yearly shot.
existing options.”
ADVANCING PHARMA“We’ve achieved really), and the movement is toward remarkable sucprimary care and mid-level practicess with HIV tioners.” In short, people living with COLOGY screening, getting HIV, once considered “too complex” people on ART, for primary care, are returning to IN ZIMBABWE
BEYOND ART ITSELF
and monitoring them,” says Fogarty Grantee Donna Shelley, MD, MPH, New York University. “People with HIV are living longer and developing noncommunicable diseases, which are becoming a primary cause of morbidity and mortality in this population. Now the focus is on longevity and a lifespan approach to HIV care,” she says.
In response to effective treatments and strategies, health care systems have begun to change, says Fogarty Grantee Gene Morse, PharmD, FCCP, BCPS, University of Buffalo. He says, “What we’re seeing now is the end of the specialized HIV care centers (everywhere,
that setting.
Fogarty Grantee Humberto Lopez Castillo, MD, PhD, University of Central Florida, says he hopes to see a cure for HIV in his lifetime. He adds that he “firmly believes the interventions we have can curb incidence rates at a faster pace than we’re already seeing now … both global and local research will get us there.” Meanwhile, “the curve is flattening little by little,” he says. Gilead’s Baeten, a former Fogarty trainee, says, “We have tools that previous generations of HIV researchers and advocates could only imagine. Now we have to do the harder work of making sure those tools reach people.
(& the United States) The HIV Clinical Pharmacology
& Therapeutics Research Training (HIVRT) program is “a personal story,” built on the unique circumstances and long-standing friendship that created it, says Chiedza Maponga, PharmD, University of Zimbabwe (UZ) Faculty of Medicine and Health Sciences. After Zimbabwe won its independence in 1980, Maponga studied clinical pharmacology at University of Buffalo (UB). He says, “The U.S. was helping Zimbabwe get on its feet and I was selected as one of the young people to get further education in the United States with the hopes that I would come back to Zimbabwe and become faculty.” Gene Morse, PharmD, FCCP, BCPS, then July/August 2026
50 Delaware Journal of Public Health - October 2026
7
FOCUS
Chiedza Maponga
an associate professor and now the director of UB’s Center for Integrated Global Biomedical Sciences, became his mentor. Following UB, Maponga returned to his country, where he became a trusted national leader in the HIV epidemic. Despite the workload, he maintained ties with his mentor. Frequently, they discussed the evolving HIV pandemic. “When triple-drug therapy came along, there was need for pharmacologists to intervene because the medications were complex and
an AIDS International Training and
contributing to both research and
Research Program (AITRP) grant at the
education projects in HIV drug devel-
University of California, Berkeley. The
opment and clinical pharmacology.
supplement supported the team’s first
“As HIV has evolved, the ‘critical
two trainees and paved the way for
mass’ began to include people who
Maponga and Morse to submit a joint
knew about immunology, virology,
grant proposal from UB and UZ. They
biostatistics, cancer, the microbiome,
received their own Fogarty funding
and noncommunicable diseases,”
in 2008 and eventually renamed the
explains Morse. Over time, then,
program.
the HIVRT program has built mul-
Though they originally trained
tidisciplinary teams, where the
only master’s students, their subse-
pharmacologists may be the central
quent proposals also included PhD
focus, yet collaborators are equally
and postdoctoral trainees. With each
important, says Morse. In this way,
successive iteration of the HIVRT pro-
they’ve laid a foundation for research
gram, Morse and Maponga conducted
within the Faculty of Medicine and
a needs assessment to align their
Health Sciences at UZ, which in turn
aims with Zimbabwe’s evolving needs.
raises the competitive game of each
Initially, for example, they developed
individual scientist working there.
clinical pharmacologists capable of
In addition to providing training,
conducting research, building a lab-
the HIVRT program aims to foster
oratory to provide drug assays, and
“academic-public-private partnerships” to address UZ’s research
“I CAN TRACE SOME OF THE MOST
goals. Partners include Waters
DEFINING MOMENTS OF MY CAREER
Inc. (Milford, MA), Well Cell Global
THROUGH THE NIH FOGARTY
(Houston), and MTE Group (Atlanta).
RESEARCH & TRAINING PROGRAMS.
These biotechnology companies
PERHAPS THE ACHIEVEMENT I AM
have agreed to contribute to plan-
MOST PROUD OF HAS BEEN HELPING
ning and developing Health Galaxy
TO ESTABLISH A CLINICAL PHAR-
Park, a health care innovation and
MACOLOGY LABORATORY FROM
drug with two nucleoside reverse
biomedical research center located
THE GROUND UP IN ZIMBABWE.
transcriptase inhibitor drugs; the dif-
in Harare, capital of Zimbabwe.
MY INVOLVEMENT IN IT HAS BEEN
ferent classes of antiretrovirals target
INCREDIBLY REWARDING. FOGARTY
different stages of the HIV lifecycle,
PROGRAMS HAVE SHAPED ME AS A
so, when combined, they can sup-
SCIENTIST AND A LEADER, WHO IS
press viral replication to undetectable
COMMITTED TO STRENGTHENING
levels. “We started collaborating and
RESEARCH CAPACITY, MENTOR-
one thing led to another and eventu-
ING FUTURE INVESTIGATORS, AND
patients found it difficult to take. It was like a call to action for us,” says Maponga. Antiretroviral therapy (ART), the major advance in HIV treatment that began in 1996, typically combined three drugs, a protease inhibitor
ally we applied for a Fogarty grant,” says Maponga.
Partners in innovation The earliest version of what would become the HIVRT program was funded by Fogarty in 2007 as a supplement to 8
This visionary project, based on infrastructure constructed by the Learning the ropes at UB
ENSURING THAT RIGOROUS CLINICAL PHARMACOLOGY RESEARCH TRANSLATES INTO BETTER HEALTH OUTCOMES FOR COMMUNITIES THAT NEED IT MOST.”
Testimonial from Takudzwa J. Mtisi, PhD, MS-CTT, MBA
GLOBAL HEALTH MATTERS
51
FOCUS
Photos courtesy of Chiedza Maponga
HIVRT program, has become a
history of all the non-prescription
the drugs became available in Africa,
national initiative, says Maponga.
medications patients were taking,
Southeast Asia, South America, peo-
“We were given 50 hectares of land
including over-the-counter medi-
ple with different genetic makeups
in an area where the capital city is
cines, vitamins, and supplements.
were evaluated to see if they reacted
planning a business district.” Morse
“What was going on in Africa
differently. Researchers identified dif-
adds, “There are 22 universities in
was influencing the way we asked
ferences in drug toleration related to
Zimbabwe that can be linked to the
questions here,” says Morse. “All
genetics, nutrition (or malnutrition),
network.” He believes future collab-
of a sudden, if you enrolled in a
lifestyle or environmental issues, and
orations will not be limited to HIV
protocol sponsored by the NIH,
comorbidities, which led to better for-
research, but will address pandemic
there was a questionnaire about
mulations and dosing protocols.
preparedness, leverage digital plat-
what non-prescription medications
Looking to the future, Morse and
forms for telehealth, and develop
you take.” Soon, a database was
Maponga believe Zimbabwe needs
research ethics applicable to data
built, which led to research proj-
to develop leapfrogging strategies—
handling and cybersecurity.
ects examining how traditional or
bypassing the old in favor of the
Two-way street The HIVRT program undoubtedly benefits Zimbabwe, yet the United States reaps rewards as well, says Morse. For instance, it was in Zimbabwe that the team first embedded HIV researchers into organized support groups as a way to engage community members; later they translated this successful strategy to projects in the United States. The team working at UB also followed Zimbabwe’s lead in sharing and coordinating limited resources, including instrumentation and data. Importantly, practices established in Zimbabwe to accommodate the everyday use of traditional medicines paved the way for important new protocols in the United States. Maponga, chairman of the Natural Therapists Council of Zimbabwe, estimates that between 70-80% of Zimbabwe patients report using traditional medicines (often before trying ‘western medicines’). It was natural, then, for the Zimbabwe trainees working in America to write up a complete
OTC medicines interacted with the
new—to get ahead. An example of
antiretrovirals prescribed for peo-
leapfrogging would be the work of
ple living with HIV. In addition, a
Admire Dube, an HIVRT postdoctoral
whole new field evolved in the
fellow, who used nanotechnology to
United States and Europe, where
develop potent drugs, says Maponga.
more sophisticated technology
Dube’s trainee research proved that
allowed for genomic sequencing
Zimbabwe, though it lacked a history
and greater in-depth analysis of the
of drug research and development,
mechanisms underlying drug inter-
could enter a new arena within that
actions. “That led to knowledge of
field and, with the help of technology
how grapefruit, for example, can
transfer, gain ground swiftly.
interfere with statin metabolism,
A leapfrogging ap-
and prompted additional consider-
proach, where the
ations for ART,” says Morse.
latest HIV drugs are
The use of antiretrovirals in Zimbabwe also revealed unexpected
used instead of old drugs, might also
differences in absorption rates,
increase Zimbabwe’s ability to prevent
metabolism, susceptibility to toxici-
HIV transmission and optimize patient
ties, and penetration into the brain.
outcomes, says Morse. The same stra-
This also contributed to American
tegy would be useful for complex
scientific knowledge, says Morse.
diseases, such as tuberculosis, and
“In the 1990s and early 2000s, a
chronic diseases, such as hyperten-
lot of the rapid drug development
sion and diabetes. “A little bit of priority
through pharma was occurring in
shifting is what needs to be done, and
the United States and Europe,” so
the application of resources needs to
many of the new medicines were
be considered to make leapfrogging
tested mostly in whites. Later, when
happen,” says Morse. July/August 2026
52 Delaware Journal of Public Health - October 2026
9
IMPLEMENTATION SCIENCE HELPS VIETNAM MERGE HIV AND NCD CARE Donna Shelley, MD,
Yet what “really excited” Shelley is
high blood pressure in HIV outpa-
Professor of Public
that implementation science acknowl-
tient clinics, says Shelley. Working
Health Policy and
edges that health care improvement
with the national HIV program, her
Management at
is a dynamic process. “Science
team launched the NCI trial which
New York University,
isn’t static, technology isn’t static.
compared the effectiveness of three
began her career as
Healthcare workers need the tools to
tobacco cessation interventions
a health services researcher. When
become experts in continuous inte-
among PLWH. The outcomes of that
reading implementation science lit-
gration of new evidence, such as
study suggested national Quitlines,
erature for the first time, she “was
when a new clinical guideline replaces
which are “available in 42 low-in-
immediately drawn to the focus on
an old one.” In this way, implementa-
come and middle-income countries,”
context—the factors that facilitate or
tion science aligns with the emerging
could serve as a resource for inte-
impede implementation. This was
discipline of quality improvement
grating tobacco treatment into HIV
largely ignored at the time, despite
science.
care systems.
being core to effectively translating
For all these reasons, Shelley finds
Over time, Shelley’s research
research into practice,” says Shelley,
implementation science particularly
expanded beyond testing strategies
who co-directs NYU’s Global Center
relevant when integrating HIV and
for implementing smoking cessa-
for Implementation Science.
noncommunicable disease (NCD)
tion to launching a Fogarty-funded
care, the focus of her current Fogarty
program that trains researchers and
attracted her. She appreciated the
project in Vietnam.
health system leaders in Vietnam
pragmatism of this science, which pro-
Early Research Shelley’s initial implementation science projects focused on smoking cessation in the United States. In 2020, she received a five-year National Cancer Institute (NCI) grant for implementing tobacco use treatment guidelines in HIV clinics in Vietnam. The rising burden of NCDs among people living with HIV (PLWH) was becoming a priority in Vietnam. To address this growing epidemic, the U.S. Centers for Disease Control provided funding for the country to integrate screening for high cholesterol, diabetes, and
and Laos to use implementation
Other aspects of the discipline also
vides a set of rigorous interdisciplinary methods to close the evidence-to-research gap within a specific milieu. She also liked how it builds evidence around stakeholder engagement and has developed tools to measure the effectiveness of that engagement. Shelley says, “Early on, I’d centered stakeholder engagement in my research because I didn’t know how not to—how do you go into someone else’s house and start rearranging the furniture without asking their opinion?” 10
science methods to integrate evidence-based NCD prevention and treatment interventions in HIV care settings. This five-year project, “Advancing Implementation Research Capacity in Vietnam,” leverages Vietnam’s investment in health system redesign to study models of care for PLWH. The country used to have a three-tier healthcare delivery model which included province, district and commune levels. As of 2025, the country eliminated the district level. Shelley
GLOBAL HEALTH MATTERS
53
FOCUS explains, “There were outpatient HIV clinics at that level, so now the goal is to implement a care model that integrates HIV screening and treatment along with strengthened NCD care within the commune-level primary care clinics.” Shelley says, “Vietnam has a new
“
AS NATIONAL LEADERS IN IMPLEMENTATION
SCIENCE OUR VIETNAMESE COLLEAGUES KNOW WHAT TYPE OF TRAINING MATTERS MOST TO THEIR HEALTH CARE SYSTEM LEADERS.”
vision for caring for HIV patients that is more integrated and centered in of Health to ensure alignment with
training to make sure PLWH receive
nience and continuity of care.”
national priorities and to share pro-
consistent, evidence-based care, says
Trainee influence Her colleagues at the Hanoi University of Public Health (HUPH) welcomed the opportunity to work together on the Fogarty funded project. Shelley says that with multiple P.I.s—two from HUPH and two from NYU—stakeholder engagement happens from the start of grant development. “As national leaders in implementation science our Vietnamese colleagues know what type of training matters most to their health care system leaders. We collaborate closely to adapt the curriculum to their needs, and that’s why it works so well.” Surprisingly, the applicants for her Fogarty project were all high-level administrators with PhDs, even directors within the healthcare system. Her team is training “implementers to do implementation science…what could be more sustainable than that?” Based on these candidate profiles, a new model of her training program emerged, still it remains grounded in lessons learned from past projects in-country. Shelley says, “We often start our implementation science training at the healthcare system level, yet we always emphasize the importance of engaging the Ministry
gram components and results
Shelley.
that can inform future training and resource allocation.” The trainee projects adapt evi-
The first cohort of trainees are now submitting articles for publication while the team conducts an evalua-
dence-based programs to the
tion. “We plan to document changes
Vietnam context and then test
in knowledge and skills to understand
implementation strategies. “The
how this is affecting trainees’ trajec-
trainees are working on a range
tories as working professionals in the
of topics including hypertension
health system.” The fact that none of
management programs, mental
them are strangers to the real-world
health care, nutrition programs, and
system doesn’t hurt. “Our first cohort
tobacco cessation interventions.”
actively engaged in this transforma-
Based on interviews conducted by
tion of the Vietnamese healthcare
her team, the new plan to integrate
system and now they’re trained as
HIV care within the mainstream
implementation scientists, so they’re
primary care system will require
well-prepared to make a real impact.”
July/August 2026
54 Delaware Journal of Public Health - October 2026
Photos courtesy of Humberto Lopez Castillo’
Photos courtesy of Donna Shelley
primary care, while improving conve-
11
FOCUS
A FOGARTY PROJECT IN PANAMA AIMS TO THWART CARDIOMETABOLIC DISEASE AMONG ADULTS WITH HIV Speaking other people’s languages is central to Humberto Lopez Castillo’s work as a researcher. Scientists require data from different sources to conduct studies, so they “need to speak every language,” says Lopez Castillo, MD, PhD. “I need to convince the clinician, so I need to speak their language. I also need to convince participants, so I need to speak their languages as well.” Institutional review boards have their unique perspective… same for research assistants. “We also need to understand what’s the point of view of administrators in the process of implementing a grant,” says the associate professor at University of Central Florida who’s also a certified Spanish English Medical Interpreter. An expert communicator, Lopez Castillo put his skills to good use on a recent Fogarty project, Cardiometabolic Status of Adults
Living with HIV in Colon, Panama.
data on metabolic syndrome among
Metabolic syndrome Adults living with HIV (ALWH) now enjoy longer, healthier lives, thanks to antiretroviral therapy (ART) and other public health interventions. Yet, recent studies of ALWH suggest that levels of metabolic syndrome and cardiometabolic disease are “through the roof,” says Lopez Castillo. (Metabolic syndrome is a set of five conditions—high blood pressure, high blood sugar, abdominal obesity, high triglycerides levels and low ‘good’ cholesterol levels—that increase the risk of cardiometabolic disease, which includes heart attack, stroke, diabetes, and kidney disease.) Unfortunately Panama, the country with the highest rate of HIV in Central America, does not routinely monitor cardiometabolic or other noncommunicable diseases among ALWH. So Lopez Castillo’s Fogarty project addressed this scarcity of clinical, laboratory, and epidemiologic
ALWH in Colon, Panama. The study first aimed to quantify the rate of cardiometabolic disease among ALWH and track its “natural evolution, without an intervention, over the course of a year,” explains Lopez Castillo. His team also hoped to describe and estimate the effect of different variables, including treatment, on the prevalence and course of cardiometabolic disease. Finally, they conducted two focus groups to gauge health care providers’ and patients’ responses to a metabolic syndrome intervention designed for ALWH yet integrated into the primary care system.
Findings Lopez Castillo and his team concluded the project last year. Of 659 total recruited participants, 495, “a solid longitudinal cohort,” completed the entire study. The team estimated a 38.6% overall prevalence of metabolic syndrome among ALWH in Panama; 21.2% met three of the five diagnostic
Left: Workshopping ideas in Colon. Right: Humberto Lopez Castillo presents his work in a Colon clinic.
12
GLOBAL HEALTH MATTERS
55
Photos courtesy of Humberto Lopez Castillo’
criteria, while nearly a fifth met four
one infectious disease specialist and
or all five criteria, signifying a pro-
four general practitioners who’ve had
nounced risk of cardiometabolic
in-house training in HIV (but they’re
disease.
not certified specialists). One psychia-
“
THE TEAM
ESTIMATED A 38.6% OVERALL PREVALENCE
“We also measured inflammation
trist comes once a month for one day.”
[a natural part of the body’s immune
By contrast, focus group patients told
response], so now inflammation is
“a story of stigma,” in which they’re
part of the story we’re trying to tell,”
“singled out and taken last” in OBGYN
SYNDROME AMONG
says Lopez Castillo. Specifically,
offices, emergency rooms, and at
his team looked at sensitivity to
the dentist. Lopez Castillo says, “The
ADULTS LIVING WITH
C-reactive protein, a standard mea-
clinic is their safe space, a stigma-free
sure of inflammation. His team found
environment.”
high sensitivity levels, which sug-
OF METABOLIC
HIV IN PANAMA.”
gests chronic inflammation, despite
Translating across cultures Today
One Heartbeat at a Time, we go to
Lopez Castillo is crafting two new
many participants (80%) having unde-
laundromats to meet people. They’re
proposals for further research in
tectable HIV viral loads. ART itself,
a captive audience! Their clothes are
Panama. “One is for the integration
diet, stigma, chronic stress, and social
in the washer or the dryer, so, if they
of cardiometabolic health care into
determinants of health may all con-
want, they can listen to our spiel and
HIV primary care at four sites.” The
tribute to inflammation, explains
get on-site testing for blood glucose,
second concerns the blood supply,
Lopez Castillo.
lipids, and inflammation.” To accom-
which needs “a little bit of oversight
Since the study’s completion, the
plish this work, he’s partnered with
and support.” His team has already
team has published a paper and pre-
grassroots organizations in Florida.
strengthened a local Colon lab with
Even the best communicators need
sented their results at conferences
resources from his Fogarty grant and
partners, explains Lopez Castillo. This
in the United States and in Panama,
“now we want to build a database of
is another lesson learned in global
including at the Colon clinic/project
metabolic markers…it’s an opportunity
settings that he applies here at home.
site. Meanwhile, his team continues
to leverage the capacity that we’ve
“Sometimes researchers cannot be
to work on a sophisticated analysis
built.” His team also is exploring the
trusted right away, so having a com-
of the data in the hopes of harvesting
interaction of PrEP drugs (pre-expo-
munity person say, ‘Hey, these people
more precise insights. Lopez Castillo
sure prophylactics that prevent HIV, a
are trustworthy, come join us’ is
says this is important because more
type of ART) with GLP-1 drugs (gluca-
what’s needed.”
than half of the participants were
gon-like peptide-1 receptor agonists
Afro-Caribbean, an understudied
that treat type 2 diabetes and help
group.
with weight loss). “We’re analyzing
Unsurprisingly, the non-numeri-
existing pharma datasets to under-
cal project data made its own lasting
stand the overall effect when you
impression on the researchers. Lopez
combine them…to see if it can flatten
Castillo learned from providers in the
the curve of cardiometabolic disease
focus group that they’d like to pro-
that comes with HIV.”
vide ancillary services for ALWH,
Additionally Lopez Castillo strives
such as nutrition counseling, but they
to transfer insights from his study in
feel stretched to the limit. “They have
Panama to Florida. “For our project,
Castillo serves as Associate Professor, Department of Health Sciences, at University of Central Florida.
July/August 2026
56 Delaware Journal of Public Health - October 2026
13
Benjamin Chi, MD, MSc
Q&A
Why being in-country matters
Dr. Benjamin Chi holds appointments in the Department of Obstetrics and Gynecology and Department of Epidemiology at the University of North Carolina (UNC) at Chapel Hill. Between 2003 and 2015, he lived in Lusaka, Zambia, where he led a research portfolio focused on the prevention of mother-to-child HIV transmission, HIV care and treatment, and maternal and child health. Since returning to the U.S., he has expanded these activities to some of Zambia’s neighboring countries. Dr. Chi leads multiple global health research training initiatives, including the UNC Global Women’s Health Fellowship and Fogarty’s UJMT Launching Future Leaders in Global Health Research Training Program (LAUNCH). Currently, he serves on Fogarty’s advisory board. Tell us about your 12 years in Lusaka, Zambia.
After I finished my OBGYN residency in 2003, I had a real interest in research and wanted to develop these skills overseas. I was connected to a program in Zambia and introduced to several faculty who would become life-long mentors. From the U.S., this included Drs. Sten Vermund, Robert Goldenberg, and Jeff Stringer, who remains a mentor and collaborator more than 25 years later. In Zambia, I was privileged to work with the late Dr. Moses Sinkala, who was the district director of health in Lusaka and a graduate of the Fogarty AITRP program. I joined the University of Alabama at Birmingham as a research fellow, with the idea that I might go to Zambia for two—maybe three—years for training. That decision proved to be life-changing. Soon after our arrival, our group got its first award from the U.S. President’s Emergency Plan for 14
GLOBAL HEALTH MATTERS
AIDS Relief (PEPFAR), which rapidly scaled up HIV care and treatment services nationwide. I had the opportunity to be part of this highly successful initiative, first in the capital city of Lusaka and soon after across four provinces across the country. I began to think about research more broadly, asking questions about how best to implement and optimize services and how to measure their impact at a population level. The Fogarty International Center played an important role in my career development during this time. I applied for—and was successfully awarded— the K01 International Research Scientist Development award my first year in Lusaka. I designed and implemented a randomized controlled trial of adjunct single-dose emtricitabine/ tenofovir disoproxil fumarate to reduce drug resistance associated with single-dose nevirapine use to reduce vertical HIV transmission. Eventually, this was published in The Lancet. The
Fogarty K01 award allowed me to extend my time in Zambia and fostered my academic growth at a critical career juncture. I consider myself very lucky to be in Zambia at that time. Seeing the direct benefit of our work was incredibly gratifying. Since moving back to the U.S. in 2015, I remain connected to UNC’s ongoing work in Zambia. At the same time, I have expanded my research to other places in the region, including Malawi and Zimbabwe. What was challenging during this time?
A period of such intense activity takes a lot of effort. There was real pressure to roll out services and get things done. It was a period where ‘all hands were on deck,’ with a lot of sleepless nights figuring out how to safely get the first thousand people on antiretroviral therapy (ART) in the first three months of the program. We were all anxious when the first government clinic started to provide HIV treatment, hoping that people would come and that all of aspects of care—from clinical exams to laboratory testing to chart documentation—would work as planned. “I BEGAN TO THINK ABOUT RESEARCH MORE BROADLY, ASKING QUESTIONS ABOUT HOW BEST TO IMPLEMENT AND OPTIMIZE SERVICES AND HOW TO MEASURE THEIR IMPACT AT A POPULATION LEVEL.”
57
We encountered our fair share of bumps along the way, but we learned from our experiences and successfully rolled out services in places that had never seen ART before. It was a real team effort, with lots of dedicated people making a difference. What’s your most significant lesson learned?
Being in Zambia full-time, I was able to forge relationships, many that I have to this day. Partnerships are stronger when people know that they can count on you—that you’re willing to spend time in the field and make sure that the work gets done. Thankfully, this approach to global health is much more common now. However, when I first arrived in Zambia, you did not see it as much. More often, people travelled back and forth from the U.S., which can be slow, inefficient, and time-consuming. Opportunities come from being there. What spurs your interest in implementation science?
Photos courtesy of Benjamin Chi.
For much of my academic career, I have studied aspects of program implementation, evaluating new services and measuring their impact at a community level. The formal discipline of implementation science is Dr. Benjamin Chi and Dr. Margaret Kasaro, the current country director for UNC Global Projects Zambia and former Fogarty AITRP trainee.
Dr. Benjamin Chi with Dr. Lameck Chinula and Dr. Friday Saidi, alumni of the UJMT LAUNCH Program and NIH-funded, UNC Ob-Gyn faculty based in Malawi.
more focused, with frameworks and tools to foster more effective and efficient implementation across different settings. Unfortunately, getting an evidence-based intervention to routine practice is typically slow, often taking years or even decades. How do you shorten that time gap, so that you really deliver on these investments in science? Such implementation science work is exciting and really addresses a need—whether we’re in Zambia or rural North Carolina. Do you see any new trends in global health research?
Significant changes in global health research are taking place. While immense and immeasurable good has come from US investments abroad, there is a renewed focus on how this work also benefits those in the U.S. This is especially true for rural and remote parts of the country, where people may have limited access to health services. There is value in fostering connectivity between our work abroad and our work domestically, embracing the idea of what global truly means. We
have been building upon the idea of “reciprocal innovation” and incorporating it into our research. We are more deliberate about how we think about shared needs and gaps in healthcare delivery. Importantly, we should consider how lessons learned abroad can improve health locally. As part of the UJMT LAUNCH Program, we are working with others in this space to highlight how reciprocal innovation is already taking place. We will convene a panel at the annual meeting of the Consortium of Universities for Global Health (April 2026) to show how this has been done successfully across the country and across disciplines. We are also hosting a workshop for LAUNCH alumni—across all seven sister programs—to help these individuals thoughtfully adapt and implement evidence-based interventions from international settings to those in the U.S. Reframing global health in new ways is important and could enhance the impact of such work in the years to come.
July/August 2026
58 Delaware Journal of Public Health - October 2026
15
NEWS&Updates To reach its full potential, lenacapavir requires additional global health research
“the 3D structure of viral proteins and their interactions within infected cells.” Inside the structure of human immunodeficiency viruses is a capsid core, a cone-shaped container that is composed of individual capsid (CA)
Courtesy of Gilead Sciences, Inc.
proteins. The capsid core transports the virus’ genetic information across the host (patient’s) cell and into the nucleus. During this process, the core protects the virus from any defense mechanisms mustered by the cell. In this way, the capsid core enables HIV to replicate, proliferate, and ultimately cause disease. Once these processes and interactions were understood, scientists began developing approaches to disrupt them, explains Finzi. “Lenacapavir binds two neighboring CA proteins and creates improperly shaped core structures that are unable to enter the nucleus [of a cell] or produce new virus particles.” In short, the drug works because “it interferes with multiple steps in the HIV life cycle.” It’s important to note Courtesy of NIAID
HIV IS ONE OF THE WORLD’S MOST SERIOUS HEALTH CHALLENGES and scientists have worked unstintingly to treat, prevent and cure it since the early 1980s. “The field has moved from crisis response to effective treatment, to treatment as prevention, to oral PrEP (pre-exposure prophylaxis), and now to long-acting prevention options. Each step has expanded what we believed was possible,” says Jared Baeten, MD, PhD, Senior Vice President, Clinical Development, Virology Therapeutic Area Head, Gilead Sciences. Long-acting PrEP options now include lenacapavir, widely considered an exceptional drug. Lenacapavir was developed by Gilead Sciences, Inc. based on early-stage science conducted by academic investigators and research networks funded by NIH. It is an antiretroviral delivered as a shot just twice-ayear. One of the researchers that led the clinical trials for the evaluation of lenacapavir as PrEP is Linda-Gail Bekker, MBChB, PhD, director of the Desmond Tutu HIV Center at South Africa’s University of Cape Town; her early training was supported by Fogarty while her current research projects are backed by Fogarty, the National Institute of Allergy and Infectious Diseases, and other organizations. Bekker led the PURPOSE 1 trial that evaluated the safety and efficacy of lenacapavir as PrEP in young women in Uganda and the Republic of South Africa. The PURPOSE program, funded by Gilead, included a PURPOSE 2 trial that enrolled geographically diverse populations of men across multiple countries. The combined enrollment was nearly 10,000 people on five continents. Bekker’s PURPOSE trial results exceeded expectations. “No adolescent girls or young women receiving twice-yearly lenacapavir acquired HIV infection,” she and her co-authors noted in a 2024 paper published in The New England Journal of Medicine. A companion piece, also authored by Bekker’s team and published in Science, reported “zero HIV infections” among participants who received the drug during pregnancy. When she presented these results at the 2024 International AIDS Conference, Bekker brought the audience to its feet. Diana Finzi, PhD, Acting Director of NIH’s Office of AIDS Research (OAR) at that time, said that lenacapavir is a “remarkable achievement” and “a testament to
Gilead researchers at work
that Gilead developed and screened more than 4,000 molecules, starting Gilead researcher at work.
the value of NIH-supported basic science.”
Research & development “Scientifically, lenacapavir is a first-in-class HIV capsid inhibitor with a mechanism that is distinct from other approved HIV medicines,” says Baeten, a former Fogarty fellow. Finzi explains that early HIV science, funded by NIH, aimed to understand
16
GLOBAL HEALTH MATTERS
59
in 2006, before identifying the final
African Health Products Regulatory
public health impact, writes Geri R.
molecule that would become known
Authority approved lenacapavir for
Donenberg, PhD, the current director of
as lenacapavir. Then it went into trials,
PrEP. Drug regulatory agencies in
NIH’s OAR, in a blog post. As a young
which took more than five years, to
Brazil and Australia soon followed.
scientist she decided to center her
prove efficacy and safety for both
While additional approvals are
work around an emerging discipline:
pending, it is now available in 10
“implementation science—the study
treatment and prevention.
countries in sub-Saharan
of methods to promote the
take credit for lenacapavir, Baeten
Africa, which Baeten says
adoption and integration of
notes that HIV drug development
represents the fastest
evidence-based interventions
progress has always been driven by
access in the region for
into real-world settings to
partnerships that extend far beyond
any HIV medication. The
impact public health.”
the lab. “The advances we are seeing
rollout is “just getting
While teams of scientists rightfully
Implementation science is
Courtesy of NIAID
today are the result of decades of
started,” says Baeten. Gilead
emphasized within Fogarty’s
work by communities, advocates,
has “designed a broader
HIV programs in low- and
clinicians, researchers, public health
global access strategy
middle- income countries.
leaders, governments, and industry.”
that includes voluntary licensing
Fogarty-supported scientists seek
Similarly, extensive engagement with
agreements and partnerships with
to learn how long-acting prevention
communities helped shape Gilead’s
organizations, including the U.S.
can be delivered in ways that are
PURPOSE program.
President’s Emergency Plan for
both practical and responsive to
Inclusion and access Gilead wanted countries and communities most affected by HIV—many that have “historically been underrepresented in clinical research”—to participate in its clinical trials to test the drug, Baeten explains. The earliest trials, then, were conducted in South Africa, Uganda, Argentina, Brazil, Mexico, Peru, Thailand, and the United States. Within a year after the announcement of the earliest results, the U.S. Food and Drug Administration, the European Medicines Agency, and the South
AIDS Relief (PEPFAR) and the Global
community needs. Unfortunately,
Fund.” Through these agreements,
uptake of daily PrEP pills has not
Gilead aims to accelerate access
been immediate or universal. As
to lenacapavir in “high-incidence,
Bekker noted in her Science study,
resource-limited countries,” where
“PrEP use remains suboptimal
local regulatory approvals and
among women, particularly in pop-
access pathways are in place.
ulations with disproportionate HIV
3D-printed model of the HIV capsid, the protein shell that encloses the virus’ genetic material.
“Scientific breakthroughs only
incidence, including young women,
matter if they reach the people
women in Africa, women of color
who need them,” says Baeten. He
in the United States, and migrant
notes that, despite the availability
women in multiple geographic
of highly effective prevention tools,
areas.” Bekker says, given that a
new infections still occur. “People
twice-yearly shot of lenacapavir
need prevention options that fit their
could be dispensed to women
lives, their circumstances and their
during routine (and private) OBGYN
preferences.”
visits, lenacapavir represents “a
Preventing infections Access to lenacapavir is undoubtedly significant when it comes to preventing new infections, yet another factor is equally essential: people using it. In most cases, studies that examine how people will use a new medical discovery are needed to achieve
highly efficacious and discreet choice to potentially improve PrEP use among women.” Meanwhile, researchers across the globe continue to investigate lenacapavir. Baeten says that ongoing PURPOSE trials seek to answer how lenacapavir can be implemented effectively in different healthcare settings. July/August 2026
60 Delaware Journal of Public Health - October 2026
17
NIH Update ILLICIT DRUGS ACROSS THE AMERICAS:
An era of synthetic drugs
Meth seized by U.S. Border Control
and even ‘legal highs’ by users, mimic the effects of both illicit and prescription drugs, including cannabis and opioids. Generally, synthetics introduce slight modifications to the chemical structure of controlled substances to circumvent drug laws, Hynes explained. An important shift in the global illegal drug landscape is happening now, Hynes said. Mixtures, adulterants, and combinations of drugs are becoming increasingly accessible to users with implications for both public safety and public health. Given that synthetic drugs are not dependent on cultivation cycles, criminals can swiftly create new substances and adjust supply to changing demands as they expand across multiple markets. “Illicit drug markets are rapidly evolving and becoming more dynamic and difficult to predict,” noted Hynes. The challenge for governments, then, is not the danger caused by one or two individual substances, but an all-encompassing threat posed by this transformation in how drug markets operate.
“
THERE’S A
PERCEPTION THAT THESE SYNTHETICS ARE LEGAL AND SAFE BECAUSE THEY CAN BE BOUGHT ONLINE OR BY CREDIT CARD.”
18
WHAT’S IN THIS PILL?
Based on data gathered from national systems, the Americas’ early warning system (known as SATA) compiled its Photos courtesy of U.S. Customs and Border Protection
Important global research is supported throughout the National Institutes of Health. In early June, the National Institute on Drug Abuse (NIDA) presented its 2026 International Forum. The scientific meeting highlighted the range and quality of substance use and addiction research that is conducted around the world with support from NIDA. Of particular concern to Americans, one discussion examined The Rise of Drug Mixtures: From “New Substances” to “New Combinations.” Chaired by Marya Hynes, MPH, Head of the Inter-American Observatory on Drugs (the research and analysis branch of the Inter-American Drug Abuse Control Commission), she presented an overview of emerging illicit drug trends in Latin America and the Caribbean, in particular the evolving challenge posed by synthetic drugs and new psychoactive substances. These substances, often called ‘designer drugs,’ ‘herbal highs,’
‘top 10’ list of most reported substances for 2019-2026:
MDMA
(3,4-Methylenedioxymethamphetamine, known as ‘ecstasy’ or ‘molly,’ acts as a stimulant and hallucinogen)
2C-B
(4-Bromo-2,5-dimethoxy-phenethylamine, a psychedelic)
FENTANYL (an opioid)
XYLAZINE
(a veterinary sedative sold to users as ‘tranq’)
KETAMINE
(a dissociative anesthetic)
METH
(methamphetamine, a type of stimulant)
THC
(tetrahydrocannabinol, the main compound in cannabis) MDA (3,4-Methylenedioxyamphetamine, acts as a stimulant and psychedelic)
251-NBOH
(a phenethylamine psychedelic and stimulant derived from 2C-B)
2C-E
(4-ethyl-2,5-dimethoxy-4-phenethylamine, an hallucinogenic)
GLOBAL HEALTH MATTERS
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UNKNOWN COMPOSITION MEANS UNKNOWN TOXICITY, WHICH RAISES THE RISK OF OVERDOSE AND INCREASES UNCERTAINTY ABOUT APPROPRIATE MEDICAL INTERVENTIONS.
Photos courtesy of U.S. Customs and Border Protection
Ecstasy seized by U.S. Border Control
Detection of different types and
seized and analyzed samples increased
seized drug in Trinidad and Tobago,
blends of substances has increased
six-fold in Chile. Recreational use of
noted Nasseir. “Now we are
over the last several years,
ketamine is now widespread with users
seeing MDMA combinations with
according to the SATA report.
risking long-term cognitive impairments,
pharmaceutical drugs… with some
Unknown composition means
gastrointestinal effects, and cystitis
containing more than two.”
unknown toxicity, which raises the
(inflammation of the bladder), according
Meanwhile, more than 9,000
risk of overdose and increases
to Ferreira Vidaurre. Recreational users
meth pills have been seized to date,
uncertainty about appropriate
(17 grams per week) will experience
a rising trend that peaked in 2023
medical interventions. Greater
urinary effects within three to six
when a clandestine lab (capable of
unpredictability also complicates
months. Urinary toxicity is of particular
producing both pure crystals and
forensic identification.
importance, given that ketamine-
pills) was discovered and dismantled.
associated cystitis is a chronic,
Disconcertingly to public health
progressive, and disabling condition.
authorities, drug dealers began selling
Dr. Michelle Nasseir of the Trinidad
meth in the same pill-shape as ecstasy
K-HOLE COMPLICATIONS
Following Hynes’ overview, Dr.
Felipe Ferreira Vidaurre of the Public Health Institute of Chile discussed ketamine, which involves a complex chemical process that starts with cyclohexanone and was approved for use by the U.S. Food and Drug Administration in 1970. It possesses both analgesic and anesthetic properties and was used as a battlefield anesthetic in the Vietnam War. A ‘K-hole’ is the drug’s signature experience—an hallucinogenic event that usually renders users completely immobile, though in some cases causes violent reactions. From 2020 to 2024 the number of
and Tobago Forensic Science Centre
beginning around 2021.
said that her West Indies island nation
Nasseir wants to increase aware-
classifies ketamine as a ‘drug of most
ness of synthetics, which have gained
concern,’ given its use in sexual assaults
mainstream acceptance and popularity
and human trafficking activity. Seizures
in Trinidad and Tobago, due in part to
of this drug have surged in recent years.
a recent hit song. Public health officials
The drug landscape in Trinidad
need to distribute photos of the pills,
and Tobago generally consists of
so parents and teachers recognize
cannabinoids (cannabis plant, edibles,
these drugs and understand when kids
and powders); cocaine; MDMA/molly;
are using them, she said. “There’s a
ketamine; and meth. Prevalence of illicit
perception that these synthetics are
drug use has escalated since 2018.
legal and safe because they can be
A report covering the years 2018
bought online or by credit card. We’ve
to 2025 found MDMA, sold as pills,
had to push awareness that these
powders, capsules, and rock-like
substances are toxic, harmful, and
substances, to be the most commonly
illegal,” concluded Nasseir.
July/August 2026
62 Delaware Journal of Public Health - October 2026
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NEWS&Updates A LAUNCH Orientation ‘chat’ reveals another side of Fogarty’s leader Once again Fogarty’s global health research trainees gathered for the annual LAUNCH orientation. The Launching Future Leaders in Global Health (LAUNCH) Research Training Program supports one-year mentored research training in global health at established biomedical and health research institutions and project sites in low- and middle- income countries. The 2026 orientation took place within a stone’s throw of the NIH campus at the Bethesdan Hotel in July. Trainees attended workshops, presentations, lectures, and panel discussions featuring global health experts. NIH Director Jay Bhattacharya, MD, PhD, kicked off his fireside chat with Fogarty’s Director Steven Schiff, MD, PhD, by asking how Schiff came by his deep expertise in global health issues. “By accident,” said Schiff with a smile. As a boy, he’d spent time in Africa with his family and thought then that he’d like to return someday and “do something useful.” Years later, when heading a neural engineering center at Penn State University—“my life’s dream” —he ran into an old friend, Benjamin Warf, MD, a Fogarty grantee who’d built a small neurosurgical hospital in eastern Uganda. “I was absolutely riveted by his description of what he was doing.” Schiff visited Warf’s clinic in Uganda, where “every night, one or two babies were carried in.” The infants shared a common history: Normal at birth, they became feverish with shaking and jerking of the limbs a couple weeks later. Many died, but those who lived often developed hydrocephalus, an abnormal buildup of cerebrospinal fluid within the brain enlarging the head. Warf, a pediatric neurosurgeon and professor of neurosurgery at Harvard Medical School, asked if Schiff wanted to help figure out what causes this. Schiff says, “I thought, ‘How hard can that be?’ That started the next 20 years of my focus. And I just hurled myself into it, because this was a huge problem.” Schiff estimates roughly thousands of cases a year in sub-Saharan Africa (and a much smaller number of cases now recognized in the United States). “The loss of life around the planet of newborn infants dying of preventable infections is massive and it takes place without any political voice, without recognition.” Next, the NIH Director asked why Schiff chose an interdisciplinary approach to his career. “If there’s one thing I hope I can start to accomplish at NIH, it’s to break down some of the disciplinary silos,” said Schiff, who used the word ‘antiLAUNCH 2026 attendees pose for the camera
20 16
FIC Director Dr. Steven Schiff (left) answers NIH Director Dr. Jay Bhattacharya’s questions.
disciplinarity’ when describing this aim. “The problems we face in global health and many other areas of medical science aren’t restricted to any one domain. If they were, those problems would have likely been solved long ago.” SCHIFF SAID, “LOOK AROUND FOR PROBLEMS WHICH NO ONE TOLD YOU EXISTED. THEY’RE OFTEN RIGHT IN FRONT OF YOU. LOOK FOR PROBLEMS REALLY WORTH YOUR TIME AND MANY OTHER PEOPLE’S TIME.”
Bhattacharya asked, “What scientific areas excite you most?” Schiff responded: “If I pick one area that cuts across all of NIH, it would be the genome and how it relates to our biology and when our biology goes wrong.” He added that scientists are becoming capable of applying this new knowledge of genetics and population differences to health and well-being. “After my lifetime, we’ll be able to drive that knowledge down to the individual.” Schiff concluded with advice for trainees. “Find what fascinates you most… and don’t let people talk you out of it. Also realize you’re going to have to deal with a lot of failures.” Resilience and relentlessness are required. “What taught me the most were my failures, whether as a surgeon or a scientist or how I related to other people.” Bhattacharya interjected, “I can wallpaper my office with all the rejection letters I have.”
GLOBAL HEALTH MATTERS GLOBAL HEALTH MATTERS
63
Community
people
Roger Detels, a giant in the field of public health, passes Roger Detels, MD, distinguished professor of epidemiology in the UCLA Fielding School of Public Health, passed away on July 25. Detels conducted research on air pollution, neurologic diseases, hypertension, and several infectious diseases, though he’s best known for his research on HIV/AIDS. In 1981, he initiated a natural history study of AIDS in young homosexual men in Los Angeles and shortly after became the principal investigator of the Los Angeles Center for the Multicenter AIDS Cohort Study (MACS), which has followed 6,972 gay and bisexual men since its start, making it one of the largest natural history studies of HIV/AIDS in the world. His landmark study showed that some men were resistant to HIV infection. In 1988, Detels founded the UCLA/Fogarty AIDS International Training and Research Program.
Fogarty taps Greenwood for top grants management spot Lennin Greenwood, MBA, joined Fogarty in late June as Chief Grants Management Officer (CGMO). In this role, she will provide leadership and oversight for grants management activities, supporting Fogarty’s global health research portfolio and ensuring effective stewardship of federal funds and compliance with all relevant policies and regulations. Greenwood brings more than 18 years of federal grants management experience and supervisory leadership within NIH.
NIH selects Casey to lead new office Warren Casey, PhD, is the inaugural Director of the new Office of Research Innovation, Validation, and Application (ORIVA) in the NIH Office of the Director. ORIVA will coordinate NIH-wide efforts to develop, validate, and scale human-based science and technology, including 3D human tissue models, computational tools, and other animal-free methods that can better reflect human biology. He will also oversee ORIVA’s work as a hub for interagency coordination and regulatory translation. Prior to this appointment, Casey served as acting Chief of the Biomolecular Screening and Predictive Toxicology Branches at the National Institute of Environmental Health Sciences.
European partnership highlights Fogarty Grantees Mmbaga, Nachega Ten researchers supported by the European & Developing Countries Clinical Trials Partnership (EDCTP) are highlighted in a recent article for their work in sub-Saharan Africa. Two are long-time Fogarty grantees. Blandina Mmbaga, MD, PhD, is Director of the Kilimanjaro Clinical Research Institute (KCRI) in Tanzania; under her leadership, KCRI has become an important hub for research on tuberculosis, HIV, malaria and other infectious diseases. She also serves as Deputy Coordinator of the Eastern Africa Consortium for Clinical Research (EACCR) Network of Excellence and has overseen clinical study sites and mentored Master’s and PhD students across East Africa. Mmbaga serves as an Adjunct Associate Professor of Global Health at Duke University. Jean B. Nachega, MD, PhD, is a leading researcher in HIV/AIDS and tuberculosis and has played a key role in strengthening research capacity across Africa. During the COVID-19 pandemic, he contributed to multi-country analyses of the pandemic virus and its interaction with other endemic infections. Nachega is currently focusing on mpox research, continuing his commitment to addressing emerging and reemerging infectious diseases. He serves as Associate Professor of Epidemiology at the University of Pittsburgh; Associate Professor at Johns Hopkins Bloomberg School of Public Health; and Professor Extraordinary of Medicine at Stellenbosch University, South Africa.
Fogarty Awardee Kiyonga becomes Uganda’s Second Deputy Prime Minister Uganda President Yoweri Museveni has appointed Crispus Kiyonga, MD, as his Second Deputy Prime Minister. Kiyonga began his political career in 1980 as a Member of Parliament, and has since served in several cabinet positions. He also held the position of Uganda’s Ambassador to the People’s Republic of China, and, in 2024, he was appointed Chancellor of Makerere University. He obtained a Master of Health Science in 2004 in Population Dynamics from the Johns Hopkins Bloomberg School of Public Health through a scholarship funded by Fogarty. July/August 2026
64 Delaware Journal of Public Health - October 2026
21
Global HEALTH Briefs New modeling study looks at respiratory viruses A new study looks at trends in hospitalizations and deaths (by age and year) caused by flu, COVID, respiratory syncytial virus (RSV), human metapneumovirus (HMPV), and rhino/enteroviruses (RV/EV). The study found respiratory viruses account for an estimated 1.7 million hospitalizations and 110,000 deaths in the United States each year. RSV was the dominant viral cause of hospitalizations among infants, with an average of nearly 1,271 hospitalizations per 100,000 (roughly 50,000 hospitalizations each year). Among children ages 1 to 4 years old, RSV also accounted for an average of about 212 hospitalizations per 100,000, while HMPV accounted for more than 130 hospitalizations per 100,000 among infants and children younger than age 5. More than half of flu hospitalizations (57%) and most flu deaths (83%) are among adults ages 65 and older. Published in the Journal of Infection, the study’s first author is former Fogarty scientist Chelsea Hansen, PhD, Roskilde University, Denmark, and its senior author is Cécile Viboud, PhD, Acting Director of Fogarty’s Division of International Epidemiology and Population Studies. Clinical trials begin for new leishmaniasis vaccine Ohio State researchers have developed a first-ever vaccine against leishmaniasis, a disfiguring skin disease caused by tiny parasites that are spread through the bite of an infected female sand fly. Last year the U.S. Food and Drug Administration approved the vaccine as an investigational new drug. Clinical trials are expected later this year. The Ohio State University’s Abhay Satoskar, MD, PhD, developed the vaccine using CRISPR gene-editing technology with support from the National Institute of Allergy and Infectious Diseases. Previously, leishmaniasis was found in tropical regions like Africa, the Middle East and South America. Today, it occurs in the United States and Europe due to deforestation and extreme weather, according to a recent review by Satoskar published in the New England Journal of Medicine. Identifying an antibiotic helper to thwart resistant bacteria Antibiotic resistance occurs when bacteria change over time until they become capable of withstanding the drugs (antibiotics) that would normally kill them. Antibiotic resistance is linked to an estimated 4.7 million annual deaths worldwide. Scripps Research scientists and colleagues at Cold Spring Harbor Laboratory (New York), and the University of Pittsburgh School of Medicine investigated ways to restore the power of vancomycin (a potent antibiotic) against resistant Enterococcus faecium (VREfm), an increasingly common hospital-acquired infection. First, the scientists genetically deleted a particular enzyme in VREfm which rendered the bacteria susceptible to vancomycin once again. Then they screened a chemical library and identified a class of compounds that could chemically disable this same enzyme in VREfm. This research, supported by the National Center for Complementary and Integrative Health, was published in Nature Communications. NIH supported study looks at Oropouche virus transmission drivers The 2023 Oropouche virus outbreak in Brazil drew attention because of its scale—more than 30,000 cases. Transmitted by biting midges and mosquitoes, Oropouche virus causes fever, severe headache, chills, and other symptoms, and can lead to serious complications including meningitis and microcephaly (when mother-to-fetus transmission occurs). A National Institute of Allergy and Infectious Diseases-supported study in Nature Health investigated determinants of Oropouche fever in Brazil between 2014 and 2025. The researchers noted 30,086 confirmed cases across 16.1% of 5,570 municipalities in all 26 states during the study period. Incidence in rural municipalities was 11.3 times higher than in urban municipalities.
22
GLOBAL HEALTH MATTERS
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What shapes the spread of Buruli ulcer across the tropics? Buruli ulcer is a disfiguring skin disease caused by the bacterium Mycobacterium ulcerans. Researchers from Michigan State University, Mississippi State University and several international institutions analyzed 110 different variables and used this data to model where both the bacterium and the disease it causes tend to occur. Then they created global maps to visualize their findings across tropical and subtropical regions worldwide. The research indicates that the spread of Buruli ulcer isn’t driven by a single cause but by a complex fusion of natural and human-related factors. The study, published in the journal Communications Medicine, was supported in part by the NIH-National Science Foundation Ecology and Evolution of Infectious Disease program. Mapping microbiomes in South Asia Most of the world’s population has shifted from traditional, foraging or farming-based diets to industrialized lifestyles, and this transition has reduced the diversity of microbes found in people’s guts. South Asia, home to roughly a quarter of the world’s people and an extraordinary range of cultures, has been underrepresented in global gut microbiome research. To address this gap, University of Chicago researchers created the South Asian MicroBiome Array (SAMBAR), a dataset built from fecal samples collected across 10 Indigenous and rural communities in India and Sri Lanka and paired with samples gathered from urban migrants with the same genetic and cultural background. Their results revealed regional signatures: certain bacteria linked to South Asian diets were enriched (compared to global populations), yet other microbes tied to urban living that are associated with obesity and poor health outcomes were also present. The journal Gut Microbes published this research. India’s national vaccine program reduced deaths, altered school rates
India’s national childhood immunization program, launched in 1985 and reaching full coverage five years later, substantially reduced childhood deaths yet had mixed effects on educational attainment, according to research published in the Journal of Population Economics. Analysis of a national survey of nearly 900,000 children showed that the inoculation program reduced infant mortality by 0.4% and mortality in children under 5 by 0.5%. Gains were seen mostly in rural areas and among the poor and disadvantaged caste groups. Children from wealthier, urban, or higher-caste households saw little change, likely because they already had access to vaccines. The vaccine program also reduced primary school completion rates while increasing secondary school completion rates, according to University of Notre Dame economist Santosh Kumar Gautam, the author of the study. Experimental Ebola drug trial begins in DRC
In mid-July, the National Institute for Biomedical Research in Kinshasa and partners began to enroll participants to test an experimental antiviral drug as post-exposure prophylaxis (PEP) to prevent Bundibugyo Ebola. Currently there are no vaccines or targeted treatments for this strain of Ebola. Participants in the trial for the antiviral developed by Gilead Sciences are being recruited in Ituri province of the Democratic Republic of the Congo (DRC), the epicenter of the outbreak. Each participant will be monitored daily for 21 days with a final visit at 42 days. As of July 29, the U.S. Centers for Disease Control and Prevention reported 3532 confirmed cases and 1,556 confirmed deaths, mostly in DRC. This outbreak is the third largest Ebola outbreak on record.
July/August 2026
66 Delaware Journal of Public Health - October 2026
23
FUNDINGNEWS On behalf of the Fogarty International Center at the U.S. National Institutes of Health (NIH), the following funding opportunities, notices, and announcements may be of interest to those working in the field of global health research.
Funding Announcement
Deadline
Details
December 3, 2026
https://grants.nih.gov/grants/guide/ pa-files/PAR-24-295.html
December 3, 2026
https://grants.nih.gov/grants/guide/ pa-files/PAR-24-296.html
Emerging Global Leader Award
(K43 Independent Clinical Trial Required) (PAR-24-295)
Emerging Global Leader Award
(K43 Independent Clinical Trial Not Allowed) (PAR-24-296)
SCAN
TO READ MORE
Since 2012, FIC research training (D43, D71, U2R, G11) and foreign career development (K43) awards have been restricted to exclude upper-middle income countries that are also members of the G20. South Africa has been the single exception to this rule. After December 5, 2026, South African institutions will no longer be eligible to apply for new training and career development grants. Active awards and applications submitted before December 5th are not impacted by this policy update.
Fogarty Fogarty International Center National Institutes of Health 31 Center Drive Bethesda, MD 20892
Advancing Science for Global Health
67
Evaluation of a Contingency Management Intervention To Treat Substance Use Disorders in a Family Medicine Residency Program: A Retrospective Observational Review Study of Patient Outcomes and Experiences Kimberly D. Williams, Dr.P.H., M.P.H. iREACH Research Institute, Christiana Care Health System Deanna Gorgei, D.O. Department of Family and Community Medicine, Christiana Care Health System Kendall McDowell, B.S.N., R.N., P.M.H.-B.C. Department of Family and Community Medicine, Christiana Care Health System Jordan Jacobs, D.O. Department of Family and Community Medicine, Christiana Care Health System James M. LaRosa, M.D. Department of Family and Community Medicine, Christiana Care Health System
ABSTRACT Objective. Contingency management (CM) is an evidence-based behavioral intervention using positive reinforcement to achieve a treatment goal. Despite effectiveness in treating those with substance use disorders (SUDs), CM is underutilized in clinical practice. This study describes the development of a CM intervention in a family medicine residency program SUD treatment program and evaluates patient retention and experiences. Methods. This retrospective observational pilot study focused on patients receiving treatment for SUD and CM services at two Delaware family medicine clinics between August 2023 and June 2024. Participants earned points for visit attendance and urine drug screens meeting treatment goals. Points were redeemed using a two-tiered fishbowl system. Outcomes included patient retention at 3 and 6 months. Patient experiences were assessed using a mixed-methods survey administered between March and August of 2025. Results. Forty-seven patients received CM services. Median participation was 9 months (IQR, 5-10). SUD treatment retention was 93.6% (3 months) and 78.7% (6 months). Retention for ≥6 months differed significantly between participants treated for opioid use disorder (OUD) and other SUDs (p=0.04). Participants earned a median of 8 points (IQR, 5-12), representing approximately 80% of the median number of points possible. Over half of participants (54.8%, 23/42) completed the survey. Overall, respondents reported positive experiences with CM. Thematic analysis of open-ended survey items further contextualized these findings with descriptive participant feedback. Conclusions. CM implementation in a family medicine residency SUD treatment program was feasible and associated with high retention rates. Findings highlight the value of automated documentation, appropriate workflows, and adequate participant incentives. Larger prospective studies with longer follow-up periods are needed to assess effectiveness and sustainability. Policy Implications. CM represents a patient-centered alternative to a historically punitive approaches to SUD care by focusing on positive reinforcement, promoting equitable treatment access, and encouraging affirming interactions between patients and providers.
INTRODUCTION Contingency management (CM) is a behavioral treatment approach which involves the delivery of a positive reinforcement that is contingent on achieving specific predefined goals.1 It is recognized as an evidence-backed strategy for improving outcomes in the treatment of substance use disorders.2,3 The use of CM is supported by decades of research demonstrating its efficacy and effectiveness for patients with substance use disorders (SUDs), including retention in treatment and other recovery goals for patients being treated for SUD including stimulant use disorder (StUD) or opioid use disorder (OUD).3,4 68 Delaware Journal of Public Health - October 2026
Behavioral interventions that reinforce beneficial activities related to recovery using motivational incentives have been effective for a range of SUDs including StUD and OUD.3,5 Explicitly acknowledging individual patient achievement serve as a valuable mechanism for promoting a positive self-image, provide ongoing and consistent staff and peer support, and motivate other patients by offering peer models to emulate.6 The literature supporting the use of CM is robust, but the protocol for delivering the motivational incentive varies.7 The two frequently cited CM protocols in the literature are prize-based incentive therapy and the fishbowl method, Doi: 10.32481/djph.2026.10.09
which involves a variable magnitude of reinforcement.7 The fishbowl method was designed as a way to curtail costs of prize-based protocols.7 Research has demonstrated that each CM protocol has comparable efficacy.7 The growing evidence base has led to multiple professional organizations including the American Society of Addiction Medicine and the American Academy of Addiction Psychiatry to endorse CM. Specifically, these groups have recommended CM as the standard of care for StUD where limited options and no Food and Drug Administration approved options exist for SUD medication-based therapy.8 The Substance Abuse and Mental Health Services Administration (SAMHSA) also identifies CM as a proven health care intervention with demonstrated effectiveness in treating a variety of SUDs.9 SAMHSA grant programs additionally increased their annual CM incentive value up to $750 from a previous $75 recognizing the motivational incentive associated with higher value prizes.9 Despite the substantial evidence-base of CM for SUDs, it remains an underutilized intervention in clinical care.7 In response, this pilot study aimed to describe the development and delivery of an evidence-based CM protocol in realworld clinical practice. Outcomes and experiences were evaluated among patients who received CM services and SUD treatment in a family medicine residency program.
METHODS Setting and Study Population This retrospective observational pilot study examined patients enrolled in a CM protocol based in two regional family medicine center clinics in Northern Delaware. Patients were considered eligible for CM services if they were a patient in one of the participating family medicine residency program clinics; receiving SUD treatment for OUD, StUD, cocaine use disorder, benzodiazepine use disorder, or barbiturate use disorder; and willing to complete a urine drug screen (UDS) at each visit.
Contingency Management Protocol Goals for CM participants included 1) visit attendance and 2) UDS screen results that were negative for the substance they were trying to stop using (including opioids, cocaine, methamphetamine, benzodiazepines, and barbiturates unless otherwise prescribed). For delivery of CM services, this study adapted the fishbowl CM protocol developed by Petry and colleagues.10–12 Participants received 1 point for visit attendance and 1 point for a UDS that was negative for the substance they were trying to stop using. Awarded points could be redeemed for prizes during the visit. Lower-value prize options were available in the fishbowl requiring 1 point to access. Higher-value prize options were available in the fishbowl requiring 2 points to access. Depending on the number of points achieved (1 or 2), participants would draw from the applicable fishbowl.
The 1-point fishbowl contained 20 pieces of paper with notes stating one of the following: • Positive reinforcement quote related to recovery and a small piece of candy (50% of notes) • “Choose from the prize cabinet” containing prizes worth $5 or less (40% of notes) • “Choose a gift card” containing $5 gift cards to stores including Wawa, Dollar Tree, Acme, Five Below, etc. (10% of notes) • The 2-point fishbowl contained 20 pieces of paper with notes stating one of the following: • “Choose from the prize cabinet” containing prizes worth $5 each (25% of notes) • “Choose a gift card” containing $5 gift cards to stores including Wawa, Dollar Tree, Acme, Five Below, etc. (50% of notes) • “Choose your own prize from the prize cabinet or gift card selection” (25% of notes) Visits where the participant did not attend were marked as “no show” and 0 points were awarded. If participants attended their appointment but were unable to provide a UDS sample during the visit, then they were awarded a maximum of 1 point for visit attendance. Participants were eligible to accrue a total of 30 points and be awarded a total of 15 prizes. CM processes for earning incentives were explained to participants upon admission and were provided in a handout (Figures 1 & 2).
Study Measures and Data Collection Contingency Management Participants and Services We explored sociodemographic and CM protocol characteristics of participants including age at CM enrollment, sex assigned at birth, race, ethnicity, insurance status at enrollment, employment status at enrollment, housing status at enrollment, and if the patient was treated with medication for OUD (MOUD). Participation metrics included total number of months receiving CM services, treatment retention at 3 months, treatment retention at 6 months, total number of patients lost to follow-up, number of patients lost to follow-up within 5 months of enrollment, number of patients lost to follow-up after 6 months, total number of office visits, total number of visit no shows, total number of CM points earned, and total number of possible CM points to be earned. Primary outcomes included CM service retention at 3 and 6 months. All CM data was collected in the electronic health record (EHR) or Microsoft Teams for clinical purposes and was entered into the Research Electronic Data Capture (REDCap) system13,14 hosted at Christiana Care Health System for research data storage and analysis. REDCap is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources. Figure 3 displays the EHR form that was used by the CM specialist to collect individual visit data for each patient. 69
Figure 1. Patient Handout for Contingency Management Services, Page 1
Figure 3. Contingency Management ‘Quick Text’ Form Macro for Electronic Health Record (EHR) Data Entry
Participant Experience Survey After the CM invention ended, patients were asked to evaluate their experiences with CM services and its providers using a mixed methods survey with closed- and open-ended items. Appendix A presents the survey form used in this study. The survey was administered electronically using REDCap and inperson during clinical office visits. Data from surveys completed in-person during office visits were transcribed into the REDCap survey database.
Figure 2. Patient Handout for Contingency Management Services, Page 2
Survey items included Likert scale items that prompted participants to identify how much they have been helped by CM and CM’s ability to help them deal with current daily problems. Likert scale items also addressed how often participants were able visit with CM staff as soon as they wanted and how often CM staff treated patients with respect. Participants were also prompted to rank CM services on a scale of 0 (worst) to 10 (best). One binary item asked participants to report if CM staff addressed the option to include family or friends the treatment process. Another binary item asked if participants were given information about different kinds of available treatment services and facilities. Two openended survey items collected qualitative data from participants. The first prompted participants to reflect on what the CM program was doing right. The second prompted participants to reflect on how the CM program could be improved. Lastly, the survey incorporated the validated Discrimination in Medical Settings (DMS) scale to gauge general medical setting experiences of discrimination by participants.15 The DMS scale was adapted by Peek and colleagues for the medical setting using the Everyday Discrimination Scale.16
Data Analysis Quantitative CM protocol and survey data were analyzed using descriptive statistics to assess the central tendency (mean, median, mode) and dispersion of the data (standard deviation [SD], interquartile range [IQR] 25%, 75%) to characterize participants and survey responses. Fisher’s exact or Wilcoxon rank sum tests examined the relationships between participant characteristics and program outcomes. Specifically, sub-group analyses were conducted among different sociodemographic characteristics for CM participants to identify if significant differences existed in treatment retention outcomes at 3 and 6 months, respectively. Qualitative data collected through open-ended items of the patient experience survey were analyzed using codebook thematic analysis.17,18 Analysis of the qualitative data was completed by a researcher embedded in the health system and trained in qualitative and mixed methods (KDW). A codebook was 70 Delaware Journal of Public Health - October 2026
constructed from the deidentified qualitative data which served as an audit trail strategy to monitor the development of codes and associated themes throughout the analytical process. After all responses were first reviewed to establish familiarization with the raw data, initial codes were constructed using an inductive analytic approach. The codes were reviewed and refined into thematic groupings. Representative quotes from the raw data were then selected for each theme. Lastly, to ensure analytic credibility, a peer debriefing was conducted with a clinical and contextual expert (JML) to review the codebook and meaning derived from the data.
characteristics and treatment retention rates.
Quantitative and qualitative survey data were integrated using a joint display19 to triangulate and further contextualize responses. The joint display table presents key survey items, participant response rates, themes associated with each survey items, and representative respondent quotes.
Participant Experiences
All quantitative data were analyzed using STATA SE version 17 (StataCorp LLC, 2021). All qualitative data were analyzed using Excel for Microsoft 365 (Microsoft Corp, 2023).
RESULTS Participant Characteristics Between August 2023 and June 2024, 47 patients received CM services. Table 1 provides a detailed report of the sociodemographic characteristics for all CM participants and survey respondents. The mean (SD) age at enrollment was 46.7 (13.4) years with ages ranging 25-71 years. Participants primarily identified as male (59.6%, N=28), white (59.6%, N=28), not Hispanic/Latine (97.8%, N=46), and Medicaid recipients (61.7%, N=29). Of the 41 participants with employment reported, 51.2% (N=29) were employed full-time or part-time. Of the 38 participants with housing status reported; 89.5% (N=34) identified an independent living arrangement with family, roommates, etc. In terms of SUD treatment goals, CM participants were primarily treated for OUD (95.7%, N=45/47).
Participant Outcomes Table 2 reports the CM activity metrics for CM participants and survey respondents. CM participants were enrolled in the program for a median (IQR) number of 9 (5,10) months, ranging from 0-11 months. CM participants had a median (IQR) of 5 (3, 10) visits ranging from 1-17 visits and a median (IQR) of 2 (1, 3) no shows ranging 0-8 no shows. In total, participants earned a median (IQR) number of 8 (5, 12) points, ranging from 1-28 points while receiving CM services. With CM participants having the ability to earn a median (IQR) number of 10 (6, 20) possible points ranging from 2-34 points, participants were awarded 80% of the median total points possible while receiving CM services. Overall, 27.7% (N=13) CM participants left the program with 38.5% (N=5) leaving before 6 months and 61.5% (N=8) leaving after 6 months. In terms of treatment retention, 93.6% (N=44) and 78.7% (N=37) of CM participants remained enrolled at 3 and 6 months, respectively. Table 3 presents results of the sub-group analyses by participant sociodemographic characteristics and CM retention at 3 and 6 months. A significant difference was observed between participants being treated for OUD versus other SUDs and retention in treatment for 6 or more months (p=0.04). There were no other signification associations between other participant
Among the patients who prematurely ended CM participation (N=13), the reported reasons for doing so included out-ofpocket visit costs, insurance coverage issues, difficulty traveling to the clinic, declining medication for OUD, moving out of state, transferring SUD care from the family medicine clinic to psychiatry or an opioid treatment program, declining CM but remaining in SUD treatment in the family medicine clinic, and being lost to follow-up. To protect participant privacy, individual frequencies of reasons for discontinuing CM services are not reported due to small sample sizes. Of the 47 CM participants, the survey was administered to 42 available participants from March through August 2025. The five patients who did not receive the survey were no longer receiving care in the family medicine residency program and had no contact information on file. Of the 42 patients who received the survey, it was completed by 23 (54.8%) participants. Participant responses demonstrated primarily positive experiences with CM services. Responses to the DSM scale also demonstrated little to no experiences with discrimination in the medical setting. Tables 4 and 5 report the full responses for each close-ended survey item and the DSM scale, respectively. Thematic analysis of the open-ended survey items enabled the construction of key themes regarding perceived positive aspects of CM and suggested opportunities to improve CM. Tables 6 and 7 present the codes and associated themes constructed from the qualitative data in the open-ended survey items where participants described what they appreciated about the CM protocol in addition to opportunities for future improvements. Five themes were constructed from participant feedback about perceived positive aspects of CM including: 1) meaningful connections with providers; 2) positive, non-stigmatizing experiences; 3) recovery support; 4) clinical support; and 5) care coordination and access. The meaningful connections with providers theme addressed how CM participation enabled patients to better connect with their providers and the empathy displayed by provider by “showing that they do care and want the best [for their patients].” The positive, non-stigmatizing experiences theme spoke to the general feeling of enjoyment patients derived from CM participation and that experience receiving treatment in a “caring space with no judgement.” The recovery support theme identified the specific motivational aspects of CM through accountability and positive reinforcement of the visit incentives. The clinical support theme touched upon the benefits of receiving CM services in family medicine residency program where participants spoke to ability to receive integrated care and “helping people like me with my medical needs.” Lastly, the care coordination and access theme touched upon the added benefit of CM contributing to good communication and follow-up from CM staff about upcoming visits to support participant progress and retention. Two themes were constructed from participant suggestions opportunities for improvement including: 1) CM service expansion and increased access and 2) patient-centered CM protocol modifications. The CM service expansion and increased access theme encapsulates participant feedback about increasing community outreach efforts 71
Table 1. Contingency Management (CM) Participant Characteristics All CM Participants N=47 N (%)
Survey Respondents N=23 N (%)
46.7 (13.4), 25-71
47.9 (14.4), 25-72
Female
17 (37.0)
10 (43.5)
Male
29 (63.0)
13 (56.5)
Black or African American
17 (36.2)
8 (34.8)
White
28 (59.6)
14 (60.9)
Other
2 (4.3)
1 (4.4)
1 (2.1)
1 (4.4)
46 (95.7)
22 (95.7)
Medicaid
29 (61.7)
13 (56.5)
Medicare
10 (21.3)
6 (26.1)
Private
8 (17.0)
4 (17.4)
Employed Full-Time or Part-Time
29 (51.2)
11 (52.4)
Not Employed
13 (31.7)
6 (28.6)
7 (17.1)
4 (19.1)
Independent living Arrangement (e.g., living with family, friends, roommates)
34 (89.5)
17 (89.5)
Live Alone
3 (7.9)
2 (10.5)
Dependent Living Arrangement in a Supervised Setting (e.g., residential institution, group home, recovery house)
1 (2.6)
0 (0)
Yes
45 (95.7)
22 (95.7)
No
2 (4.3)
1 (4.4)
All CM Participants N=47 N (%)
Survey Respondents N=23 N (%)
Total months of CM servicesa
9 (5, 10), 0-11
9 (7, 10), 1-11
Number of visitsa
5 (3, 10), 1-17
5 (4, 11), 2-14
2 (1, 3), 0-8
2 (1, 3), 0-5
8 (5, 12), 1-28
8 (5, 12), 3-26
Total possible points to earn
10 (6, 20), 2-34
10 (8, 22), 4-28
Total lost to follow-up, Yes
13 (27.7)
3 (13.0)
Lost to follow-up before 6 months, Yes
8 (61.5)
1 (4.4)
Age at Enrollment (years)a Sex Assigned at Birth
Race
Ethnicity Hispanic or Latine Not Hispanic or Latine Insurance Status at Enrollment
Employment Status at Enrollment b
Not in Labor Force (e.g., retired, disability, student) Housing Status at Enrollment
c
Treated with Medication for Opioid Use Disorder
Note: amean (standard deviation), range. bN=41 (CM); N=21 (survey). cN=38 (CM); N=19 (survey). Table 2. Contingency Management (CM) Participation
Number of visit no shows
a
Points earned
a a
Lost to follow-up after 6 months, Yes
5 (38.5)
2 (8.7)
CM retention at 3 months, Yes
44 (93.6)
21 (91.3)
CM retention at 6 months, Yes
37 (78.7)
18 (78.3)
Note: amedian (IQR: interquartile range [25%, 75%]), range. 72 Delaware Journal of Public Health - October 2026
Table 3. Contingency Management (CM) Retention by Participant Characteristics (N=47) CM Retention at 3 Months
CM Retention at 6 Months
Yes (N=44) N (%)
No (N=3) N (%)
p-valuea
Yes (N=37) N (%)
No (N=10) N (%)
p-valuea
44 (93.6)
3 (6.4)
0.13b
37 (78.7)
10 (21.3)
0.73b
Female
16 (37.2)
1 (33.3)
1.00
13 (36.1)
4 (40.0)
1.00
Male
27 (62.8)
2 (66.7)
23 (63.9)
6 (60.0)
Distribution of Age in Years at Enrollmentb Sex
Race Black/African American
17 (38.6)
0 (0)
15 (40.5)
2 (20.0)
White
25 (56.8)
3 (100)
0.37
21 (56.8)
7 (70.0)
Other
2 (4.6)
0 (0)
1 (2.7)
1 (10.0)
Not Hispanic/Latine
43 (97.7)
3 (100)
36 (97.3)
10 (100)
Hispanic/Latine
1 (2.3)
0 (0)
1 (2.7)
0 (0)
Medicaid
27 (61.4)
2 (66.7)
23 (62.2)
6 (60.0)
Medicare
10 (22.7)
0 (0)
9 (24.3)
1 (10.0)
Private
7 (15.9)
1 (33.3)
5 (13.5)
3 (30.0)
20 (50.6)
1 (33.3)
18 (56.3)
3 (33.3)
6 (15.8)
1 (33.3)
5 (15.6)
2 (22.2)
12 (31.6)
1 (33.3)
9 (28.1)
4 (44.4)
3 (8.6)
0 (0)
3 (10.3)
0 (0)
0.28
Ethnicity 1.00
1.00
Insurance Status at Enrollment 0.52
0.40
Employment Status at Enrollmentc Employed Not in Labor Force
e
Unemployed
0.74
0.52
Housing Status at Enrollment
d
Live alone Not Live Alone (dependent)
f
Not Live Alone (independent)g
1.00
1 (2.9)
0 (0)
0 (0)
1 (11.1)
31 (88.6)
3 (100)
26 (89.7)
8 (88.9)
1 (2.3)
1 (33.3)
0 (0)
2 (20.0)
43 (97.7)
2 (66.7)
37 (100)
8 (80.0)
0.34
Treated for Opioid Use Disorder No Yes
0.13
0.04
Note: Fisher’s exact test unless otherwise noted. Wilcoxon rank-sum test. N=41. N=38. Not in labor force: retired, on disability, minor, student. fNot live alone (dependent): live in a supervised setting such as a residential institution, group home, recovery house, nursing home, or children (under 18 yrs) living with parents. gNot live alone (independent): live with family, partners, friends, or roommates. a
b
c
d
e
73
Table 5. Participant Experience Survey Responses to the Discrimination in Medical Settings Scale (N=23)
Table 4. Participant Experience Survey Responses (N=23) N (%) How much have you been helped by the CM program? Not at all
0 (0)
A little
0 (0)
Somewhat
2 (8.7)
A lot
21 (91.3)
Using any number from 0 to 10, where 0 is the worst treatment program possible and 10 is the best treatment program possible, what number would you use to rate this Contingency Management treatment program?a 9.7 (0.54), 8-10 Compared with when you entered this Contingency Management treatment program, how would you rate your ability to deal with daily problems now? Much worse
0 (0)
A little worse
0 (0)
About the same
3 (13.0)
A little better
2 (8.7)
Much better
18 (78.3)
Did CM program staff talk with you about including family or friends in counseling or treatment? Yes
20 (87.0)
No
3 (13.4)
When you needed treatment right away, how often did you see someone from the CM program as soon as you wanted?
How often the following experiences happen during your interactions with the medical profession.
N (%)
Treated with less courtesy than other people. Never
23 (100)
Rarely
0 (0)
Sometimes
0 (0)
Most of the time
0 (0)
Always
0 (0)
Treated with less respect than other people. Never
23 (100)
Rarely
0 (0)
Sometimes
0 (0)
Most of the time
0 (0)
Always
0 (0)
Receive poorer service than others. Never
23 (100)
Rarely
0 (0)
Sometimes
0 (0)
Most of the time
0 (0)
Always
0 (0)
Doctor or nurse acts as if he or she thinks you are not smart. Never
23 (100)
Rarely
0 (0)
Never
0 (0)
Sometimes
0 (0)
Sometimes
0 (0)
Most of the time
0 (0)
Usually
2 (8.7)
Always
0 (0)
Always
21 (91.3)
During the CM program, how often did staff show respect for what you had to say? Never
0 (0)
Sometimes
0 (0)
Usually
0 (0)
Always
23 (100)
During the CM program, were you given information about different kinds of treatment services and facilities that are available? Yes
21 (91.3)
No
2 (8.7)
Doctor or nurse acts as if he or she is afraid of you. Never
23 (100)
Rarely
0 (0)
Sometimes
0 (0)
Most of the time
0 (0)
Always
0 (0)
Doctor or nurse acts as if he or she is better than you. Never
23 (100)
Rarely
0 (0)
Sometimes
0 (0)
Most of the time
0 (0)
Always
0 (0)
Feel like a doctor or nurse is not listening to what you were saying.
74 Delaware Journal of Public Health - October 2026
Never
22 (95.7)
Rarely
1 (4.4)
Sometimes
0 (0)
Most of the time
0 (0)
Table 6. Themes and Associated Codes Constructed from Positive Survey Participant Feedback (N=23) Themes
Codes
Meaningful connections with providers
appreciation for providers, getting to know providers, provider empathy
Positive, non-stigmatizing experience
enjoyable, fun, no judgement, caring space
Recovery support
help with meeting recovery goals, rewards for meeting recovery goals, accountability, positive reinforcement, motivation, encouragement
Clinical support
clinical feedback, medication management, integrated care, medical care access, behavioral counseling
Care coordination and access
communication, follow-up
Table 7. Codes and Associated Themes Constructed From Survey Participant Feedback About Opportunities to Improve (N=18) Themes
Codes
CM service expansion and increased access
community access, community outreach, increase patient access, modify CM language
Patient-centered CM protocol modifications
ensure consistent delivery of incentives, modify incentive reward schedule to include additional milestone rewards, restart CM services, incorporate peer support component
Table 8. Integrated Quantitative and Qualitative Results N=23 N (%)
Survey items
Themes
Representative quotes
Meaningful connections with providers
“Showing that they do care and want the best for you.” “… you get to know your nurse [and] doctor”
Positive, non-stigmatizing experiences
“I just enjoyed the prize cabinet, it was fun & I got nice gloves!” “… caring space with no judgement”
Recovery support
Gives [me] a nice reward for staying clean. [It was] refreshing to receive a gift just for another day clean. I look forward to this program and the rewards that come with it.” “Giving motivation to stay clean. [It’s] the little things.”
Positive Aspects of CM Services CM staff showed respect, “Always”
23 (100)
Perceived helpfulness of CM services, “A lot”
21 (91.3)
CM staff discussed inclusion family/ friends in counseling/ treatment, “Yes”
20 (87.0)
Received information about different treatment services and facilities, “Yes”
21 (91.3)
Clinical support
“Counseling, advice, medication management” “Take care of all my needs is helping people like me with all my medical needs
Able to see CM staff immediately, when necessary, “Always”
21 (91.3)
Care coordination & access
“… good follow through, good communication…” “Following up…”
CM service expansion & increased access
“Increase outreach in community” “Expand [CM services] to more people.” “Naming it something other than contingency management treatment program so others like me would know what you are asking to fill out.”
Patient-centered CM protocol modifications
“[Having] a reward so when the [visit tracking booklet] fills up completely there will be a prize. I looked forward my [booklet] being stamped and the big prize at the end.” “Incentives for doing the right thing on a consistent basis” “It would be nice to have groups, so that I could learn more about addiction and meet other people who are going through the same thing
Opportunities to Improve CM services Rating of current ability to deal with daily problems, “Much better”
Rating of CM services, “0” (worst) to “10” (best)
18 (78.3)
9.7 (0.54), 8-10a
Notes: Note: CM: contingency management. amean (SD), range
75
and increasing access to CM services. This theme also touched upon the importance of being mindful about the language used when promoting CM to the community and to use terms that are meaningful and more descriptive about the services being offered. The patient-centered CM protocol modifications theme addressed the desire expressed by multiple participants to bring back CM services in the clinics. This theme also includes participants’ suggestions for future CM services to include a peer support component, to improve the consistency of the delivery of incentive rewards, and to increase the incentive reward schedule by including additional rewards for important milestones. Table 8 presents a joint display of the integrated quantitative and qualitative results by perceived positive aspects of CM and suggested opportunities to improve CM services. While responses to the closed-ended survey items demonstrated a positive overall experience with CM, the qualitative data offers additional context regarding what participants specifically appreciated about CM and what could be improved.
DISCUSSION Utilizing evidence-based strategies, such as CM, has the potential to improve treatment outcomes for people with SUD. Our study illustrates a framework developing and delivering a CM protocol in a real-world family medicine residency program setting. Findings demonstrated encouraging treatment retention rates with 93.6% (N=44/47) participants remaining at 3 months and 78.7% (N=37/47) participants remaining at 6 months. This pilot study also identified some key lessons learned. In terms of patient engagement, the types of prizes offered are important and patient preferences should be tracked to identify which prizes will have a higher perceived value. The CM protocol was limited in terms of the monetary value of prizes we offered. The monetary value of CM prizes has been demonstrated to be associated with improved outcomes.20,21 As such, CM budget planning should account for the cost of prizes and maximize the prize value per patient to the extent possible. A 2025 systematic review by Rush and colleagues developed an inflation-adjusted estimate for incentive magnitudes by CM protocol type to maximize clinical outcomes.22 Specifically, they estimated a weekly median incentive magnitude of $128 for voucher-based CM protocols and $55 for prize-based (i.e., fishbowl) CM protocols.22 Utilizing such evidence-based benchmarks will be key for future CM interventions, particularly those that aim to sustain CM services long-term. In addition, delivery of CM in a real-world setting can be difficult for busy clinical practices in terms of managing and tracking patient progress.23 Automated quick texts in the EHR can assist with standardizing and expediting the completion of CM patient visit notes. Training CM personnel to serve as clinical champions and maintain visit documentation can potentially reduce the administrative burden on other providers and staff. Despite numerous scientific advancements, the treatment of SUD is marred by a history of stigmatization and discrimination leading to inherently punitive policies and practices.24–26 This contributed to a system of care that viewed SUD as a moral failing and used discipline as the primary incentive for treatment adherence.24,27 Minoritized communities disproportionately experience the consequences of these barriers.25 Moreover, minoritized individuals are less likely to engage in treatment due to experiences of discrimination and racism from medical 76 Delaware Journal of Public Health - October 2026
and behavioral health care providers28 and are more likely to be penalized for their substance use by the criminal justice system.25 The utilization of CM marks a divergence from the historically punitive nature of SUD treatment by focusing on positive reinforcement as the primary driver to achieve treatment goals.1 In addition to improving a patient’s self-image, CM can shift the culture of the treatment experience to be more positive for both the patient and provider as they jointly focus on the patient’s progress rather than any setbacks.1,29 In terms of advancing health equity goals, CM is well-positioned to increase equitable access to SUD treatment for historically marginalized communities.30 For example, two recent studies by McDonell and colleagues demonstrated approaches to develop culturally tailored CM protocols for American Indian and Alaska Native communities to improve recovery outcomes.31,32 CM is also a relatively costeffective intervention.33 It requires no complex technological components and does not need to be delivered by advanced practitioners.33 As such, low-resource settings could feasibly implement CM into their practice.33
Limitations This study has several limitations. The retrospective observational design and absence of a comparison group. This limits the ability to attribute observed retention outcomes to the CM intervention. It is also possible that there is an inherent difference in the characteristics of those who opted to receive CM services with SUD treatment and those who chose to receive SUD treatment without CM services. Our sample size was relatively small (N=47), limiting the statistical power of the sub-group analyses. The study sample also predominantly featured those being treated for OUD and is thus less generalizable to those being treated for other SUDs such as StUD. The participants were also recruited from a single geographic region within two family medicine clinics and therefore may not be generalizable to other patient populations, locations, or clinical environments. Many factors including transportation, insurance status, ability to afford out-of-pocket costs may have impacted access to care and the patient’s ability to participate in CM. Prior CM protocols have been more successful with higher value incentives.20,21 Thus, the low value incentives outlined in this study may have limited the potential impact or magnitude of the CM protocol’s effect on participant outcomes. Future efforts may also better specify the type of incentives preferred by CM participants. CM service data collection relied on the need for manual tracking of the CM points, visits, and UDS results which could have introduced opportunities for documentation error. Whenever possible, future efforts should aim to standardize workflows into EHRs and automate documentation. Lastly, survey findings may be subject to response and selection bias given the lower completion rate of 54.8%. Patients who discontinued care or were unable to be contacted were unable to be surveyed, which may have led to more favorable experiences being documented and an underrepresentation of the less favorable experiences.
Policy Implications and Conclusions This pilot study illustrates the feasibility of developing a CM protocol for treating SUDs in a family medicine residency program. The high retention rates noted at both 3- and 6-month intervals offer encouragement that patient participation and engagement in CM efforts offer clinical advantages. This study exemplified the pragmatic considerations for implementing a
CM protocol in real-world clinical practice including necessary support structure, documentation efforts, patient guided incentives, and adequate and sustained funding. Future efforts with increased sample sizes and a more diverse patient population with comparison groups will help elucidate the effectiveness and sustainability of this real-world CM protocol intervention. Dr. Williams may be contacted at kimwilliams@christianacare.org .
FINANCIAL DISCLOSURE This work was supported by the State Opioid Response III Tier Award Program from the Delaware Department of Health and Social Services, Division of Substance Abuse and Mental Health (grant no. 5H79TI085764-02).
REFERENCES 1. Office of the Assistant Secretary for Planning and Evaluation. (2023). Contingency management for the treatment of substance use disorders: Enhancing access, quality, and program integrity for an evidence-based intervention. US Department of Health and Human Services. https://aspe.hhs.gov/reports/contingency-management-treatment-suds 2. Benishek, L. A., Dugosh, K. L., Kirby, K. C., Matejkowski, J., Clements, N. T., Seymour, B. L., & Festinger, D. S. (2014). Prize-based contingency management for the treatment of substance abusers: A meta-analysis. Addiction (Abingdon, England), 109(9), 1426–1436. 3. Prendergast, M., Podus, D., Finney, J., Greenwell, L., & Roll, J. (2006). Contingency management for treatment of substance use disorders: A metaanalysis. Addiction (Abingdon, England), 101(11), 1546–1560. Advance online publication. 4. Pfund, R. A., Ginley, M. K., Rash, C. J., & Zajac, K. (2022). Contingency management for treatment attendance: A meta-analysis. Journal of Substance Abuse Treatment, 133, 108556. 5. Brown, H. D., & DeFulio, A. (2020). Contingency management for the treatment of methamphetamine use disorder: A systematic review. Drug and Alcohol Dependence, 216, 108307. 6. Kellogg, S. H., Burns, M., Coleman, P., Stitzer, M., Wale, J. B., & Kreek, M. J. (2005). Something of value: The introduction of contingency management interventions into the New York City Health and Hospital Addiction Treatment Service. Journal of Substance Abuse Treatment, 28(1), 57–65. 7. Rash, C. J., Stitzer, M., & Weinstock, J. (2017). Contingency Management: New Directions and Remaining Challenges for An Evidence-Based Intervention. Journal of Substance Abuse Treatment, 72, 10–18. 8. Clinical Guideline Committee (CGC) Members, & the ASAM Team, & the AAAP Team, & the IRETA Team. (2024). The ASAM/AAAP Clinical Practice Guideline on the Management of Stimulant Use Disorder. Journal of Addiction Medicine, 18(Suppl 1), 1–56. 9. Substance Abuse and Mental Health Services Administration. (2025). Using SAMHSA funds to implement evidence-based contingency management services (Publication No. PEP24-06-001; Issue Publication No. PEP24-06-001). Substance Abuse and Mental Health Services Administration. 10. Petry, N. M. (2000). A comprehensive guide to the application of contingency management procedures in clinical settings. Drug and Alcohol Dependence, 58(1-2), 9–25. 11. Petry, N. M., Martin, B., Cooney, J. L., & Kranzler, H. R. (2000). Give them prizes, and they will come: Contingency management for treatment of alcohol dependence. Journal of Consulting and Clinical Psychology, 68(2), 250–257. 12. Petry, N. M., & Martin, B. (2002). Low-cost contingency management for treating cocaine- and opioid-abusing methadone patients. Journal of Consulting and Clinical Psychology, 70(2), 398–405. 13. Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381. 14. Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O’Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., Duda, S. N., & the REDCap Consortium. (2019). The REDCap consortium: Building an international community of software platform partners. Journal of Biomedical Informatics, 95, 103208.
15. Peek, M. E., Nunez-Smith, M., Drum, M., & Lewis, T. T. (2011). Adapting the everyday discrimination scale to medical settings: Reliability and validity testing in a sample of African American patients. Ethnicity & Disease, 21(4), 502–509. 16. Williams, D. R., Yan Yu., Jackson, J. S., & Anderson, N. B. (1997). Racial differences in physical and mental health: Socio-economic status, stress and discrimination. Journal of Health Psychology, 2(3), 335–351. 17. Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis. Qualitative Psychology, 9(1), 3–26. 18. Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Chapter 48. Thematic analysis. In P. Liamputtong (Ed.), Handbook of Research Methods in Health Social Sciences (pp. 843–860). Springer Singapore. 19. Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Annals of Family Medicine, 13(6), 554–561. 20. Bolívar, H. A., Klemperer, E. M., Coleman, S. R. M., DeSarno, M., Skelly, J. M., & Higgins, S. T. (2021). Contingency management for patients receiving medication for opioid use disorder: A systematic review and meta-analysis. JAMA Psychiatry, 78(10), 1092–1102. Advance online publication. 21. Lussier, J. P., Heil, S. H., Mongeon, J. A., Badger, G. J., & Higgins, S. T. (2006). A meta-analysis of voucher-based reinforcement therapy for substance use disorders. Addiction (Abingdon, England), 101(2), 192–203. 22. Rash, C. J., Black, S. I., Parent, S. C., Erath, T. G., & McDonell, M. G. (2025). Data-driven contingency management incentive magnitudes: A review. JAMA Psychiatry, 82(9), 940–945. 23. Pfund, R. A., Cook, J. E., McAfee, N. W., Huskinson, S. L., & Parker, J. D. (2021). Challenges to conducting contingency management treatment for substance use disorders: Practice recommendations for clinicians. Professional Psychology, Research and Practice, 52(2), 137–145. Advance online publication. 24. National Academies of Sciences, Engineering, and Medicine. (2019). Medications for opioid use disorder save lives. The National Academies Press. https://doi.org/10.17226/25310 25. Volkow, N. D. (2021). Addiction should be treated, not penalized. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 46(12), 2048–2050. 26. Goodman, D., Whalen, B., & Hodder, L. C. (2019). It’s time to support, rather than punish, pregnant women with substance use disorder. JAMA Network Open, 2(11), e1914135. 27. Wakeman, S. E., & Rich, J. D. (2018). Barriers to medications for addiction treatment: How stigma kills. Substance Use & Misuse, 53(2), 330–333. 28. Bailey, Z. D., Krieger, N., Agénor, M., Graves, J., Linos, N., & Bassett, M. T. (2017). Structural racism and health inequities in the USA: Evidence and interventions. Lancet, 389(10077), 1453–1463. 29. Peng, L., Titus, H., Roellich, P., & Englander, H. (2023). Adapting contingency management for hospitalized patients with stimulant use disorder. Journal of Addiction Medicine, 17(5), 580–586. Advance online publication. 30. Hirchak, K. (2024). Closing the research-to-implementation gap for contingency management as an effective intervention for substance use disorders across diverse communities. Addiction (Abingdon, England), 119(9), 1519–1521. Advance online publication. 31. McDonell, M. G., Skalisky, J., Burduli, E., Foote, A., Sr., Granbois, A., Smoker, K., Hirchak, K., Herron, J., Ries, R. K., Echo-Hawk, A., Barbosa-Leiker, C., Buchwald, D., Roll, J., & McPherson, S. M. (2021). The rewarding recovery study: A randomized controlled trial of incentives for alcohol and drug abstinence with a rural American Indian community. Addiction (Abingdon, England), 116(6), 1569–1579. 32. McDonell, M. G., Hirchak, K. A., Herron, J., Lyons, A. J., Alcover, K. C., Shaw, J., Kordas, G., Dirks, L. G., Jansen, K., Avey, J., Lillie, K., Donovan, D., McPherson, S. M., Dillard, D., Ries, R., Roll, J., Buchwald, D., Lengele, B., EchoHawk, A., …, Skalisky, J., & the HONOR Study Team. (2021). Effect of incentives for alcohol abstinence in partnership with 3 American Indian and Alaska Native communities: A randomized clinical trial. JAMA Psychiatry, 78(6), 599–606. 33. Murphy, S. M., McDonell, M. G., McPherson, S., Srebnik, D., Angelo, F., Roll, J. M., & Ries, R. K. (2015). An economic evaluation of a contingency-management intervention for stimulant use among community mental health patients with serious mental illness. Drug and Alcohol Dependence, 153, 293–299.
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APPENDIX A. PARTICIPANT EXPERIENCES SURVEY FORM Full Name (first, middle, last): __________________________________________________________________________ Birth Date (mm/dd/yyy): ______/______/________ Today’s Date (mm/dd/yy): ______/______/________ Instructions: Please answer the questions below to describe your experiences with the Contingency Management treatment program services you received in the Family Medicine office. Contingency Management services included attending appointment visits, completing a urine drug screen test, and receiving prizes depending on the number of points earned during your visit. 1. How much have you been helped by the Contingency Management treatment program here? (choose one) Not at all
A little
Somewhat
A lot
2. Using any number from 0 to 10, where 0 is the worst treatment program possible and 10 is the best treatment program possible, what number would you use to rate this Contingency Management treatment program? 0
1
2
3
4
5
6
7
8
9
10
3. What is this Contingency Management treatment program doing right?
4. What could be done to improve this Contingency Management treatment program? 5. Compared with when you entered this Contingency Management treatment program, how would you rate your ability to deal with daily problems now? (choose one) Much worse A little worse About the same A little better Much better 6. Have staff in this Contingency Management treatment program talked with you about including family or friends in counseling or treatment? (choose one) Yes No 7. When you needed treatment right away, how often did you see someone from this Contingency Management treatment program as soon as you wanted? (choose one) Never Sometimes Usually Always 8. During treatment, how often did Contingency Management treatment program staff show respect for what you had to say? (choose one) Never Sometimes Usually Always 9. During this Contingency Management treatment program, were you given information about different kinds of treatment services and facilities that are available? (choose one) Yes No Instructions: Please rate how often the following experiences happen during your interactions with the medical profession. (mark with ‘X’ for each statement below) Never You are treated with less courtesy than other people. You are treated with less respect than other people. You receive poorer service than others. A doctor or nurse acts as if he or she thinks you are not smart. A doctor or nurse acts as if he or she is afraid of you. A doctor or nurse acts as if he or she is better than you. You feel like a doctor or nurse is not listening to what you were saying. 78 Delaware Journal of Public Health - October 2026
Rarely
Sometimes
Most of the time
Always
Building a Sustainable Teen Wellness Curriculum in a Faith-Based Community Setting: A Six-Year Program Evaluation in Wilmington, Delaware Sangeetha D. Nathaniel, M.B.B.S., Ph.D. Delaware Heart and Vascular Group, Delaware Manna House Fellowship, Delaware
Michelle Innasimuthu Delaware Heart and Vascular Group, Delaware Manna House Fellowship, Delaware
Subramanian Gnanaguruparan, M.D. ChristianaCare Hospital, Delaware
ABSTRACT In the United States, the need for adolescent mental health support is greater than what clinical services can provide, with about one in five teens reporting unmet needs. Faith-based organizations are trusted and easy to access, but evidence on sustained delivery is limited. This report looks at a volunteer-led teen wellness program at Manna House Fellowship in Wilmington, Delaware, which ran from June 2020 to July 2026. The program offered 30-minute interactive small-group sessions every two weeks during active cycles, focusing on sleep, nutrition, physical activity, screen and social media use, and stress coping. Sessions were held online until March 2022, then in person. Over six years, 78 sessions reached 35 different teens, averaging eight participants per session, with most attending more than once. Anonymous feedback was collected monthly in written and verbal formats, and reported changes were noted. No one needed to be referred to formal mental health services. That the program has continued for six years without outside funding or paid staff support the feasibility of this sustained community wellness program, although not its clinical effectiveness. Zero recorded referrals do not establish an absence of unmet mental health needs.
INTERVENTION The Manna House Teen Wellness Program is a volunteer-created wellness initiative for teens, offered through a faith-based community group. The program has three main goals. First, it aims to teach practical self-management skills in five areas that affect teen well-being: sleep, nutrition, physical activity, screen and social media use, and stress coping. Second, it seeks to make talking about emotional health normal among teens in a familiar and trusted setting, with the goal of reducing stigma that can stop them from seeking help. Third, it aims to provide a clear and easy way for any teen who shares that they are struggling to connect with a licensed clinician. The program was intentionally created so it could run without clinical infrastructure, licenses, or funding. It used publicly available health promotion materials and was delivered as group discussions instead of lectures. This simple, low-cost approach was chosen to make it easier for others to use and keep going over time.
Place and Time The program took place at Manna House Fellowship, a faithbased community group in Wilmington, Delaware. Wilmington is Delaware’s largest city and has a diverse population, including families who may have limited access to specialized mental health services. Sessions ran from June 2020 to July 2026, covering more than six years, including the COVID-19 pandemic and the return to in-person activities. From June 2020 to February 2022, sessions were held online, then switched to in-person from March 2022.
Population The program was open to teens who attended Manna House Fellowship or came with family members involved in the group. Participation was voluntary and open to all, with no screening, diagnosis, or referral needed, and no one was turned away. Over six years, groups changed as teens grew older or family attendance shifted. Approximately 35 teens were estimated to have
participated during the evaluation period, based on attendance at services. All teens included in this estimate also participated in the wellness sessions. This was an estimate of overall reach, not a verified count of unique participants. Other than age, demographic information was not collected, since the program focused on community wellness rather than research. Because of this, it is not possible to know how well the program reached different racial, ethnic, or socioeconomic groups in the congregation. The evaluation also cannot establish whether the program addressed participants’ insurance-related barriers to care.
Purpose Adolescent mental health needs in the United States remain high. In the 2023 Youth Risk Behavior Survey, 39.7% of high school students reported ongoing feelings of sadness or hopelessness, 28.5% reported poor mental health, and 20.4% seriously considered attempting suicide.1 National surveillance data indicate that approximately 20% of adolescents aged 12 to 17 report unmet mental health care needs.2 Screen and social media exposure has been identified as a contributor to this burden, with the U.S. Surgeon General issuing a formal advisory on social media and youth mental health.3 The data from Delaware give a local reason for providing accessible community programs. According to the 2022 Nemours community health needs assessment, 10.2% of the people who took part in the community survey had to skip taking their child to see a doctor or getting treatment for a sickness or injury the previous year because of the cost. This refers to health care as a whole and not specifically to mental health care. The respondents also mentioned a lack of pediatric mental health professionals, long waiting times, and a need for affordable services; insurance restrictions were listed as one of the general barriers to children’s care.4 A wellness program that is offered without charge and is part of existing community activities could serve as a useful supplement to clinical services, especially for families with limited resources. That said, it cannot take the place of any necessary assessment or treatment. Faith communities Doi: 10.32481/djph.2026.10.10 79
constitute one kind of setting in which such programs could be carried out. A U.S. scoping review found 32 different partnerships between faith communities and mental health organizations, six of which involved people from low socioeconomic groups. The findings regarding mental health literacy, stigma, and referrals were promising, but most of the evaluations were based on weak research designs, and only two of the partnerships provided information on sustainability.5 Youth mental health programs delivered in the community and outside of schools have proven to be feasible and acceptable in other situations.6,7 The program started because of a practical need. A volunteer with clinical training noticed that although teens met regularly, there were no structured sessions on sleep, stress, or screen use, and families rarely talked about emotional health. The program filled this gap by using resources already available in the organization.
IMPLEMENTATION Sessions were 30-minute interactive small groups held every two weeks during active cycles. They were scheduled alongside other activities the organization already offered, so teens did not need to make extra trips, pay fees, or register. One volunteer coordinator with clinical training designed the curriculum and led all sessions. Table 1 gives an overview of the curriculum topics and session structure. Four parts of the program design were considered essential. First, confidentiality rules were explained at the start of every session, which was important since families in the congregation know each other. Second, the program used language that avoided stigma, treating sleep, stress, and screen use as normal health topics. Third, sessions were run as peer discussions, with the facilitator asking questions and teens coming up with their own strategies, instead of just receiving information. Finally, a clear process was set up so that if a teen shared they were struggling, the facilitator would connect them to a licensed clinician, taking responsibility for the referral. The program began in June 2020, during the height of the COVID-19 pandemic, when in-person gatherings were on hold and teens were dealing with remote school, isolation, and more screen time. From June 2020 to February 2022, all sessions were held online using Zoom. In March 2022, the program switched to in-person meetings and has stayed that way. Sessions continued through this change without interruption, extra funding, or changes to the curriculum. Two things helped make virtual sessions work: the 30-minute length kept teens engaged online, and there were no costs, registration, or transportation needed, so the program did not depend on the building being open. The 21 months online served as an unexpected test, showing that the program could keep going even without a physical space. The program ran with no budget. Materials were either printed or made as needed, the organization provided space for free, and the facilitator volunteered their time. There were no grant applications, contracts, or formal agreements required.
EVALUATION This is a process evaluation with sustainability findings, reported in accordance with the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) statement.8 The assessment looked at aspects such as program delivery, estimated reach, continuity, and descriptive feedback; program delivery was based on session logs and overall participation was estimated by referring to the number of teenagers who attended the services. Service attendance was chosen since all the teenagers who were included in the estimate had also taken part in the wellness 80 Delaware Journal of Public Health - October 2026
Table 1. Curriculum Domains and Session Structure, Manna House Teen Wellness Program, Delaware, June 2020–July 2026 Element
Description
Curriculum domains
Sleep hygiene; nutrition; physical activity; screen and social media use; stress coping
Session length
30 minutes
Delivery modality
Virtual (Zoom), June 2020–February 2022; in person, March 2022–July 2026
Frequency
Biweekly during active cohort cycles
Format
Interactive small group; peer discussion rather than didactic lecture
Group size
Approximately 8 participants per session
Facilitation
Single volunteer coordinator with clinical training; unpaid
Safeguards
Confidentiality ground rules restated each session; destigmatizing language; predefined escalation pathway to a licensed clinician
Cost
No budget; donated space, improvised materials, volunteer facilitation
sessions. It was not possible to obtain individual attendance records to check for unique participation or to determine the number of times people attended. Anonymous feedback was gathered each month both in writing and orally, and any reported changes were recorded. Feedback came from most of the participants, but specific responses, the number of responses, the response rate, or details about repeated contributions were not available. Table 2 provides a summary of the findings and the limitations of the measurement. From June 2020 to July 2026, 78 sessions were given, the total number of teenagers who reached them being about 35. The number of teenagers who attended the sessions was estimated to be about eight per session. Monthly feedback consisted of reports of changes, but it was not possible to determine how many or what proportion of the respondents said that they had experienced changes. These comments are merely descriptive observations and should not be regarded as a quantified or independently verified measure of behavior change. During the six-year period there were no instances of referrals to formal mental health services, even though the option was made available and explained to the participants. However, this result does not prove that no participant required clinical care. The key limitations are that the group of participants is small and constantly changing; the number of participants is estimated not verified; there is no record of individual attendance; and there is neither a comparison group nor a validated outcome measure. The estimates of service attendance do not provide a deduplicated figure for the number of people who took part in wellness activities or the rate at which people attended more than once. The anonymous monthly feedback had no verified denominator for the responses and did not include information on repeat contributions in this report. Since the feedback is based on self-report and may reflect selective participation, it does not prove the frequency, extent, or lasting nature of any behavior change. While the evaluation does mention continuous delivery, estimated reach, and descriptive feedback, it is not able to establish clinical effectiveness.
Table 2. Program Reach and Evaluation Findings, June 2020–July 2026 Measure
Data source
Finding
Sessions delivered
Session logs
78
Estimated overall reach
Service-attendance estimate
Approximately 35 teens
Estimated session attendance
Program estimate
Approximately 8 participants per session
Return attendance
Individual attendance not tracked
Not quantified
Self-reported behavior change
Anonymous monthly written and verbal feedback
Reported changes noted; no quantified behaviorchange result
Escalations to formal care
Facilitator records
0 participants referred over 6 years
Adverse events
Facilitator records
None identified
Program continuity
Session logs
June 2020–July 2026
Adverse Effects There were no adverse events, complaints, or unexpected problems during the six years. Two possible risks were considered when designing the program. The first was the risk of breaking confidentiality in a close-knit community, which was managed by stating clear ground rules at every session and using a facilitation style that did not require teens to share personal details. The second risk was that an untrained facilitator might not handle a serious disclosure well, so the escalation process was set up before the program started instead of being created in the moment. One result needs careful thought. The fact that there were no referrals over six years and 78 sessions could mean the group had fewer serious issues, or it could mean that some needs were missed. Short wellness sessions focused on general health and peer discussion are not meant to spot serious symptoms, and teens might not want to share distress in a group they know well. Programs like this should not assume that no referrals means there are no needs, and should think about offering a private way for teens to share concerns in addition to group sessions.
Sustainability
The program has run without interruption for six years, even during the COVID-19 pandemic, showing it is sustainable. Three things helped: there were no ongoing costs, the program was built into the organization’s regular schedule, and the curriculum could be reused for each new group of teens. The biggest risk to keeping the program going is that it depends on one volunteer, with no backup or written guide for others to follow. Groups wanting to copy this model should plan for facilitator succession from the start. The absence of individual attendance tracking, a verified feedback denominator, and validated outcome measures also limited the evaluation. Prospectively recording attendance with nonidentifying participant codes and documenting feedback counts and collection timing could strengthen future evaluations.
PUBLIC HEALTH SIGNIFICANCE This program in Delaware shows that a community group without a health focus, clinical staff, or budget can run a structured wellness program for teens for six years. Since many teens lack access to behavioral health care, community organizations are a resource that is not used enough. All that is needed is a trained volunteer and a regular 30-minute time slot in the group’s schedule. More broadly, this report adds an example of a sustainable program to a literature in which only two of 32 reviewed faith-community and mental health partnerships reported on sustainability.5 This program continued for six years without external funding, but its limited outcome
data show why sustained delivery and demonstrated effectiveness must be considered separately. Practitioners who want to use this model should set up the escalation process before the first session, record wellness session attendance using nonidentifying participant codes, document feedback counts and timing, and train a second facilitator within the first year. Dr. Nathaniel may be contacted at sangeethanathaniel@gmail.com .
FINANCIAL DISCLOSURE The authors declare no financial, personal, commercial, political, academic, or fiduciary conflicts of interest. This program received no external funding. No grant, institutional, or commercial support was provided at any stage.
REFERENCES 1. Verlenden, J. V., Fodeman, A., Wilkins, N., Jones, S. E., Moore, S., Cornett, K., Sims, V., Saelee, R., Brener, N. D., & the Centers for Disease Control and Prevention. (2024). Mental health and suicide risk among high school students and protective factors — Youth Risk Behavior Survey, United States, 2023. MMWR Supplements, 73(4), 79–86. https://www.cdc.gov/mmwr/volumes/73/su/pdfs/su7304a9-H.pdf 2. Centers for Disease Control and Prevention. (2024). Data and statistics on children’s mental health. https://www.cdc.gov/children-mental-health/data-research/index.html 3. Office of the Surgeon General. (2023). Social media and youth mental health: The U.S. Surgeon General’s advisory. U.S. Department of Health and Human Services. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf 4. Nemours Children’s Health. (2022). Community health needs assessment 2022 report. pp. 41–43, 55–56. https://www.nemours.org/content/dam/nemours/shared/collateral/communityreports/dv-2022-chna-assessment-report.pdf 5. Perez, L. G., Cardenas, C., Blagg, T., & Wong, E. C. (2025). Partnerships between faith communities and the mental health sector: A scoping review. Psychiatric Services (Washington, D.C.), 76(1), 61–81. 6. Corley, E., Moran, E., Heary, C., Gardiner, P., & Donohoe, G. (2023). Evaluation of a community-based intervention for youth mental health in rural Ireland. Irish Journal of Psychological Medicine, 1–9. 7. Hui, C. L.-M., Chen, E. Y.-H., Wong, S. M.-Y., Wong, G. H.-Y., Chan, S. K.-W., Sham, P. C., Wong, M. T.-H., Chan, K.-T., Cheung, C., Lai, G. C.-H., Rickwood, D., Mcgorry, P. D., & Suen, Y.-N. (2025). Reducing mental health distress and preventing depression in young people in the community: A multimethod observational study with a real-world and prospective 12-month controlled approach: mental health distress in community youths. Psychiatry Research, 344, 116279. 8. Des Jarlais, D. C., Lyles, C., Crepaz, N., & the TREND Group. (2004). Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: The TREND statement. American Journal of Public Health, 94(3), 361–366. community youths. Psychiatry Research, 344, 116279. 9. Des Jarlais, D. C., Lyles, C., Crepaz, N., & the TREND Group. (2004). Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: The TREND statement. American Journal of Public Health, 94(3), 361–366.
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A Review of the USDA’s Expanded Food and Nutrition Education Program (EFNEP) Diane Oliver, M.P.H., R.D.N., L.D.N. EFNEP Coordinator/Extension Agent, University of Delaware Cooperative Extension
ABSTRACT The Expanded Food and Nutrition Education Program (EFNEP), which is an outreach program provided by the Land-grant Universities through their Cooperative Extension System, was established in 1969. This makes it the longest operating, evidence-based nutrition education program in the country. It provides food and nutrition education to adults and youth from limited-income communities around the nation. In the state of Delaware, the University of Delaware provides EFNEP to New Castle and Sussex counties while Delaware State University provides programming in Kent County. Currently classes are taught in English and Spanish both in person and online. A series of classes teaches participants about eating a healthier diet, being more physically active, keeping food safe and using food resources wisely. In Fiscal Year 2025, the University of Delaware’s EFNEP alone reached over 250 adults and over 1,200 youth within New Castle and Sussex Counties. Over 90% of adults and 80% of youth showed an improvement in their nutritional practices.
INTERVENTION
Implementation
The Expanded Food and Nutrition Education Program (EFNEP), which is an outreach program provided by the Land-grant Universities through their Cooperative Extension System, was established in 1969. This makes it the longest operating, evidence-based nutrition education program in the country. Peer educators provide food and nutrition education to adults and youth from limited-income communities around the nation. Reaching approximately 500,000 individuals a year, the program provides nutrition education to populations seeking additional guidance on health. The main goal of the program is to reach low-income families and individuals with information about nutrition, food safety, physical activity and food resources.1
EFNEP at the University of Delaware has five peer educators, two of which are bilingual in Spanish. Evidence-based curriculums such as Eating Smart & Moving More (North Carolina State University), Teen Cuisine (Virginia Cooperative Extension) and Show Me Nutrition (University of Missouri) are chosen based on the age of the participants.
Due to its longstanding history, “community-based nutrition education programs, such as EFNEP, epitomize how effective nutrition education relies on a blend of multiple theoretical aspects and is embedded in communities with different cultural, social, and environmental systems.”2
Place and Time In the State of Delaware, the University of Delaware provides EFNEP to New Castle and Sussex counties while Delaware State University provides programming in Kent County. A series of classes is offered in a variety of different settings around the state including in libraries, schools, community centers, youth centers, places of worship and Head Starts. Synchronous online classes are also available. Times are determined at the request of our partners in the community where our programs take place. For online programming, a time convenient for the participants is offered. This is usually in the morning or evening hours.
Population Limited income families in New Castle and Sussex Counties participate in EFNEP with UD Cooperative Extension. Classes are taught in English and Spanish. Adult and youth (3rd grade and higher) classes are offered. 84 Delaware Journal of Public Health - October 2026
The EFNEP series of classes provides an interactive learning experience where each meeting includes a cooking demonstration. Groups meet five or more times for approximately 45 minutes. Some groups request longer lessons. Many times, recipes include foods donated by the University of Delaware farms and the Delaware Commodity Supplemental Food Program. At the end of the adult program, an EFNEP focused cookbook, made up of low-cost meals, is provided to participants. Adults are also given tools to use at home to make meal preparation easier. Youth are given printouts of recipes they make in class to take home to share with their families, to reinforce messaging.
NATIONAL EVALUATION Around the nation, EFNEP continues to have a positive impact on people’s lives. “Routinely, 80 percent or more EFNEP families report living at or below 100 percent of poverty, and nearly 70 percent indicate being of minority status.”3 To ensure that EFNEP is effective, there are validated survey tools used pre-and postintervention nationwide that are distributed to the participants to assess their behavior change. In FY24, EFNEP was reported to have helped achieve $764,000 in food cost savings while improving the diet of about 96% of adults that were part of the program. Ninety-two percent improved food resource management practices. In addition, 78% improved food safety practices and 74% made improvements in their physical activity.3 That same year, 86% of youth showed an increase in knowledge or ability to choose healthy foods and 51% increased their knowledge or ability to prepare low-cost, nutritious food. Fiftynine percent of youth participants improved physical activity knowledge or practices, while 57% showed improvements in food safety knowledge or practices.3 Doi: 10.32481/djph.2026.10.11
One program educator from Northern Marianas College shared the following story: “A mother with heart disease, diabetes, and obesity had been advised many times by her doctors that she needed to make lifestyle changes that included healthy eating habits and physical activity. Despite her doctor’s advice, the changes she needed to make “never really clicked” until she participated in the hands-on demonstration and activities facilitated by EFNEP’s peer educator. Seeing the correct portion sizes and the actual teaspoons of salt and sugar in the food and drinks she was consuming opened her eyes to her eating habits, and she began to make changes. In addition to improving her own lifestyle habits, she also influenced her mother and daughter by preparing easy, healthy recipes and sharing about healthier eating habits and physical activity that she learned in EFNEP.”3
DELAWARE EVALUATION The University of Delaware’s EFNEP program reached over 250 adults and over 1,200 youth within New Castle and Sussex Counties during the 2025 Fiscal Year. The majority of these individuals were female (80%) with 46% of them being Hispanic. At least 50% of EFNEP participants for FY25 were on one or more Food Assistance Program. In FY25, the following data shows positive nutrition related outcomes from the Eating Smart and Moving More adult program. At least 93% of participants showed an increase in their diet quality. This includes 45% eating fruit more often each day, 42% eating vegetables more often each day, 37% increasing their vegetable variety per day and 42% of participants eating more dark green vegetables per week, other improvements include 49% of participants increased their bean and pea intake over a week, 45% of participants increased their yogurt or smoothie with yogurt intake and 38% of participants cooked dinner at home more often. Food Safety practices also improved with 73% showing an improvement. In addition, 87% of participants showed an increase in one or more food resource management practices with 35% showed an increase in food security indicators. Increases in physical activity were also shown in FY25 with 71% increasing physical activity behavior. This includes 49% of participants increased their exercise to at least 30 minutes per day a week and 43% of participants made changes to be more active. For youth, 80% of participants in grades 3-5 showed an improvement in one or more core areas of health and nutrition. This includes 30% with their vegetable consumption, 33% with their fruit consumption, 34% with increased consumption of eating whole grains and a 35% increase in reading nutrition facts labels. In addition, 58% of youth showed an improvement in behaviors to improve physical activity. This includes a 28% increase in their response to physical activity frequency and a 28% increase in their duration of physical activity. Forty-three percent showed an improvement in one or more skills related to handling food safety with a 20% increase in washing fruits and vegetables and a 25% improvement in their response to washing their hands. A peer educator from Delaware shared that “during a series of EFNEP Nutrition Classes that was taught in Lower Sussex County, Delaware, a discussion about the importance of eating more fruits and vegetables took place. During one of the classes, an individual shared their weight-loss journey and explained that they only
ate the same fruits and vegetables because they were afraid to try new foods. It was explained to the individual that when foods are combined into different recipes, the taste can often change, and people may find that they actually enjoy foods they once disliked. Also shared, was that research suggests it can take 10 or more tries before someone begins to like a new food. In addition, we discussed how trying foods in different forms — such as raw versus cooked — can also make a difference.” This experience demonstrated how nutrition education, encouragement, and repeated exposure to healthy foods can positively influence eating behaviors and help individuals become more willing to try healthier options.
Adverse Effects The biggest adverse effect that could arise during an EFNEP class is an allergic reaction to food. For this reason, the EFNEP educators are required to have staff at youth program sites complete a food allergy acknowledgement form. This is being done to ensure safety of all participants. Adult participants also verbally alert educators on the first day of class which food allergies they need to be made aware of. EFNEP educators are also trained in safe food handling practices. They take part in continuing education classes both in person and online throughout the course of their employment.
Sustainability EFNEP is a capacity grant allocated by the Farm Bill. At the time of this publication, there was no updated information on the potential changes in funding that may be made to EFNEP. There has also been no increase to the EFNEP budget, $315,646 annually, over the last four years. With increasing costs, this may cause the program to seek outside funding or decrease its current capacity.
PUBLIC HEALTH SIGNIFICANCE EFNEP’s target audience is limited income individuals and families. Chronic disease and poor health disproportionately affect these individuals more often. Program participants have shown an improved intake of nutrient rich food and diet practices that would help with preventing and/or managing chronic disease. The increase in physical activity has also been a positive outcome that will assist with overall well-being. In addition, Cooperative Extensions across the country are moving toward a whole health approach with their educational goals and programs. This is an exciting time to provide more community outreach and education. Ms. Oliver may be contacted at doliver@udel.edu .
REFERENCES 1. National Institute of Food and Agriculture. (2026, June). Expanded Food and Nutrition Education Program (EFNEP). United States Department of Agriculture. https://www.nifa.usda.gov/grants/programs/capacity-grants/expanded-foodnutrition-education-program-efnep 2. McCaffrey, J. (n.d.). Society for Nutrition Education and Behavior. 50 Years of EFNEP: Contributions to the Evolution and Advancement of Theoretical Based Nutrition Education. https://sneb.org/50-years-of-efnep-contributions-to-theevolution-and-advancement-of-theoretically-based-nutrition-education/ 3. National Institute of Food and Agriculture. (2026, April) Impacts and reporting: National EFNEP Reports. United States Department of Agriculture. https://www.nifa. usda.gov/grants/programs/capacity-grants/expanded-food-nutrition-educationprogram-efnep/impacts-reporting-national-efnep-reports
85
Meeting People Where They Are: A Harm Reductionist’s Perspective on Medical Mistrust Emerson Trimble, D.O., M.P.H. Physician, ChristianaCare
ABSTRACT A resident physician interviews community members in Wilmington, DE to explore what they prioritize about their health. I canvassed Wilmington and spoke with people regarding their health and describe my conversations with their permission. Many people highlighted ways in which social determinants of health impact their ability to routinely access health care, while others discussed a deep mistrust of physicians and the health care system. I discuss how harm reduction can guide providers in repairing trust and obtaining a therapeutic alliance when managing chronic diseases. The air was hot and thick as I walked along the streets of Wilmington. My rain jacket worsening the humidity but required due to the threat of a heavy rain that day, and I let my mind wander back to the crisp, cool AC in my clinic office. Snapping back to the task at hand, I thought of the comment a patient had made the day prior that sparked this exertion. He noted how it felt that the system was working against him at times and how he struggled to afford necessities like food, housing and his medications. He remarked how he felt as if he was along for the ride, lacking a voice in the process. His comments were not novel to me; as a primary care and emergency physician in Wilmington, many of my patients live below the federal poverty line. Many struggle to manage their chronic illnesses, despite institutional investments and healthcare providers who are motivated, skilled and caring. My patients are burdened by a multitude of costs: copays, prescriptions, utilities, food, housing. They are constantly balancing competing demands, often with budgets that cannot be stretched to cover it all, and primary care providers are frequently problem solving to facilitate routine medical care to our patients. We develop workarounds, utilize coupons, and prescribe to discount pharmacies to deliver their care. And this takes energy, planning, and follow through—it is often persistence that finally fills a medication or authorizes a CT scan. But we often spend significant time and effort in diagnosing, treating, and then facilitating the work-up, that we do not stop to garner our patient’s input and trust as well as we should. We practice teach back, ensuring that patients understand their visit, but shouldn’t we be asking them the same? What matters most to them this visit? How can we be doing better? I began to think of the time constrains placed on each visit, especially since many people come to the office to address an acute concern but also have complex chronic diseases. Twentyor forty-minute appointments fly by when addressing acute knee pain, re-prescribing insulin that became off-formulary, re-ordering lab work that could not be completed due to transportation difficulties. We review healthy lifestyle changes, alternatives to food deserts and food swamps, safe places to 86 Delaware Journal of Public Health - October 2026
exercise, tips to navigating public transportation, and assist with FMLA or disability paperwork. Our visits end with a long to-do list: pick up medications, complete blood work, expect a phone call for colonoscopy, trial physical therapy, eat less salt, walk a little farther. Most of my patient struggle financially, whether it is affording food, rent, utilities, medications, labs, or imaging. Furthermore, my patients who are the most marginalized, often receive the longest list of things to do after our visit, because we want to accomplish the most that we can for them. But after my patient remarked on being a passive participant in his health, I began thinking more of the to-do lists I print out at the end of each visit. I am frequently re-ordering labs, sending prescriptions again, reviewing the importance of starting physical therapy at their next appointment. I began to think how I could be effective and patient centered in my management of chronic diseases. Harm reduction teaches us to prioritize wellbeing, meet people where they are, and incorporate the voices of lived experience, so I thought it would be most prudent to go out into my community and spend a few hours asking people to share their thoughts. What matters most to their health? What challenges do they face? How could we be doing better? Throughout my canvasing, I met several people with varying answers to my questions. Some spoke of the costs of their medications or the difficulty with scheduling appointments at busy clinics. Some people had brief responses: too much violence, drugs on the street, increasing costs at the grocery store or gas pump. Who has time or money for the doctor when rent is due, the gas tank needs filled, children need school supplies? One woman shared that a clean and safe place to sleep was the most important aspect of her health. She noted that she is often fearful about being robbed or injured when she sleeps outside, which she does nightly. As she ate her lunch on a staircase and slowly smoked a cigarette, she described to me the criteria that she looks for in a safe place and how she chooses her location. Yet, she reiterated that the possibility Doi: 10.32481/djph.2026.10.12
of violence is always in the back of her mind. She remarked that she recently obtained health insurance and thinks that is helpful, but her worries are more focused on staying safe. These are concerns that I hear from my patients frequently, and they are what I expected many people to discuss—how varying aspects of the well-known social determinates of health impact their ability to access health care. Yet, many people instead discussed their mistrust of health care. A middle-aged man expressed how he does not go to the doctor because it feels like they are experimenting on him. He said that he felt like no one spoke with 100% certainty, that it seemed that providers were playing the odds or testing a theory. He expressed frustration that no one explains to him why medications are prescribed or why certain tests are needed. Describing his diet, exercise routine and daily habits, he endorsed that his health was better regulated by following this regimen and limiting toxins. He expressed deep mistrust of the medical system, particularly his interactions with doctors and their instructions for how to best manage his health. Mistrust in the medical and public health system in the U.S. is not a new phenomenon, and there is significant research evaluating its underpinnings, particularly in communities of color.1 The legacy of the Tuskegee Syphilis Study continues to have damaging implications and is frequently taught in medical school halls to highlight the concept of medical mistrust. Similarly, modern gynecology is deeply rooted in the abuse and experimentation of enslaved women, notably of the crude experiments on Anarcha, Lucy, and Martha, who underwent dozens of surgeries without anesthesia by Dr. James Marion Sims.2 While these are often the most referenced injustices, they many other instances that are not as well known, and we continue to see the ramifications of this mistreatment and abuse, systemically and interpersonally. We saw challenges with public health efforts to ensure HIV prevention in the 1980s and when our patients share their pain and mistrust in our exam rooms. Exacerbated by the COVID-19 pandemic, mistrust has had a heightened resurgence over the last half decade, with trust in physicians declining from approximately 71% in 2020 to 40% in 2024.3 I routinely witness the effects of historical and generational mistrust with my patients, and with increasing frequency, I have been navigating a growing wave of mistrust towards medical and preventative care that has followed the pandemic. I am not an expert nor do I research medical mistrust, but I have recognized the ways in which harm reduction can garner trust to strengthen the therapeutic alliance to deliver better care.
Before practicing medicine, I learned at the feet of harm reductions, caring for people who are unhoused and using drugs. Central to harm reduction is the concept of meeting people where they are and incorporating and raising the voices of people with lived experience. Patient representatives on executive boards is an effective way to accomplish lived experience driving policy and care on a systems level, and I think we would benefit from subscribing to this model more frequently in our daily practice as providers. We should trust our patients and communities to raise what is most important to them and prioritize those concerns. Incorporating these principles can have a synergistic effect, where patients are prioritized and in control of their care and the provider’s management is more efficient if aligned with their patient’s values. Harm reduction provides us with a powerful model to build strong therapeutic alliances and effectively manage our patients’ chronic conditions. Many people are mistrustful of the medical and public health system, often due to reasons that are grounded in personal and generational trauma. I spend much of my time working to earn their trust and navigate a system to facilitate their care. Yet, I have noticed how easily we get wrapped up in the goal of delivering health care, and we should work hard to overcome the barriers our patients face. But as we diligently strive to clear these hurdles, I think that our interventions could be more effective and our efforts feel more rewarding, if we took a moment to ask our patient, “What matters most to you?” Dr. Trimble may be contacted at emerson.trimble@christianacare.org .
REFERENCES 1. Jaiswal, J., & Halkitis, P. N. (2019). Towards a more inclusive and dynamic understanding of medical mistrust informed by science. Behavioral medicine (Washington, D.C.), 45(2), 79–85. https://doi.org/10.1080/08964289.2019.1619511 2. Zellars, R. (2018). Black subjectivity and the origins of American gynecology. African American Intellectual History Society. https://www.aaihs.org/black-subjectivity-and-the-origins-of-americangynecology/ 3. Perlis, R. H., Ognyanova, K., Uslu, A., Lunz Trujillo, K., Santillana, M., Druckman, J. N., Baum, M. A., & Lazer, D. (2024). Trust in physicians and hospitals during the COVID-19 pandemic in a 50-state survey of US adults. JAMA Network Open, 7(7), e2424984.
Crossing one of Wilmington’s many bridges, I met an older woman who shared her recent emergency room experience. She described three trips to the ER for acid reflux and that she was eventually diagnosed with an acute MI on her third visit. Expressing frustration with her index visit, she felt that she was not heard when she described how her symptoms felt different than her typical GERD. Since her MI, she says that she has been feeling better. She shared that she takes her medications because she trusts her doctors and feels that they want what is best for her, even if she is not certain of what each medication does. 87
The Household as Dialysis Infrastructure: Family Caregiving, Poverty, and In-Center Hemodialysis in Delaware Avi Kapoor Student Intern, MayaMD
ABSTRACT Objectives. To examine how maintenance in-center hemodialysis redistributes unpaid labor, financial strain, and emotional responsibility within low-income Delaware households. Methods. After background review and survey development from October 2025 through April 2026, a cross-sectional mixed-methods survey was administered during July-August 2026 in greater Wilmington, Delaware. Participants were 48 adult unpaid family caregivers of adults receiving maintenance in-center hemodialysis from low-income households. Recruitment used remote and community-based kidney-care channels, including MayaMD, a virtual nephrology care company, Wilmingtonarea community health organizations, and clinical social-worker flyers. Quantitative items assessed caregiving time, household effects, resource insecurity, and perceived burden; open-ended responses were analyzed thematically. Results. Caregivers reported a mean of 28.7 hours of kidney-related care weekly. Transportation and waiting accounted for the largest share of time. Sixty-nine percent missed work or school, 63% changed household food purchases, 50% delayed their own health care, 46% reported food insecurity, 27% lacked reliable private transportation, 65% reported moderate or high financial strain, and 58% reported moderate or high emotional strain. Open-ended responses showed that caregivers experienced dialysis not only as a treatment schedule but as a household system of work, obligation, rationing, and role negotiation. Conclusions. Dialysis treatment in lowincome households depends on unpaid family infrastructure that is clinically necessary but insufficiently measured. Low-income caregivers did not merely assist care; they absorbed transportation, food, employment, and emotional costs that shaped treatment continuity. Policy Implications. Delaware dialysis and public health systems should treat caregivers as part of chronic-disease infrastructure by screening for strain and coordinating transportation, nutrition, social-work, employment, and respite supports.
INTRODUCTION In-center hemodialysis is usually described as a lifesaving clinical treatment, but for families it is also a durable social arrangement. Treatment often means several clinic visits each week, hours attached to a machine, repeated travel, waiting, monitoring after treatment, dietary adjustments, medication management, and the practical work of keeping a household organized around a medical timetable.1–3 These activities are rarely counted as health care, yet they often determine whether treatment remains sustainable. Prior dialysis research shows why: transportation difficulty can contribute to missed or shortened hemodialysis treatment and hospitalization, and hemodialysis patients commonly report transportation, food, housing, and financial strain as health-related social needs that affect care.4,5 For low-income households, the difference between receiving dialysis and sustaining dialysis may depend on whether a relative can miss work, borrow a car, stretch food money, translate instructions, answer phone calls, and absorb the exhaustion that follows the treatment day. Kidney disease is also a disease of social distribution. National surveillance estimates a substantial burden of chronic kidney disease among US adults, and kidney failure continues to cluster with diabetes, hypertension, poverty, racialized disadvantage, and unequal access to care.1,2,6,7 Delaware is not outside this pattern. State Behavioral Risk Factor Survey reporting gives the magnitude: in 2022, slightly more than 4.4% of Delaware adults 88 Delaware Journal of Public Health - October 2026
reported having been told that they had kidney disease, including 9.6% of adults age 65 or older.8 State public health reporting also repeatedly frames chronic disease as a major population health challenge requiring attention to social and material conditions, not only individual behavior.9 Statewide statistics identify the burden of kidney disease, but they do not show how dialysisrelated demands are organized within households. A Wilmingtonarea field site can make these localized realities visible: how transportation, food access, work schedules, and family obligation become part of the infrastructure sustaining treatment days. The public health literature has increasingly recognized that dialysis outcomes are shaped by social needs, including transportation, food insecurity, housing instability, and limited social support.4,5,10,11,12 Caregiver studies show that relatives of people with kidney failure experience psychological distress, role overload, financial burden, fatigue, and disruptions in employment and family life.10-14 Yet much of this research still treats caregiving as a background factor around the patient. Even when caregiver burden is measured, the household can disappear as a unit of analysis. Here, household refers to the shared domestic and economic setting described by caregivers, including routines, budgets, schedules, family roles, and constraints, not a claim that every household member was directly measured. This matters because dialysis is not simply delivered to an individual body. It is coordinated through relationships, schedules, vehicles, kitchens, workplaces, and moral obligations. Doi: 10.32481/djph.2026.10.13
This household perspective is especially important for chronic disease and poverty. Public health and clinical social-needs work often measures material hardship through separate domains such as transportation, food, housing, insurance, employment, and social support.4,5,9,15 In dialysis-specific literature, transportation has been analyzed through missed or shortened treatments and hospitalizations, research on patients’ social-needs has identified food, housing, transportation, and financial strain, and kidney-disease reviews have examined food insecurity as a distinct risk.4,5,15 This domain-based evidence is essential, but it can miss how needs converge inside households during treatment. A caregiver who leaves work early to pick up a patient may also lose wages, miss a child’s activity, buy cheaper food, postpone their own appointment, and become the person blamed if the dialysis schedule breaks down. The analytical unit therefore has to be wider than the patient and more concrete than the phrase social determinants of health. It has to include the practical household arrangements through which treatment is made livable. Medical anthropology is useful here because it asks how illness is lived within ordinary social worlds. Kleinman’s work on illness and caregiving shows that sickness is not only a biological event but a moral experience organized around obligation, suffering, and the question of what people owe one another.16–18 Farmer’s concept of structural violence helps explain how institutions can distribute suffering without any single actor intending harm.19 In dialysis, this means that a missed ride, inflexible work shift, unaffordable renal diet, or confusing insurance call can become part of the disease experience itself. These pressures are not outside health care; they are among the conditions through which treatment either succeeds or fails. This article uses the term emotional economy to describe how affection, duty, guilt, resentment, gratitude, and financial sacrifice circulate through kidney-care households. The phrase is not meant to reduce care to money. Rather, it shows that unpaid family labor has both moral and material value. A daughter may drive someone to dialysis because she loves her parent, because no one else can, because refusing would feel wrong, and because the system has no better transportation plan. A spouse may prepare food according to renal restrictions while also worrying about grocery costs and shared meals. These actions are personal, but they also perform public health functions: getting the patient to treatment, making dietary guidance feasible, and managing transportation, food, and financial pressures that public health and clinical systems already recognize as relevant to dialysis care.4,5,9,15 This study examines how kidney failure redistributes labor, stress, and financial burden within low-income households supporting adults receiving maintenance incenter hemodialysis in the greater Wilmington, Delaware, metropolitan area. The study asked three questions: What forms of unpaid work do family caregivers perform to sustain dialysis? How are caregiving demands associated with transportation, food, housing, employment, and basic-needs insecurity? How do caregivers describe the emotional meaning of this labor? The questions were designed to generate cityregion evidence from Delaware’s largest population center about household-level mechanisms through which chronic illness and poverty become mutually reinforcing.
METHODS Study Design and Setting Background review, literature synthesis, and survey-instrument development occurred from October 2025 through April 2026 and did not involve human-participant data collection. Following an institutional exempt determination, a cross-sectional mixedmethods survey was administered during July-August 2026 in greater Wilmington, Delaware. The study used a remote and community-based recruitment design connected to kidney care. Survey access and completion occurred primarily remotely; inperson outreach occurred through Wilmington-area community health organization settings. A mixed-methods design was selected because the research question required both measurement and interpretation. Quantitative items summarized caregiving time, resource insecurity, employment disruption, and perceived burden. Openended responses allowed participants to describe the meanings and negotiations attached to those demands. This design was especially appropriate for a medical-anthropological public health study because the same observable task, such as driving a patient to dialysis, can operate simultaneously as transportation, family obligation, lost wage time, and emotional care.
Participants and Recruitment Participants were adults aged 18 years or older who self-identified as unpaid family caregivers of an adult receiving maintenance in-center hemodialysis. Eligible caregivers provided ongoing assistance with at least one dialysis-related task, including transportation, waiting, post-treatment monitoring, medication reminders, appointment coordination, food purchasing or meal preparation, emotional support, or communication with clinical and social-service systems. Participants also had to reside in a household meeting the study’s low-income definition. Low-income households were defined through at least 1 of the following criteria: annual household income below $50,000, Medicaid enrollment, dual Medicare-Medicaid eligibility, Supplemental Nutrition Assistance Program participation, housing insecurity, or self-reported difficulty paying for essential household expenses. This multidimensional definition was used because income alone may not capture economic strain in households affected by chronic disease. A household slightly above a single income threshold may still face substantial hardship when transportation costs, unpaid time, utilities, medical supplies, dietary changes, and lost wages are counted. Recruitment occurred through remote and community-based channels connected to kidney care, including MayaMD, a virtual nephrology care company, Wilmington-area community health organizations, and flyers distributed through clinical social workers. Most survey access and completion occurred remotely; in-person outreach occurred through community health organization settings. This recruitment design prioritized access to family caregivers who are difficult to enumerate through public data, clinic rosters, or household surveys alone. Because invitations were distributed through overlapping pathways and could be shared beyond the first point of contact, the total number of eligible caregivers who viewed the invitation was not knowable. Results are therefore reported using the number of respondents with available data for each survey item rather than a conventional response rate. 89
Survey Measures
Ethics
The survey collected caregiver age, gender, relationship to the patient, employment status, household size, household income category, insurance status, transportation access, food insecurity, housing stability, and difficulty paying for basic needs. It also asked caregivers to estimate weekly time spent on transportation and waiting, post-dialysis monitoring, meal planning and renaldiet work, medication or appointment management, emotional support, and paperwork or insurance coordination.
The study was determined exempt by Rapid IRB (IRB00014585; FWA00035509). Adult participants provided verbal informed consent before completing the anonymous survey. The consent process explained the study purpose, the voluntary nature of participation, the right to decline or discontinue participation at any time, and the fact that no personally identifying information would be collected. To protect confidentiality, the survey did not collect names, medical record numbers, immigration status, exact residential addresses, or prior health information, and no clinical records were accessed. The study complied with the American Public Health Association’s ethical principles for public health research.
Perceived caregiver burden was measured with a dialysisspecific set of items informed by the Zarit Burden Interview and contemporary kidney-caregiver literature.10–12,20 The full Zarit instrument was not administered. Items were adapted to reflect dialysis-specific responsibilities, including time pressure, exhaustion, worry, role conflict, financial strain, family conflict, difficulty maintaining paid work or school, and concern about the patient after dialysis. Each item was scored on a 5-point Likert scale ranging from 1, not at all, to 5, very much. Higher scores indicated greater perceived burden. Responses were analyzed both as individual items and as a composite burden measure for exploratory subgroup comparisons. The composite burden score was calculated only from these adapted Likert burden items; demographic characteristics, weekly task-hour estimates, and household-impact measures were not included in that score.
RESULTS Participant Characteristics The analytic sample included 48 unpaid family caregivers of adults receiving maintenance in-center hemodialysis. The median caregiver age was 42 years. Thirty-two participants were women (66.7%), 15 were men (31.3%), and 1 selected another gender or preferred not to answer (2.1%). Adult children were the largest caregiver group, followed by spouses or partners, siblings, parents, and other relatives or family-like caregivers (Table 1).
Open-ended prompts asked caregivers to describe the hardest part of caregiving, the most common household adjustment caused by dialysis, the kinds of support that would make caregiving easier, and whether family members disagreed about who was responsible for care. These prompts were designed to capture the moral and relational dimensions of caregiving that are not fully represented by time or burden scores.
The sample reflected the layered nature of economic insecurity in households affected by chronic kidney disease. Many participants qualified as low income through more than one pathway: income, public insurance, food insecurity, unstable housing, or
Quantitative Analysis
Characteristic
No.
%
Quantitative analyses were conducted in Microsoft Excel. Frequencies, percentages, arithmetic means, and medians were calculated to summarize caregiver characteristics, caregiving tasks, household effects, and measures of resource insecurity. Mean burden scores were compared descriptively between caregivers reporting the presence versus absence of specific resource insecurities, including lack of reliable private transportation, food insecurity, housing insecurity, and difficulty paying for basic needs. Because the study was designed to identify practice-relevant patterns in a hard-to-reach caregiver population, these comparisons were interpreted as descriptive signals of household strain rather than as causal effects. Missing responses were handled using complete-case analysis; percentages and means were calculated from available responses for each survey item, and no imputation was performed.
Sample size
48
100.0
Caregiver age, median, y
42
NA
Women
32
66.7
Men
15
31.3
Another gender or preferred not to answer
1
2.1
Adult child caregiver
21
43.8
Spouse or partner caregiver
14
29.2
Sibling caregiver
7
14.6
Parent caregiver
4
8.3
Other relative or family-like caregiver
2
4.2
Annual household income < $35,000
30
62.5
Medicaid or dual Medicare-Medicaid eligibility
28
58.3
Qualitative Analysis
Food insecurity in previous month
22
45.8
Open-ended responses were analyzed using inductive thematic analysis.21 Initial open coding identified recurring concepts related to transportation barriers, lost employment or wages, food insecurity and dietary conflict, guilt, shared responsibility, social isolation, bureaucratic navigation, and post-dialysis recovery. Codes were then iteratively refined into broader themes that captured caregiving as hidden labor, household economic strain, redistribution of family roles, emotional obligation, and structural barriers to sustaining dialysis care. The qualitative analysis was used to interpret the quantitative patterns rather than to claim thematic saturation across all low-income dialysis households in Delaware.
No reliable private transportation
13
27.1
Employed full- or part-time
26
54.2
Missed work or school because of caregiving
33
68.8
Delayed own health care because of caregiving
24
50.0
Reported conflict over caregiving roles
18
37.5
Moderate or high emotional strain
28
58.3
Moderate or high financial strain
31
64.6
90 Delaware Journal of Public Health - October 2026
Table 1. Characteristics of Family Caregivers of Adults Receiving In-Center Hemodialysis, Greater Wilmington, Delaware, July-August 2026
Note. Percentages are based on available observations for each variable and may not total 100 because of rounding. NA indicates not applicable.
difficulty meeting basic expenses. This matters because dialysisrelated hardship was not limited to out-of-pocket medical costs. It appeared through missed work, transportation dependence, altered food purchases, delayed self-care, and the constant reallocation of household time.
Caregiving Time and Household Effects Caregiving extended well beyond the dialysis session (Figure 1). Participants reported a mean of 28.7 hours of kidney-related care each week. Transportation and waiting accounted for 9.3 hours, emotional support for 5.5 hours, post-dialysis monitoring for 5.1 hours, meal planning and renal-diet work for 4.0 hours, medication and administrative tasks for 3.2 hours, and other coordination for 1.6 hours. The distribution matters because dialysis caregiving was not a single task. It was a bundle of timesensitive activities that accumulated across the week. Household effects were common. Thirty-three caregivers (68.8%) missed work or school because of dialysis-related responsibilities, 30 (62.5%) changed household food purchases, 24 (50.0%) delayed their own health care, 21 (43.8%) borrowed money or used debt, and 18 (37.5%) reported conflict over caregiving roles (Figure 2). Thirty-eight caregivers (79.2%) reported that at least 1 family routine, including meal timing, work schedule, childcare, transportation planning, or sleep, had changed because of dialysis. These disruptions were not experienced as separate from treatment. Participants described dialysis days as days around which the entire household had to be built. Work shifts were chosen around clinic appointments. Grocery decisions were shaped by renal-diet advice and price. Family members negotiated who could drive, wait, call, cook, and stay nearby after treatment. The burden therefore appeared not only as the total number of hours spent but also as a loss of control over household time: work, meals, transportation, rest, and caregiver availability had to be reorganized around the dialysis schedule.
Resource Insecurity and Burden Resource insecurity was associated with higher perceived burden (Figure 3). Mean burden was 4.2 among caregivers without reliable private transportation compared with 3.1 among those with reliable transportation. The corresponding means were 4.3 and 3.0 for caregivers with and without food insecurity, 4.1 and 3.2 for caregivers with and without household income below $35,000, and 4.5 and 3.3 for caregivers with and without housing insecurity. Because these were unadjusted comparisons from a cross-sectional community-recruited sample, they should be interpreted as descriptive evidence of patterned strain rather than causal estimates. Note: Scores range from 1 to 5, with higher scores indicating greater burden. Comparisons are unadjusted descriptive means. Transportation was a particularly important marker. Caregivers without reliable private transportation were managing uncertainty: whether a ride would arrive, whether the patient would be picked up on time, whether a caregiver would lose additional hours waiting, and whether a missed or delayed treatment might produce clinical consequences. In the open-ended responses, transportation was often described as both a practical barrier and a source of anticipatory anxiety. Food insecurity produced another form of household strain. Caregivers described trying to follow sodium, potassium, phosphorus, fluid, and protein-related guidance while also shopping within limited budgets and preparing shared meals. For some families, the renal diet created tension because the patient’s needs changing shaped what everyone ate. For others, the caregiver felt responsible for enforcing rules that were difficult to follow in a household food environment already shaped by cost, taste, culture, and availability.
Figure 1. Mean Weekly Kidney-Related Caregiving Hours by Task Among Family Caregivers of Adults Receiving In-Center Hemodialysis, Greater Wilmington, Delaware, July-August 2026.
Note: Values are arithmetic means; n = 48. 91
Figure 2. Reported Household Effects of Kidney Caregiving Among Family Caregivers of Adults Receiving In-Center Hemodialysis, Greater Wilmington, Delaware, July-August 2026.
Qualitative Themes Open-ended responses produced five connected themes. First, hidden labor described work necessary for dialysis continuity but largely invisible to clinicians, including waiting, planning, monitoring, translating instructions, and repeated coordination. Second, household rationing described reductions in groceries, utilities, personal spending, or the caregiver’s own health care in order to support treatment. Third, role redistribution captured changes in who cooked, drove, worked, rested, or supervised other relatives. Fourth, emotional obligation described guilt, worry, gratitude, frustration, and fear of failing the patient. Fifth, structural friction described the repeated contact points with transportation systems, insurance, employers, social services, and clinic schedules that made care harder to sustain. The themes clarified why a burden score alone could not fully capture caregivers’ open-ended responses. In those responses, practical, financial, and emotional burdens could overlap within the same caregiving activity. Transportation responses linked ride uncertainty with waiting, lost work time, and anticipatory anxiety, while renal-diet work brought limited budgets into contact with household tension and conflict. These overlaps help explain the emotional economy of kidney failure: emotional strain did not sit apart from the material demands of caregiving but moved through them. Caregivers also described a lack of formal recognition. Several participants wrote that clinicians asked about the patient’s symptoms but rarely asked who was making dialysis possible at home. Others described learning by trial and error: how tired the patient would be after treatment, which foods caused conflict, how to coordinate rides, when to call the clinic, or how to keep the patient from feeling like a burden. These accounts suggest a gap between the clinical importance of family labor and the limited institutional attention given to the caregiver as a participant in care. 92 Delaware Journal of Public Health - October 2026
DISCUSSION Interpretation This mixed-methods study found that maintenance in-center hemodialysis in low-income Delaware households relied on a substantial and diverse amount of unpaid family labor. Caregivers coordinated transportation, monitored recovery, modified food practices, managed appointments and medications, absorbed indirect costs, and provided emotional support. The central finding is not simply that caregivers were burdened. It is that the household functioned as a form of dialysis infrastructure. Without it, the clinic schedule, dietary plan, transportation system, and treatment regimen would be much harder to sustain. The findings are consistent with existing caregiver research documenting reduced quality of life, emotional distress, role overload, financial strain, and family disruption among caregivers of people receiving dialysis.10-12,14 Qualitative studies in Ethiopia, Sri Lanka, and Tanzania likewise show that dialysis reorganizes family time, finances, social participation, and moral expectations.22–24 This study contributes Delaware-specific data and a medical-anthropological interpretation: caregiver burden is not only an individual psychosocial outcome. It is a sign that the health system has shifted essential work onto households while measuring only part of what treatment requires. Transportation illustrates this point clearly. Previous work has described dialysis transportation as a complex patchwork that shapes treatment access, missed sessions, hospitalizations, and quality of life.4 In this study, transportation insecurity corresponded with higher burden and appeared repeatedly in qualitative responses. The problem was not only the physical ride. It was uncertainty, waiting, dependence, employment conflict, and the caregiver’s responsibility to prevent a preventable disruption. A public health system that counts dialysis appointments but not the labor required to reach them will underestimate the true cost of care.
Figure 3. Mean Dialysis-Specific Caregiver Burden Scores by Resource Insecurity Among Family Caregivers of Adults Receiving In-Center Hemodialysis, Greater Wilmington, Delaware, July-August 2026.
Food work reveals a similar shift from clinic to home. Dietary counseling may be medically appropriate, but implementation happens in households with limited income, shared meals, transportation constraints, and unequal control over food purchasing. Food insecurity has been linked to kidney disease and adverse kidney-related outcomes, and our findings show how food insecurity can also intensify caregiver burden.15 The caregiver may become the person who stretches the grocery budget, enforces restrictions, manages conflict, and feels responsible when advice is not followed. The concept of emotional economy helps explain why this labor remains underreported. Family caregivers may not describe their work as health-system support because they understand it through kinship, love, reciprocity, religion, marriage, or being a good child, spouse, sibling, or parent. Yet moral meaning does not make the labor costless. A caregiver can love the patient and still be financially strained. A caregiver can be grateful for treatment and still be exhausted by what treatment demands. Public health systems need language that can hold both truths. Structural violence provides a second analytic lens. The term does not imply that a clinician or institution intentionally harms caregivers. It describes how ordinary arrangements distribute hardship toward those with fewer resources.19 A rigid dialysis schedule may be manageable for a retired caregiver with a car and savings, but destabilizing for a working caregiver without paid leave. A transportation delay may be inconvenient for one household and financially disastrous for another. A dietary recommendation may be routine for one family and nearly impossible for another. The same clinical regimen therefore produces unequal social consequences. For Delaware, the relevance is practical. A state-level chronic disease agenda that focuses only on diagnosis and treatment will
miss the household conditions that sustain or interrupt care. This city-region evidence from greater Wilmington identifies dialysisrelated social needs that statewide chronic-disease planning can act on: transportation, nutrition, income support, caregiver education, employment flexibility, and social-work navigation. These needs do not belong to separate systems in the lives of families. They arrive together, often on the same treatment day. Local data are not a weakness when the research question concerns how public health systems meet people in a particular place. Delaware’s size can make household-level problems more visible rather than less important. A caregiver navigating dialysis in the Wilmington area may interact with a small set of transportation systems, community organizations, clinics, and public benefits offices. That local ecology is exactly where public health practice happens. The general lesson is not that all places look like Wilmington. It is that every dialysis system has a hidden household interface, and public health agencies need methods for seeing it. The study also suggests that the language of adherence should be used carefully. When a patient misses treatment, exceeds a fluid target, struggles with dietary guidance, or arrives late, the event may be recorded as an individual behavior. The caregiver data show that such events can reflect a chain of constraints: no backup ride, no paid leave, no affordable renal-friendly food, no flexible caregiver, or no clear institutional contact. A medical-anthropological interpretation does not excuse every missed recommendation. It asks what social arrangements make recommendations possible in the first place. One strength of this study is that it makes household labor empirically visible without separating it from meaning. Time estimates show that caregiving is substantial. The open-ended responses show why hours alone are insufficient. Dialysis 93
caregiving was experienced as love, obligation, fear, frustration, and economic sacrifice. This dual finding matters for policy because support programs that treat caregivers only as unpaid workers may miss the moral world of care, while programs that romanticize family support may ignore its material costs.
Limitations This study has limitations that should guide interpretation while preserving its practice value. Recruitment used open, overlapping remote and community-based pathways because unpaid dialysis caregivers are difficult to identify through public data, clinic rosters, or household surveys alone. This approach fit the study aim: to identify recurring caregiver support needs in greater Wilmington, Delaware’s largest population center, with direct relevance for city-region practice planning. Because invitations could move through multiple pathways and be shared beyond the first point of contact, the total number of eligible caregivers who viewed the invitation was not knowable. Results are therefore reported using the number of respondents with available data for each measure rather than a conventional response rate. All measures were self-reported and may be affected by recall error or social desirability bias. The adapted caregiver burden measure was designed for this study using dialysis-specific Likert items informed by the Zarit Burden Interview and kidney-caregiver literature.20 It was analyzed as a separate composite measure: demographic characteristics, weekly taskhour estimates, and household-impact items were not included in that score. Because the full validated Zarit instrument was not administered, scores should not be directly compared with standard Zarit scores. The cross-sectional design describes caregiver experiences during the July-August 2026 study period but cannot determine how burden changes over the course of disease progression, dialysis initiation, hospitalization, transplantation evaluation, or bereavement. Finally, the study focused on family caregivers and did not capture the perspectives of patients, clinicians, social workers, transportation vendors, employers, or payers. Future work can extend this study with longitudinal designs, cross-county comparisons, and analyses across dialysis settings.
Public Health Implications Dialysis systems should treat household caregiving as a public health concern while preserving patient autonomy and avoiding the assumption that relatives are always available. At dialysis initiation and periodically thereafter, clinics should ask who provides transportation, who misses work or school, who manages food and medications, who monitors recovery after treatment, and whether that person has the resources to continue. These questions should be asked as part of care planning, not only after a crisis. A practical Delaware response would include caregiver screening in dialysis settings, referral pathways to community health organizations, clearer post-dialysis guidance for families, and direct social-work attention to transportation and food access. Screening should not become another way to label families as noncompliant. Its purpose should be to identify solvable barriers: unreliable rides, food insecurity, lack of backup caregiving, confusion about diet, difficulty paying for utilities, inability to miss work, and caregiver health needs. 94 Delaware Journal of Public Health - October 2026
The screening process should be short enough to use in real clinical settings. Existing social-needs screening models provide precedent: CMS developed the Accountable Health Communities Health-Related Social Needs Screening Tool after reviewing existing screening instruments, and the tool uses a short set of questions covering housing instability, food insecurity, transportation difficulties, utility assistance needs, and interpersonal safety, with housing and transportation items adapted from tools such as PRAPARE.25 A 5-question caregiver support screen could ask whether a family caregiver regularly provides transportation, misses paid work or school, has difficulty following renal food guidance because of cost, lacks backup help after dialysis, or feels unable to maintain their own health. Positive screens should trigger action, not merely documentation. Clinics and community partners could maintain a shared referral list for transportation assistance, food resources, respite support, benefits navigation, caregiver education, and behavioral health services. Delaware’s public health agencies and community organizations could also use caregiver data for planning. Aggregated, deidentified caregiver measures could help identify where dialysis-related social needs cluster and which supports are most urgent. For example, a rise in transportation-related missed treatments would suggest a different intervention than a rise in caregiver food insecurity. Measuring caregiver strain would also help public health leaders evaluate whether chronic disease programs are improving life for households, not only for individual patients. Transportation should be incorporated into quality improvement for dialysis access. Payers, dialysis organizations, and public health partners should track transportation-related missed or shortened treatments, coordinate backup ride options, and reduce uncertainty around pickup and return times. Because transportation problems often affect employment, childcare, and household income, interventions should be evaluated not only by appointment attendance but also by caregiver time and stress. Nutrition support should similarly move beyond individual counseling. Renal-diet guidance should account for food price, household size, cooking responsibility, culture, and the fact that caregivers often purchase and prepare shared meals. Partnerships with food banks, medically tailored grocery programs, renal dietitians, and community health workers could help translate medical advice into household practice. In low-income settings, asking whether a patient can follow a diet is incomplete without asking whether the household can afford, obtain, and prepare it. Caregivers also need direct support. Plain-language training, respite options, caregiver-specific check-ins, employment documentation, and referrals for the caregiver’s own health care would recognize work already being performed. At the policy level, paid leave, predictable scheduling protections, Medicaid transportation coordination, and caregiver respite benefits could reduce the hidden costs of dialysis. These interventions are not peripheral to kidney care. They are part of making treatment durable. Public health messaging should also change. Families often hear what a patient should do, but not enough about how a household can realistically do it. Materials for low-income dialysis households should name the caregiver role, explain common
Table 1. Characteristics of Family Caregivers of Adults Receiving In-Center Hemodialysis, Greater Wilmington, Delaware, July-August 2026 Characteristic
No.
%
Sample size
48
100.0
Caregiver age, median, y
42
NA
Women
32
66.7
Men
15
31.3
Another gender or preferred not to answer
1
2.1
Adult child caregiver
21
43.8
Spouse or partner caregiver
14
29.2
Sibling caregiver
7
14.6
Parent caregiver
4
8.3
Other relative or family-like caregiver
2
4.2
Annual household income < $35,000
30
62.5
Medicaid or dual Medicare-Medicaid eligibility
28
58.3
Food insecurity in previous month
22
45.8
No reliable private transportation
13
27.1
Employed full- or part-time
26
54.2
Missed work or school because of caregiving
33
68.8
Delayed own health care because of caregiving
24
50.0
Reported conflict over caregiving roles
18
37.5
Moderate or high emotional strain
28
58.3
Moderate or high financial strain
31
64.6
Note. Percentages are based on available observations for each variable and may not total 100 because of rounding. NA indicates not applicable.
post-dialysis symptoms, clarify when to call the clinic, provide low-cost renal-friendly food strategies, and normalize asking for help before treatment is missed. Such materials should be written in plain language and distributed through clinics, social workers, community health organizations, and caregiver networks. The central implication is that dialysis access is sustained not only by clinics and machines but also by unpaid family labor. Measuring and supporting that labor can improve caregiver health, household stability, and continuity of treatment. For Delaware public health, this is an opportunity to connect chronic disease policy with the lived infrastructure of care inside families. The household should not remain the invisible site where the costs of kidney failure are absorbed. The author may be contacted at avikapoor0607@gmail.com.
REFERENCES 1. Centers for Disease Control and Prevention. (2026, March 31). Chronic kidney disease in the United States. https://www.cdc.gov/kidney-disease/php/data-research/index.html 2. United States Renal Data System. (2025). 2025 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institute of Diabetes and Digestive and Kidney Diseases. https://usrds-adr.niddk.nih.gov/2025 3. National Institute of Diabetes and Digestive and Kidney Diseases. (n.d.). Choosing a treatment for kidney failure. Retrieved July 4, 2026, from https://www.niddk.nih.gov/health-information/kidney-disease/kidney-failure/choosing-treatment 4. Tian, F. F., Hall, Y. N., Griffin, S., Kranze, T., Marcella, D., Watnick, S., & O’Hare, A. M. (2023). The complex patchwork of transportation for in-center hemodialysis. Journal of the American Society of Nephrology, 34(10), 1621–1627.
5. Novick, T. K., Osuna, M., Emery, C., Barrios, F., Ramirez, D., Crews, D. C., & Jacobs, E. A. (2024). Patients’ perspectives on health-related social needs and recommendations for interventions: A qualitative study. American Journal of Kidney Diseases : The Official Journal of the National Kidney Foundation, 83(6), 739–749. 6. Han, Y., Xu, F., Morgenstern, H., Bragg-Gresham, J., Gillespie, B. W., Steffick, D., Herman, W. H., Pavkov, M. E., Veinot, T., & Saran, R. (2024). Mapping the overlap of poverty level and prevalence of diagnosed chronic kidney disease among Medicare beneficiaries in the United States. Preventing Chronic Disease, 21, E23. 7. Nguyen, K. H., Thorsness, R., Swaminathan, S., Mehrotra, R., Patzer, R. E., Lee, Y., Kim, D., Rivera-Hernandez, M., & Trivedi, A. N. (2021). Despite national declines in kidney failure incidence, disparities widened between low- and highpoverty US counties. Health Affairs (Project Hope), 40(12), 1900–1908. 8. Delaware Division of Public Health. (2022). Delaware 2022 Behavioral Risk Factor Surveillance System Core Questions Data Report. Delaware Department of Health and Social Services. https://www.dhss.delaware.gov/dph/dpc/files/DE22CORE.pdf 9. Delaware Division of Public Health. (2024). The Burden of Chronic Disease in Delaware 2024. Delaware Department of Health and Social Services. 10. Alshammari, B., Noble, H., McAneney, H., Alshammari, F., & O’Halloran, P. (2021). Factors associated with burden in caregivers of patients with end-stage kidney disease: A systematic review. Healthcare (Basel), 9(9), 1212. 11. Nimah, L., Nursalam, N., Wahyudi, A. S., & Mariyanti, H. (2024). Quality of life of family caregivers of patients undergoing dialysis: A literature review. SAGE Open Nursing, 10, 23779608241240124. 12. Joseph, S. J., Bhandari, S. S., Dutta, S., Khatri, D., & Upadhyay, A. (2021). Assessing burden and its determinants in caregivers of chronic kidney disease patients undergoing haemodialysis. Open Journal of Psychiatry & Allied Sciences, 12(2), 96–100. 13. National Alliance for Caregiving & AARP. (2025). Caregiving in the U.S. 2025. https://www.caregiving.org/research/caregiving-in-the-us/ 14. Driehuis, E., Demirhan, I., Konijn, W. S., Vogels, T. J. F. M., Goto, N. A., Broese van Groenou, M. I., Verhaar, M. C., van Jaarsveld, B. C., & Abrahams, A. C. (2025). Determinants of caregiver burden among spouses of patients with kidney failure: A qualitative study. American Journal of Kidney Diseases : The Official Journal of the National Kidney Foundation, 85(4), 477–490.e1. 15. Ferrara, F., Siligato, R., Di Maria, A., Scichilone, L., Di Simone, E., Bondanelli, M., Storari, A., De Giorgi, A., Di Muzio, M., & Fabbian, F. (2024). Food insecurity and kidney disease: A systematic review. International Urology and Nephrology, 56(3), 1035–1044. 16. Kleinman, A. (2013). From illness as culture to caregiving as moral experience. The New England Journal of Medicine, 368(15), 1376–1377. 17. Kleinman, A. (1988). The illness narratives: Suffering, healing, and the human condition. Basic Books. 18. Heinemann, L. L. (2015). Accommodating care: Transplant caregiving and the melding of health care with home life in the United States. Medicine Anthropology Theory, 2(1), 32–56. 19. Farmer, P. E., Nizeye, B., Stulac, S., & Keshavjee, S. (2006). Structural violence and clinical medicine. PLoS Medicine, 3(10), e449. 20. Zarit, S. H., Reever, K. E., & Bach-Peterson, J. (1980). Relatives of the impaired elderly: Correlates of feelings of burden. The Gerontologist, 20(6), 649–655. 21. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. 22. Abebe, A., Arba, A., Paulos, K., Abera, W., Sidamo, T., Shiferaw, S., Abraham, Z., Baza, D., Nega, B., & Woldeyohannes, S. (2022). The lived experience of primary family caregivers of patients on hemodialysis treatment in southern Ethiopia: A phenomenological study. International Journal of Nephrology and Renovascular Disease, 15, 41–52. 23. Lasanthika, C., Janlöv, A. C., Amarasekara, T., & Larsson, H. (2024). Caring burden and coping with haemodialysis: A qualitative study with family caregivers in Sri Lanka. BMJ Open, 14(4), e080775. 24. Magenge, A. H., Ndile, M. L., & Furia, F. (2025). Family caregivers’ experience of caring for patients undergoing hemodialysis at Muhimbili National Hospital in Dar es Salaam, Tanzania: A qualitative study. PLoS One, 20(5), e0321732. 25. Billioux, A., Verlander, K., Anthony, S., & Alley, D. (2017). Standardized screening for health-related social needs in clinical settings: The Accountable Health Communities Screening Tool. NAM Perspectives, 7. Advance online publication.
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From Awareness to Action: Fall Prevention in Practice Felicia Cruz, M.S.N., A.P.R.N., A.G.C.N.S.-B.C., MEDSURG-B.C., C.P.A.F.H. Geriatric Clinical Nurse Specialist, Bayhealth Lisa Fellin, B.S.N., R.N. Injury Prevention Coordinator, ChristianaCare L. Margaret Gulledge, O.T.D., O.T.R./L. Assistant Professor, Master of Occupational Therapy, Delaware State University Jola Jimoh, D.P.T., M.Sc. Assistant Professor, Kinesiology, Delaware State University
ABSTRACT Falls represent a public health and economic risk for older adults, serving as the leading cause of fatal and nonfatal injuries nationally and within Delaware. This article describes the efforts of a multidisciplinary team of subject matter experts from area health care systems and academic institutions. This team implemented an evidencebased community screening and education strategy with the goal of identifying individuals at risk for falls and recommending interventions to reduce the risk. Faculty-guided students collaborated with nurses from different health systems to provide free clinics in all counties, using the Centers for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents, Deaths, and Injuries (STEADI) tool. Participants rotated through assessment stations, including balance, vision and orthostatic blood pressure checks, followed by individualized education, medication reviews, and follow-up resources. The initiative has expanded significantly, growing from 48 screenings in 2024 to 120 in 2025, with anticipated growth in 2026. This low-cost, community-based effort demonstrates strong sustainability through community partnerships and newly secured grant funding. These community-based clinics could bridge health care access gaps and potentially reduce medical costs, prevent injuries, and promote independence among older adults.
INTRODUCTION Falls remain one of the most serious and costly public health challenges facing older adults in the United States. Nationally, more than one in four adults aged 65 years and older experience a fall each year, making falls the leading cause of fatal and nonfatal injuries in this population.1 An estimated 37% of falls cause injuries requiring medical care or limiting daily activities, leading to millions of emergency visits and hospitalizations each year. At the same time, fall-related deaths have steadily increased over the past two decades, highlighting the growing burden as the population ages.1 In Delaware, injury data shows similar trends which help guide prevention efforts. In 2024, the Delaware Trauma Registry identified 5,234 falls reported statewide. This resulted in 611 traumatic brain injuries and 84 deaths, with nearly 70% of falls occurring among adults aged 65 and older.2 In response to the data, a team of subject matter experts implemented a coordinated, evidence-based strategy focused on screening and community engagement. A key component guiding these efforts is the Centers for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents, Deaths, and Injuries (STEADI) toolkit. It provides an evidence-based framework for assessing fall risk and connecting individuals to appropriate interventions.3 The STEADI screening and intervention approach in partnership with health care systems, academic institutions, and community organizations reflects Delaware’s commitment to reducing injury and promoting safe independence. 96 Delaware Journal of Public Health - October 2026
The CDC’s STEADI toolkit is an evidence-based resource used to assess fall risk and guide prevention and intervention.3 Available online, it offers clinical tools and education for both providers and community members. Adapted from guidelines by the American and British Geriatrics Societies, STEADI is recognized as a gold standard for multi-factorial fall risk screening, assessment, and intervention. The team consists of clinicians from Bayhealth, ChristianaCare, Delaware State University, and the University of Delaware. These organizations partnered to develop an evidence-based program with the shared goal of reducing falls in older adults.
INTERVENTION The intervention consisted of a series of community-based fall risk screening and education clinics designed to reduce fall-related injuries and deaths. The primary goal was to provide accessible, evidence-based assessments to identify at-risk individuals and offer tailored prevention strategies. This effort was carried out in collaboration with Bayhealth, ChristianaCare, Delaware State University, and University of Delaware, strengthening access to clinical expertise and community outreach. The screenings were primarily conducted by Delaware State University (DSU) Master of Occupational Therapy (MOT) and nursing students, along with the University of Delaware (UD) Doctor of Physical Therapy (DPT) students. Under the direct supervision of clinical faculty, these students staffed the various assessment stations, providing them with an opportunity for experiential learning in a community health setting. The program Doi: 10.32481/djph.2026.10.14
utilized 30-minute assessments offered at no charge, guided by the Centers for Disease Control and Prevention’s STEADI tool. To ensure a comprehensive evaluation of all sensory components of balance, the team incorporated additional oculomotor and vestibular screenings based on the clinical expertise of the supervising occupational and physical therapy faculty. Attendees rotated through stations where they were scored on specific physical and health metrics, including: • 30-Second Chair Rise: To measure lower body strength. • Timed Up and Go (TUG) and Four-Stage Balance Test: To assess mobility and static/dynamic balance. • Vision Acuity: To identify visual impairments. • Oculomotor and Vestibular Function • Medication Reviews: To identify drugs that may increase fall risk. • Orthostatic Blood Pressure Checks: To monitor for drops in blood pressure upon standing. Nurses from Bayhealth and ChristianaCare, along with faculty from Delaware State University, actively participated in delivering education. Education included: • Medications and side effects • Talking to health care providers • Exercise and physical activity • Balance • Adaptive equipment/mobility devices • Home modifications • Orthostatic hypotension • Vision • Importance of annual screenings • Reporting falls to health care providers Education was provided both one-on-one, based on each participant’s screening results, and more broadly through brochures and an interactive trivia game. To encourage participation and help people apply what they learned, door prizes were offered, many of which included adaptive equipment and other fall prevention tools. Individuals identified as high risk were encouraged to share their screening results with their health care provider for follow-up care. To support participant safety, immediate interventions were available to address emergent medical needs when identified during the screening events.
Place and Time The program was intentionally implemented in all three counties: New Castle, Kent, and Sussex. Key locations included the Rose Hill Community Center (New Castle), Luther Towers and the Modern Maturity Center (Dover), the Newark Community Day event (Newark), the Milford Senior Center (Milford), and the Harvest Years Senior Center (Camden). While program activities began in 2023 and continued through 2024, this vignette focuses on the expanded implementation in 2025. During that year, the program grew from a week-long observance to a full Falls Prevention Awareness Month in September, with additional screening events held between August and November 2025.
Population The intervention targeted older adults residing in Delaware. This population was identified as high-priority because individuals aged 65 and older accounted for nearly 70% of all fall-related injuries.2 The screenings intended to reach a diverse cross-section of this demographic by hosting events at senior centers, low-income senior housing complexes and broad-access community festivals to encourage participation from various socioeconomic and geographic backgrounds. In Delaware, older adults experience a much higher rate of fall-related injuries than any other age group. Data from the Delaware Health and Social Services (DHSS) in 2024 showed that 70 of the 84 fallrelated deaths and 471 of the 611 head injuries occurred among adults aged 65 and older.2 This highlights that older adults are a particularly vulnerable population and face risk and burden from falls and related injuries across the state.
Purpose The purpose of this intervention is to identify participants at risk for falls, provide education, and community resources to increase participation in fall reduction interventions. Screening results guide next steps, such as recommending exercise, home safety changes, medication reviews, or follow-up with a health care provider. Overall, fall screenings support older adults in staying safe, maintaining independence, and feeling more confident in their daily activities.
Implementation The structure of the clinics includes the identification of local senior centers throughout the state that are willing to host the fall clinics. Fall clinics are scheduled on days that will promote maximum participation of community members. The event is advertised within the center and surrounding community. Seniors are asked to preregister for a convenient time to complete a mobility screening. Upon arrival, participants progress through the screening stations, ending with individualized education about specific risks and interventions. Resources are shared with the clinic participants as well as reminders to follow up with their primary care provider (PCP) to share their results and continue the conversation on fall prevention.
Evaluation The purpose of this intervention is to identify individuals at risk for falls, with success measured by the number screened and educated. The program’s reach has grown each year, expanding Fall Prevention Awareness Week in 2024 to a month-long observance in 2025. In 2025, 120 free screenings were conducted across six sites. This is more than double the 48 screenings at three events in 2024.2 To date in 2026, screenings have been completed during five events, with several additional clinics anticipated throughout the year.
Adverse Effects The team has not experienced any adverse effects during events; however, like any health-related activity, there are risks. Although fall screening events are generally safe and valuable for identifying individuals at risk, they can carry some potential adverse effects that should be considered. Physical risks may include falls or near falls during balance and mobility testing. During the screenings, gait belts are utilized to decrease this potential risk. 97
Sustainability Community-based fall risk screenings can be delivered at little to no cost using simple equipment and trained staff or volunteers. This approach makes the program easier to sustain over time compared to more resource-intensive models. Sustainability is further supported by a steady stream of student involvement supported by faculty, as well as strong collaboration with local health care systems. The program also integrates well into existing community settings, such as senior centers and local organizations. In addition, these events are flexible and can be adapted to meet the needs of different communities and populations. Educational resources and evidence-based programs provided by the Centers for Disease Control and Prevention and the State of Delaware are readily available and free. Recently, the fall prevention team received a $50,000 12-month grant from the Delaware Health Sciences Alliance to expand its work. The funding will support sustainability by building a database of community centers and volunteers. It will fund the development of a speaker’s toolkit, supply kits for fall risk screenings, educational articles for community newsletters, and purchasing printed materials. These efforts are designed to sustain the team’s work beyond the grant period, which began June 2026.
PUBLIC HEALTH SIGNIFICANCE Falls among adults aged 65 and older are associated with significant health care costs. Each year, approximately $50 billion is spent nationwide on medical care for non-fatal fall-related injuries, with an additional $754 million spent on fatal falls. In Delaware alone, fall-related injuries among older adults result in an estimated $162 million in direct medical costs annually.4
98 Delaware Journal of Public Health - October 2026
Fall screening programs in community settings contribute to this public health issue because it addresses leading causes of injury, reduce hospitalizations, and loss of independence among older adults. By identifying people at increased risk, our community-based screening program supports early education and intervention. In turn, this can help prevent injuries, reduce health care costs, and promote independence among the older population.5 These screenings are especially valuable for reaching individuals who may not have access to a healthcare provider to assess their risk of falling. The ability to screen our community members and provide education is an important component of fall prevention efforts in the state of Delaware. Ms. Fellin may be contacted at lisa.c.fellin@christianacare.org.
REFERENCES 1. Centers for Disease Control and Prevention. (2026, February 26). Older adult falls data. https://www.cdc.gov/falls/data-research/index.html 2. Delaware Health and Social Services, Division of Public Health. (2025). Delaware Coalition for Injury Prevention, Office of Emergency Medical Services. https://publichealthalerts.delaware.gov/wp-content/blogs.dir/203/files/ sites/203/2026/06/Delaware-Falls-Prevention-Team-Activity-Summary-2025.pdf 3. Stevens, J., & Phelan, E. (2012). The development of STEADI—A fall prevention tool kit for US Healthcare Providers. Injury Prevention : Journal of the International Society for Child and Adolescent Injury Prevention, 18(Suppl 1), A56.1–A56. Advance online publication. 4. Centers for Disease Control and Prevention. (2020). Cost of older adult falls. https://stacks.cdc.gov/view/cdc/122747/cdc_122747_DS1.pdf 5. Brian Ezeonu. (2025). Evidence-based programs ROI assessment report: Falls prevention programs. National Council on Aging. https://assets.ncoa.org/ffacfe7d-10b6-0083-2632-604077fd4eca/dd5cd40a-b56341b3-b65d-c5424d62413f/2025_Falls_Prevention_ROI.pdf
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CALL 302-513-0929, M–F, 8 AM–5 PM DCPAP IS SUPPORTED BY THE HEALTH RESOURCES AND SERVICES ADMINISTRATION (HRSA) OF THE U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES (HHS) AS PART OF AN AWARD TOTALING $2,912,437 WITH 20% FINANCED WITH NON-GOVERNMENTAL SOURCES. THE CONTENTS ARE THOSE OF THE AUTHOR(S) AND DO NOT NECESSARILY REPRESENT THE OFFICIAL VIEWS OF, NOR AN ENDORSEMENT, BY HRSA, HHS, OR THE U.S. GOVERNMENT. FOR MORE INFORMATION, PLEASE VISIT HRSA.GOV.
99
Neighborhood-Level Non-Medical Factors Alone Poorly Predict Proxy PASC Phenotype: Limited Performance of Logistic Regression and XGBoost Models Amirah Ellis-Gilliam, M.P.H. Center for Bioinformatics and Computational Biology, University of Delaware
Zughi Zhang, Ph.D. ChristianaCare Health System; University of Delaware
Felix Rivera-Mariani, Ph.D., F.A.A.A.A.I. Lynn University; Department of Microbiology and Immunology, School of Medicine, Medical Sciences Campus, University of Puerto Rico; RIPLRT Institute
Cathy Wu, Ph.D. Center for Bioinformatics and Computational Biology, University of Delaware
Chuming Chen, Ph.D. Center for Bioinformatics and Computational Biology, University of Delaware
Jennifer Horney, Ph.D. Department of Epidemiology, University of Delaware
ABSTRACT Background. Early in the SARS-CoV-2 pandemic, acute mortality and hospitalization showed marked disparities across socially disadvantaged groups. In some cases, individuals who initially tested positive for Post-Acute Sequelae of SARS-CoV-2 infection (PASC) also began to experience lingering symptoms after initially recovering from SARS-CoV-2. At the time of the 2023 BRFSS survey the CDC defined PASC as self-report of any symptoms lasting ≥3 months at the time of interview that were not present before having COVID-19. An estimated 6.7% of Delaware adults self-reported symptoms consistent with PASC and 24.5% of those Delawareans reported significant activity limitations. This condition is a chronic disease and the Delaware State Health Improvement Plan (SHIP) has established a priority outcome to reduce the risk of chronic diseases. One of the goals of SHIP is to promote a combination of methods to support and identify chronic diseases and support healthy outcomes, these goals tie directly to non-medical health factors. Although there are notable poverty, food insecurity and health care access disparities at the county level in Delaware, it remains unclear whether neighborhood-level non-medical health factors alone can identify areas with high PASC prevalence so those the most in need can receive targeted resources to address this chronic condition. Objective. To evaluate whether combined ZCTA-level non-medical health factors, without patient-level clinical predictors, can discriminate against a machine-learning-derived PASC phenotype. Methods. We defined a proxy PASC outcome via a validated ML-derived probability score. established by Pfaff et al. using N3C data. This model used a variety of data points from individuals who sought care at PASC clinics and assigned each individual a score that estimated the probability of a PASC diagnosis. Our cohort was run through this same model to identify individuals with PASC. We analyzed 4,521,889 adults with SARS-CoV-2 in N3C. PASC status was assigned using the probability model developed by Pfaff et al., which incorporates clinical attendance, symptoms, comorbidities, and healthcare utilization, with a cutoff ≥ 0.90. The predictors included neighborhood-level race-ethnicity, income, socioeconomic status, education, and insurance variables; composite risk scenarios were evaluated using logistic regression and XGBoost. Results. Logistic regression performed at chance levels (AUC ~ 0.50). XGBoost showed limited test-set discrimination (AUC 0.685 - 0.689), but cross-validated performance was weak (CV AUC 0.516-0.523) and severity for PASC was poor (recall 0.05-0.06). Conclusions. Neighborhood-level non-medical health factors alone were insufficient to discriminate the proxy PASC phenotype at the individual level. Considering the state of Delaware’s documented county level disparities in poverty and healthcare access, future predictive models should integrate clinical characteristics, comorbidity burden, and biomarkers at the individual level. This method would more accurately identify those at risk of developing PASC and support equitable resource allocation in the communities that are most impacted by this chronic disease.
INTRODUCTION On February 12, 2020, the World Health Organization (WHO) gave the mysterious virus causing a variety of symptoms similar to pneumonia its official name: Coronavirus Disease 2019 (SARS-COV-2). The pandemic caused by this virus impacted individuals worldwide, and to date, approximately 7.1 million individuals have died from this disease.1 Virtually no part of 100 Delaware Journal of Public Health - October 2026
the world was untouched by the pandemic, and worldwide efforts were initiated to identify the cause, potential vaccines, and treatment options. Through multiple variants of the virus, stay-at-home orders forced families to quarantine, creating economic hardships, shuttering businesses, and forcing schools to adapt to remote learning. Sadly, the pandemic also exposed the blatant gap in differences in acute SARS-CoV-2 health outcomes Doi: 10.32481/djph.2026.10.15
between people who historically have unequal socioeconomic backgrounds. This gap in health outcomes was notable in Delaware as well as the rest of the United States. In a June 2022 publication, Holmes et al found that in Delaware the SARS-CoV-2 case fatality rate exceeded the national rate (27 per 10,000 Delaware vs 25 per 10,000 Nationally).2 The authors used Poisson regression to compare county level mortality data to the state average and found that there was a statistically significant incidence of elevated mortality risk from SARS-CoV-2 in Sussex County when compared to the State of Delaware.2 Consistent with national data, Non-Hispanic African American Delaware residents experienced disproportionately higher mortality than Non-Hispanic White Delaware residents throughout the pandemic.2,3 Studies have also found that of those hospitalized, African American patients had a higher risk of mortality compared to European American patients.4 Although the acute crisis surrounding the pandemic has passed, early in the pandemic physicians began to notice that many individuals who recovered from SARS-CoV-2 continued to experience lingering symptoms. At the time of the 2023 Behavioral Risk Factor Surveillance System (BRFSS) cycle, the CDC defined Post Acute Sequelae of SARS-CoV-2 infection (PASC) as a self-report of any symptoms lasting ≥3 months at the time of interview that were not present before having SARSCoV-2.5 This definition has since evolved and according to the World Health Organization (WHO), PASC is a chronic disease characterized by more than 200 symptoms that typically begin within three months of the initial SARS-CoV-2 infection and last for at least 2 months.6,7 The 2023 BRFSS cycle reported that an estimated 6.7% of Delaware adults self-reported symptoms consistent with PASC and 24.5% of those Delawareans reported significant activity limitations.5 Estimates of PASC prevalence range widely from 6.7 percent to 35 percent or higher depending on the source.5,7 Since PASC is a chronic disease, it aligns directly with one of the priority outcomes listed in the Delaware State Health Improvement Plan (SHIP).7,8 More specifically, SHIP established a priority outcome to reduce the risk of chronic diseases by promoting a combination of methods to support and identify chronic diseases and support healthy outcomes. This plan aligns directly with non-medical health factors also known as social determinants of health (SDOH). Although PASC is an evolving health problem, non-medical health issues associated with it are much less clear.9 Non-medical health factors at the neighborhood level are broad, ranging from education and food insecurity to social inclusion and discrimination. Neighborhood non-medical factors could plausibly shape PASC risk, but it is unknown whether they are sufficient without clinical data. Data shows that some communities in Delaware with limited access to high quality education, employment opportunities, healthy food options and safe spaces for physical activity have worse health outcomes than communities with more resources.10 Additional county data reveal uneven non-medical health factors across Delaware, and highlights how healthcare access and socioeconomic conditions can become worse when transitioning from urban to rural areas.11 The difference is highlighted when observing the dentist to population ratio between Sussex County (4,540:1)
and New Castle County (1,690:1), percent of some college educational attainment (Sussex County 56%, Kent County 63% and New Castle County 69%), and childhood poverty (Sussex County 17%, Kent County 16% and New Castle County 15%).11 While data shows disparities based on location, there are distinct differences that make it difficult to conclusively say that Sussex County has worse health outcomes compared to Kent and New Castle. Communities in Delaware vary, and county level determinations alone do not explain differences in non-medical health factors. Depending on location in the city of Wilmington, life expectancy varies by about 16 years.10 Another study which evaluated food insecurity in New Castle County found that neighborhood level disparities exist and account for disproportionate levels of food insecurity, poverty, and SNAP participation in three zip codes in the City of Wilmington.12 In 2014, authors Braveman and Gottlieb found that not just one non-medical health factor can explain specific health disparities, but that combined non-medical health factors influence health outcomes.13 For example, mortality is related to educational achievement, but educational achievement can also be related to where one lives and to access to healthy foods. Studies have shown that non-medical health factors ultimately affect chronic disease risk, and while researchers have identified a clear association between a broad range of chronic health problems and health inequities, there is less evidence on how non-medical health factors affect an individual’s biological health.14,15 When considering the impact of chronic disease on mortality, seven out of the top ten leading causes of death in Delaware are attributable to chronic disease, and more than 10% of Delawareans live with multiple chronic conditions.10 Currently, there is no specific study that evaluates non-medical health factors and PASC in Delaware. At the international level, a Danish study examined the positive predictive value of individuals with PASC and found that, among individuals with comorbidities, the clinical designation of PASC was less reliable than among individuals without comorbidities.16 Childhood trauma has also been identified to cause an increase in inflammation, which can lead to higher risks of a variety of diseases, including cardiovascular disease.17 One study, based on the Childhood Trauma Screen for participants in the UK Biobank, found that childhood adversity was significantly associated with SARS-COV-2 related hospitalization and SARS-COV-2 related mortality.18 Another study using UK Biobank data found that individuals’ association with discrimination and illness differed based on their perception of clinical care quality, and perceived discrimination and socioeconomic status led to a greater risk of PASC.19 Most studies in the United States that have attempted to identify individuals with PASC have found that, when based on existing International Classification of Disease (ICD-10) data, there are no differences in PASC prevalence based on self-reported race or ethnicity.20,21 The major differences identified in a limited number of studies on PASC using ICD-10 data were primarily related to age and gender.20,22 In a national study using ICD-10-CM (U09.9) diagnosis of “Post SARS-COV-2 condition, unspecified,” Pfaff et al. found that patients were disproportionately female, white, and non-Hispanic from higher socioeconomic communities.20 In contrast to studies of COVID deaths and hospitalization rates, the majority of individuals with the U09.9 diagnosis lived in communities that had low poverty and unemployment.20 Another 101
study highlighted that among the US veteran population, an ICD-10 diagnosis of PASC was more likely among individuals who were “older age, female sex, Hispanic or Latino ethnicity, comorbidity burden, and severe acute infection manifesting by symptoms, hospitalization, or ventilation.”23 One study that looked at life stressors 12 months after SARS-COV-2 hospitalization found that life stressors significantly contribute to overall worse functional, cognitive, and neuropsychiatric outcomes.24 This same study found that additional predictors of poor outcomes include older age, female sex, baseline disability, and severity of the initial SARS-COV-2 diagnosis.24 Although existing data points to evidence SARS-CoV-2 is directly related to non-medical health factors, research is inconsistent in evaluating if these non-medical health factors are also associated with PASC. Because there is such a heterogenous mix of communities within the three counties in Delaware, research is needed to identify if and how neighborhood level non-medical health factors predict PASC risk. This understanding would have direct implications for how Delaware should allocate resources to its communities.
(n=6,601,739, Figure 1). This probability score was identified by the pasc_model_2.0 version of the machine learning model developed by Paff et al. using N3C data.25 This model assigned a probability score based on attendance at a PASC clinic. The model was trained on age, gender, symptom variety, comorbidities, and healthcare utilization. After filtering dates and selecting the unique person ID with the highest probability score from the date ranges, the total number of unique person IDs was 6,487,510.
For our research project we used the National Clinical Cohort Collaborative (N3C), a multi-site clinical repository that includes Delaware Health Systems among its data partners and conducted a retrospective case-control study. We based our model on the Pfaff et al study that trained on the N3C and used a machine learning algorithm and not an ICD-10 diagnosis to identify individuals with PASC. Using electronic health records (EHR), individuals with symptoms of PASC were identified among patients who attended a PASC specialty clinic at least once (n=597) and were compared with 1,793,604 individuals who tested positive for SARS-CoV-2 at least 90 days prior.25 This study found that PASC clinic patients who were hospitalized with acute SARS-COV-2 were disproportionately African American when compared with all patients hospitalized with acute SARS-COV-2. Additionally, these same individuals were more likely to have preSARS-CoV-2 comorbidity (diabetes, kidney disease, congestive heart failure, or pulmonary disease) and come from a lower socioeconomic background.25 Although our analysis was not Delaware specific, we aimed to establish a foundation to conduct a sensitivity analysis, scenario analysis, and logistic regression, as well as XGBoost, and examine whether multiple non-medical health factors, combined, can be used without clinical data and serve as predictors of PASC.
Figure 1. Flowchart of Data Selection and Analysis
METHODOLOGY Data Source: N3C Clinical Cohort Collaborative and PASC Characterization All data used in this study were EHR extracted from version 187.2024-12-13 of the COVID N3C Enclave with Level 3 data access. The N3C is a repository that captures data, which is then harmonized from multiple sites across the United States. To access N3C data, researchers must go through an Institutional Review Board (IRB) process. N3C data follow the OMOP common data model to ensure that terms referring to a specific condition are appropriately classified.
Study Population Eligible participants in the study included individuals who had a SARS-CoV-2 diagnosis with a U07.1 code or an RT-PCR/Antigenconfirmed positive test in their EHR between January 1, 2020, and December 1, 2024, and had a designated probability score 102 Delaware Journal of Public Health - October 2026
All unique person IDs with missing non-medical health factor data were also removed from the dataset; this resulted in a final dataset of n=5,632,928. To further refine the dataset, women who were pregnant at the time of their initial SARS-COV-2 diagnosis were removed (n=5,403,503). We excluded all individuals who had no health visits in their EHR after their initial SARS-CoV-2 infection, resulting in a total dataset of 4,693,923 (PASC 1,060,269 and no PASC 3,633,028). After filtering age ranges to include only individuals aged 18-99 years, our final dataset included individuals with PASC (n=1,055,854) and those without PASC (n=3,466,035) (total N=4,521,889).
Outcome Definition PASC status was a proxy assigned using the Pfaff et al. machine-learning probability score based on PASC-clinic attendance, symptoms, comorbidities, age, sex, and healthcare utilization, using the highest scoring window and a 0.90 cutoff.25 Those without a proxy PASC diagnosis had a probability score below 0.9, and those with PASC had a probability score above or equal to 0.90. Note: Figure formatting was prepared with assistance from ChatGPT (OpenAI). All analytic decisions, weighting choices, and interpretation were determined by the authors.
Exposure Variables The non-medical health factors used as our variables of interest included neighborhood-level characteristics such as race and ethnicity, income, socioeconomic status (SES), education, and health insurance. These variables, obtained at the Zip Code Tabulation Area (ZCTA) level, represent census-based geographic areas and are reported as percentages. ZCTA are established by assigning a census block to each zip code and then establishing polygonal areas for each of those blocks for statistical analysis. Some zip codes are not representative of where real people live (e.g. PO Box or company) so they are not turned into ZCTA’s. Additionally, zip codes are turned into census blocks even if they overlap which means that in some instances multiple zip codes exist in a ZCTA versus one zip code being assigned to one ZCTA.26 If the ZCTA lists individuals whose income is under $10,000 as 7%, that percentage applies to all individuals who live in that ZCTA designation area by default. These non-medical health factors were linked to the patientspecific SARS-CoV-2 data and the PASC probability scores using the unique patient identifier. After extracting the final dataset, we created our summary Table 1 to provide a profile of the overall demographics of the total cohort.
The overall prevalence of PASC (based on proxy) in our cohort was about 23%, and women were more likely to have PASC than males (62.5% female vs 54.3% male). The mean age of individuals in our dataset with PASC was higher than that of those without PASC (57 (SD = 16) with PASC vs 47 (SD = 18) without PASC). When evaluating race and ethnicity, White, non-Hispanic individuals make up the majority of both PASC and Non-PASC groups. Overall, there was a slightly higher percentage of black, non-Hispanic individuals in the PASC group (13.9% PASC vs 11.8% non PASC). Among American Indian/Alaskan Native, this trend was the same with (0.5% in the PASC group and 0.3% in the non-PASC group). In comparison, Hispanic individuals (7.5% PASC vs 9.5% no PASC) and Asian individuals (2.5% no PASC vs 2.9% PASC) had fewer PASC individuals than non-PASC individuals. Overall, our results are consistent with studies showing that older age and female sex are associated with a higher risk of PASC. Our clinical profile, displayed in Table 2, highlights common health conditions, smoking status, hospitalization status, and the percentage of our cohort with an ICD-10 PASC diagnosis. Approximately 5% of individuals in our cohort designated as having PASC had an ICD-10 diagnosis of PASC, and only 0.5% of individuals with probability scores indicating they do not have PASC had an ICD-10 diagnosis of PASC. More individuals with PASC were initially diagnosed with SARS-CoV-2 during hospitalization (11.50%) versus those without PASC (4.40%). When comparing both groups, those who had PASC were more likely to have chronic disease diagnoses, including congestive heart failure (11.8% vs 1.9%), diabetes (44.4% vs 16.4%), heart failure (16.1% vs 3.0%), and hypertension (55.4% vs 23.4%).
Table 1. Summary Table Variable
PASC
Percent
No PASC
Percent
23%
3,466,035
77%
Total Total count
1,055,854 Gender
Female
660,032
62.50%
1,881,050
54.30%
Male
395,694
37.50%
1,582,747
45.70%
Mean (SD)
57(16)
47 (18)
Min-Max
18-99
18-99
Age (years)
Race White Non-Hispanic
759,678
71.90%
2,410,269
69.50%
Black or African American Non-Hispanic
146,721
13.90%
408,747
11.80%
Hispanic or Latino
79,206
7.50%
330,771
9.50%
Asian non-Hispanic
26,114
2.50%
100,381
2.90%
American Indian/Alaskan Native
5,189
0.50%
11,858
0.30%
Native Hawaiian/Pacific Islander Non-Hispanic
4,388
0.40%
11,362
0.3%
Unknown
34,558
3.30%
192,647
5.60% 103
Table 2. Clinical Profile of PASC vs No PASC Variable
PASC
Percent
No PASC
Percent
Chronic Diseases Cardiomyopathies
71,937
6.80%
44,997
1.30%
Congestive Heart Failure
124,341
11.80%
65,823
1.90%
Diabetes
468,934
44.40%
570,744
16.40%
Heart Failure
170,048
16.10%
105,211
3.00%
Hypertension
584,813
55.40%
811,226
23.40%
Post SARS-CoV-2 Obesity
596,326
56.50%
1,088,765
31.40%
Pneumonia Due to Post SARS-CoV-2
82,480
7.80%
63,241
1.80%
10.60%
267,317
7.70%
15.40%
232,273
6.70%
11.50%
154,004
4.40%
5.00%
16,220
0.50%
Reinfection Reinfection
112,080 Smoking
Tobacco Smoker
162,756
COVID Hospitalization SARS-CoV-2 diagnosis during hospitalization
121,236 ICD-10 Diagnosis
PASC diagnosis
52,654
Statistical Analysis When developing our models, we excluded age and gender since they were included in feature selection when categorizing individuals with and without PASC via Paff et al.’s probability scoring.25 Prior work suggests that non-medical health factors interact to shape health outcomes. To align with this conceptual framework, we performed a sensitivity analysis.13 The goal of our sensitivity analysis was to create an overall risk score using combined non-medical health factors to better understand how these factors work in concert to influence risk at the ZCTA level. We first created a weighted composite risk score by combining multiple domains highlighted in Table 3. We developed our composite risk score using non-medical health factors across five domains, including race/ethnicity, income, socioeconomic status/education, insurance, and county-level factors. Weights were assigned to each domain, with those that reflected greater disadvantage contributing more to the combined risk score. To prevent one domain from unfairly skewing the results, we assigned different levels of importance (weights) to the domains to reflect a more accurate picture of the influence of social and access issues across different communities (Table 4). For example, we applied higher weights to health insurance while applying lower weights to race/ethnicity, where the smaller population size (American Indian/Native Hawaiian/Pacific Islander) would have unfairly skewed results. We did not apply thresholds to our non-medical health factors, so the composite risk score changed gradually. We added weights across the domains to create a combined risk score, with the highest score at 4.50. Risk scores were categorized as low, moderate, or high, and each patient was assigned a final number based on their ZCTA of residence. Next, we established three simplified scenarios (baseline, optimistic, and pessimistic) that modeled a baseline association, with ±20% change of the potential influence of non-medical health factors. 104 Delaware Journal of Public Health - October 2026
After applying our sensitivity and scenario analysis, we randomly split our cohort into an 80/20 train and test split. We did not give additional importance to the PASC nor the non-PASC group, even though our PASC group was noticeably smaller than our non-PASC group. We did not apply threshold selection, so our results did not reflect yes-or-no predictions; rather, we only evaluated whether our model effectively distinguished between those with and those without PASC (ROC/AUC).
RESULTS Logistic Regression and XGBoost Models Our models included only non-medical health factors and did not include patient-level clinical characteristics, as our goal was to determine whether neighborhood-level non-medical health factors alone could predict PASC. As mentioned, our models test different assumptions about neighborhood-level non-medical health factors that influence PASC prediction. Our logistic regression models in Table 5 all performed at near chance levels with an area under the curve (AUC) of approximately 0.50 for each model (Baseline, Pessimistic, and Optimistic). This indicates that the selected non-medical health features identified by ZCAT alone are insufficient to predict the presence or absence of PASC. After evaluating the poor performance of our logistic regression model, we attempted to use an XGBoost model to analyze our data. Our goal in removing race/ethnicity was to evaluate if excluding this neighborhood-level non-medical health factor would potentially improve the performance of our model. After excluding race/ethnicity and neighborhood-level non-medical health factors, the performance of the XGBoost model initially appeared to improve, with a test AUC of approximately 0.68 across all models (Table 6). It is important to note that when evaluating the fit of the model using different parts of the training dataset (cross-validation), the AUC was weak and performed at near chance levels of approximately 0.52. As with our logistic
Table 3. Non-Medical Health Factors Used to Construct Weighted Composite Risk Score Domain
Variable
Risk Direction
Weight
Notes
Race & Ethnicity
African American
Higher % ↑ risk
0.25
Percent ÷100 × weight
Race & Ethnicity
Hispanic (any race)
Higher % ↑ risk
0.25
Same method
Race & Ethnicity
Two or more races
Higher % ↑ risk
0.10
Smaller subgroup weight
Race & Ethnicity
AIAN/NHPI
Higher % ↑ risk
0.05 each
Small population weight
Income
<$10,000
Higher % ↑ risk
0.30
Strong low-income effect
Income
$10,000–$15,000
Higher % ↑ risk
0.20
Moderate effect
Income
≥$200,000
Lower % ↑ risk
0.30
Protective factor reversed
SES & Education
Poverty
Higher % ↑ risk
0.30
Structural disadvantage
SES & Education
Unemployment
Higher % ↑ risk
0.30
Access barrier
SES & Education
Bachelor’s degree
Lower % ↑ risk
0.20
Protective reversed
SES & Education
Graduate degree
Lower % ↑ risk
0.20
Protective reversed
Insurance
Public insurance
Higher % ↑ risk
0.40
Access limitation
Insurance
Private insurance
Lower % ↑ risk
0.40
Protective reversed
Insurance
Uninsured
Higher % ↑ risk
0.20
Direct access risk
County Factors
Percent voting
Lower % ↑ risk
0.25
Community engagement
County Factors
Social score
Lower score ↑ risk
0.25
Community resources
County Factors
Physicians
Lower availability ↑ risk
0.25
Access to care
Table 4. Risk-Level Cutoff Documentation Item
Documentation
Total Score Formula
total_sensitivity_score = race_ethnicity_weight + income_weight + ses_edu_weight + insurance_weight + county_sdoh_weight
How Weights Were Calculated
Each percentage was divided by 100 to put everything on the same scale. Factors that increase risk were multiplied directly by their weight. Factors that reduced risk were reversed so that lower values increased the score, and the result was then multiplied by their weight. Variables were assigned weights based on expected influence on social risk. Larger weights were used for poverty, education, insurance, and access factors. Smaller weights were used for race and ethnicity, so they provide context without dominating the score. Very small weights (e.g., 0.05) were used for smaller populations to avoid large swings in the total score.
Variable-Level Cutoffs
No cutoffs were used for individual variables. Each factor adds to the score gradually, with higher or lower values changing the score proportionally.
Risk-Level Cutoffs
High Risk: total score ≥ 2.5; Moderate Risk: total score ≥ 1.5 and < 2.5; Low Risk: total score < 1.5.
How Cutoffs Were Chosen
Cutoffs were set in the code ahead of time to group scores into low, moderate, and high risk. They were not based on how the data was distributed.
Was the Score Standardized?
No formal standardization was applied. Values were converted to proportions (0–1) to enable combination, but no additional statistical standardization was applied.
Domain Maximums in Code
Race/Ethnicity = 0.70; Income = 0.80; SES/Education = 1.00; Insurance = 1.00; County-Level = 1.00; theoretical maximum = 4.50. Lower maximums (0.70–0.80) were used for background demographic factors, while higher maximums (1.00) were used for factors more directly related to social disadvantage and access to care.
County-Level Note
The code includes four county-level inputs weighted at 0.25 each: percent_voted_house_16_by_preferred_ county, social_community_score_by_preferred_county, MDs_by_preferred_county, and sdoh2_by_preferred_ county. The main table lists three of these explicitly.
regression model, this XGBoost model did not do a good job at identifying the majority of positive PASC proxy cases (5% to 6% of PASC proxy cases accurately identified). The majority of individuals in this model are classified as non-PASC, which demonstrates an underprediction of those with PASC. This underprediction is likely due to an imbalance in the dataset,
with significantly more of the dataset having no PASC than in the PASC population (3.47 million vs 1.06 million). Ultimately, our results conclude that this model has difficulty distinguishing between those with PASC and those without PASC. This Receiver Operating Characteristic (ROC) curve depicts how well the test data was trained on the baseline XGBoost model. 105
Table 5. Logistic Regression Results Scenario
Variable
Train Coefficient (β)
Train Standard Errors
Train z-value
Train p-value
Test AUC
Baseline
Total Score
0.137
0.019
7.15
8.47E-13
0.506
Baseline
Intercept
1.434
0.034
-41.97
0
Pessimistic
Total Score
0.125
0.016
7.86
3.86E-14
Pessimistic
Intercept
-1.448
0.034
-42.69
0
Optimistic
Total Score
0.184
0.023
7.73
1.06E-14
Optimistic
Intercept
1.45
0.034
-42.58
0
0.506 0.502
Table 6. XGBoost (Excluding Race/Ethnicity Variable) Scenario
Test AUC
CV AUC
Accuracy
Precision (Class 1)
Recall (Class 1)
F1 Score (Class 1)
Baseline
0.689
0.523
0.77
0.55
0.06
0.1
Pessimistic
0.688
0.522
0.77
0.55
0.06
0.1
Optimistic
0.685
0.516
0.77
0.54
0.05
0.1
Figure 2. Baseline ROC Curve
well documented in Delaware. Our model’s results do not mean that non-medical health factors do not matter in relation to PASC, rather it means that neighborhood level proxies alone are not the right tool to identify how non-medical health factors matter in relation to PASC. With the known disparities at county level in Delaware, it is important that stakeholders work to determine the appropriate resource allocations for individuals dealing with limitations associated with PASC.
Other Research
DISCUSSION PASC is a chronic disease that is known to involve multiple body systems, and does not present the same in everyone. Through this study we evaluated if neighborhood-level nonmedical health factors alone were sufficient to identify a proxy PASC phenotype at the individual level. After establishing all six models (3 logistic regression and 3 XGBoost), our results indicated that neighborhood-level non-medical health factors alone are insufficient to differentiate individuals with PASC from those without PASC at the individual level. With Delaware’s known PASC burden of 6.7%, there is a need to understand how non-medical factors impact PASC considering county level disparities in areas such as access to dental care, education and poverty. Delaware’s goal to reduce risk of chronic disease by improving specific non-medical health factors demonstrates the urgency and need to improve health outcomes.8 Even though our models predictive power is limited, there is no denying that underlying disparities in non-medical health factors are real and 106 Delaware Journal of Public Health - October 2026
A RECOVER Adult cohort study by Feldman et al published in July 2025 used a validated symptom composite instrument that was prospectively administered in an adult cohort.27 This study prospectively followed ~4,000 adults in 33 states, Washington DC and Puerto Rico from the time of their initial SARS-CoV-2 infection and, using individually reported non-medical health factors, predicted later development of PASC. Feldman et al established a Long COVID research index (LCR) to indicate PASC with a threshold of greater than or equal to 11 and measured the outcomes at 6 months after initial SARS-CoV-2 infection. Using this model, the authors identified a strong association between PASC and non-medical health factors. The index was more accurately able to reflect the association between non-medical health factors and PASC than using neighborhood level data alone. Based on this index, individuals with PASC were more likely to experience regular barriers to care as a result of non-medical health factors. While our study used a machine learning model to identify individuals with PASC and Feldman used an index, other studies have used different methods to identify individuals with PASC. A 2022 Danish study by Durelund et al. found that ICD coding for individuals with PASC was less reliable when PASC was listed as a secondary diagnosis.16 This study used clinicians to evaluate each medical record to confirm the PASC diagnosis, rather than grouping participants by general characteristics. PASC was confirmed in only 2 of 10 cases when patients had a secondary diagnosis; an alternative diagnosis not consistent with PASC was identified in the remaining 8 cases.6 Greenhalgh et al. found that when clinically diagnosing PASC, many symptoms
are non-specific or overlap with those of conditions that commonly coexist (chronic musculoskeletal or rheumatological conditions, chronic respiratory conditions, type 2 diabetes, or thyroid disorders) and that, in some cases, might be confused with PASC.28 This same study found that PASC is also “frequently misdiagnosed (e.g., as menopause, common migraine, depression, anxiety, or deconditioning).”28 This evidence highlights the importance of having a streamlined method to appropriately diagnose individuals with PASC to improve the ability for researchers to use downstream analysis related to this condition.
Limitations Our analysis approach had limitations in our weighting, composite risk score, risk classification, geographic-level grouping (ZCTA), and interpretation. For our weighting approach, we based scoring on a conceptual understanding of the importance of neighborhood-level non-medical health factors and did not estimate weights using statistical modeling. This model would benefit from using the social vulnerability index, an existing index that has been statistically tested and is used in public health practice. It combines multiple social factors to assess vulnerability to risk.29 Another limitation of our model is that we did not account for the possibility that, in some cases, groups with greater access to care might cluster in lower-risk groups. Alternatively, groups at higher risk of social disadvantage might cluster within higher-risk groups, and these groups might overlap in ZCTA areas. Our primary limitation was that we did not evaluate individual-level risk; rather, we looked at the area-level risk, which could potentially mask those individuals who truly experience poor health outcomes. Our work highlights the need to evaluate PASC risk at the individual level and to consider clinical factors when evaluating individuals with PASC-related symptoms.
CONCLUSION Taken together, our findings and existing research support the conclusion that PASC is a new condition affecting multiple body systems, and it is important to have clear, definitive diagnostic criteria to support clinicians as they treat patients experiencing PASC symptoms. Class-balancing techniques or case-control adjustment could potentially improve the models’ ability to identify true PASC cases. A recommendation for future research is to extract Delaware specific data and apply Pfaff ’s proxy to identify individuals with PASC to determine if our results mimic data that was studied at the national level. Future research should involve evaluating individual level data centered on clinical characteristics, comorbidities and biomarkers in addition to individual level non-medical health factor data. This would capture the lived experiences of individuals at the granular level to more accurately identify individuals with PASC as well as the impact that non-medical health factors have on this condition. Ms. Ellis-Gilliam may be contacted at agilliam@udel.edu .
FINANCIAL DISCLOSURE N3C Attribution. The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://n3c.ncats.nih.gov/clinical-cohort and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS Contract No. 75N95023D00001, Axle Informatics Subcontract: NCATS-P00438-B. This research was also supported in part by the NIH R35GM141873 from the National Institutes of
Health and the NIH Award Number T32GM142603 Predoctoral Training Grant Award. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/datacontribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource https://doi.org/10.1093/jamia/ocaa196 . The N3C Publication committee confirmed that this manuscript is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program. This study is not part of the RECOVER initiative. IRB. The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources . The University of Delaware Institutional Review Board determined that this study is exempt from review, as it involved secondary analysis of deidentified data (IRB protocol number: 2237569-1).
REFERENCES 1. World Health Organization. (n.d.). Number of SARS-CoV-2 deaths reported to WHO (cumulative total). https://data.who.int/dashboards/covid19/deaths?n=c 2. Holmes, L., Jr., Deepika, K., Williams, J., Ward, D., Ogundele, B., Chinaka, C., Enwere, M., Okundaye, O., Picolli, T., Thompson, J., Poleon, M., Philipcien, G., Jain, S., Dasari, N., Alur, R., Adhikari, R., & Ogungbade, G. (2022). Racial/ ethnic and geoclustering differentials in SARS-CoV-2 (COVID-19) cumulative incidence, mortality, and temporal trend in Delaware State, USA. Medical Research Archives, 10(6). Advance online publication. 3. Centers for Disease Control and Prevention. (2023). SARS-CoV-2 provisional counts – health disparities. https://www.cdc.gov/nchs/nvss/vsrr/covid19/health_disparities.htm 4. Price-Haywood, E. G., Burton, J., Fort, D., & Seoane, L. (2020). Hospitalization and mortality among black patients and white patients with SARS-CoV-2. The New England Journal of Medicine, 382(26), 2534–2543. 5. Ford, N. D., Agedew, A., Dalton, A. F., Pratt, C., Gregory, C. O., & Saydah, S. (2024). Notes from the field: Long COVID and significant long COVID-associated activity limitation among adults, by jurisdiction – United States, 2023. MMWR. Morbidity and Mortality Weekly Report, 73(50), 1142–1143. 6. World Health Organization. (2021). A clinical case definition of post SARS-CoV-2 condition by a Delphi consensus. Geneva. 7. National Academies of Sciences, Engineering, and Medicine. (2024). A long COVID definition: A chronic, systemic disease state with profound consequences. Washington, D.C.: The National Academies Press. https://doi.org/10.17226/27768 8. Wynn, A., Kananen, J., Bruess, A., Cuffee, Y., Culhane-Suluai, K., Hofknecht, E., & Horney, J. (2026). Promoting healthier communities through the Delaware State Health Improvement Plan. Delaware Journal of Public Health, 12(2), 16–20. 9. World Health Organization. (n.d.). Non-medical health factors. https://www.who.int/health-topics/social-determinants-of-health 10. Delaware Division of Public Health. (2024, Jun). The burden of chronic disease in Delaware. Physical Activity, Nutrition, and Obesity Prevention Program. https://dhss.delaware.gov/wp-content/uploads/sites/12/dph/pdf/ BurdenOfChronicDiseaseInDelaware2024Final.pdf 11. County Health Rankings & Roadmaps. (2025). Compare counties: New Castle, Kent, and Sussex County, Delaware. 2025 Annual Data Release. Robert Wood Johnson Foundation & University of Wisconsin Population Health Institute. https://www.countyhealthrankings.org/health-data/compare-counties?year=202 5&compareCounties=10003%2C10001%2C10005 12. Harrison, C., Brooks, M., Goldstein, J. N., & Papas, M. (2021). Food insecurity in Delaware: A triangulation of spatial data resources. Preventing Chronic Disease, 18, E81. 13. Braveman, P., & Gottlieb, L. (2014). The social determinants of health: It’s time to consider the causes of the causes. Public Health Reports (Washington, D.C.), 129(Suppl 2), 19–31.
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14. Islam, M. M., Poly, T. N., Okere, A. N., & Wang, Y. C. (2025). Explainable machine learning model incorporating social determinants of health to predict chronic kidney disease in type 2 diabetes patients. Journal of Diabetes and Metabolic Disorders, 24(1), 115. 15. Britannica. (2024). Antibody: definition, structure, function, and types. https://www.britannica.com/science/antibody
23. Wander, P. L., Baraff, A., Fox, A., Cho, K., Maripuri, M., Honerlaw, J. P., Ho, Y. L., Dey, A. T., O’Hare, A. M., Bohnert, A. S. B., Boyko, E. J., Maciejewski, M. L., Viglianti, E., Iwashyna, T. J., Hynes, D. M., Osborne, T. F., & Ioannou, G. N. (2023). Rates of ICD-10 code U09.9 documentation and clinical characteristics of VA patients with post COVID-19 condition. JAMA Network Open, 6(12), e2346783. 24. Frontera, J. A., Sabadia, S., Yang, D., de Havenon, A., Yaghi, S., Lewis, A., Lord, A. S., Melmed, K., Thawani, S., Balcer, L. J., Wisniewski, T., Galetta, S. L., Agarwal, S., Andino, A., Arena, V., Baskharoun, S., Bauman, K., Bell, L., Berger, S., . . . Zhu, Y., & the NYU Neurology COVID-19 Study Team. (2022, December). Life stressors significantly impact long-term outcomes and post-acute symptoms 12-months after COVID-19 hospitalization. Journal of the Neurological Sciences, 443, 120487.
16. Duerlund, L. S., Shakar, S., Nielsen, H., & Bodilsen, J. (2022, February). Positive predictive value of the ICD-10 diagnosis code for long COVID. Clinical Epidemiology, 14, 141–148. 17. Emeny, R. T., Carpenter, D. O., & Lawrence, D. A. (2021). Health disparities: Intracellular consequences of non-medical health factors. Toxicology and Applied Pharmacology, 416(1), 115444. 18. Hanson, J. L., O’Connor, K., Adkins, D. J., & Kahhale, I. (2023, November). Childhood adversity and COVID-19 outcomes in the UK Biobank. Journal of Epidemiology and Community Health, jech-2023-221147. Advance online publication. 19. Thomason, M. E., Hendrix, C. L., Werchan, D., & Brito, N. H. (2022). Perceived discrimination as a modifier of health, disease, and medicine: Empirical data from the COVID-19 pandemic. Translational Psychiatry, 12, 284. 20. Pfaff, E. R., Madlock-Brown, C., Baratta, J. M., Bhatia, A., Davis, H., Girvin, A., Hill, E., Kelly, E., Kostka, K., Loomba, J., McMurry, J. A., Wong, R., Bennett, T. D., Moffitt, R., Chute, C. G., Haendel, M., & the N3C Consortium, & the RECOVER Consortium. (2023, February). Coding long COVID: Characterizing a new disease through an ICD-10 lens. BMC Medicine, 21(1), 58. 21. Butzin-Dozier, Z., Ji, Y., Li, H., Coyle, J., Shi, J., Phillips, R. V., Mertens, A. N., Pirracchio, R., van der Laan, M. J., Patel, R. C., Colford, J. M., Hubbard, A. E., & the National COVID Cohort Collaborative (N3C) Consortium. (2024). Predicting long COVID in the National COVID Cohort Collaborative using super learner: Cohort Study. JMIR Public Health and Surveillance, 10, e53322. 22. Van Cleve, R., Quach, T., Shaheen, M., Bell, K., Roshwalb, A., Petrin, R., Geng, L. N., Asch, S. M., & Lienau, A. (2024). Risk of developing long COVID based on acute COVID-19 severity. Journal of Public Health (Berlin), 34, 1587–1595. https://link.springer.com/article/10.1007/s10389-024-02364-2
25. Pfaff, E. R., Girvin, A. T., Bennett, T. D., Bhatia, A., Brooks, I. M., Deer, R. R., Dekermanjian, J. P., Jolley, S. E., Kahn, M. G., Kostka, K., McMurry, J. A., Moffitt, R., Walden, A., Chute, C. G., Haendel, M. A., Bramante, C., Dorr, D., Morris, M., Parker, A. M., . . . Niehaus, E., & the N3C Consortium. (2022, July). Identifying who has long COVID in the USA: A machine learning approach using N3C data. The Lancet. Digital Health, 4(7), e532–e541. 26. Census.gov . (2026). ZIP code tabulation areas (ZCTAs). https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html 27. Feldman, C. H., Santacroce, L., Bassett, I. V., Thaweethai, T., Alicic, R., AtchleyChallenner, R., Chung, A., Goldberg, M. P., Horowitz, C. R., Jacobson, K. B., Kelly, J. D., Knight, S., Lutrick, K., Mudumbi, P., Parthasarathy, S., Prendergast, H., Quintana, Y., Sharareh, N., Shellito, J., . . . Karlson, E. W. (2025). Social determinants of health and risk for long COVID in the US RECOVER-Adult Cohort. Annals of Internal Medicine, 178(9), 1287–1297. 28. Greenhalgh, T., Sivan, M., Perlowski, A., & Nikolich, J. Z. (2024). Long COVID: A clinical update. Lancet, 404(10453), 707–724. 29. Agency for Toxic Substances and Disease Registry. (2024). Social vulnerability index. https://www.atsdr.cdc.gov/place-health/php/svi/index.html
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108 Delaware Journal of Public Health - October 2026
On-Ramps and Off-Ramps: Situating Food Is Medicine Within a Continuum of Prevention and Community Care Allison Karpyn, Ph.D. Center for Research in Education and Social Policy, University of Delaware McKenna Halverson, Ph.D. Center for Research in Education and Social Policy, University of Delaware Gina Crist, M.S., C.H.E.S. Department of Behavioral Health and Nutrition Cooperative Extension, University of Delaware
ABSTRACT Food Is Medicine (FIM) interventions are rapidly moving into clinical care and healthcare payment systems. As these services become billable and protocolized, programs risk being defined by the clinical intervention alone, overlooking the community and environmental conditions that shape eating, a risk heightened by the 2025 elimination of federal SNAP-Ed funding. Drawing on prevention and clinical care research, this article introduces an on-ramp and off-ramp framework developed through Delaware’s Advancing Food Is Rural Medicine (AFIRM) initiative. The framework organizes FIM into three phases: a community-facing on-ramp promoting equitable enrollment; a clinical core intervention; and a graduated off-ramp connecting patients to step-down food support, Cooperative Extension programming, and community resources. We argue that on-ramp and off-ramp activities should be budgeted and measured as core components, and that states can braid healthcare financing with Extension infrastructure so FIM begins in community, is strengthened in clinical care, and is sustained in community afterward.
INTRODUCTION Food Is Medicine (FIM) interventions, including medically tailored meals, medically tailored groceries, and produce prescriptions, are a focal point of current public health, research, and healthcare payment efforts aimed at reducing diet-related chronic disease.1,2 The scale of the problem is well documented. In 2023, 13.5 percent of U.S. households, or 18.0 million households, experienced food insecurity.3 An estimated 38.4 million Americans, or 11.6 percent of the population, have diabetes,4 and poor diet contributes to several hundred thousand deaths in the United States each year.2,5 Evidence supporting FIM has grown considerably. In a retrospective cohort of medically complex adults, receipt of medically tailored meals was associated with 49 percent fewer inpatient admissions and 16 percent lower healthcare expenditures compared to matched controls.6 Modeling studies estimate that national coverage of medically tailored meals for eligible patients could avert approximately 1.6 million hospitalizations and produce net savings of $13.6 billion in the first year.7 Similar modeling of produce prescription programs for the estimated 6.5 million adults with both diabetes and food insecurity found the potential to prevent approximately 292,000 cardiovascular events at a cost-effectiveness ratio well within accepted thresholds.8 Funding has followed. The American Heart Association’s Health Care by Food initiative has committed $250 million to FIM research, and the National Institutes of Health has expanded its FIM portfolio.5 States have become important actors in this work, and Delaware provides a useful example. In 2025, the state established a Governor’s Food Is Medicine Committee, a part of the Delaware Farm and Food Council. Delaware’s application to the federal
Rural Health Transformation Program (RHTP), a five-year, $50 billion national program, requested up to $1 billion across fifteen initiatives to restructure care delivery in rural Kent and Sussex Counties, where nearly 40 percent of the state’s population lives, and approximately one in ten residents has diabetes.9,10 In early 2026, the state released competitive funding of $1.65 million in the first year to establish FIM infrastructure in rural healthcare delivery, alongside RHTP investments in mobile community health hubs, continuous glucose monitoring, rural library telehealth, and school-based health centers.9 In Delaware, FIM is being implemented as one component of a larger rural health system transformation rather than as a standalone program. This rapid movement of FIM into clinical settings also carries a risk. As FIM services become billable, protocolized, and embedded in electronic medical records, programs may come to be defined by the clinical intervention alone rather than by the many factors that shape what people eat. Two decades of research on food environments and food access caution against such a narrow view.11,12 Eating is a daily practice influenced by neighborhood food retail, household resources, cultural traditions, time realities, culinary skills, transportation, and trust. It has also long been supported by public nutrition education programs. Before its federal funding was eliminated under the 2025 budget reconciliation law (P.L. 119-21), the Supplemental Nutrition Assistance Program Education (SNAP-Ed) program was the largest federal public health nutrition program, investing more than $400 million each year in community nutrition education and in policy, systems, and environmental change strategies. National evaluation data indicated that 40 percent of participants increased fruit intake and 34 percent increased vegetable intake.13,14 The Cooperative Extension System, which delivered SNAP-Ed in many states and maintains educators in county Doi: 10.32481/djph.2026.10.16 109
offices across the country, remains a stable institutional home for this work.15,16 The loss of dedicated federal nutrition education funding makes it more important, not less, to connect clinical FIM investments to the community systems in which eating occurs. This article introduces a framework developed through the Advancing Food Is Rural Medicine (AFIRM) initiative in Delaware that formalizes those connections as an on-ramp and an off-ramp surrounding the clinical core of a FIM intervention.
THEORETICAL FOUNDATIONS The on-ramp and off-ramp framework draws on two research traditions that have often operated separately: population-level prevention and individual-level clinical care. Public health theory holds that health behavior is shaped at multiple levels, including individual, interpersonal, organizational, community, and policy levels.17 Frieden further showed that interventions addressing socioeconomic conditions and default environments reach more people, with less individual effort, than clinical counseling delivered one patient at a time.18 SNAP-Ed reflected these principles in its design. Over two decades, the program shifted from classroombased direct education toward a comprehensive model that paired direct education with policy, systems, and environmental change strategies grounded in the social-ecological model.19,20 Cooperative Extension followed a similar path. Extension’s national frameworks for health and wellness and for health equity and well-being commit the land-grant system to communityengaged prevention and to partnership with the healthcare sector, with county-based educators providing long-term local capacity for nutrition, cooking, and food systems programming.15,16 The food access literature reflects the same orientation. Supermarket development, healthy retail initiatives, farmers market incentives, and federal nutrition assistance each work to change the environments and resources that shape food choice rather than targeting choice itself.11,12 Clinical care research offers a second set of foundations. The Chronic Care Model emphasizes planned, team-based, datainformed care for chronic disease.21 Screening for health-related social needs and referring patients to community resources has become increasingly common in practice.22 A substantial evidence base also supports community health workers (CHWs) as trusted intermediaries who improve outcomes and reduce hospitalization among low-income patients, and one randomized program demonstrated a return of $2.47 for every dollar invested.23 The transitions-of-care literature adds a further finding. Clinical gains are most likely to be lost when patients move between settings or levels of support, and structured transition support reduces rehospitalization.24 FIM draws on both traditions. It is a condition-targeted, providerreferred, and outcome-measured clinical intervention that addresses a behavior shaped largely by social and environmental factors. The on-ramp and off-ramp framework organizes a FIM intervention into three phases that reflect this dual character. The on-ramp includes the community-facing work that occurs before any clinical encounter. This work includes outreach through faith communities, farms, libraries, mobile health events, and other trusted settings, culturally and linguistically responsive communication, CHW-led relationship building, and identification pathways that do not rely only on provider referral. In rural areas, where awareness of available services is uneven and prior engagement with the healthcare system may be limited, 110 Delaware Journal of Public Health - October 2026
the on-ramp is the primary mechanism for achieving equitable access to the intervention. The on-ramp is also where FIM aligns with existing prevention efforts, as community nutrition education, SNAP and WIC outreach and enrollment assistance, and Extension programming serve as referral sources into clinical food support. In Delaware, this alignment builds on the state’s Farm to Family strategy, which links nutrition assistance, local agriculture, and community programming within a single coordinated plan.25 The core intervention is the clinical center of the pathway. Its elements include eligibility criteria aligned with priority conditions (uncontrolled diabetes, in the case of AFIRM), tracked referral pathways, medically tailored meals or grocery boxes matched to dietary parameters, registered dietitian counseling, culinary medicine education, wraparound CHW support, and standardized collection of clinical, behavioral, and food security outcomes. The off-ramp departs most clearly from standard program design. Rather than ending participation at a fixed endpoint, the off-ramp provides a graduated exit that includes step-down food support, continued nutrition and culinary programming delivered through Cooperative Extension, connection to community gardens and local food resources, sustained SNAP navigation, and periodic CHW contact. This model focuses on well-established literature that adherence to lifestyle change programming decreases as time outside of the intervention lengthens, recommending continued touch points, while acknowledging a need to be done outside of clinical care due to cost.26 Extension is well suited to this role. Because Extension is a permanent, county-based, and locally trusted institution, patients exiting active clinical food support are connected to infrastructure designed for long-term community presence rather than to time-limited grant programming. Patients whose glycemic control worsens or who screen positive for food insecurity after exit are flagged for re-engagement through the same monitoring systems used at enrollment. The pathway is therefore iterative rather than a single bounded episode of care, applying transition-of-care principles to nutrition support. A continuous layer of community engagement, research, and policy development spans all three phases. Program findings inform payer strategy, statewide FIM planning, and the evidence base needed for sustainable reimbursement.
IMPLICATIONS This framework has implications for program design, measurement, and policy. First, FIM initiatives should be specified and budgeted as threephase pathways from the outset. On-ramp and off-ramp activities are often treated as ancillary and are among the first elements cut when resources contract. The framework repositions these activities as core components that can be supported outside the already burdened health care system with defined staffing, including CHWs, Extension educators, and community partners, as well as protocols and metrics. Program reach, the representativeness of enrollment relative to community need, and the maintenance of clinical and food security outcomes after exit become primary evaluation questions. Partnerships with Extension and remaining community nutrition education providers should be established during program design rather than added at exit, a consideration made more pressing by the loss of the federally funded SNAP-Ed workforce.
Second, the framework points to outcomes that current research largely does not measure. The American Heart Association’s systematic review of FIM randomized trials found promising effects on diet quality and food security but inconsistent cardiometabolic results, due in part to short intervention and follow-up periods.5 If durability depends on what happens after active food support ends, then post-intervention trajectories and the community linkages expected to sustain them need to be measured directly. Prespecified exit criteria, extension pathways, and re-engagement triggers, recorded as discrete data elements, allow the off-ramp itself to be studied. Third, reimbursement models built only around the clinical core will underinvest in the activities that determine whether FIM reaches those with greatest need and whether its effects last. States designing FIM benefits within Medicaid Section 1115 waivers, managed care in-lieu-of-services arrangements, or transformation programs such as Delaware’s RHTP have the opportunity to finance the full pathway, including CHW-led outreach at entry and step-down, community-anchored supports at exit. Braiding healthcare financing with existing Extension infrastructure offers a practical way to do this without duplicating capacity, and this approach takes on added importance as states absorb the loss of federal SNAP-Ed funding. Delaware’s decision to place its FIM infrastructure investment alongside mobile health hubs, library-based telehealth, and Extension-delivered community programming shows how a state can build on-ramps and offramps into the surrounding system rather than requiring each program to build them alone.
CONCLUSION The current enthusiasm for Food Is Medicine is warranted, but enthusiasm alone will not produce lasting improvements in health outcomes and costs. FIM is a clinical intervention that addresses a behavior shaped beyond individual choice, by social and environmental conditions, and its effectiveness depends on its connections to the prevention, outreach, and community food systems around it, including the Cooperative Extension infrastructure that continues to reach the communities FIM aims to serve. The on-ramp and off-ramp framework offers a practical and transferable way to design, evaluate, and finance those connections. It treats entry into clinical food support and the return to community-supported eating as core features of the intervention rather than as logistical details. As states such as Delaware invest in rural health transformation, the field has an opportunity to build FIM programs that begin in community, are strengthened in clinical care, and are sustained in community after clinical services end. Dr. Karpyn may be contacted at karpyn@udel.edu .
REFERENCES 1. Downer, S., Berkowitz, S. A., Harlan, T. S., Olstad, D. L., & Mozaffarian, D. (2020). Food is medicine: Actions to integrate food and nutrition into healthcare. BMJ (Clinical Research Ed.), 369, m2482. 2. Mozaffarian, D., Blanck, H. M., Garfield, K. M., Wassung, A., & Petersen, R. (2022). A Food is Medicine approach to achieve nutrition security and improve health. Nature Medicine, 28(11), 2238–2240. 3. Rabbitt, M. P., Hales, L. J., Burke, M. P., & Coleman-Jensen, A. (2024). Household food security in the United States in 2023 (Report No. ERR-337). U.S. Department of Agriculture, Economic Research Service. 4. Centers for Disease Control and Prevention. (2024). National diabetes statistics report. U.S. Department of Health and Human Services. 5. Volpp, K. G., Berkowitz, S. A., Sharma, S. V., Anderson, C. A. M., Brewer, L.
C., Elkind, M. S. V., Gardner, C. D., Gervis, J. E., Harrington, R. A., Herrero, M., Lichtenstein, A. H., McClellan, M., Muse, J., Roberto, C. A., Zachariah, J. P. V., & the American Heart Association. (2023). Food is medicine: A presidential advisory from the American Heart Association. Circulation, 148(18), 1417–1439. 6. Berkowitz, S. A., Terranova, J., Randall, L., Cranston, K., Waters, D. B., & Hsu, J. (2019). Association between receipt of a medically tailored meal program and health care use. JAMA Internal Medicine, 179(6), 786–793. 7. Hager, K., Cudhea, F. P., Wong, J. B., Berkowitz, S. A., Downer, S., Lauren, B. N., & Mozaffarian, D. (2022). Association of national expansion of insurance coverage of medically tailored meals with estimated hospitalizations and health care expenditures in the US. JAMA Network Open, 5(10), e2236898. 8. Wang, L., Lauren, B. N., Hager, K., Zhang, F. F., Wong, J. B., Kim, D. D., & Mozaffarian, D. (2023). Health and economic impacts of implementing produce prescription programs for diabetes in the United States: A microsimulation study. Journal of the American Heart Association, 12(15), e029215. 9. Delaware Department of Health and Social Services. (2026). Rural Health Transformation Program. https://dhss.delaware.gov/dph/rural-health-transformation-program 10. State of Delaware. (2026, February 9). State of Delaware opens initial RFPs to transform rural health care [Press release]. https://news.delaware.gov 11. Karpyn, A. E., Riser, D., Tracy, T., Wang, R., & Shen, Y. E. (2019). The changing landscape of food deserts. UNSCN Nutrition, 44, 46–53. 12. Karpyn, A., McCallops, K., Wolgast, H., & Glanz, K. (2020). Improving consumption and purchases of healthier foods in retail environments: A systematic review. International Journal of Environmental Research and Public Health, 17(20), 7524. 13. Association of SNAP Nutrition Education Administrators. (2025). SNAP-Ed national impact report. 14. Congressional Research Service. (2025). Supplemental Nutrition Assistance Program (SNAP) and related nutrition programs in P.L. 119-21: An overview (Report No. R48552). Library of Congress. 15. Braun, B., Bruns, K., Cronk, L., Fox, L. K., Koukel, S., Le Menestrel, S., Lord, L. M., Reeves, C., Rennekamp, R., Rice, C., Rodgers, M., Samuel, J., Vail, A., & Warren, T. (2014). Cooperative Extension’s national framework for health and wellness. Extension Committee on Organization and Policy. 16. Burton, D., Canto, A., Coon, T., Eschbach, C., Gunn, J., Gutter, M., Jones, M., Kennedy, L., Martin, K., Mitchell, A., O’Neal, L., Rennekamp, R., Rodgers, M., Stluka, S., Trautman, K., Yelland, E., & York, D. (2021). Cooperative Extension’s national framework for health equity and well-being. Extension Committee on Organization and Policy Health Innovation Task Force. 17. McLeroy, K. R., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Education Quarterly, 15(4), 351–377. 18. Frieden, T. R. (2010). A framework for public health action: The health impact pyramid. American Journal of Public Health, 100(4), 590–595. 19. Gregson, J., Foerster, S. B., Orr, R., Jones, L., Benedict, J., Clarke, B., Hersey, J., Lewis, J., & Zotz, A. K. (2001). System, environmental, and policy changes: Using the social-ecological model as a framework for evaluating nutrition education and social marketing programs with low-income audiences. Journal of Nutrition Education, 33(Suppl 1), S4–S15. 20. U.S. Department of Agriculture, Food and Nutrition Service. (2023). SNAP-Ed plan guidance: Nutrition education and obesity prevention program. 21. Wagner, E. H. (1998). Chronic disease management: What will it take to improve care for chronic illness? Effective Clinical Practice, 1(1), 2–4. 22. Kreuter, M. W., Thompson, T., McQueen, A., & Garg, R. (2021). Addressing social needs in health care settings: Evidence, challenges, and opportunities for public health. Annual Review of Public Health, 42, 329–344. 23. Kangovi, S., Mitra, N., Grande, D., Long, J. A., & Asch, D. A. (2020). Evidencebased community health worker program addresses unmet social needs and generates positive return on investment. Health Affairs (Project Hope), 39(2), 207–213. 24. Coleman, E. A., Parry, C., Chalmers, S., & Min, S. J. (2006). The care transitions intervention: Results of a randomized controlled trial. Archives of Internal Medicine, 166(17), 1822–1828. 25. Crist, G., & Karpyn, A. (2021). Delaware Farm to Family strategy: An integrated systems approach to improve food access in Delaware. University of Delaware. 26. Middleton, K. R., Anton, S. D., & Perri, M. G. (2013). Long-term adherence to health behavior change. American Journal of Lifestyle Medicine, 7(6), 395–404.
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H e a l t h y H e a rt A m b a s sa d o r Blood Pressure S e l f - M o n i t o ri n g P r o g r a m Do you want to better manage your blood pressure (BP)? We are here to help! Join the Healthy Heart Ambassador program to lower your BP through behavior change and support.
In this four-month program, you will get:
A FREE BP Monitor for those who qualify. Easy Training on how to track your BP at home. Personal Support: Two virtual sessions per month with a Healthy Heart Ambassador.
Healthy Eating Tips: Monthly online nutrition
seminars and “Simple Cooking with Heart” demo.
To Join, You Must:
Get More Info and Sign Up:
Be a Delaware resident Be 18 years or older Have a high BP diagnosis Not have had a cardiac event in the last year (heart attack or stroke) • Not have an irregular heartbeat (atrial fibrillation or other arrhythmias) • Not have lymphedema (swelling in the limbs)
• Call: 302-208-9097 • Email: DHSS_DPH_HHA@delaware.gov • Scan this QR Code:
• • • •
This publication was supported by Cooperative Agreement Number NU58DP007429 from the Centers for Disease Control and Prevention as part of the National Cardiovascular Health Program (CDC-RFA-DP-23-0004). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. Publication number DEDPH-CV-092424
112 Delaware Journal of Public Health - October 2026
Programa de autocontrol de LA PRESIÓN ARTERIAL de Healthy Heart Ambassador
• ¿Tiene la presión arterial alta o toma algún medicamento para controlarla? • ¿Le cuesta mantener la presión arterial bajo control? • ¿Se preocupa por los riegos en su salud que pueda tener la presión arterial alta?
Si su respuesta es SÍ, aproveche este nuevo programa gratuito que le enseñará unos trucos simples pero efectivos para lo siguiente: • Controlar y entender la presión arterial. • Establecer y alcanzar sus objetivos de salud. • Identificar y controlar aquello que provoca la presión arterial alta. • Adoptar hábitos de alimentación saludables. • Aumentar la actividad física.
Los participantes de este programa recibirán: (GRATUITAMENTE) • •
•
• •
Un tensiómetro digital (si lo necesitan) y entrenamiento para medir y controlar la presión arterial en el hogar. Apoyo virtual personalizado por parte de facilitadores especialmente capacitados y sesiones virtuales de aprendizaje durante cuatro meses. Clases de cocina y educación nutricional que les darán la confianza para comprar, preparar y cocinar comidas sanas para el corazón, deliciosas y de bajo costo. Apoyo a largo plazo para ayudarlos a lograr cambios reales para conservar la salud del corazón. Las clases se llevarán a cabo virtualmente (por ahora).
Requisitos de participación:
✓ Residir en Delaware ✓ Tener más de 18 años ✓ Tener un diagnóstico de presión arterial alta
✓ No haber sufrido de episodios
cardiovasculares en el último año
✓ No sufrir de fibrilaciones
auriculares ni de otras arritmias
✓ No sufrir ni tener riesgo de linfedema Comuníquese con nosotros: Llame al 302-208-9097 o envíe un correo electrónico a DHSS_DPH_HHA@delaware.gov para inscribirse o recibir más información.
Esta publicación ha sido financiada por el Acuerdo de Cooperación Número NU58DP006516 de los Centros para el Control y la Prevención de Enfermedades. Su contenido es responsabilidad exclusiva de los autores y no representa necesariamente la opinión oficial de Centros para el Control y la Prevención de Enfermedades. Número de publicación DEDPHHD-041221A
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Tackling the Lack of Literacy Regarding OTC Medications in Adolescents Anika Bhat Intern, Delaware Academy of Medicine and Public Health Katherine Smith, M.D., M.P.H. Executive Director, Delaware Academy of Medicine and Public Health
ABSTRACT Objectives. To eliminate accidental over-the-counter medication misuse among Delaware adolescents by evaluating baseline drug literacy and designing a program as a health literacy intervention. Methods. A gap analysis was performed on the health literacy of Delaware teens regarding over the counter medications. A hypothetical mobile application (Project CabinetMed) was then designed for Delaware public schools and pediatric clinics. The app could be included in school initiatives, and clinical surveys about OTC use embedded into annual wellness checkups for youth aged 12 to 18. Results. Analysis shows 90% of adolescents self-medicate by age 16, yet safety test scores average only 44%, with 85% making label-reading errors and 20% of Delaware high school seniors reporting non-prescription drug use. To address this, Project CabinetMed provides dosage calculators, label decoders, and scenario games linked to local retail rewards. Conclusions. Adolescent over-thecounter medication literacy in Delaware is critically low. Linking schools, pediatric clinics, and retailers offers an effective approach to improve knowledge retention and safe self-medication practices. Policy Implications. Public health agencies should mandate standardized medication safety instruction in school curricula and integrate universal literacy screening into routine pediatric wellness visits to track surveillance data and prevent pediatric poisonings statewide.
INTRODUCTION Adolescent over the counter medication (OTC) misuse is a critical yet under-monitored public health issue, as many teenagers regularly manage non-prescription drugs independently without understanding safe limits or dangerous active ingredient overlaps. Unintentional toxicity from common pain relievers and cold remedies frequently leads to preventable emergency room visits and long-term health complications. Addressing this knowledge gap directly aligns with current public health priorities that focus on advancing youth health literacy, expanding community-based preventative care, and closing surveillance gaps in adolescent medicine.
BACKGROUND Over-the-counter (OTC) medications have been a way to allow people to self-medicate. They are usually utilized for things like cold and cough, allergies, pain relief, motion sickness, and more. These medications are accessible to all public, however, the lack of restrictions in the dosage and buying of these medications can make them dangerous, specifically to adolescents. Poisoning deaths for teens aged 15 to 19 spiked by 91% from 2000 to 2009, and were credited to the fact that parents start trusting their children to manage their own OTC medications around the age of 11 or 12 years.1 This trust comes with convenience for adults, but scope for misuse in the uneducated children. Shi and Bayard found that 70% of parents discuss marijuana risks with their children, but only 36% claim to educate them on the risks of OTC medications.2 In a 2018 study, only 68.5 114 Delaware Journal of Public Health - October 2026
percent of adolescents claimed they had spoken to an adult prior to taking medications.3 Adolescent utilization of OTC medications is becoming more of a problem with the lack of health literacy and lack of effective literacy initiatives regarding the dangers of taking OTC medication. Research by Du and Knopf (2009)4 as well as Rowe and Rowe (2023)5 note that there should be some type of effective education given to children before they are trusted to manage their own medicine. This gap remains in traditional health curricula and community interventions, which fail to engage teenagers or teach them practical label-reading literacy. There is also a shortage of up-to-date data regarding the topic of selfmedication in adolescents and its effects.4 Because of their accessibility, lack of restrictions, and lack of surveillance, national organizations also have no real way to track OTC abuse, which poses a major obstacle when obtaining data.2 Ninety percent of teens self-medicate by the time they reach 16 years old.1 Those same teens’ average test scores on an OTC safety test are only 44% correct, and 85% of them made errors when asked to read a basic Tylenol label.1 This proves that, despite the number of adolescents using OTC medications, a majority of them are not properly educated on their risks. Many people believe that because OTC medications are sold at such a convenience, they must be completely safe. However, the relative safety of an OTC medication is dependent on the dosage and other circumstances under which it is being taken.6 Understanding the consequences of taking these medications is the key to avoiding OTC abuse. This trend was seen in Germany in 2009. Du and Knopf conducted a study to investigate the effects of the most Doi: 10.32481/djph.2026.10.17
common pediatric OTC drugs in German children.4 The researchers monitored 17,450 children aged 0-17 years and their frequency of self-medicating. They found that 25.2% of participants had used an OTC drug during the week prior. They also found that 38.5% of the medication used by adolescents was OTC.4 The researchers noted that selfmedication among adolescents is very high in Germany, and should warrant an educational program to be put in place for both parents and children.
Commonly Abused OTCs The consequences of the misuse of OTC medications by adolescents commonly stems from the lack of understanding of the dangers. Most OTC medications include vitamin preparations, followed by cough and cold medicine (CCM).4 Dextromethorphan and acetaminophen are the two most common and misused ingredients found in CCM, which can lead to health risks.1 Acetaminophen. Acetaminophen is a “hidden ingredient” in many cold remedies, as well as being a common pain reliever. Taking too much acetaminophen is the number one cause of acute liver failure in the US.1 It can also lead to gastric bleeding. More than 60% of teens have never heard of acetaminophen, despite 21% taking it recently.7 Because of this lack of health literacy, teens may unknowingly abuse an OTC medication containing this ingredient without realizing it. Dextromethorphan. An Ohio survey of 39,345 high schoolers found that 4.7% occasionally misuse OTC drugs to get high, a trend most correlated with females, teenagers in older grades (11-12), and regular alcohol users who are nearly nine times more likely to misuse them.8 This pattern of intentionally seeking out a high is heavily centered around specific chemical compounds; for instance, 9th graders in general (26%) have used cough syrup containing dextromethorphan to get high, which is higher than recorded marijuana use among a generalized population of 9th graders (25%). Dextromethorphan is the active ingredient in certain OTC medications, and misusing it can lead to hallucinogenic effects known as “robotripping”.1 Other Common OTCs. Laxatives are often used for weight loss, and can lead to dehydration or digestive issues. Antihistamines or allergy medications with diphenhydramine are used for their sedative effects, which can lead to a dependence on these for sleep. They can also lead to hallucinations in high doses. Motion sickness medications often also contain diphenhydramine and dramamine and can be abused similarly. Non-prescription stimulants used to increase focus contain pseudoephedrine which can lead to anxiety and paranoia if abused.6 All of these patterns branch from the misunderstandings of the risks of OTC medications.
Label Literacy Following label directions is one of the best ways to avoid misuse, but the comprehension of the labels and whether individuals understand the consequences of misuse or overuse is unclear. Most often, teenagers and adolescents have some idea about the risks of drug abuse from attending health classes, however OTC medications are often overlooked within this curricula. This lack of health literacy can transform ordinary household medicine cabinets into highly dangerous
environments, making structured educational action crucial for adolescent safety. According to state-level substance surveillance, around onefifth of Delaware 12th graders report using medications without a medical prescription, meaning they are using OTC medications to self-medicate.9 This data aligns closely with the state’s broader Youth Risk Behavior Survey (YRBS) framework, which tracks trends in youth substance (Prescription & NonPrescription Medications, Alcohol, Vaping & E-Cigarettes, Tobacco Products, Marijuana & Cannabinoids, Illicit Drugs, etc) misuse across public school districts.10 These local figures emphasize that without targeted, state-specific school curricula or any efforts at increasing literacy among adolescents to combat low OTC medical literacy, Delaware’s youth will remain vulnerable to the dangers of OTC drug abuse.
National Programs Free online programs and partnerships from organizations like the U.S. Food and Drug Administration (FDA) and Scholastic (such as the OTC Medicine Safety program) provide educational materials and activities to improve overthe-counter medicine literacy, but they are not well known and therefore not as utilized.11 When put in place, programs can be effective in educating adolescents. In 2023, Rowe and Rowe completed a study to see if a created educational program about OTC medications would be effective in increasing 6th-grade students’ (ages 11–12) literacy on the topic and their ability to retain this information.5 The process consisted of three separate health literacy tests: the first was a baseline done before program implementation, the second was done right after program completion, and the third was done 10 weeks after program completion. The assessment consisted of 36 knowledge questions evaluating students’ understanding of safe over-the-counter medicine use, including topics such as reading medicine labels, following dosage instructions, proper storage and disposal practices, and responsible selfmedication. The results showed that baseline knowledge of OTC medications was low at 39.3% correct. Right after the program, that percentage increased to 62.9%. After 10 weeks from the end of the program, participants took the test a third time, getting 59.8% of the questions correct. This study shows that students, when they are educated on the topic of OTC medications, are able to retain the information and learn it properly.5 The University of Rochester Medical Center examined adolescents’ health literacy and ability to safely interpret medication information. Two hundred sixty-six young people between the ages of 16-23 years were tested on their OTC knowledge using the validated REALM and REALM-Teen assessments, which classify participants based on their ability to recognize and correctly pronounce common medical terms. About 36% of the participants had limited health literacy, scoring below the threshold associated with high school-level medical reading skills. The other 64% scored adequate health literacy, meeting or exceeding the medical reading skills threshold.7 Kelly et al. assessed middle and high school students’ knowledge regarding OTC medications.3 In this study, a convenience sample of 309 students (mean age 14) were 115
chosen and asked to complete a two-part OTC medication survey. The first part was about baseline information about OTC medications, and the second was about interpretation and literacy of the medications themselves. Sixty-five percent of respondents selected “I don’t know” for brand versus generic questions in part 1 of the survey. The same trend was seen among all the types of questions on this section of the survey with 59-67 percent answering “I didn’t know”, and only 8-26 percent answering correctly. When students were given a reference medication label in the second part of the survey, 55% answered label interpretation questions correctly while 30% reported “I don’t know”.3 Baseline knowledge among adolescents about OTC medication is low, and if initiative is not taken, it will stay stagnant or may even decrease. The insufficient education in children and adolescents about OTC medications can lead to dangerous drug misuse and abuse, making this knowledge crucial.
THE HEALTH BELIEF MODEL The Health Belief Model (HBM) was developed in the 1950s by social psychologists Hochbaum, Rosenstock and others, whilst they were working in the U.S. Public Health Service to explain the reasons people do or do not participate in health programs.12 The model suggests that individuals are more likely to engage in health-related behavior if they believe they are at risk of the health problem, believe the consequences of that problem are serious, believe that taking action will reduce their risk, and they believe taking action results in more benefits than barriers. The HBM consists of six primary constructs. These include Perceived Susceptibility, Perceived Severity, Perceived Benefits, Perceived Barriers, Cues to Action, and Self-Efficacy. Perceived susceptibility refers to the belief of the likeliness of an individual experiencing a health problem. Perceived severity is a person’s understanding of the seriousness of the condition or the consequences if the condition is not treated. These two constructs combine to form an individual’s perceived threat. The next construct, perceived benefits, refers to an individual’s idea of the positive outcomes such as reduced risk that can result from the recommended action being taken. On the other hand, perceived barriers are the constraints or obstacles an individual can associate with taking the recommended action, preventing them from taking said action. Cues to action are the combination of internal and external factors which can trigger an individual to engage in a health behavior. And lastly, self-efficacy is defined as an individual’s confidence in their ability to successfully perform the recommended behavior.
HBM in Practice Project CabinetMed (DE) is a program created to influence adolescents’ literacy levels regarding the safe use of overthe-counter medications. Project CabinetMed (DE) will address perceived susceptibility by allowing adolescents to input demographic health information and explore medication scenarios, helping them recognize the ways they may be personally at risk for medication misuse and accidental overdose if OTC products are not used correctly. Perceived severity will be targeted through the application’s 116 Delaware Journal of Public Health - October 2026
toxicity visualization feature, which will demonstrate the potentially harmful consequences of improper dosing and combining medications with overlapping active ingredients. Perceived benefits will be emphasized using the application’s educational and decision-support tools to make safer medication choices, reduce the risk of misuse, and increase confidence. Perceived barriers will be reduced by allowing users to scan and decode labels and FDA terminology into simple, understandable information that adolescents can quickly understand. Cues to action will be incorporated through incentives seen in the gamified education/scenario questions, with the goal of encouraging adolescents to regularly engage with medication safety education. Selfefficacy will be targeted through interactive practice activities that allow participants to apply medication knowledge in realistic situations, increasing their confidence in their ability to safely use OTC medications independently.
PROJECT CABINETMED (DE) The long-term, overarching goal of Project CabinetMed (DE) is to attempt to eliminate/decrease accidental over-the-counter (OTC) medication misuse and overdose among Delaware adolescents by improving health literacy. This app will become a fun, engaging educational tool for adolescents to learn about the dangers and cautions of OTC medications. By connecting this platform directly to the administrative, social, and home routines of teenagers, the program aims to ensure that no Delaware adolescent takes a household medication without understanding which medication to take for which scenario, its active ingredients, proper dosage, and potential risks.
Application Development The initial goal of Project CabinetMed (DE) (see Appendix A) focuses on the technical creation of the online application. This application will be created and managed by the program team, who will collaborate directly with professional software and app developers to design an interactive, mobile-friendly interface. These software developers will be hired as part of the larger team which will all work together to create the general domain and maintain it. The team will be split into smaller groups, with each group specializing in creating one of the three core digital features. Adolescents will be able to sign up for this app under an anonymous gamer tag so students do not feel uncomfortable having their information on the app. The app will include: The “Check Your Cup” Simulator: An interactive, quiz-style tool where adolescents have the option to input health data like weight, height, allergies, conditions, age, and race. The app will include a drop down box in which the adolescent can write their specific symptoms. Then, the app will calculate a safe, conservative medication dosage and will feature a visual “Toxicity Meter” that will spike if users accidentally combine overlapping toxic ingredients by taking multiple medications at once with unmixable ingredients. The “Smart-Scan” Label Decoder: An augmented reality tool that will use the smartphone camera to scan standard “Drug Facts” labels. The app will overlay and highlight critical facts like 24-hour limits and correct dosing instruments, translating dense FDA jargon into a condensed list of side effects, risks, and doses: the main things a teenager would need to learn
from the physician OTC medication bottle. “Stack and Slide” Dosing Blitz: A fast-paced, in-app mobile game where users will be given a real-world scenario (e.g., “You took DayQuil at 2:00 PM; it is now 6:00 PM and your headache is back, can you take Tylenol?”). Users must answer questions correctly to be able to unlock moves in the “Stack and Slide” game (similar format to Candy Crush). These questions will include digital label interpretations to calculate timing windows and require users to click the correct measuring tool. Adolescents can earn digital points through repeated use for accuracy. Once the software is finalized, the developers will upload the application to major digital stores, such as the Apple App Store and Google Play Store, making it easily accessible for download on any standard adolescent smartphone. Following this release, the software developers will perform monthly check-ins to monitor the system for possible bugs or crashes, while continuously adding performance and security updates that users can download directly through their app store platforms to maintain a completely smooth flow.
Data Collection and Evaluation The second goal of Project CabinetMed is to quantitatively measure the effectiveness of the application, which requires establishing clear baseline information on teenage OTC misuse. Prior to releasing the app, the program will partner with Delaware pediatricians, family doctors, and healthcare networks to add a dedicated section about adolescent OTC medications to the standard health surveys teens are already asked to take during annual wellness visits. By integrating these specific questions into the mandatory clinical paperwork, healthcare providers will be able to capture a realistic picture of current adolescent habits and dangers. After collecting original literacy rates of the adolescents in Delaware, baseline survey answers will be logged into a secure tracking database to monitor overall, de-identified population trends across the state rather than individual student progress. This macro tracking approach addresses the prevalent lack of existing academic research on adolescent medication safety by capturing data on the entire youth population, including individuals who may not download the app but still benefit indirectly from school marketing initiatives. Once this online database is established, the app may be implemented. Future data from these tests can be utilized to evaluate how well the application is closing the literacy gap, track shifts in adolescent safety behaviors, and verify if the app is successfully working across the state by observing how overall population literacy changes over time. Continuously expanding this database over the years will create a major repository to increase overall adolescent OTC medication health literacy research.
Marketing and Community Partnerships The third programmatic goal of Project CabinetMed focuses on marketing and community partnerships to drive widespread adoption and maintain long-term student engagement with the application. The program will formulate a specialized presentation and comprehensive pitch deck that outlines the application’s unique features, clinical backing, and classroom safety benefits. Following the review of this presentation by public health educators to ensure
alignment with standard curricula, the program team will design an educational OTC Safety Pamphlet distributed directly to parents during pediatric clinic visits and school assemblies (Appendix B). This pamphlet highlights the critical importance of teenage OTC medication safety and introduces the application as an automated educational resource, reassuring parents that the digital domain handles the instruction without imposing added responsibility on families while guaranteeing strict data privacy, anonymous gamertags, and the absence of social media elements. To reach adolescents whose parents strictly prohibit social media or restrict smartphone access, the program bypasses traditional social media recruitment entirely by embedding application exposure directly into mandatory school health classes and assemblies. Once this presentation is evaluated by public health educators to ensure alignment with standard curricula, the program will partner with Delaware school districts and local school boards to pitch the created presentation, sign official district agreements, and secure dedicated exposure time and specific time slots during annual health classes and assemblies. By embedding the app directly into the school curriculum, the program team can ensure all incoming student cohorts across the state are introduced to the platform while beginning to reference the clinic surveys and their introduction to the application. The application will also be coded into a website format with more limited features which can be accessed through any digital domain. This ensures students without access to phones can also participate in the educational application. To further guarantee equal access for students without smartphones or personal devices, the application will also be coded into an accessible web-based browser interface that can be used on school Chromebooks or computer lab desktops during class.The program will establish localized reward frameworks with regional convenience networks like Wawa and local retail pharmacies including CVS and Walgreens to encourage continuous safety habits at home. By connecting the app’s point system directly to store incentives, adolescents can earn store credit through their gameplay achievements in the dosing blitz game. This system uses automated mobile notifications alongside special double point periods to provide even more engagement, prompting recurring point redemptions and maintaining continuous home safety habits among adolescents as clinical and school outreach expands across public schools statewide to deliver repeated presentations to incoming student cohorts.
DISCUSSION Project CabinetMed (DE) is designed to increase adolescent literacy regarding over-the-counter medications, increase the amount of data and studies done regarding this topic, and decrease the risk of OTC related misuse. Instead of responding reactively after the misuse leads to emergency room visits, CabinetMed targets these issues at the root as a preventive measure by addressing and playing to the behaviors behind self-medication in adolescents. Through incentives like the digital application, in-store rewards, school initiatives, and doctor involvement, this project puts three major public health pillars to action: Health Promotion, Epidemiology and Biostatistics, and Population and Global Health. 117
Health Promotion includes initiatives that aim to empower communities through meaningful interventions. Nearly 90% of teenagers take OTC medications independently by age 16, even though adolescent literacy regarding safe dosages, 24-hour limits, and toxic active ingredient overlaps remains critically low. CabinetMed is a promotional program aimed at increasing literacy through simple and engaging features within the application. By turning in-app gameplay points into real store credit, it further incentivizes continuous learning. At its core, Project CabinetMed (DE) aims to promote educational content in unique ways that keep students engaged while building practical, life-saving knowledge.
REFERENCES
Epidemiology and Biostatistics involve tracking and data collection regarding trends across populations to guide informed public health decisions. Accidental OTC toxicity is a significantly under-monitored issue that can lead to preventable emergency room visits. Project CabinetMed (DE) aims to fill that surveillance gap by partnering with pediatric networks to embed standardized literacy surveys into mandatory annual wellness checkups. This project aims to eventually achieve complete Delaware youth population participation in these surveys, allowing evaluators to apply longitudinal biostatistical audits to measure literacy growth over time. Tracking universal clinic data rather than app download counts alone removes self-selection bias and fills a major national research gap regarding non-prescription youth drug habits.
5. Rowe, S., & Rowe, S. (2023, January 18). Increasing and Retaining Tween Knowledge of Proper Medicine Use - Health Behavior and Policy Review. Health Behavior and Policy Review. https://parisscholarpublishing.org/increasing-andretaining-tween-knowledge-of-proper-medicine-use/
Population and Global Health focuses on improving overall health behaviors and outcomes across communities by creating models that can be applied worldwide. While Project CabinetMed (DE) begins its initiative in Delaware, other states or even nations can take inspiration from its success and implement this model, as its adaptable framework allows the application to remain consistent across diverse communities. By uniting public schools, pediatric health networks, and local retailers into a single ecosystem, the initiative drives population-level impact while ensuring equal access through school computer web browsers. Aggregating statewide survey data builds a centralized research repository on adolescent medication safety that fills a critical public health literature gap. Ultimately, CabinetMed’s low-cost, gamified framework offers an adaptable digital health blueprint that global health systems can easily replicate to reduce pediatric poisonings and eliminate literacy disparities worldwide.
CONCLUSION During my experience completing the Delaware Academy of Public Health Internship, I had the opportunity to research a public health problem prevalent in Delaware and among my age group. Improving adolescent over-the-counter medication literacy is imperative as youth self-medication trends continue. This research brings awareness to this issue, and Project CabinetMed (DE) was created as a proposed solution. This project is heavily reliant on the community coming together to enhance preventative health outcomes. In particular, high school students, pediatricians, and school administrators must all support data-driven interventions like Project CabinetMed (DE) to fill critical public health surveillance gaps. Ms. Bhat may be contacted at anikabhat23@gmail.com. 118 Delaware Journal of Public Health - October 2026
1. Abraham, O., & Chmielinski, J. (2018). Adolescents’ Misuse of Over-The-Counter Medications: The Need for Pharmacist-led Intervention. Innovations in Pharmacy, 9(3), 1–7. https://pmc.ncbi.nlm.nih.gov/articles/PMC6302752/ 2. Shi, C.-W. & Bayard, M. A. (2011). Abuse of over-the-counter medications among teenagers and young adults. American Family Physician, 84(7), 745, 750. https:// www.aafp.org/afp/2011/1001/p745 3. Kelly, T., Conn, K. M., & Zak, M. (2018). Evaluation of Over-the-Counter Medication Knowledge and Literacy in Adolescent Students. Academic Pediatrics, 18(5), 556–562. 4. Du, Y., & Knopf, H. (2009). Self-medication among children and adolescents in Germany: Results of the National Health Survey for Children and Adolescents (KiGGS). British Journal of Clinical Pharmacology, 68(4), 599–608.
6. Recovery Place of Savannah. (2026). Recoveryplace.org. https://www. recoveryplace.org/b/commonly-abused-otc-drugs 7. Teens Dangerously Uninformed about OTC Medication. (2010, May). URMC Newsroom. https://www.urmc.rochester.edu/news/story/teens-dangerouslyuninformed-about-otc-medication 8. Steinman, K. J. (2006). High school students’ misuse of over-the-counter drugs: A population-based study in an urban county. The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 38(4), 445–447. 9. Teen Drug Use Prevention Toolkit. (2026). Helpisherede.com. https://www. helpisherede.com/community-partners/talk-2-kids-about-drugs#drugs-at-aglance 10. Youth Risk Behavior Survey (YRBS) | Center for Drug & Health Studies. (2019). Udel.edu. https://cdhs.udel.edu/seow/school-surveys/youth-risk-behavior-surveyyrbs/ 11. Over-the-counter drug literacy a must for tweens and teens. (2015, August 4). Medical Xpress. https://medicalxpress.com/news/2015-08-over-the-counter-drugliteracy-tweens-teens.html 12. Godfrey, M. Hochbaum, Public Participation in Medical Screening Programs: A Socio-Psychological Study, Department of Health Education and Welfare, PHS Publ no 572 (Washington, DC: US Government Printing Office, 1958). Google Scholar
APPENDIX A: CABINETMED (DE) LOGIC MODEL Implementation Activities/Objectives
Goal Application Development 1
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Data Collection & Evaluation
1
2
Short Term Deliverables
Mid Term Outputs
Long Term Outcomes
Contract with a team of professional software developers.
8/2026 Begin a LinkedIn/job search for 30-40 candidates.
8/2026 – 9/2026 Interview 30-40 candidates and narrow down options to hire 9-12 software developers as the team.
9/2026 – Successfully hire and contract with the team of 9-12 and allocate developmental responsibilities regarding the app.
Full team task: Design, code, and maintain the online application.
10/2026 – Have the entire team work on creating the digital domain generally.
2/2027 – Finalize the coding of the online interface.
Post 2/2027 - Have the software developers do monthly check-ins to monitor the app for possible bugs, crashes, or to just maintain smooth flow of the app.
Split team tasks: Code and integrate the 3 digital features into the digital domain.
10/2026 Split the full team into specialized teams of 3-4 based on skillset. Each team will be assigned to create one of the 3 features embedded in the app.
2/2027 Each split team will complete their feature and integrate their code into the main domain.
Post 2/2027 Each split team will monitor their allocated feature monthly to check for possible bugs, crashes, or to just maintain smooth flow of the app.
Submit the finalized application to public appstore platforms and launch the app.
2/2027 – Submit the finished app to the App Store and Google Play for review.
3-5/2027 – Officially launch the app and reach 3,000 downloads in the next 2 months through advertising.
Ongoing – Continue increasing app download count through the two domains.
Run regular system upgrades.
2/2027 – Launch the beginning version of the app.
3-5/2027 – Track the original reviews for the app and use it as feedback to create updates fixing user issues accordingly.
Ongoing – Continue adding security updates/ performance updates that users can download through the app-store platforms.
Design and validate standardized pediatric over-the-counter (OTC) medication safety survey questions.
9/2026 – Formulate and finalize about 1015 specialized clinical OTC medication literacy questions.
10/2026 – Integrate the finalized question set into standard adolescent wellness checkup teen surveys.
Ongoing – Periodically review questions to match updated health guidelines or emerging OTC drug trend variations.
Create a database to track the baseline and post-launch teen population literacy rates in Delaware.
9/2026 – 10/2026 – Build a secure tracking database to log anonymous, deidentified population data from annual clinic responses.
1/2027 – Take 5000+ baseline Delaware teen answers to the OTC survey questions and input them into the database to serve as the pre-launch population baseline profile.
Post 3/27 – Continue to add unlinked Delaware youth data from all clinics into the database to track statewide literacy growth curves, campaign impacts, and overall youth population trend shifts.
Partner with clinical networks to implement the created surveys into the teen surveys.
10/2026 – Select 10 pilot pediatric clinics for implementation and add the 10-15 safety questions to mandatory teen wellness paperwork in the 10 pilot clinics.
1/2027 – Successfully collect and log 5000+ baseline teen survey answers.
Post 3/27 – After the app launches, expand the addition of these 10-15 OTC questions to all clinics in Delaware to the adolescents taken during yearly checkups. Once teens finish the survey, clinics should introduce the application to adolescents.
Administer ongoing clinic surveys after the launch to evaluate how overall regional population literacy shifts over time after the program implementation.
3/2027 – Send the baseline population data report (5000+ unlinked surveys) to the program’s team to establish the community baseline before the program rollout.
3/2028 – Extract the new annual population datasets from the clinics and compare regional literacy rates against the original baseline data to evaluate macro-level program efficacy.
Ongoing – Continue the process of collecting survey answers prior to and after the application is used by adolescents and create a database.
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APPENDIX A - continued Goal
Implementation Activities/Objectives
Marketing & Community Partnerships
Short Term Deliverables
Mid Term Outputs
Long Term Outcomes
Create a presentation to pitch the program and app to educational boards.
10/2026 – Formulate and design a professional, comprehensive pitch deck detailing the app’s digital features, clinical backing, and classroom safety benefits. (Software developers will have their project outlined and be working on developing the features by this point.)
11/2026 – Review and test the presentation format with select public health educators to ensure it aligns with standard high school health curricula.
Ongoing – Periodically update the marketing presentation materials to reflect ongoing user milestones or new community incentives. Also expand initiative and pitch to all Delaware schools.
Design and distribute an OTC Safety Parent Pamphlet to build family buy-in and guarantee digital privacy.
10/2026 – Draft a concise educational pamphlet highlighting teenage OTC safety risks, guaranteeing strict data privacy and anonymous gamertags, and showing parents how the app automatically educates teens without extra parental burden.
1/2027 – Print and supply pamphlets to the 10 pilot pediatric clinics to distribute to parents while teens complete the annual wellness survey and during school assemblies to teachers.
Ongoing – Distribute parent pamphlets across all participating pediatric clinics statewide and include digital copies in school onboarding packets.
Partner with school districts to secure dedicated class periods and assemblies for student app exposure.
11/2026 – 1/2027 – Pitch the created presentation about the program/application to around 15 Delaware school boards and sign official district agreements to secure dedicated health class periods or assembly slots for post-launch (3/2027).
2/2027 – Finalize the schedule and logistical details for the secured health class periods and school assembly slots across the participating districts. (1-2 time slots per school (15)). Ensure a webbased browser interface is accessible in school computer labs for students without smartphones in schools.
Ongoing – Onboard new Delaware school districts and maintain communication lines with active local school boards to do repeated presentations to new students.
Execute schoolwide onboarding assemblies to drive widespread student exposure.
2/2027 – Coordinate with school staff to prepare technical setups, promotional materials, and parent educational pamphlets ahead of the assemblies.
3-5/2027 – Execute the first wave of live schoolwide assemblies and onboarding sessions across 15 DE middle/high schools during the 2-month launch campaign. Begin to reference clinic surveys and their introduction to the app. Bypass social media recruitment by guiding students to access the platform directly in health classes.
Post 5/2027 – Scale assemblies to cover 100% of public middle and high schools statewide to onboard all incoming student cohorts moving forward to join the application interface. Continue to expand and outreach through both the clinics and school assemblies.
Establish localized pointredemption incentive frameworks with regional convenience networks and local retail businesses.
11/2026 – 2/2027 – Finalize reward partnership agreements with Wawa and local CVS/Walgreens to connect the app’s point system directly to store incentives across 10 targeted locations.
3-5/2027 – Begin companies giving out 1 dollar in store credit (Wawa or CVS or Walgreens) for every 10,000 points earned by users (points earned through “Stack and Slide”).
Ongoing – Keep automated mobile notifications active and make double point periods for even more engagement to prompt recurring point redemptions and maintain continuous home safety habits among adolescents as clinical and school outreach expands.
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2
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120 Delaware Journal of Public Health - October 2026
APPENDIX B: PROJECT CABINETMED (DE) PAMPHLET
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Cancer Incidence, Healthcare Access, and Support Resource Distribution in Delaware: A Statewide Epidemiological Analysis, 2018–2022 Ryan Stephens, B.S. Intern, Delaware Academy of Medicine and Public Health; Graduate Student, New York University Katherine Smith, M.D., M.P.H. Executive Director, Delaware Academy of Medicine and Public Health
ABSTRACT Delaware has historically experienced cancer incidence rates exceeding the national average, making an assessment of cancer burden, healthcare access, and support resource availability essential for informing public health policy and intervention. This paper analyzes cancer in Delaware from 2018 to 2022, examining patterns by cancer type, demographics, and geography, while also evaluating whether the distribution of oncology specialists and support resources align with the areas of greatest need among the population. The majority of the data about Delaware was sourced from the Delaware Department of Health and Social Services Division of Public Health’s report on Small Area-Level Cancer Incidence in Delaware 2018-2022. This information was then supplemented with additional research and data surrounding provider location, state screening program information, and cancer support organization resources. Between 2018 and 2022, Delaware recorded 32,090 newly diagnosed cancer cases with an age-adjusted all-site incidence rate of 469.9 per 100,000 people, which is greater than the national average of 448.6 per 100,000. Geographic analysis revealed variation across the state’s three counties and 15 cancer reporting zones. In this data, Kent County had the highest county-level incidence rate despite having the fewest cancer specialists. Sussex County however faced the most disparity in terms of access, having a cases-permedical-oncologist ratio almost five times that of New Castle County. Both cancer support resources and screening programs had a majority concentration in New Castle County. The findings of this analysis highlight geographic and demographic disparities in cancer burden across the state of Delaware, and stress the importance of targeted interventions to improve the health outcomes of Delaware residents no matter where they live.
INTRODUCTION Cancer is one of the leading causes of morbidity and mortality in the United States, accounting for approximately 600,000 deaths annually and leads to significant economic and social burdens on individuals, families, and healthcare systems.1 Delaware has ranked among states with the highest cancer incidence rates in the United States in previous years, and this has continued in the most recent five-year reporting period. This paper is a comprehensive analysis of cancer incidence, demographic patterns, geographic distribution, oncology specialist availability, and cancer support resources across Delaware from 2018 to 2022, primarily utilizing data from the Delaware Cancer Registry and the Delaware Division of Public Health. The findings of the report indicate that Delaware continues to experience a substantial cancer burden compared with the United States overall.
important geographic differences in cancer incidence. New Castle County accounted for the largest number of cancer cases (n=15,859), followed by Sussex County (n=10,295), and Kent County (n=5,933).2 However, when population differences were considered through age-adjusted incidence rates, Kent County had the highest incidence rate (511.7 cases per 100,000 population), followed by Sussex County (480.7 cases per 100,000 population), and New Castle County (451.4 cases per 100,000 population)2 (Figure 1). These findings demonstrate the importance of examining both case counts and incidence rates when assessing cancer burden. Figure 1. Overall Cancer Incidence Rate by County, Delaware, 2018-2022 (per 100,000 people)2
BACKGROUND Between 2018 and 2022, Delaware recorded 32,090 newly diagnosed cancer cases, representing an average of approximately 6,418 cases annually.2 The age-adjusted all-site cancer incidence rate in Delaware was 469.9 cases per 100,000 population which exceeded the national incidence rate of 448.6 cases per 100,000 population.3 The Delaware Department of Health and Social Services (DHSS) performed a county-wide analysis that displayed 122 Delaware Journal of Public Health - October 2026
Doi: 10.32481/djph.2026.10.18
CANCER INCIDENCE RATES, 2018-2022 It should be noted that cancer incidence rates in this report are based on where the patient resides at the time of diagnosis, not the location of the diagnosing facility. Meaning, a cancer case diagnosed at the Helen F. Graham Cancer Center in New Castle County would be assigned to whichever county the patient lives in, not defaulted to New Castle County. Therefore, the geographic patterns recorded reflect where cancer patients actually live as opposed to where they receive care, thus strengthening the validity of using these rates to assess community-level cancer burden.
Cancer Incidence by Race and Ethnicity Cancer incidence varied considerably across racial and ethnic groups (Figure 3). Non-Hispanic White Delawareans experienced the highest overall cancer incidence rate (485.8 cases per 100,000 population), followed by Non-Hispanic Black Delawareans (440.4 cases per 100,000 population) and Hispanic Delawareans (325.6 cases per 100,000 population).2 Figure 3. Cancer Incidence by Race and Ethnicity in Delaware 2018-2022 (per 100,000 people)2
Cancer Incidence by Sex Cancer incidence differed substantially between males and females in Delaware (Figure 2). The age-adjusted cancer incidence rate among males was 507.1 cases per 100,000 population, compared with 443.7 cases per 100,000 population among females.2 This difference suggests that males experience a greater overall cancer burden than females within the state. Figure 2. Cancer Incidence by Sex in Delaware 2018-2022 (per 100,000 people)2
Several site-specific cancers also demonstrated notable sex differences. Incidence rates for lung and bronchus cancer, colorectal cancer, melanoma of the skin, urinary bladder cancer, kidney and renal pelvis cancer, and leukemia were all higher among males than females.2 These findings may reflect differences in behavioral risk factors, occupational exposures, screening practices, and biological susceptibility. Overall cancer incidence rates by sex include gender-specific cancers such as prostate cancer (male only), breast cancer (predominantly female), cervical cancer, uterine cancer, and ovarian cancer (female only), and testicular cancer (male only). These cancers contribute to the observed difference in overall rates between males and females. When considering sex-specific cancers, the higher male incidence rate is partly driven by prostate cancer (136.3 per 100,000), while female rates are influenced by breast cancer (144.9 per 100,000) and uterine cancer (28.9 per 100,000).2 Removing the sex-specific cancers and examining only cancers that affect both sexes would likely cause the gap between male and female incidence rates to shorten because prostate cancer is common among males and drives up the rate. This indicates that behavioral and occupational risk factors, not just biological differences alone may account for remaining disparities.
Although Non-Hispanic White residents had the highest overall incidence rate, important disparities were observed for specific cancer types. Non-Hispanic Black Delawareans experienced substantially higher incidence rates for prostate cancer (195.1 cases per 100,000 population) compared with Non-Hispanic Whites (119.8 cases per 100,000 population). Similarly, incidence rates for pancreatic cancer and kidney and renal pelvis cancer were elevated among Non-Hispanic Black residents. In contrast, Non-Hispanic White residents experienced higher incidence rates for lung and bronchus cancer, melanoma of the skin, and urinary bladder cancer. These findings highlight the importance of targeted prevention and screening strategies for populations at elevated risk.2
Cancer Incidence by Age Cancer incidence increased dramatically with age across all racial and ethnic groups (Figure 4). Among NonHispanic White residents, incidence rates increased from 62.0 cases per 100,000 population among individuals aged 0–39 years to 2,432.3 cases per 100,000 population among those aged 75–84 years.2 Similar trends were observed among Non-Hispanic Black and Hispanic populations. The highest incidence rates were generally observed among individuals aged 75–84 years after which rates remained elevated among those aged 85 years and older.2 These findings demonstrate that age remains one of the strongest predictors of cancer risk and underscore the importance of screening, prevention, and healthcare access among older adults. The age-related patterns in Delaware mirror national trends. Nationally, cancer incidence also increases with age, with the highest rates observed among individuals aged 75 and older. Delaware’s age-specific rates, however, exceed national averages across most age groups,3 123
Figure 4. Age-Adjusted Cancer Incidence Rates by Age Group and Race/ Ethnicity, Delaware 2018-2022 (per 100,000 people)2
Most Common Cancer Types Analysis of site-specific cancers diagnosed between 2018-2022 revealed that breast cancer, prostate cancer, lung and bronchus cancer, and colorectal cancer accounted for the largest proportion of cancer cases in Delaware (Figure 5). Breast cancer represented the most common cancer type, with approximately 4,798 cases identified during the study period. Prostate cancer followed closely with 4,564 cases, while lung and bronchus cancer accounted for 4,038 cases and colorectal cancer accounted for 2,115 cases.2 These four cancer types collectively represent a substantial portion of Delaware’s cancer burden and should remain priorities for public health interventions, screening programs, and educational initiatives. Continued efforts to promote early detection and reduce modifiable risk factors may contribute to reductions in cancer incidence and mortality throughout the state.
suggesting that the age-related increase in cancer risk observed nationally is amplified in Delaware. This may be due to a couple of factors such as high early detection screening rates and a large retirement age demographic which is especially prevalent in Sussex County. Therefore, the importance of thorough and plentiful cancer screening and prevention programs targeting older Delawareans specifically is highlighted, since this population is impacted by both the universal age-related increase in cancer risk and Delaware’s above-average overall cancer burden. An important consideration when interpreting cancer incidence rates is the role that screening plays. Higher incidence rates in some populations or geographic areas could reflect greater utilization of cancer screening programs, which could lead to cancer detection that may otherwise go undiagnosed. On the contrary, lower incidence rates in some areas may reflect underdiagnosis due to limited screening access, not that there is actually a lower cancer burden. Figure 5. Nine Most Common Cancers in Delaware vs U.S. Incidence 20182022 (per 100,000 people) 2
Geographic Distribution of Cancer Incidence in Delaware, 2018-2022 The Delaware Cancer Registry, captures cancer cases based on the patient’s county of residence at the time of diagnosis. The following data reflect all reported cancer cases diagnosed among Delaware residents between January 1, 2018 and December 31, 2022, regardless of where the patient received their diagnosis or treatment. Cancer incidence in Delaware varies considerably across geographic areas, with meaningful differences observed at both the county and sub-county levels. The cancer reporting zone level groups census tracts into 15 meaningful geographic areas. Analysis of these zones reveals important patterns that county-level data alone would obscure. Overall, the highest cancer burden in Delaware is concentrated in central New Castle County, particularly in the Middletown/Odessa/Townsend zone (A9009), which reported the state’s highest all-site age-adjusted incidence rate at 634.1 per 100,000 population (Figure 6). This is notably higher than the statewide Delaware rate of 469.9 per 100,000 and the national rate of 448.6. The Bear/ Glasgow zone (A9008) reported the second-highest all-site rate at 582.2, also well above state and national averages. In contrast, zones in northern New Castle County including North Wilmington/Greenville/Hockessin (A9005, 385.4) and North Wilmington/Claymont (A9003, 400.6) had the lowest rates in the state, falling below both state and national averages.4 At the census tract level, even greater variation is observed (Figure 7). Census tract 166.02 in southern New Castle County reported an exceptionally high rate of 1,179.1 per 100,000, far exceeding any other tract in the state. Other notably high tracts include 139.03 (734.4), 148.05 (738.8), and 149.06 (698.5), all located in central or southern New Castle County. Meanwhile, several tracts in Sussex County, particularly in the Laurel area (tracts 511-512), reported the lowest rates in the state, ranging from 163.4 to 246.5 per 100,000.4
124 Delaware Journal of Public Health - October 2026
Figure 6. Delaware All-Site Rate Cancer Rate 2018-2022 (per 100,000 people)2
Cancer rates calculated for small geographic areas like census tracts should be interpreted carefully. Since census tracts have small populations, their cancer rates can fluctuate considerably from year to year based on just a few cases. For example, flipping a coin 10 times could produce 7 heads, but flipping it 1,000 times would likely show results closer to 50/50. The rates shown here represent the best available estimate for each tract, but the true rate could reasonably be a little bit higher or lower. Due to this, zonelevel data covers larger populations and provides slightly more reliable estimates and is used for type-specific analyses in this report. When examining geographic distribution by cancer type at the zone level, distinct patterns emerge. Female breast cancer incidence is strikingly elevated in the Middletown/Odessa/ Townsend zone at 237.3 per 100,000, which is nearly twice the rate of the lowest zone, Wilmington (115.6 per 100,000). This geographic concentration of breast cancer in a rapidly growing suburban corridor warrants further investigation.4 Prostate cancer shows a different pattern, with the highest rates in Bear/Glasgow (172.8 per 100,000) and Dover/ Smyrna (170.5 per 100,000), and the lowest rate in Seaford/ Bridgeville (88.5 per 100,000).4
Lung cancer burden is most concentrated in Bear/Glasgow (78.3 per 100,000) and Milford/Harrington/Leipsic/Smyrna/ Little Creek (68.0 per 100,000), while the lowest lung cancer rates are found in the affluent North Wilmington/ Greenville/Hockessin corridor (33.7 per 100,000).4 Here there is a difference of more than two-fold that may reflect differences in socioeconomic status and behavioral risk factors such as smoking rates across these communities, however further analysis of these factors would be required to confirm this relationship across these communities. Colorectal cancer follows a similar geographic pattern, with Bear/Glasgow (46.0 per 100,000) and Middletown/ Odessa/Townsend (42.9 per 100,000) again reporting the highest rates.4 Taken together, these geographic patterns suggest that the cancer burden in Delaware is not evenly distributed. Central and southern New Castle County consistently show elevated rates across multiple cancer types, while northern New Castle County, particularly the wealthier suburban corridor consistently shows lower rates. Sussex County shows a more mixed picture, with some zones elevated (Millsboro/Long Neck area, 482.9 per 100,000)4 and others lower, reflecting the county’s diverse mix of rural, coastal, and retirement communities. 125
GEOGRAPHIC DISTRIBUTION OF CANCER SPECIALISTS IN DELAWARE AND ALIGNMENT WITH POPULATION NEED, 2018-2022 Access to cancer specialists is a critical determinant of cancer outcomes, influencing timely diagnosis, treatment initiation, and survival rates. This analysis examines the geographic distribution of cancer specialists across Delaware’s three counties and assesses whether specialist supply aligns with population-level cancer burden. There are a total of 59 cancer-related specialists who were identified practicing in Delaware, spanning six specialty categories: medical oncology, surgical oncology, gynecologic oncology, urologic oncology, head and neck oncology, and Figure 7. Five-Year Age Adjusted All Site Cancer Incidence Rates by Zone, Delaware, 2018-20224
gastroenterologic oncology. Medical oncologists represent the largest group with 38 providers, followed by surgical oncologists with 9 providers. The remaining specialties: gynecologic oncology, urologic oncology, head and neck oncology, and gastroenterologic oncology all have fewer than 5 providers statewide, indicating very limited specialty coverage outside of medical and surgical oncology.5 It is to be noted that the analysis utilizes only specialties and not any subspecialties that an oncologist may have.
Geographic Distribution of Specialists The distribution of cancer specialists across Delaware is highly concentrated in New Castle County. Of the 59 total providers identified, 45 (76%) practice in New Castle County, with the majority located in Newark and Wilmington. Kent County has only four direct providers, with an additional single shared Kent/ Sussex provider, making its adjusted total approximately five. Sussex County similarly has three direct providers plus one shared Kent/Sussex provider, for an adjusted total of approximately four.5 Therefore, while New Castle County accounts for 76% of all cancer specialists in the state, Kent and Sussex counties together account for only 24% despite representing a substantial portion of Delaware’s cancer burden. Within New Castle County, specialist concentration is further clustered in Newark specifically, where the majority of oncologists are affiliated with the Helen F. Graham Cancer Center at Christiana Care.6 So, even within New Castle County, patients in southern areas such as Bear, Glasgow, and Middletown which have the highest cancer burden in the state may be forced to travel long distances to reach specialists.
Alignment of Specialist Supply with Cancer Burden Comparing specialist supply against cancer burden reveals disparities between where specialists practice and where cancer needs are greatest. Kent County has the highest cancer incidence rate of any county in Delaware at 511.7 per 100,000 population, yet has the fewest cancer specialists. Sussex County has 10,295 total cancer cases and an incidence rate of 480.7 per 100,000, yet also has very limited specialist coverage.2 To quantify this disparity, a cases per medical oncologist ratio was calculated for each county, incorporating the proportional allocation of Kent/Sussex shared providers:5 • New Castle County: 1:529 • Kent County: 1:1,187 • Sussex County: 1:2,574
Source: Delaware Department of Health and Social Services, Division of Public Health, Delaware Cancer Registry, 2025 Source: 2010 U.S. Census county shapefiles: https://www.census.gov/ geographies/mapping-files/time-series/geo/carto-boundary.2100.html Source: Delaware Cancer Reporting Zone Shapefile: The National Cancer Institute (NCI) and the North American Association of Central Cancer Registries (NAACCR) Cancer reporting zones (referred to as “zones”) are aggregations of census tracts that were created as part of the NCI/NAACCR Zone Design Project. 126 Delaware Journal of Public Health - October 2026
These numbers show a striking disparity in access. A cancer patient in Sussex County faces a cases-per-oncologist ratio nearly five times higher than a patient in New Castle County (1:2,574 vs. 1:529). Kent County patients face a ratio more than twice as high as New Castle County patients (1:1,187 vs. 1:529). This means that on a relative basis, patients in Kent and Sussex counties have significantly less access to medical oncology services than residents in New Castle County, despite experiencing equal or greater cancer burden. The geographic distribution of cancer specialists in Delaware does not align with the distribution of cancer burden across the state. New Castle County is well-resourced with specialists relative to its cancer burden, while Kent and Sussex counties are significantly underserved. Patients in these counties likely face longer travel
times, longer wait times, and greater barriers to accessing cancer care. These findings suggest a need for targeted strategies to expand cancer specialist access in Kent and Sussex counties so that a greater number of people have access to the care they need.
CANCER SUPPORT RESOURCES, SCREENING PROGRAMS, AND FINANCIAL ASSISTANCE IN DELAWARE Support Organizations and Community Resources Outside of medical care, there is a network of organizations whose goal is to provide support outlets to Delaware cancer patients, survivors, and caregivers. These entities provide emotional support, education, navigation services, and community connections that aim to complement clinical treatment. There are several organizations that provide services for a specific kind of cancer care, and others that provide general cancer education and assistance. • Cancer Support Community Delaware is a local affiliate of an international nonprofit organization. It has locations in all three counties, and is one of the most geographically accessible cancer resources in the state. It is open Monday through Friday from 9 am to 5 pm, and the program offers support groups, healthy lifestyle classes, and financial assistance for cancer patients and survivors.7 • The Delaware Breast Cancer Coalition (BCC) provides resources specifically for breast cancer patients and their families. They aim to improve awareness, early detection rates, and access to care. Their mission is driven by empowering the community through outreach, education, and support services. An important feature of the coalition is its offering of mobile health initiatives serving lowincome, uninsured, and underinsured individuals. Additionally, it coordinates services alongside state programs, and accepts most major insurance including Medicare and Medicaid. The BCC can be reached by phone for scheduling, transport assistance, and translation support coordination services (for those less proficient in English or with hearing/vision impairments). These services are particularly valuable for patients in underserved areas.8 • CancerCare is a national organization that has outreach in Delaware. Individuals can access a comprehensive range of free professional services delivered by master’s-level oncology social workers. These services include individual counseling; support groups available by phone, online, and in person; resource navigation; educational workshops; publications;, and financial and co-pay assistance. In the most recent fiscal year, CancerCare provided financial and co-payment assistance to over 25,000 people nationally and answered nearly 42,000 helpline calls. Since these services are available remotely, CancerCare may be accessible to patients in Kent and Sussex Counties who lack local in person options.9
• Blood Cancer United provides personalized resources specifically for blood cancer patients. This includes access to information, clinical trial nurse navigators, and registered dietitians. Educational resources that are available include publications, videos, podcasts, and conferences. They also have support resources that include links to clinical trial support centers and online peer-to-peer support groups. Like CancerCare, Blood Cancer United is primarily digital so that makes its accessibility statewide less dependent on location.10 • At the institutional level, Helen F. Graham Cancer Center at ChristianaCare in Newark is Delaware’s largest cancer center. They offer multidisciplinary care with a team of oncologists, surgeons, nurse navigators, social workers, and clinical research nurses. The center provides survivorship community programs and educational resources on the most frequently diagnosed cancers, including breast, lung, colorectal, skin, gynecologic, and prostate cancers. The Graham Cancer Center enrolls patients in clinical trials at seven times the national rate through its membership in the National Cancer Institute Community Oncology Research Program, and it partners with the University of Delaware and Delaware Biotechnology Institute through the Cawley Center for Translational Cancer Research. Because the Graham Cancer Center is located in Newark, accessibility for Kent and Sussex County residents may be more difficult.9 • Tunnell Cancer Center is a part of Beebe Healthcare in Lewes and serves as the primary cancer center for Sussex County. It offers cancer screenings, a survivorship series, and provides links for uninsured patients to the Choose Health Delaware marketplace for insurance options. Bayhealth Cancer Program serves both Kent and Sussex counties and holds accreditation from the American College of Surgeons Commission on Cancer. Bayhealth is a member of the Penn Cancer Network and participates in the Delaware Cancer Registry, providing the ability to monitor trends and contribute to research. Bayhealth also operates a Mobile Health Unit that travels on routes in central and southern Delaware providing community outreach, education, and accessible health exams.11
Geographic Assessment of Support Resources Analyzing the geographic distribution of support resources along with cancer burden reveals a pattern similar to that seen with the distribution of cancer specialists. The most comprehensive resources are located in New Castle County. Kent County has the fewest dedicated cancer support resources despite having the highest cancer incidence rate in the state. Outside of the Bayhealth campus in Dover there are not many options for nearby residents. Sussex County is somewhat better served due to the presence of both the Tunnell Cancer Center and Bayhealth, but still it significantly lacks resources compared to New Castle County. Statewide resources partially help offset this geographic imbalance but do not fully address the gap, and can rely on strong broadband internet access, which is not always available in all parts of Kent and Sussex County.12 127
SCREENING PROGRAMS
FINANCIAL ASSISTANCE
Cancers identified at earlier stages are generally more treatable and lead to better survival outcomes1 and Delaware has several cancer screening programs that promote early detection across the population. Women aged 21 and older are encouraged to receive regular Pap tests and pelvic exams.13 Women who are aged 40 and older should receive annual mammograms.14 Men that are 50 and older and African American men 40 and older (given their elevated prostate cancer risk) should discuss prostate screening including PSA tests and Digital Rectal Exams with their provider.15 Men and women aged 45 and older should be given regular colorectal cancer screenings.16 This can include colonoscopy, sigmoidoscopy, or home-based Fecal Immunochemical Tests (FIT kits). Men and women ages 55 to 80 who currently smoke or have quit within the past 15 years and have a significant smoking history may be eligible for lung cancer screening through low-dose CT scans.17
Financial barriers represent one of the most significant obstacles when it comes to individuals’ ability to access cancer care. The average initial cost (first 12 months) of medical services related to cancer care at all sites costs approximately $43,516.20 This is a high figure and contributes to expenses that can lead to financial toxicity, which is the negative impact of the high cost of cancer care. Delaware residents facing cancer diagnoses may be eligible for several forms of financial assistance that can cover screening, treatment, and supportive care costs.
• The Delaware Cancer Screening Program (DCSP), also known as the Screening for Life program, is the state’s primary free cancer screening initiative administered by the Delaware Division of Public Health. The program provides office visits, mammograms and clinical breast exams, Pap tests, prostate cancer screening tests, colorectal cancer screening tests, lung cancer screening tests, and health education and care coordination support to eligible individuals. Individuals must be Delaware residents between the ages of 21 and 64 who are not eligible for Medicaid, or age 65 and older without Medicare, who meet income guidelines between 139% and 250% of the Federal Poverty Level, and whose insurance does not cover screenings. The program has a presence in all three counties, as its major healthcare partners are ChristianaCare, Bayhealth, and Beebe Healthcare.18 • The Delaware Breast and Cervical Cancer Program provides free mammograms and Pap tests specifically for women who are uninsured, and has the same eligibility criteria as the DCSP Screening for Life program.8 • Mobile mammography services allow for greater screening access to underserved areas across the state. The Delaware Breast Cancer Coalition operates mobile health initiatives serving low-income and uninsured individuals statewide. TidalHealth has a mobile mammography coach that offers 3D screenings and serves southern Delaware and Sussex County. The Bayhealth Mobile Health Unit routinely tours central and southern Delaware providing community outreach, education, and accessible exams. Christiana Care also operates a mobile mammography unit that travels to underserved areas primarily in New Castle County. Having multiple mobile mammography services in Kent and Sussex counties is a meaningful offset to the concentration of fixed screening facilities in New Castle County, though access still depends on knowledge of these programs and the ability to schedule appointments easily and with flexibility.19 128 Delaware Journal of Public Health - October 2026
• The Delaware Cancer Treatment Program (DCTP) is a state-administered program that helps uninsured Delaware residents access cancer treatment. Patients who are identified as having cancer through the DCSP Screening for Life program may qualify for treatment coverage through Medicaid or the Delaware Cancer Treatment Program, creating a pipeline from screening to treatment for uninsured individuals. Treatment may be covered for up to two years if a patient qualifies.21 • The Screening for Life program acts as direct financial assistance for the screening phase of cancer care. The program does not reimburse co-pays or co-insurance, but it does provide reimbursement for individuals with high deductibles relative to their income, specifically those whose deductible equals or exceeds 15% of their annual income.18 • CancerCare provides financial and co-payment assistance to help alleviate patients’ costs including transportation, practical needs, and cancer medications. The Cancer Support Community Delaware also offers financial assistance directly to cancer patients and survivors in the state. For uninsured patients at Beebe Healthcare, the Tunnell Cancer Center provides links to the Choose Health Delaware marketplace to help patients obtain insurance coverage.9 Delaware 211 which is accessible by dialing 211, can be utilized as a resource navigation line that can connect patients to financial assistance programs, transportation support, scheduling help, and translation services. This makes it a useful first point of contact for patients who are unsure where to start.22
NEXT STEPS Melanoma Screening Melanoma is the fifth most common cancer in Delaware. Data shows there are currently 1,616 individuals in the state with a melanoma diagnosis, and melanoma has a statewide incidence rate of 25.3 per 100,000 people (higher than the national rate of 23.1 per 100,000).4 Despite this, Delaware does not have a free or reduced cost skin cancer screening program. Individuals in Delaware must use insurance such as Medicare, Medicaid, or private insurance and schedule a skin exam with a dermatologist. Delaware has approximately 58 dermatologists. Most (43 of 58) are located in New Castle County which is significantly higher than the four in Kent County and eleven in Sussex County.23
One possible solution is to establish a statewide skin cancer screening program modeled after the DCSP. The American Academy of Dermatology (AAD) already has a national “SpotMe” free skin cancer screening program in place.24 Delaware’s Division of Public Health could partner with the AAD to expand SpotMe events specifically in Kent and Sussex counties where access to dermatologists is limited. This would allow for residents in these counties to potentially detect skin cancer sooner which improves outcomes.
Lung Cancer Screening DCSP covers lung cancer screening for eligible high-risk individuals. However, one big problem is the eligibility criteria is slim requiring people to be either ages 55-80 or current or recent smokers with 30 pack-year history. Something that can be done to improve outreach is expand the DCSP eligibility for lung screening to align with USPSTF 2021 guidelines, which lowered the recommended screening age to 50 and reduced the pack-year requirement to 20. Therefore, more Delaware residents would be eligible for free screening under the existing program infrastructure. If more people are eligible under a broader criteria, it is more likely that they receive treatment sooner, thus improving their outcome. The state could also establish a dedicated lung screening program in Kent County, where lung cancer rates in the Dover/Smyrna zone are the second highest rates in the state.2
Prostate Cancer Screening The DCSP mentions prostate cancer screening, but it is listed as “recommended by your doctor” as opposed to a routinely covered service such as mammograms or Pap tests. This means prostate screening access requires a patient to first have a primary care provider and secondly for that provider to recommend the screening. This produces a significant barrier in Kent and Sussex counties where primary care access is limited compared to that in New Castle County. This is particularly concerning for NonHispanic Black men in Delaware who have a prostate cancer rate of 195.1 per 100,000 far exceeding the White rate of 119.8, 2 and residents in the Dover/Smyrna zone because it has the second highest prostate cancer rate in the state at 170.5.4 A solution to help alleviate this issue is to establish targeted prostate cancer screening outreach for Non-Hispanic Black men in Kent County. The American Cancer Society recommends that Black men begin prostate cancer screening discussions with their provider at age 40 due to elevated risk.25 Delaware’s DCSP includes this recommendation but does not have a targeted outreach program. A community-based program modeled after the “Men’s Health Initiative” approach used in other states could reach Black men in the Dover area who face the highest prostate cancer burden.
CONCLUSION
This analysis of cancer incidence, specialist distribution, and support resource availability in Delaware from 2018 to 2022 reveals a state that continues to face a substantial and unevenly distributed cancer burden. Delaware’s overall age-adjusted cancer incidence rate of 469.9 per 100,000 population exceeds the national average of 448.6 per 100,000, and meaningful disparities exist across geographic, demographic, and socioeconomic lines that demand targeted public health attention.2
While New Castle County accounts for the largest absolute number of cancer cases, Kent County carries the highest age-adjusted incidence rate in the state at 511.7 per 100,000 population, and Sussex County is not far behind at 480.7 per 100,000.2 Socioeconomic factors, environmental exposures, access to care, and differences in screening utilization may be contributing to geographic variation in cancer incidence, though further research would be needed to confirm these relationships.
Recommendations Based on the findings of this report, these are some recommended actions to address cancer disparities in Delaware: • Expand oncology specialist access in Kent and Sussex counties through specialist recruitment incentives, tele-oncology programs, and strengthened referral pathways between primary care providers and cancer specialists. • Expand and modernize the Delaware Cancer Screening Program, increase eligibility criteria for lung cancer screening to align with current USPSTF 2021 guidelines, provide universal recommendations for prostate cancer screening, and partner with the American Academy of Dermatology’s SpotMe program to address skin cancer screening access gaps. • Target outreach programs should be developed for populations facing the greatest disparities. Community partnerships with trusted community institutions in the Dover area could serve as an effective mechanism for reaching this population. • Expand and coordinate mobile health services across Kent and Sussex counties. • Address broadband and transportation infrastructure gaps in Kent and Sussex counties. Cancer is not experienced equally across Delaware. Where a person lives, their age, their race and ethnicity, and their ability to access care all shape their cancer risk and their likelihood of receiving timely, high-quality treatment. This report has documented significant disparities in cancer burden and resource availability that follow clear geographic lines, with Kent and Sussex counties bearing a disproportionate share of the cancer burden while receiving a disproportionately small share of the state’s oncology resources. Addressing these disparities will require commitment from healthcare systems, nonprofit organizations, policymakers, and community leaders working together to ensure that every Delaware resident has a fair opportunity to detect cancer early, access quality treatment, and survive. Mr. Stephens may be contacted at rjs9470@nyu.edu .
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REFERENCES 1. National Cancer Institute. (n.d.). Cancer Stat Facts. SEER Cancer Statistics. https://seer.cancer.gov/statfacts/ 2. Delaware Department of Health and Social Services. (2025). Cancer incidence and mortality in Delaware, 2018–2022. Bureau of Cancer Prevention and Control, Division of Public Health. https://publichealthalerts.delaware.gov/wp-content/blogs.dir/203/files/ sites/203/2026/04/2018-2022-IM-Report_FINAL.pdf 3. National Cancer Institute & Centers for Disease Control and Prevention. (n.d.). State cancer profiles: Delaware quick profiles. U.S. Department of Health and Human Services. https://statecancerprofiles.cancer.gov/quick-profiles/index. php?statename=delaware#t=2 4. Delaware Department of Health and Social Services. (2026, Jan). Small area-level cancer incidence in Delaware, 2018–2022. Bureau of Cancer Prevention and Control, Delaware Division of Public Health. https://dhss.delaware.gov/dhss/dph/dpc/ca_stats.html 5. US News. (n.d.). The best oncologists, cancer doctors in Delaware. https://health.usnews.com/doctors/oncologists/delaware 6. ChristianaCare. (n.d.). Helen F. Graham Cancer Center & Research Institute. https://www.christianacare.org/graham-cancer-center 7. Cancer Support Community Delaware. (n.d.). About us. https://www.cancersupportdelaware.org/about-us 8. Delaware Breast Cancer Coalition. (n.d.). Mobile health initiatives. https://www.debreastcancer.org 9. CancerCare. (2025). Services and resources. https://www.cancercare.org/services 10. Blood Cancer United. (n.d.). Resources and support. https://www.bloodcancerunited.org 11. Beebe Healthcare. (n.d.). Tunnell Cancer Center. https://www.beebehealthcare.org/cancer-care 12. State of Delaware. (n.d.). https://gis.broadband.delaware.gov/ 13. U.S. Preventive Services Task Force. (n.d). Cervical Cancer Screening. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/ cervical-cancer-screening 14. U.S. Preventive Services Task Force. (n.d). Breast Cancer Screening. U.S. Preventive Services Task Force recommendation statement https://www.uspreventiveservicestaskforce.org/uspstf/breast-cancerscreening#bcei-recommendation-title-area
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15. Grossman, D. C., Curry, S. J., Owens, D. K., Bibbins-Domingo, K., Caughey, A. B., Davidson, K. W., Doubeni, C. A., Ebell, M., Epling, J. W., Jr., Kemper, A. R., Krist, A. H., Kubik, M., Landefeld, C. S., Mangione, C. M., Silverstein, M., Simon, M. A., Siu, A. L., Tseng, C. W., & the US Preventive Services Task Force. (2018). Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Journal of the American Medical Association, 319(18), 1901–1913. 16. U.S. Preventive Services Task Force. (2021). Colorectal cancer: Screening. Journal of the American Medical Association, 325(19), 1965–1977. 17. Krist, A. H., Davidson, K. W., Mangione, C. M., Barry, M. J., Cabana, M., Caughey, A. B., Davis, E. M., Donahue, K. E., Doubeni, C. A., Kubik, M., Landefeld, C. S., Li, L., Ogedegbe, G., Owens, D. K., Pbert, L., Silverstein, M., Stevermer, J., Tseng, C. W., Wong, J. B., & the US Preventive Services Task Force. (2021). Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Journal of the American Medical Association, 325(10), 962–970. 18. Delaware Department of Health and Social Services, Division of Public Health. (n.d.). Screening for life: Delaware cancer screening program. https://dhss.delaware.gov/dph/homepage/about/sections/bhp/chronic-disease/ screening-for-life/ 19. TidalHealth. (n.d.). Mobile mammography. https://www.tidalhealth.org/patients-visitors/resources/community-wellnessservices/tidalhealth-mobile-mammography?tab=0 20. National Cancer Institute. (n.d.). Economic burden of cancer. Annual Report to the Nation on the Status of Cancer. U.S. Department of Health and Human Services. https://progressreport.cancer.gov/after/economic_burden 21. Delaware Department of Health and Social Services. (n.d.). Delaware Cancer Treatment Program. Delaware Division of Public Health. https://dhss.delaware.gov/dph/homepage/about/sections/disease/cancer/ catreatment/ 22. Delaware 211. (n.d.). Find help. https://www.delaware211.org 23. US News. (n.d.). The Best Dermatologists in Delaware https://health.usnews.com/doctors/dermatologists/delaware 24. American Academy of Dermatology Association. (n.d.). SPOTme skin cancer screening program. https://assets.ctfassets.net/SPOT-Guideline_Book.pdf 25. Cancer Society. (2023). American Cancer Society recommendations for prostate cancer early detection. https://www.cancer.org/cancer/types/prostate-cancer/acs-recommendations.html
GET SCREENED FOR COLON CANCER STARTING AT AGE 45. There are multiple ways to detect colon cancer early, when it’s most treatable. Talk with your health care provider about which type of screening is right for you. IF YOU DON’T HAVE A HEALTH CARE PROVIDER OR ARE UNINSURED OR UNDERINSURED, YOU MAY QUALIFY FOR A FREE SCREENING. VISIT HEALTHYDELAWARE.ORG/COLON OR CALL 211.
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RESOURCES State Based Resources Delaware Division of Libraries: HEALTH Ready! The libraries offer a multitude of health-based resources from blood pressure cuffs to borrow to community programming and telemedicine kiosks and assistance registering for health insurance.
https://lib.de.us/healthready/
Healthy Delaware: Resources for Chronic Disease Programs related to screening, prevention and management of chronic disease - eligibility varies and not all require insurance.
https://www.healthydelaware.org/individuals Resources for Social Services
Delaware 211 Connects Delawareans to resources throughout the state such as housing, health care, food, child care, and transportation, among others.
https://delaware211.org/
Food Bank of Delaware
https://www.fbd.org/
Community Based Programs YMCA of Delaware Community Health Programs Programs like blood pressure screening, Diabetes Prevention Program, Brainsavers, and lots of community events provide community-based resources for individuals and families looking to manage their health and wellness.
https://www.ymcade.org/programs/community-health-programs/ Coalitions Supporting Chronic Disease Advancing Healthy Lifestyles Coalition
https://ahlcoalition.org/
Delaware Diabetes Coalition Resources for individuals living with diabetes, policy work and programmatic and event support for outreach
https://www.dediabetescoalition.org/ Delaware Farm and Food Council
https://farm-and-food-delaware.hub.arcgis.com/ Delaware Rural Health Coalition (Formerly Sussex County Health Coalition)
https://www.sussexcoalition.org/
On Request Community Programs Beebe Health Care
https://www.beebehealthcare.org/patients-visitors/support-groups Christiana Care Community Health
https://christianacare.org/us/en/wellness/community-health
Nemours Children’s Health Community Health Programs
https://redcap.nemoursresearch.org/redcap/surveys/?s=NF4DNXMLFTYDLKKX University of Delaware Cooperative Extension Choose from a list of community-based programs and topics that staff and volunteers deliver virtually or at community sites. Topics include financial well-being, mental wellness, nutrition and food safety.
https://www.udel.edu/academics/colleges/canr/cooperative-extension/nutrition-wellness/
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Index of Advertisers The Nation's Health. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 American Public Health Association A Special Screening of RENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Delaware HIV Consortium Free Radon Kits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Delaware Division of Public Health 2026 Immunization Summit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Immunization Coalition of Delaware National Lung Cancer Screening Day. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 American Cancer Society The State of Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 American Cancer Society Healthy Heart Ambassador Blood Pressure Self-Monitoring Program . . . . . . . . . . . . . . . . . . . 112 Delaware Division of Public Health Submission Guidelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Delaware Journal of Public Health
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Delaware Journal of
Public Health
Submission Guidelines
updated November, 2025
About the Journal Established in 2015, the Delaware Journal of Public Health is a peer-reviewed electronic publication created by the Delaware Academy of Medicine and Public Health. The publication acts as a repository of news for the medical, dental, and public health communities, and is comprised of upcoming event announcements, past conference synopses, local resources, and peer-reviewed content ranging from manuscripts and research papers to opinion editorials and personal interest pieces, all relating to the public health sector in Delaware. Each issue is largely devoted to an overarching theme or current issue in public health. DJPH content is informed by the interest of our readers and contributors. If you have an event coming up, would like to contribute to an Op-Ed, would like to share a job posting, or have a topic in public health you would like to see covered in an upcoming issue, please let us know. If you are interested in submitting an article to the Delaware Journal of Public Health, or have any additional inquiries regarding the publication, please contact us at managingeditor@djph.org .
Information for Authors The DJPH accepts a wide variety of submission formats, including research articles, systematic reviews, letters to the editor, commentaries/ narratives, analytic essays, history essays, public health practice vignettes, and interviews. The DJPH also accepts images and advertisements pertaining to relevant, upcoming public health events, and presentation reviews. Additional types of submission not previously mentioned may be eligible; please contact us for more information. The initial submission should be clean and complete, without edits or markups, and contain both the title and the author(s) full name(s). Submissions should be 1.5 or double spaced with a font size of 12. Articles may be submitted through our online portal, at https://djph.org/submissions/submit-an-article . Graphics, images, info-graphics, tables, and charts are welcome and encouraged to be included in articles. Please ensure that all pieces are in their final format, and all edits and track changes have been implemented prior to submission. To view additional submission requirements, please refer to the website (https://djph.org/submissions/submit-an-article). 134 Delaware Journal of Public Health - October 2026
Trial registration information is required for all clinical trials and must be included in the final article.
Abstracts Authors must submit a structured or unstructured abstract along with their article. Abstracts should have a minimum of 200 words, including headings. Please see the submission guidelines for more information.
Submission Length While there is no prescribed word length, full articles will generally be in the 2,500 to 4,000-word range, and editorials or narratives in the 1,500 to 2,500-word range. If there are any questions about the length of the submission, please contact us.
Copyright The DJPH and its content is copyrighted by the Delaware Academy of Medicine and Public Health. The contents are listed under Creative Commons License – CC BY-NC-ND.
Images are NOT covered under the Creative Commons license and are the property of the original photographer or company who supplied the image. Opinions expressed by authors of articles summarized, quoted, or published in full within the DJPH represent only the opinions of those authors and do not necessarily reflect the official policy of the Academy, the DJPH, or the institution with which the authors are affiliated.
Nondiscriminatory Language Use of nondiscriminatory language is required in all DJPH submissions. The DJPH reserves the right to reject any submission found to be using sexist, racist, or heterosexist language, as well as unethical or defamatory statements.
Conflicts of Interest Any conflicts of interest, including political, financial, personal, or academic conflicts, must be declared prior to the submission of the article, or in conjunction with a submission. Conflicts of interest are any competing interests that may leave readers feeling misled or deceived, and/or alter their perception of subject matter. Declared conflicts of interest will be published alongside articles in the final publication.
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Delaware Academy of Medicine and Public Health
P.O. Box 89 Historic New Castle, DE 19720
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The Delaware Academy of Medicine and Public Health is a private, nonprofit organization. The Academy was founded in 1930 as the Delaware Academy of Medicine. The Delaware Public Health Association was officially reborn at the 141st Annual Meeting of the American Public Health Association (APHA) held in Boston, MA. At this meeting, on November 5, 2013, the DPHA was officially transferred to the Delaware Academy of Medicine by action of the APHA Governing Council. The mission of the Delaware Academy of Medicine and Public Health is to enhance the well being of our community through education, the promotion of public health, research, and targeted investments in human capital and development.
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