Scientia - Spring 2012

Scientia A Journal by The Triple Helix & Pharmakon

Spring 2012 Issue I

Scientia

Published Jointly by The Triple Helix and Pharmakon at the University of Chicago The title of this journal was inspired by the U of C motto: Crescat scientia; vita excolatur: Let

Scientia Economics of Health:

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Influence of an individual’s education level on chronic conditions and its economic revelations Siu Hon Jeffrey Lam

knowledge grow from more to more; and so be human life enriched. Likewise, we hope to inspire our fellow undergraduates to explore their passions and contribute to the growth of knowledge by sharing their research with peers. We chose this dramatic cover, an artistic depiction of disembodied

Recovery Lapse: Native Language Reversion Upon Emergence from General Anesthesia Rahul Snehal Bhansali

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neon lights and their reflections, starkly contrasted against a deep black background, to express the exciting scientific developments we expect to see in the coming years – developments brought into existence by our peers. A bright future awaits us, one rich with innovations that no one can truly predict.

The Politics of Disease: The Importance of Political Advocacy in Addressing Health Disparities Matthew Green

Adderall and Its Abuse: Scientific, Legal, and Corporate Considerations Chan Hee Choi, Simo Huang, Eileen Shiuan, Michael Helzer, Navtej Singh

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Produced by The Triple Helix and Pharmakon Layout and Design by Christina Chan, Jong Hyun Chung, & Andrew Kam About The Triple Helix and Cover Letter Written by Luciana Steinert, Director Of Marketing, The Triple Helix About Pharmakon Written by Navtej Singh, President, Pharmakon

Scientia

Spring 2012

About The Triple Helix

T

at The University of Chicago

he Triple Helix, Inc. (TTH) is the world’s largest completely student-run organization dedicated to taking an interdisciplinary approach toward evaluating the true impact of historical and modern advances in science. Of TTH’s more than 25 chapters worldwide, the University of Chicago chapter is one of the largest and most active. Over the past year, we, TTH at The University of Chicago have vastly expanded the size and scope of our chapter.

We have held more public lectures than ever before, and increased attendance at these events by over 100%. Our E-Publishing division has grown immensely. In fact, it has become more active than any other chapter’s E-Publishing division. Additionally, general chapter participation at this time involves more than 300 undergraduates.

We are confident that there is a place for everyone at The College here at The Triple Helix UChicago. We would love for you to join us. Considering all of this, we have decided to expand our print division in a way no other chapter of TTH has done before. Before this spring of 2012, each of our divisions, including E-Publishing, Events and Print, served mainly as a forum for secondary evaluations and syntheses of knowledge, that is, information that is already in current circulation. But now, in addition to The Science in Society Review, which we publish biannually, we are introducing, with great pride, our latest project: Scientia. Although there are opportunities for hundreds of undergraduates to engage in interdisciplinary research at the University of Chicago, there are very few opportunities for us to share our accomplishments with the general campus population. TTH is going to change that. In Scientia, a publication of original undergraduate student research, students can and will continue to find a new, innovative showcase for the cutting edge of student scientific exploration of the issues facing society today. Welcome to a new age of intellectual connectedness at The U of C. Please contact us at uchicago@thetriplehelix.org for further information.

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About Pharmakon

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at The University of Chicago

harmakon is an inter-institutional undergraduate organization for the exploration and advancement of pharmacological disciplines, biotechnology, and other cutting edge scientific fields. We offer our members a unique perspective into the foundational disciplines of the industries at the forefront of finding cures, building unforeseen machines, and helping adapt the world to human needs.

Independently organized and administered, Pharmakon is one of the best funded and fastest growing Registered Student Organizations at the University of Chicago where it was founded in 2011. Our work spans multiple journals, community development initiatives, and projects of every size and scope, from speaker series to campus-wide surveys to industry site visits. We welcome you to join our growing family of thinkers, tinkerers, and all-around passionate people invested in the growth and development of science that changes lives.

Thank you for supporting Scientia. We are honored and delighted to share with you this new peer- and faculty-reviewed undergraduate publication facilitating multidisciplinary scientific research. We hope you’ll find the content engaging, our methods rigorous, and the entire enterprise meaningful for science, academia, and the world we live in. For further correspondence, please contact pharmakon@lists.uchicago.edu.

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Scientia

Spring 2012

Economics of Health:

Influence of an individual’s education level on chronic conditions and its economic revelations Siu Hon Jeffrey Lam Contact: siuhon@uchicago.edu

In many studies, the relationship between education level and number of chronic conditions remains obscure. Through the longitudinal Americans’ Changing Lives dataset from 1986 to 1994, this paper examines the relationship and mediating factors between education level and the number of chronic conditions for an individual, with highest year of education completed as the indication for education level. Analyses of lagged regression indicate an increase in one year of education of an individual decreases his or her number of chronic condition by approximately 0.0222, demonstrating a significant effect. Directly extrapolating from the data, an increase of one year of schooling across the general population would decrease the number of chronic conditions by, at a conservative estimate, approximately 1.4 percent, providing multiple possible benefits for the economy with public policy. From the results, financial stress, depression and relative body weight mediate approximately 51% of the effects of education level of an individual on his or her number of chronic conditions, revealing an important part of the mechanism.

Introduction For centuries, chronic conditions, ”disease or conditions that are long lasting in nature,” have had a major influence on mortality (1). Today, over 100 million Americans have chronic conditions, and the number of Americans with chronic conditions is projected to increase to 160 million by 2040 (2). While approximately 75% of the people with chronic conditions do not have activity limitations, they remain a major cause of disability and consume 70% of health care spending (3) (4). Although chronic conditions are unfavorable problems in society, currently the main approach has been to increase health spending on chronic conditions. The total cost of chronic conditions in 1990 was $659 billion (5). In 2003, the total cost of chronic conditions had increased to $1.3 trillion (6). By 2050, one estimate of the total cost for chronic conditions projects $4.13 trillion in spending, demonstrating a significant – and untenable – trend of increase in the total cost for chronic conditions (6). While chronic conditions are going to be an increasingly prevalent issue in our society, the results presented in this paper suggest that focusing on education may provide one

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palliative approach.

Literature Review From existing literature, the relationship between education level and the number of chronic conditions remains ambiguous. An early article shows most people who report about their chronic conditions have fewer than 12 years of education, while the general population has a mean of 12 years of education (7). Although significant correlation may exists between education level and the number of chronic conditions, the relationship from the observations in existing literature remains open for exploration.

Some articles find that education programs about chronic conditions have no significant effects on the number of chronic conditions for people in the general population.

One extensive meta-study of 320 articles on the effects of illness education programs finds almost no change in patient’s chronic conditions from an increase in the patient’s knowledge of chronic conditions (8). Other studies confirm the small effect of education programs on patients’ chronic conditions (9) (10) (11).

Other articles find that the effect of education programs about chronic conditions on the number of chronic conditions can be significant. One study demonstrates that self-management programs can improve the health status of those suffering chronic conditions (12). Another study suggests chronic disease management principles and the characteristics of the Future of Family Medicine model can improve conditions of patients with diabetes, a type of chronic condition (13). A study from Crete indicates that health and nutrition education at local primary schools over a six year intervention period for first grade students can reduce risk levels for chronic conditions (14).

The existing literature indicates that the investigation of the relationship between education level and chronic conditions has mostly involved the effect of targeted education programs about chronic conditions on those chronic conditions. However, the highest year of education completed for an individual as an indication of education level in the relationship between education level and chronic conditions is a new direction and remains unexplored. Education programs about chronic conditions may not provide the full benefits of general scholastic education. The study examines the relationship and mediating factors between the education level of an individual and his or her number of chronic conditions, with highest year of education completed as the indication for education level. The discussion section further examines the possible theories behind the interactions of the mechanisms.

Hypothesis Existing literature shows an ambiguous relationship between education level and number of chronic conditions,

but a significant relationship can exist. This study uses the highest year of education completed to determine education level. Therefore, the first hypothesis is that, for the years 1986 to 1994 in America, as the highest year of education completed increases for an individual, his or her number of chronic conditions decreases significantly. The second hypothesis is that, from 1986 to 1994 in America, financial stress, depression and relative body weight together significantly mediated the effect of the highest year of education completed for an individual on his or her number of chronic conditions.

Analytic Model The analytic model involves the highest year of education completed for an individual, his or her number of chronic conditions, financial stress, depression (CES-D scale, center of epidemiology studies depression scale that measures depression (15)) and relative body weight (using body mass index, or BMI). Highest year of education completed for an individual affects his or her financial stress, depression and relative body weight. Changes in financial stress, depression and relative body weight affect the number of chronic conditions for an individual. The analytic model is presented below in figure 1.

Data The dataset for the study, Americans’ Changing Lives, involves four waves of data from 1986, 1989, 1994 and 2002 in a national longitudinal panel survey covering sociological, psychological, mental and physical health items (16). The first wave represents the data in 1986 and the second wave resembles data in 1989. The third wave involves the data in 1994 and the fourth wave includes data in 2002.Each wave of data was collected in the same

Figure 1. Analytic Model

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Spring 2012 models is sufficient to control for non-response in wave 2 year (16). The data type is survey data and the mode of and wave 3 together. Some of the regression models also data collection was face to face interview (16). The datainclude the mediating factors of financial stress in wave set includes the population of those 25 and older living in 2, depression in wave 2 and relative body weight in wave the continental United States’ (16). The sampling method 2. Financial stress in wave 2 is captured by the financial for the dataset includes a multistage stratified area probstress variable in. Depression in wave 2 is captured by the ability sample (16). Three thousand, six hundred and sevCES-D scale, Center for Epidemiologic Studies Depression enteen people responded in 1986, 2,867 in 1989, 2,562 Scale. Relative body in 1994, and 3,617 in Figure 2. Histogram for number of chronic conditions in Wave 3 weight in wave 2 is 2002 (16). The 2002 captured by the BMI set includes more scale, the Body Mass cases than the numIndex. ber of cases in 1989 The response variand 1994, includable is the number ing non-follow up of chronic condicases from those tions in an interval two years. This pascale, a scale with per’s analyses only an interval range. use waves 1, 2 and The variable indi3 of the Americans’ cates the number of Changing Lives datachronic conditions set to examine the of the respondent follow-up conditions based on a 10-item of people from wave scale, summing the 1 to wave 3. conditions reported To investigate the for V10219-V10228 effect of education level on the number Figure 3. Histogram of the highest year of education completed in the dataset, (17).

From the summary of chronic condiof variables (Table tions, the lagged 1 in Appendix), the regression models range of the variinclude the response able, the number of variable, number of chronic conditions, chronic conditions, is from 0 to 8 and the and the explanatory mean of the variable variable of focus, is approximately highest year of edu1.5515 chronic concation completed. ditions. The median Some lagged regresof the variable is 1, sion models include and there are 2,562 the control variobservations at this ables the number of level. There are 0 chronic conditions in missing data from wave 1, race in wave the total amount of observations for the survey in wave 1, age in wave 1, sex in wave 1, family income in wave 3, and the standard deviation of the variable is approxi1 and non-response in wave 2. The number of chronic mately 1.4453. From the histogram of the variable (Figure conditions in wave 1 is included for baseline control of 2), most individuals have fewer than 2 chronic conditions, the number of chronic conditions. Non-response in wave and the number of individuals with 0 chronic conditions 3 is perfectly correlated with non-response in wave 2, is the largest. The distribution of the histogram is bell indicating the same non-response data. The control shaped like the normal distribution with the mean at 0 for non-response at wave 2 in some of the regression

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chronic conditions.

The main explanatory variable of focus is the highest year of education completed in an interval scale. The variable indicates the highest year of education completed of the respondent, a cleaned, recoded and imputed version of V1646, variable in the dataset (17). From the summary of variables (Table 1 in Appendix), the range of the variables is from 0 to 17, and the mean of the variables is approximately 11.4689 years of education. The median of the variable is 12, and there and 3,617 observations. There are 0 missing data from the total amount of observations for the survey in wave 1 and the standard deviation is approximately 3.4736. From the histogram of the variable (Figure 3), the distribution is a bell shaped curve like the normal distribution with the mean at 11.4689 years of education, but the curve is skewed to the left. On the histogram, the highest number of observations is located at approximately 12 years of education.

The control variables are included in the multiple regression models to avoid bias in the coefficients by the confounding effect of omitted variables. These control variables have an effect on the number of chronic conditions and the highest year of education completed, showing possible confounding effects and a need to control for these variables in the models. From pair-wise correlation of all the variables (Table 2 in appendix), the control

variables correlate significantly with the response variable, number of chronic conditions and the explanatory variable of focus, highest year of education completed, ranging with correlation value from – 0.3732 to 0.6405.

The results of the pair-wise correlation further justify the need to control for these possible confounding variables.

Methods The analyses of the study use six lagged regression models to explore the longitudinal data. The first model refers to the regression of the number of chronic conditions in wave 3 on the highest year of education completed in wave 1. The second model involves the regression of the number of chronic conditions in wave 3 on the highest year of education completed in wave 1 with all the control variables. Each of the mediating factors, financial stress, depression and relative body weights is included in models 3, 4 and 5 separately with the regression of the number of chronic conditions in wave 3 on the highest year of education completed in wave 1 and all control variables.

The sixth model contains the regression of the number of chronic conditions in wave 3 on the highest year of education completed in wave 1 with all the control variables and all three mediating factors, financial stress, depression and relative body weight. The regression equations of each model are indicated below.

Model 1: Chronic conditionswave3 = β0 + Educationwave1 • c1 + e Model 2: Chronic conditionswave3

= β0 + Educationwave1 • c1 + Agewave1 • c2 + Sexwave1 • c3 + Racewave1 • c4 + Chronic conditionswave1 • c5 + Nonreponsewave2 • c6 + Familyincomewave1 • c7 + e

Model 3: Chronic conditionswave3

= β0 + Educationwave1 • c1 + Agewave1 • c2 + Sexwave1 • c3 + Racewave1 • c4 + Chronic conditionswave1 • c5 + Nonreponsewave2 • c6 + Familyincomewave1 • c7 + Financialstresswave2 • c8 + e

Model 4: Chronic conditionswave3

= β0 + Educationwave1 • c1 + Agewave1 • c2 + Sexwave1 • c3 + Racewave1 • c4 + Chronic conditionswave1 • c5 + Nonreponsewave2 • c6 + Familyincomewave1 • c7 + Depressionwave2 • c8 + e

Model 5: Chronic conditionswave3

= β0 + Educationwave1 • c1 + Agewave1 • c2 + Sexwave1 • c3 + Racewave1 • c4 + Chronic conditionswave1 • c5 + Nonreponsewave2 • c6 + Familyincomewave1 • c7 + Relativebodyweightwave2 • c8 + e

Model 6: Chronic conditionswave3

= β0 + Educationwave1 • c1 + Agewave1 • c2 + Sexwave1 • c3 + Racewave1 • c4 + Chronic conditionswave1 • c5 + Nonreponsewave2 • c6 + Familyincomewave1 • c7 + Financialstresswave2 • c8 + Financialstresswave2 • c8 c c + Depressionswave2 • 9 + Relativebodyweightwave2 • 10 + e

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Spring 2012 Table 3. Results table of standard linear model for multiple regression of model 1, 2, 3, 4, 5, & 6 Model 1 Response Variable : Number of chronic conditions (0-8)

Model 2 Response Variable : Number of chronic conditions (0-8)

Model 3 Response Variable : Number of chronic conditions (0-8)

Model 4 Response Variable : Number of chronic conditions (0-8)

Model 5 Response Variable : Number of chronic conditions (0-8)

Model 6 Response Variable : Number of chronic conditions (0-8)

-0.1358*** (0.000)

-0.0222*** (0.005)

-0.0186** (0.023)

-0.0168** (0.041)

-0.0147* (0.069)

-0.0109 (0.179)

Chronicwave1 (0 to 7)

0.5639*** (0.000)

0.5679*** (0.000)

0.5642*** (0.000)

0.5401*** (0.000)

0.5205*** (0.000)

Racewave1 (1 to 5)

0.0654* (0.065)

0.0687* (0.069)

0.0635* (0.093)

0.0573 (0.124)

0.0399 (0.285)

Agewave1 (24 to 96)

0.0170*** (0.000)

0.0183*** (0.000)

0.0174*** (0.000)

0.0178*** (0.000)

0.0200*** (0.000)

Sexwave1 (1 to 2)

0.0509 (0.264)

0.0379 (0.426)

0.0333 (0.484)

0.0658 (0.163)

0.0587 (0.212)

Nonresponsewave2 (0 to 1)

-0.0023 (0.966)

-0.0448 (0.521)

-0.0439 (0.529)

-0.0229 (0.739)

-0.0450 (0.512)

Familyincomewave1 (5,049.6 to 137,721.1) Fincstresswave2 (-1.7603 to 2.7185) Depressionswave2 (-1.1362 to 4.2540)

0.0000 (0.487)

0.0000 (0.995)

0.0000 (0.720)

0.0000 (0.580)

0.0000 (0.721)

Variables Educationwave1 (0 to 17)

Bodyweightwave2 (13.8075 to 50.7770) Constant Observation F statistic R2 Adjusted R2

3.1706 2,562 0.0000 0.0963 0.0959

0.0852*** (0.001)

0.0521** (0.044) 0.0854*** (0.000)

-0.0821 2,562 0.0000 0.4459 0.4444

-0.0300 2,348 0.0000 0.4473 0.4454

0.0234 2,348 0.0000 0.4475 0.4457

Results and Findings From table 3, the results for the standard linear model present statistically significant and substantially significant coefficients for the highest year of education in wave 1 for both model 1 and 2. To be clear about terms, statistically significant represents a significant result in terms of statistic methods and substantially significant represents a very large effect in the results. Model 1 demonstrates the total effect of the highest year of education completed in wave 1 on the number of chronic conditions in wave 3. Model 1 shows an increase in one additional year of education of an individual decreases his or her number of chronic conditions by 0.1358. By considering possible confounding variables, model 2 reveals the direct effect of the highest year of education completed in wave 1 on the number of chronic conditions in wave 3. Model 2 indicates an increase in one additional year of education of an individual decreases his or her number of chronic conditions by 0.0222. Considering the average number of chronic conditions to be approximately 1.5515 from (Table 1 in Appendix), a decrease 0.0222 in the number of chronic conditions for an additional year of education of an individual is substantially significant, representing approximately a 1.4 percent decrease. The results of model 1 and model 2 confirm the first hypothesis.

According to table 3, the results for the standard linear model also present less statistically significant and less substantially significant coefficients for the highest year of education completed in wave 1 for models 3, 4, 5 and 6. Model 3 demonstrates the mediating effect of financial stress in wave 2. Model 4 shows the mediating

effect of depression in wave 2. Model 5 indicates the mediating effect of relative body weight in wave 2. From models 2 and 3, financial stress in model 3 mediates the coefficient of the highest year of education completed in wave 1 by 0.0036 and reduces the coefficient to the 5 percent statistically significant level. From models 2 and 4, depression in model 4 mediates the coefficient of the highest year of education completed in wave 1 by 0.0054 and reduces the coefficient to the 5 percent statistically significant level. From models 2 and 5, relative body weight in model 5 mediates the coefficient of the highest year of education completed in wave 1 by 0.0075 and reduces the coefficient to the 10 percent statistically significant level.

By comparing models 2, 3, 4 and 5, relative body weight is the greatest mediator, financial stress is the weakest mediator, and depression is the medium mediator among the three mediating factors. Between models 2 and 6, the three mediating factors mediate the coefficient of the highest year of education completed in wave 1 by 0.0113 and make the coefficient not statistically significant. The comparison indicates that the three mediating factors reduce the coefficient by approximately 51 percent. The changes in the substantially significant and statistically significant coefficient of the highest year of education completed by the three mediating factors together show the substantial mediating power of combining all three mediating factors. The results from models 2, 3, 4, 5 and 6 confirm the second hypothesis. The findings about the analytic model of the mechanism are presented below in figure 4.

Figure 4. Analytic model about Mechanism and Causality

0.0801*** (0.001) 0.0373*** (0.000)

0.0373*** (0.000)

-1.028 2,348 0.0000 0.4614 0.46595

-1.147 2,348 0.0000 0.4657 0.4634

Source: Data from Americansâ&#x20AC;&#x2122; Changing Lives from years 1986, 1989, & 1994 Note: Non-standardized coefficients are reported and p values are in parentheses. *p < 0.1; **p < 0.05; ***p < 0.01

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Spring 2012 Discussion and Critiques A significant decrease of approximately 1.4 percent in the number of chronic conditions for each additional year of education of an individual has important public policy and economic implications. The total cost for the number of chronic conditions in 2003 was approximately $1.3 trillion (6). Based on the increasing trend for the total cost of chronic conditions, a decrease of 1.4 percent for the number of chronic conditions may save approximately $18.53 billion annually in the economy.

By increasing spending on education level, the government may make people healthier with fewer chronic conditions and improve people’s quality of life, achieving multiple economic benefits. Improvement in education level and reduction in chronic conditions may increase the quality of life (18). Quality of life is multidimensional, including physical wellbeing, material wellbeing, social wellbeing, emotional wellbeing, and development and activity (19) . Better changes in the quality of life may create increased GDP per capita, productivity of the worker and welfare. The increase in the quality of life may provide people with a healthier body to produce more extensively, effectively and efficiently (19). With a better human body to engage in production, people may produce more products and services for the market, increasing GDP and GDP per capita. The enhanced quality of life may also engender improved lifestyle and living conditions, creating greater welfare for an individual and a society. A greater quality of life may also lead to decreased government spending on health care, health care costs in firms and consumers in the economy. With a better quality of life, people may have fewer illnesses or none, diminishing health care spending. More efficient advancement of technology from people with better quality of life may decrease cost of production in health care for firms and cost of health care for consumers. These economic effects advance economic development and growth.

The three mediating factors provide insight into the mechanism of the relationship between education level and the number of chronic conditions. The mediating effects of financial stress, depression and relative body weight validate the close relationship between these

mediating factors and the number of chronic conditions.

By focusing on the direct effect of these mediating factors, the effect of these mediating factors on the number of chronic conditions may reveal interesting relationships, suggesting better directions to public policy interventions. The three mediating factors indicate a huge part of the potential pathways of the mechanism, partly demonstrating the influence of education level on the three mediating factors. The change in these three mediating factors may affect other variables other than the number of chronic conditions. The influence of education level on chronic conditions may generate a greater effect on other variables through the change in the three mediating factors, providing multiple possible benefits.

Some theories may help explain the mechanisms of the mediating factors. An increase in the highest year of education completed may lead to a reduction of financial stress, as a more educated person may have less to worry about regarding his or her finances. The reduction in the financial stress may then help the medical conditions of the person, reducing their number of chronic conditions.

In addition, an increase in the highest year of education completed may decrease depression, in turn alleviating the medical condition of the person. Finally, an increase in the highest year of education completed may increase personal discipline, allowing more educated individuals to maintain a lower body weight and thus lead a healthier life.

The main threat to the inference of the method of the standard linear model of multiple regression is the possible violation of mean independence through omitted variable, reverse causation and measurement error. The exclusion of confounding variables as control variables in the model demonstrates the problem of omitted variables. The lagged regression models include some crucial control variables, but some possible control variables might be missing. One of the most important possible confounding variables is the region of neighborhood, but the region of neighborhood is not included in the dataset of Americans’ Changing Lives. The region of neighborhood has an effect on the education level and affects the number of chronic conditions for

“Increasing educational level [would improve] the quality of life and health of people, creating positive economic implications for economic prosperity and growth”

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Figure 5. Scatter plot of highest year of school completed and number of chronic conditions

an individual with different environment conditions.

However, the region of neighborhood may not limit the education opportunities of an individual too much to render it a fatal flaw. The weak effect of the region of neighborhood on the education level reveals the weak effect of region of neighborhood as a control variable, generating tiny bias in the models without a region of neighborhood variable. Until more hypothesized variables are found that should be included in the models, the findings appear to incorporate relatively small amounts of bias.

The effect of reverse causation is miniscule in the lagged regression. Reverse causation confuses the direction of the causation, but lagged regression includes the temporal order of the causation, minimizing the problem of reverse causation. The reverse causation in the lagged regression models generates insignificant bias towards the findings.

Measurement error creates bias in the coefficients of the standard linear model for multiple regression.

Measurement error such as errors in measuring the variable biases the coefficient towards zero, providing an underestimation. The effect of the highest year of education completed on the number of chronic conditions in reality is larger than the effect in the result from the lagged regression models, reinforcing the substantial significance of the causality.

There are some limitations to the survey sample.

The Americans’ Changing Lives may involve some misreporting in the self-reporting aspect of the survey.

The number of chronic conditions variable provides adequate measures with many indicators. On the other

hand, respondents might have some incentive to over report their highest year of education completed to appear more educated, overestimating their actual highest year of education completed. Although the highest year of education of an individual may not reflect his or her actual education level, the highest year of education completed is a close indication for education level (20). There are no missing follow up observations in model 1 and 2. The 214 missing observations in models 3, 4, 5 and 6 is less than 10 percent of the total follow up observations, showing a small bias in models 3, 4, 5 and 6.

In the lagged regression models, multicollinearity, a statistical problem resulting from high correlation among independent variables, is not a major issue (21).

The largest value for pair-wise correlation between the independent variables is 0.6405. A correlation value of less than 0.6405 is not large enough to generate a large multicollinearity problem, indicating a small bias with multicollinearity in the results.

Including the limitations of the models, the combination of minute bias from violation of mean independence, data sample and multicollinearity demonstrates a small bias effect in the findings. A small bias in the results makes the findings a good approximation for the actual effect, strengthening the substantial findings in from the models. The overestimation of highest year of school completed and the underestimation of the number of chronic conditions demonstrate an underestimation of the actual effect of highest year of school completed on the number of chronic conditions. An increase in one year of education of an individual decreases his or her

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Spring 2012 number of chronic conditions by at least a 1.4 percent.

From the scatter plot of highest year of education completed on the number of chronic conditions (Figure 5), a negative linear line is a good approximation for the data, reinforcing the results of the standard linear model of multiple regression.

Several additional methods and alternatives might strengthen the analyses of this model. Further study of standard linear models could involve similar data in the same time period from other national surveys, such as the national longitudinal surveys to reinforce the findings (22). Another further study might incorporate possible missing omitted variables to determine a better picture of the relationship and the mechanism. Other further study of the effect of the highest year of education completed on the three mediating factors and the effect of the three mediating factors on the number of chronic conditions could provide further insight into the magnitude of the relationships in each of the pathways in the mechanism.

Further surveys with better worded questions and more technologically advanced techniques of collecting data could eliminate some measurement error, reducing bias from the limitations and making the conclusions more substantial. Surveys might also include both subjective and objective measures of the same variable to provide a more comprehensive study of the relationship and mechanisms. These new methods could also provide a

confirmation of the causality and mediating mechanisms of the findings.

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1. Glossary of Terms. Stop Chronic Disease. [Online] Metagenics, 2010. [Cited: May 17, 2010.] http://www.stopchronicdisease.com/page/glossary-of-terms.

2. Chronic Conditions. Chronic Conditions. [Online] National Academy On An Aging Society, November 1, 1999. [Cited: May 8, 2010.] http://ihcrp.georgetown. edu/agingsociety/pdfs/chronic.pdf.

3. Holman, Halsted, Lorig, Kate and Alto, Palo. Patients as partners in managing chronic disease . BMJ. [Online] February 26, 2000. [Cited: May 8, 2010.] http://www.bmj.com/cgi/content/full/320/7234/526.

4. Chronic Conditions: Making the Case for Ongoing Care: September 2004 Update. [Online] 2004. [Cited: May 17, 2010.] http://www.rwjf.org/files/ research/Chronic%20Conditions%20Chartbook%209-2004.ppt.

5. Person With Chronic Conditions. Hoffman, Catherine, Rice, Dorothy and Sung, Hai Yen. November 13, 1996, The Journal of the American Medical Association , Vol. 276, pp. 1473-1479.

6. An unhealthy society: the economic burden of chronic disease. [Online] Milken Institute, 2010. [Cited: May 17, 2010.] http://www.chronicdiseaseimpact. com/.

7. Most chronic disease are reported more frequently by individuals with fewer than 12 years of formal education in the age 18-64 united states population.

Pincus, Theodore, Callahan, Leigh F and Burkhauser, Richard V. 9, 1987, journal of chronic disease, Vol. 40, pp. 865-874.

8. Does patient education in chronic disease have therapeutic value? Mazzuca, Steven A. Great Britain : Pergamon Press Ltd, 1982, Journal of chronic disease, Vol. 35, pp. 521-529.

9. Chronic disease patient education: lessons from meta-analyses. Cooper, Helen, et al., et al. s.l. : Elsevier Science Ireland Ltd, 2001, Patient Educaiton and Counseling, Vol. 44, pp. 107-117.

10. Patient education: perspective of adolescents with a chronic disease.

Kyngäs, Helvi. 2003, Journal of Clinical Nursing, Vol. 12, pp. 744-751.

11. Self-management education programs in chronic disease. Warsu, Asra, et al., et al. 2004, Archives of internal medicine, Vol. 164, pp. 1641-1649.

12. Evidence Suggesting That a Chronic Disease Self-Management Program Can Improve Health Status While Reducing Hospitalization : A Randomized

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Conclusion According to the standard linear model for multiple regression on the data from the Americansâ&#x20AC;&#x2122; Changing Lives dataset, the findings confirm both hypotheses. The lagged regression models show that for the past eight years in America, from 1986 to 1994, as the highest year of education completed for an individual increases for one year, his or her number of chronic conditions decreases significantly by 0.0222, approximately a 1.4 percent decrease. Financial stress, depression and relative body weight together mediate the effect of the highest year of education completed for an individual on his or her number of chronic conditions by approximately 51 percent, demonstrating a significant effect. Increasing educational level for all individuals as a public policy with economic benefits would achieve multiple purposes of improving the quality of life and health of people, creating positive economic implications for economic prosperity and growth. Further research on different data sources such as the national longitudinal survey with other methods or the standard linear model for multiple regression might confirm and reveal more about the causality and mechanisms of the relationship.

Appendix Table 1. Table of explanatory summary of all variables Name of Variable Chronic Conditionswave3 (The number of chronic conditions in wave 3) Educationwave1 (Highest year of education completed in wave 1) Chronic conditionswave1 (The number of chronic conditions in wave 1) Racewave1 (Race in wave 1) Agewave1 (Age in wave 1) Sex wave1 (Sex in wave 1) Nonrespondwave2 (Non-response in wave 2) Familyincomewave1 (Adjusted family income in wave 1 converted to 2001 dollars) Fincstresswave2 (Financial stress scale in wave 2) Depressionswave2 (CES-D scale for depression in wave 2) Bodyweightwave2 (BMI scale for relative body weight in wave 2)

Number of Mean value observations 2,562 1.551522

Median value 1

Standard Deviation 1.445306

Minimum value 0

Maximum value 8

3,617

11.4689

12

3.473619

0

17

3,617

1.40282

1

1.417738

0

7

3,617 3,617 3,617 3,617

1.423002 53.63865 1.624551 0.3055018

1 57 2 0

0.6801561 17.62155 0.4843056 0.4606833

1 24 1 0

5 96 2 1

3,617

35,500.79

27,809.22

30,458.02

5,049.612

138,721.1

2,867

-0.0654919

-0.3555

0.9900084

-1.7603

2.7185

2,867

0.0232348

-0.1645

1.017223

-1.1362

4.253998

2,867

26.29981

25.53861

5.220988

13.80748

50.77698

Table 2. Table of explanatory summary for pair-wise correlation of all variables Name of Variable

Chronic Conditionswave3 (The number of chronic conditions in wave 3) Educationwave1 (Highest year of education completed in wave 1) Chronic conditionswave1 (The number of chronic conditions in wave 1) Racewave1 (Race in wave 1) Agewave1 (Age in wave 1) Sex wave1 (Sex in wave 1) Nonrespondwave2 (Non-response in wave 2) Familyincomewave1 (Adjusted family income in wave 1 converted to 2001 dollars) Fincstresswave2 (Financial stress scale in wave 2) Depressionswave2 (CES-D scale for depression in wave 2) Bodyweightwave2 (BMI scale for relative body weight in wave 2)

Chronic Conditionswave3 (The number of chronic conditions in wave 3) 1.0000

Educationwave1 (Highest year Nonrespondwave2 (Nonof education completed in response in wave 2) wave 1) -0.0086 -0.3103

-0.3103

1.0000

-0.0787

0.6405

-0.3512

-0.0619

0.0516 0.47893 0.1435 -0.0086

-0.1823 -0.3723 -0.0530 -0.0787

0.1576 -0.1166 -0.0002 1.0000

-0.2192

0.4757

-0.0898

0.0412

-0.1046

0.1192

0.1691

-0.2169

0.0731

0.2592

-0.1061

-0.0060

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Recovery Lapse:

The Implications of Native Language Reversion Upon Emergence from General Anesthesia in English-Proficient Patients Rahul Snehal Bhansali Contact: rbhansali@wustl.edu

Proper communication between the care provider and a patient is vital for maximizing patient comfort. Upon emerging from anesthesia, a patient’s level of awareness is used to determine the timing of extubation, and is often assessed by responses to commands such as “open your eyes.” However, when a language barrier arises, it may be impossible for patients to respond to commands they do not understand. In a research study at the University of Chicago Department of Anesthesia and Critical Care, we measure responsiveness of non-native English speakers to basic commands in English and the patient’s native language upon emergence from general anesthesia. In one case, a 44-year old Filipino woman reverted to her native tongue during emergence, despite claiming to be more fluent in English.

In a second, an 18-year old Mexican woman exhibited seizure-like activity upon hearing commands in English although she remained calm while hearing her native language.

These two cases have important implications in the role of language in the field of anesthesia. Other studies have shown similarities between general anesthesia and sleep states, thus attributing to the language reversion in the first case due to differences in memory storage of the native and second languages [1, 2]. Similarly, patients with a history of epilepsy may have “cerebral language reorganization” under certain clinical variables [3]. Overall, these two cases provide insight into the significance of language barriers in healthcare, and demonstrate the necessity of a solution to overcome it in order to maximize patient comfort.

The establishment of proper communication between a patient and a care provider is vital. In the field of anesthesia, the care provider must be able to judge a patient’s awareness upon wakeup in order to determine the timing of extubation, which is often done by evaluating responses to simple physical commands such as “open your eyes” or “squeeze my fingers.” However, in many cases the anesthesiologist does not speak the patient’s native language, which can lead to complications and delays in extubation, simply due to the patient’s inability to understand specific commands. Consequently, this may cause discomfort in the patient’s post-operative awakening and recovery. This can be an issue not just for patients who do not speak English but for those who speak English as a second language, as well, since they may respond more quickly to the commands in their native language, suggesting a reversion to their native tongue.

Overcoming this barrier is important to improving care

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for patients who speak English as a second language [1].

We have been involved in an ongoing Institutional Review Board (IRB) approved study at the University of Chicago that examines the impact of general anesthesia on language preference at emergence in patients who learned English as a second language. The commands “open your eyes,” “squeeze my fingers,” and “wiggle your toes” are recorded in a familiar (usually a family member’s) voice onto a laptop computer in English and in the patient’s native language. The use of a familiar voice eliminates any discrepancies due to an unfamiliar dialect, accent, or pronunciation. Additionally, the patient chooses someone that they deem proficient enough to make the recordings in order to control the language competence of the familiar person. These commands are then played back to the patient upon their emergence from general anesthesia, using headphones in order to reduce the amount of ambient noise. The commands

are played back every 30 seconds, alternating between English and the native language with five second gaps, until the patient follows the command. “Open your eyes” is played back first. Once a response is seen, “squeeze my fingers” is played, followed by “wiggle your toes.” This command order is used because motor control works in a head-to-toe manner. The patient’s background information as well as his or her response times are used to assess the degree of native language reversion.

Two particularly interesting cases have arisen during our study that deserve discussion. The first was a case involving native language reversion in a 44-year-old woman who presented with a history of breast cancer and ovarian cancer was scheduled to undergo a laparoscopic salpingo-oophorectomy under general anesthesia, in order to remove a potentially cancerous mass.

The patient had moved to the United States when she was five years old from the Philippines and subsequently began learning English in elementary school at the age of seven. Although her first language was Tagalog, the patient claimed to be significantly more fluent in English than Tagalog. On an ascending scale from one to ten, she rated her understanding of English to be a 10 and Tagalog to be a 3. Her mother recorded the commands – “open your eyes,” “squeeze my fingers” and “wiggle your toes” – in both languages, English and Tagalog. Prior to the surgery, the patient was asked if she knew the translation for all three phrases in both languages. She answered that she knew “open your eyes” in both languages but only knew the other two commands in English. Interestingly, when the commands were played back in the operating room upon emergence from general anesthesia, she responded to “squeeze my fingers” only in Tagalog, but responded to the remaining two commands in both languages. When the patient was asked in the recovery room if she remembered hearing any of the commands upon emergence, she stated that she remembered hearing “wiggle your toes,” but only in Tagalog [1].

Soon after this case, the methods of the study were altered slightly and commands were played back to the patient in the recovery room as well as in the operating room, as a control to determine levels of comprehension in both languages when the patient was in a less sedated state. Additionally, headphones were replaced with

wireless headphones in order to maximize patient comfort during wakeup and to prevent the wire interfering with other equipment in the operating room.

This led to a second interesting finding. An 18-year-old woman underwent a septoplasty under general anesthesia in order to correct a prior breathing problem. She had moved to the United States from Mexico and began to learn English at the age of five, Spanish being her first language. On the day of her procedure, she stated that her understanding of both Spanish and English were 10 out of 10. In addition, the patient had a history of having seizures after general anesthesia in the past. Upon playback of the commands during emergence from anesthesia, the patient was unresponsive to the commands in both English and Spanish.

The commands were played back to her in the recovery room in order to gauge her baseline understanding of each command, though still drowsy from the anesthesia. For each command played back in English, the patient would demonstrate seizure-like activity, while she remained still for those in Spanish. The commands were repeated approximately every thirty seconds for ten minutes, and the seizure pattern to only English commands persisted throughout this time period.

Despite the first patient’s apparent lack of understanding of two out of the three commands in Tagalog, she responded to ”squeeze my fingers” only in Tagalog while she responded to “open your eyes” and “wiggle your toes” in both English and Tagalog. This behavior is consistent with findings in the sleep literature where there is evidence of reversion to the native tongue during sleep in patients who speak English when in a conscious state [2]. This may be because the native language is learned and retained in “implicit memory systems” located in the subcortical regions of the brain, while the later-acquired second language is stored “externally” in the cerebral cortex [3]. As the patients wake up, the functionality of their “implicit” memory system is likely to recover prior to recovery of their “external” system.

As for the second patient, studies have shown that patients with “left hemisphere epilepsy have a higher likelihood of atypical language organization,” which is not surprising since the left hemisphere is dominant for language in most healthy individuals [4]. Additionally, “clinical variables [such as levels of pharmacologic sedation]

“As the patients wake up, the functionality of their ‘implicit’ memory system is likely to recover prior to recovery of their ‘external’ system”

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The Politics of Disease: Reproduced from [6]

have been shown to contribute to cerebral language reorganization in the setting of chronic seizure disorders,” though these cannot all be reliable sources of the reorientation [4]. Thus, the changing levels of anesthesia may have caused a temporary reorganization in her language comprehension during these seizing episodes.

Alternatively, the patient’s seizure focus might be adjacent to the “external” memory system where her second language (English) is stored but distant from the “implicit” memory system where her first language (Spanish) is stored.

Both of these cases have implications for anesthesia care where patients who speak English fluently may revert to their native tongue upon emergence [5] as this can lead to delays in extubation. Moreover, overcoming the language barrier can be extremely helpful in preventing extubation complications such as increased discomfort in the throat or nausea during recovery.

In this study that deals with patients’ responses to commands in their native language and English upon emergence from general anesthesia, two very intriguing cases were seen. In the first, the patient, a native Tagalog speaker who is more proficient in English after having lived in the United State for 39 years, was more responsive to Tagalog upon emergence from anesthesia. In the second case, a native Spanish speaker from Mexico who has lived in the United States for 13 years, but who is now equally fluent in English and Spanish, seized upon hearing commands in English but not in Spanish.

In both cases, there is a clear discrepancy between the responsiveness to English versus the native language underscoring the importance of providing instructions

in a patient’s primary language to some patients at emergence. Our study has evolved over time to meet this need. Originally, the care provider spoke commands written in transliterated form in the patient’s native tongue back to the patient, but this proved to be inadequate. The utilization of wireless headphones and playing back a command recorded by a native, familiar voice has, thus far, been an improvement in the study, though it may not be the most effective solution to the language barrier, as family members are not always available for this service. Ideally, translation services at the hospital would provide a translator to wake the patient in his or her native language after the procedure, but the costs of maintaining an extensively staffed translation service for all possible foreign languages can become prohibitive.

It would seem that a realistic option might be to have a database of these simple commands made in as many languages as possible, using the voices of native speakers that can be played back via headphones controlled by the anesthesia care provider. This database might also contain various other phrases in order to be prepared for a wakeup with complications. The issue of dialectic variations might still be a challenge in some languages, but the availability of properly pronounced phrases could be checked by the patient or their family prior to surgery.

Effective communication between patient and caregiver is vital in increasing patient responsiveness during emergence from anesthesia, we hope that projects like this one will help overcome some of the major challenges caused by language barriers in doctor-patient communication in the operating room.

References

language learning, Academic Press, London (1994), pp. 393–419.

4. Hamberger M, Cole J. Language Organization and Reorganization in Epilepsy. Neurophysiology Review 2011; 21(3): 240-251.

5. Akpek EA, Sulemanji DS, Arslan G. Effects of anesthesia on linguistic skills – can anesthesia cause language switches? Anesthesia and Analgesia 2002; 95: 1127.

6. Brain. [Image on the Internet] 2004 [cited 19 March 2012]. Available from: http://pubs.niaaa.nih.gov/publications/arh27-2/IMAGES/Page191.gif

1. Bhansali R, Dauber B, Glick D: Reversion to native language in an Englishdominant patient upon emergence from general anesthesia. Anesth Analg 112:C531, 2011.

2. Ozgoren et al. Brain function assessment in different conscious states.

Nonlinear Biomedical Physics 2010 4(Suppl 1):S6.

3. M. Paradis. Neurolinguistic aspects of implicit and explicit memory: Implications for bilingualism and SLA. In: N. Ellis, Editor, Implicit and explicit

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The Importance of Political Advocacy in Addressing Health Disparities Matthew Green

Contact: mcgreen@uchicago.edu While discussions of disease generally occur within the context of the medical institution, it is important to remember that the factors affecting the allocations of health resources exist in a larger social context and can be affected like any political issue. The history of Sickle Cell Anemia (SCA) in America highlights the interplay between politics and health, especially when examined in a racial context.

However, while race is undoubtedly important in determining SCA outcomes, it is merely part of a larger disparity in public awareness. In this article, we will use SCA as a case study to learn more about the larger interplay between political activism and positive health outcomes, making the case that activists seeking to improve outcomes for SCA should not let racial tensions discourage them from fighting for their cause. In order to gain greater awareness, SCA needs to find a “policy champion,” a charismatic individual or organization that will give the public a face or name to associate the condition with. Similar to what Jerry Lewis has done for muscular dystrophy, SCA needs to use political ends to achieve positive outcomes.

Disease is often discussed in the context of the medical institution, with effective prevention and treatment often attributed to strong doctor-patient relationships, adequate facilities, and cutting-edge research. However, health care is not an infinite resource, and the allocation of inputs that control the quantity and quality of care exist within a larger social context and are subject to external influence like any political or social issue.

Much of the research involving social influence in medicine focuses on racial disparities in health outcomes.

African-Americans have higher rates of morbidity and mortality than whites for conditions like heart disease, cancer, diabetes, and cirrhosis of the liver [1], even if insurance status, income, age, and severity of conditions are controlled for [2]. Sickle-cell anemia (SCA), a disease that predominantly affects individuals of African descent, is often discussed within a racial framework, with authors pointing to disparities in treatment and funding when compared to similar genetic diseases that predominantly affect Caucasians. However, can these disparities be attributed solely to race, or might there be a larger mechanism at work? Using SCA as an example,

we will examine the interplay between politics and health outcomes, arguing that while race can play a role in delivery of care, it is important to also recognize the importance of political activism and public awareness in addressing disparities.

Sickle-cell anemia is an autosomal recessive genetic blood disorder that is characterized by having two copies of a point mutation in the gene that codes for hemoglobin, causing red blood cells to be less effective in carrying oxygen and to form rigid, inflexible sickle-shaped cells in the blood. The disease originated in tropical regions as an evolutionary protection against malaria, because one copy of the point mutation causes RBCs to die more rapidly, preventing malaria from spreading. Today, having one copy is diagnosed as sickle-cell trait (SCT).

Having two copies of the mutation, diagnosed as SCA, also confers resistance. However, because patients afflicted with this condition cannot produce any normal hemoglobin, their cells polymerize and form sickled cells.

As the cells travel through the bloodstream, they have more difficulty squeezing through the smallest blood vessels. Eventually, they occlude capillaries and block

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Spring 2012 blood flow, especially in the extremities, resulting in intense pain at these sites. These “pain crises” can last for hours, days, or even weeks, and are the main symptom associated with SCA. In 1910, SCA was discovered in the United States by Chicago physician James Herrick [3], ushering in a new wave of genetic science. SCA rapidly became the most studied genetic disease [4], but early findings worked to set up barriers along racial lines. A 1922 study found that SCA was a dominant trait, meaning that having only one copy of the mutated hemoglobin would yield disease rather than SCT. This was not debunked until 1949, almost thirty years later, but both the original finding and articles in the early 1950s used SCA as a way to decry interracial marriage, claiming that “the disease originated as a by-product of racial mixture[5].” Racial tensions regarding SCA were further invoked during the civil rights movement of the 1960s and early 1970s. The Black Panther Party used SCA as a symbol of “institutionalized racism and black suffering,” and “disease lobbies” began to portray SCA sufferers as “afflicted by unjust suffering” [4].

Even today, ethnographic research demonstrates that there are still lingering racial tensions underlying perceptions about heavy pain medication usage [4].

Because pain is the most noticeable symptom of SCA, this poses great difficulties in treatment, since by its nature, pain is unquantifiable and immeasurable, except by patient self-reports. Further, because the pain for SCA is so severe, many child patients in particular are on heavy painkillers like morphine or Oxycontin. Despite clear guidelines regarding treatment of pain crises [16], many authors express concerns that frequent visits to the ER can lead health professionals to subconsciously

classify patients as “drug seekers” and delay treatment [4,6,7]. One doctor remarked that this characterization is especially dangerous for African-Americans, as “health care professionals make a connection between African-Americans and existing stereotypes” [2]. A 1999 qualitative study found that many sickle-cell patients reported feelings of mistrust from health professionals with regards to pain. One participant remarked that “[t] he nurse turned around to me and said ‘It’s not because we don’t wanna give you the painkillers, it’s cos [sic] we’re scared that you’re gonna get hooked on it and we don’t wanna see you down on the street hustling drugs,’” while another said that “[t]he doctor will look at you, and he goes ‘I don’t think that you’re in a lot of pain.’” [19] These feelings are not without evidence. A 2008 study found that racial disparities still exist with regards to pain medication prescription in emergency rooms, as black patients were 44% less likely to receive an opioid prescription than white patients, even when adjusting for time and pain severity [8]. A 2007 study found that SCA patients receive pain medications on average 70-75 minutes later than recommended by the American Pain Society [7], Additionally, there is currently no nationallycoordinated process to ensure that current guidelines are met [9], leading to massive geographical disparities in mortality outcomes related to SCA [10].

Amidst public pressure, the federal government responded with the Sickle Cell Anemia Control Act in 1972, which has resulted in over $1 billion in federal funding for SCA research [2]. Since its passing, life expectancy for those with SCA has risen from age 20 to age 45, and treatments such as hydroxyurea and bone marrow transplants have been discovered [2]. However, many authors note that SCA still suffers from a disparate

“SCA rapidly became the most studied genetic disease [4], but early findings worked to set up barriers along racial lines”

Table 1. Funding Disparities between Cystic Fibrosis and Sickle Cell Anemia [9] U.S. Prevalence NIH Funding per person (2004 FY) No. of grants funded (2004) Total public and private support per person Cystic Fibrosis Foundation 2003 Revenue Sickle Cell Disease Association of America 2003 Revenue

17

Cystic Fibrosis 80,000 $4,267 459 $9,340 N/A $498,577

Sickle-Cell Anemia 30,000 $1,125 331 $1,130 $152,231,000 N/A

lack of overall funding, especially when compared to a similar genetic disease, cystic fibrosis, which predominantly affects whites [11].

In 2003, despite affecting a fewer number of people, CF /received almost 4 times as much NIH funding per person with disease, over 38% more federal grants, and almost 9 times as much total (private and public) funding per person affected with the disease [9]. Private organizational support gaps are even more staggering, as the Cystic Fibrosis Foundation had over $150 million in revenue for 2003, compared with less than $500,000 for the Sickle Cell Disease Association of America [9].

Most of the discussion involving SCA has centered on race, suggesting that overcoming disparities will require making strides to improve racial equality. Few would deny that race is not an important issue affecting SCA outcomes, but can all the disparities be attributed only to these racial biases? Disparities in care are tragic, but guidelines for SCA pain crises are in place, so it is possible that maltreatment could be due in part to a failure of relevant organizations to promote the guidelines and train health personnel accordingly.

Additionally, funding shortages could be based on race, but the St. Jude Research Hospital, which primarily caters to low-income and minority patients, garnered revenue of over $1 billion in 2010 [12]. Upon closer examination, to suggest that disparities only exist because of underlying racism would neglect a larger problem: SCA suffers from a disparate lack of political activism and public awareness.

It may seem crass to consider the idea of “advertising” medical conditions as a way to raise funding, but at its core the concept of raising awareness about a condition through advertising, public service announcements, and celebrity endorsements is essential to the overall success of a cause. In many cases, a cause can be held up by a “policy champion,” a charismatic individual (or organization) that is able to capture the attention of the public and rally support for progress.

By the same token, medical conditions also need policy champions. There is a positive link between the number and percentage of groups registered to lobby Congress each year and the amount of media attention a cause receives [13]. Additionally, public advocacy over the past 15 years has put pressure on the NIH to adopt its mechanisms for funding allocations to allow for more public input, claiming that “scientists’ intellectual curiosity and their desire for professional advancement have produced allocation choices that diverge from public preferences and the common good” [14]. Effective

Reproduced from [20]

advocacy and public awareness can go a long way to receiving more funding.

This trend does not just apply for government funding, but also for private donations. The best example of a policy champion for medical conditions is Jerry Lewis, who hosted the Muscular Dystrophy Association (MDA) Labor Day Telethon for over 40 years before his departure in 2010 [15]. The MDA has made a concerted effort to get celebrities involved in fighting MD, and the Telethon has featured hundreds of celebrities and made connections with dozens of national sponsors [15]. During Lewis’s tenure as MDA National Chairman, the Telethon raised almost $2.5 billion dollars for MD research [16], and it is likely that even individuals who are not particularly familiar with MD are able to identify the disease with Lewis and “Jerry’s Kids.” Cystic Fibrosis also has its share of celebrity support, most notably from singer Celine Dion [17] and former NFL quarterback Boomer Esiason [18]. The Cystic Fibrosis Foundation is also known for fighting heavily for improvements in quality of care, seeking to establish clinical best practice guidelines and implement a formal accreditation process for CF centers [9]. These are important steps that improve patient care.

Conversely, SCA does not have a large celebrity backing, and the Sickle Cell Disease Association of America does not currently have the financial resources to launch largescale development programs [9]. SCA activists in the past were essential in pushing Congress to implement universal newborn screens for SCA in almost every state [9], but public awareness appears to have decreased in recent years.

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Spring 2012 It has been shown that disparities in funding and care still exist, but responsibility for alleviating SCA disparities cannot be placed solely on medical personnel. There is still a great deal of work to be done when it comes to improving outcomes for patients with SCA, and supporters of SCA research need to reach back to find their roots during the 1960s and 1970s, when they used grassroots advocacy to make substantial progress from the “racial impurity” discussions of the past.

One of the best ways to foster activism is to inspire advocacy among younger individuals, as older children and teenagers afflicted with SCA have the most to gain by improving care. Many hospitals for children are well versed in SCA treatment, especially those that deal with chronic illness. However, there are few dedicated SCA clinics for adults [9], so as these teens transition to adulthood, it will be up to them to make themselves heard to ensure that they have resources for the future.

With enough support, it may become easier for institutions to recruit celebrities and high-profile individuals to the cause. To get into the minds of the American public, SCA needs to have a face, a large organization, a set of corporate sponsors, or some other notable characteristic that will cause people to listen. The Wellstone Triangle

is a concept in political advocacy that seeks to explain how to promote and champion progressive public policy. Named for former Senator Paul Wellstone, and now championed by the Wellstone Action group, the Triangle emphasizes that grassroots advocacy, sound policy knowledge, and strong political campaigning are essential for good leadReproduced from [21] ership. Similarily, good SCA activism should strive to incorporate all of these items, focusing on engaging with the media, holding federal agencies accountable, and advocating for increased funding, Few would argue that race has not played a pivotal role in affecting the outcomes for those affected with Sickle Cell Anemia in America, but advocates for improvement should not let racial tensions discourage them from engaging in activism. SCA activists need to realize that funding and care disparities transcend race and highlight a larger disparity in public awareness. Race feeds into SCA disparities through this mechanism, but it is not the only cause of funding and care disparities. Advocacy should focus on SCA as a lifealtering disease, not merely a “black” disease. Political diseases are going to require political ends to remedy disparities, and with the right voices in place, steps can be taken to improve care for all.

References

11. Cystic Fibrosis Foundation: About Cystic Fibrosis. [Internet]. Available from: http://www.cff.org/aboutcf/testing/geneticcarriertest/ 12. St. Jude Children’s Research Hospital: Annual Report and Financial Information. 2010. Available from: http://www.stjude.org/SJFile/annual_ report_10.pdf 13. Armstrong EM, Carpenter DP, Hojnacki M. Whose Deaths Matter?

Mortality, Advocacy, and Attention to Disease in the Mass Media. Journal of Health Politics, Policy, and Law. 2006; 31(4): 730-772.

14. Dresser R. Public Advocacy and Allocation of Federal Funds for Biomedical Research. The Milbank Quarterly. 1999; 77(2): 257-274.

15. Muscular Dystrophy Association: History. [Internet] Available from : http://www.mda.org/telethon/history.html 16. Lee C. Lighting it Up. The Los Angeles Times. 2010 Sep 3. Available from: http://articles.latimes.com/2010/sep/03/entertainment/ la-et-0903-jerry-lewis-20100903 17. http://www.celinedion.com/news/ join-celine-raise-awareness-about-cystic-fibrosis 18. Boomer Esiason Foundation. http://www.esiason.org/ 19. Maxwell K, Streetly A, Bevan D. Experiences of Hospital Care and Treatment Seeking for Pain from Sickle Cell Disease: Qualitative Study.

British Medical Journal. 1999; 318: 1585-1590.

20. Sickle-Cell. [Image on the Internet] 2010 [cited 19 March 2012]. Available from: http://www.raconline.org/racmaps/mapfiles/fqhc.png 21. Wellstone Triangle. [updated 2009 Sep 7; cited 2012 31 Mar 30].

Available from http://www.wellstone.org/our-programs/camp-wellstone/ what-camp-wellstone.

1. Williams DR, Rucker TD. Understanding and Addressing Racial Disparities in Health Care. Health Care Financing Review. 2000; 21 (4): 75-90.

2. Nelson A. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Journal of the National Medical Association. 2002; 94(8): 666-668.

3. Desai DV, Dhinani H. Sickle-Cell Disease: History and Origin. The Internet Journal of Hematology. 2004; 1(2).

4. Rouse CM. Uncertain Suffering: Racial Health Disparities and Sickle Cell Disease. Berkeley: University of California Press; 2009.

5. Ashman R. Are Certain Blood Dyscrasias an Effect of Racial Mixtures?

American Journal of Physical Anthropology. 1952; 10: 217-223.

6. Green CR, Anderson KO, Baker TA, Campbell LC, Decker S, Fillingim RB, et. al. The Unequal Burden of Pain: Confronting Racial and Ethnic Disparities in Pain. Pain Medicine. 2003; 4(3):277-294.

7. Tanabe P, Myers R, Zosel A, Brice J, Ansari AH, Evans J, et. al. Emergency Department Management of Acute Pain Episodes in Sickle Cell Disease.

Academic Emergency Medicine. 2007; 14: 419-425.

8. Pletcher MJ, Kertesz SG, Kohn MA, Gonzales R. Trends in Opioid Prescribing

by Race/Ethnicity for Patients Seeking Care in US Emergency Departments.

2008; 299 (1): 70-78.

9. Smith LA, Oyeko SO, Homer C, Zuckerman B. Sickle Cell Disease: A Question of Equity and Quality. Pediatrics. 2006; 117: 1763-1770.

10. Davis H, Gergen PJ, Moore RM. Geographic differences in mortality of young children with sickle cell disease in the United States. Public Health Reports.

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Adderall and Its Abuse: Scientific, Legal, and Corporate Considerations

Chan Hee Choi, Simo Huang, Eileen Shiuan, Michael Helzer, and Navtej Singh* Advisor: Dr. R. Stephanie Huang, Asst. Professor of Medicine, University of Chicago *Primary Contact: navtej@uchicago.edu

Prescription stimulants have become an important topic in contemporary culture due to their illicit abuse and role as a performance enhancer across American colleges. Usage rates and trends in prescription stimulant abuse need to be continuously studied to better characterize the future consequences of ‘Generation Rx’. Shire Pharmaceuticals, the creator of Adderall IR in 1996, has faced adversity in its attempts to protect Adderall from both generic competitors and other brand name ADHD treatment stimulants. To maintain substantial market power, Shire filed suits against various companies generating early generic versions of Adderall IR, created an improved Adderall XR capsule, and began collaborating with other companies that carried potentially better extended-release formulations. The legality of the use and abuse of prescription stimulants like Adderall is an important topic for society ever rising to the fore with the introduction of more powerful drugs used in more vulnerable populations. The administrative approach of governmental bodies in the production, distribution, and prescription of such stimulants has been a controversial issue. Also, the corporate and financial mindset of companies that produce Adderall shows through in how it is produced, marketed, and ultimately consumed. Adderall has a variegated and diverse patent history under many famous companies, leading to consumer choice and, often, confusion. The ability of companies to meet demand has been another topic of interest due to recent droughts in supply.

Scientific Background ADHD (Attention Deficit Hyperactivity Disorder) ADHD, according to the American Psychiatric Association in the 4th edition of its Diagnostic and Statistical Manual (DSM-IV), is a neurobiological syndrome used to describe children, adolescents, or adults who are inattentive, impulsive, and hyperactive [1]. As of 2007, the Centers for Disease Control and Prevention (CDC) have reported that about 5.4 million children 4 to 17 years old have been diagnosed with ADHD, or about 9.5% of children in the United States [2]. Currently, psychostimulants such as Mixed Amphetamine Salts like Adderall are the most effective first-line treatment used by primary care physicians and pediatricians to treat ADHD. Mixed Amphetamine Salts Adderall® and Adderall Extended-Release (Adderall XR®) are formulations of chemical compounds called mixed

Figure 1. D/L-Amphetamine. Note that although D- and L-amphetamines are mirror images of each other, they are not superimposable. Such compounds are called enantiomers.

amphetamine salts (MAS), a psychostimulant introduced in 1994 to treat Attention Deficit Hyperactivity Disorder (ADHD) in children and adults [3]. Mixed Amphetamine Salts (MAS) currently used in Adderall® are a 3:1 mixture

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Spring 2012 of D- and L-amphetamine salts (Fig. 1). Both compounds have the same chemical composition but differ in their three-dimensional structure. In Figure 1, solid wedges represent bonds that point out of the paper towards the viewer whereas dashed wedges point into the paper.

One can see that although D and L compounds are mirror images of each other, they are not identical; rotating the L-compound 180° makes its methyl (-CH3) group point inwards whereas D-amphetamine’s methyl group points outwards. In organic chemistry, such compounds with non-superimposable mirror images are called enantiomers.

The initial discovery of psychostimulants’ distinct calming effect in children’s behavior was made purely by accident more than 70 years ago, by a Rhode Island psychiatrist named Charles Bradley [4]. Bradley originally intended to treat headaches in children who had various behavioral disorders but soon found that treating them with a 1:1 mixture of D/L-amphetamines known as Benzedrine® demonstrated “a spectacular improvement in school performance” with increased drive to accomplish their goals and accelerated comprehension among half of the children tested [5]. Since then, many other stimulants such as methylphenidate (Ritalin®) and lisdexamfetamine (Vyvanse®) have demonstrated therapeutic effects on ADHD patients, and it is known that Mixed Amphetamine Salts also produce qualitatively similar behavioral effects in children and adults not suffering from ADHD [6].

MAS Mechanism of Action Neurophysiology The brain is a complex network of billions of specialized cells called neurons. Although scientists understand with some confidence how each neuron transmits signals, understanding how the signals collectively lead to the macroscopic state of the mind remains a mystery [7]. Likewise, the specific neurophysiological mechanism by which psychostimulants affect neuronal cellular

processes is well-known compared to the relative lack of knowledge about the overall clinical mechanisms of stimulant action. Still, there have been significant advances in understanding the neuropsychopharmacological actions of D- and L-amphetamine salts. In order to understand the mechanism by which MAS exert their psychological effects, it is necessary to first understand some general neurophysiology of the brain. Neurons, the basic unit of the nervous system, are long, extended cells that are interconnected for input and output of signals. Within a neuron, a signal is transmitted electrically via the movement of sodium ions (Na+) across the cell membrane. At the synapse, the junction where one neuron meets another, the signal passes from one cell to another. There are two types of synapses, electrical and chemical. In the electrical synapse, the two neurons have cell membranes that are interconnected by special channels so that the electric signals can simply flow through.

In a chemical synapse, however, the two neurons are brought very close to another but are still physically apart. Hence, once the electric signal reaches a neuronal terminus, it elicits the release of chemical messengers called neurotransmitters which diffuse across the cleft and physically bind to the receptors of the other neuron receiving the signal. Binding of the neurotransmitter at the receiving neuron triggers the influx of ions into that neuron, thereby eliciting another electrical signal that can be further transmitted to its neighbors.

Dopamine, Norepinephrine, and Amphetamine’s Action Mechanism Many chemical substances, either endogenous or synthetic, are known to function as neurotransmitters. Of those, amphetamine is structurally and chemically similar to dopamine (DA) and norepinephrine (NE), neurotransmitters derived from the amino acid tyrosine and collectively known as the catecholamines (Fig. 2). The DA system is known to regulate motor output, specializing in maintaining a physical “tonic readiness to respond”

Figure 2.

Catecholamine biosynthetic pathway and D/L-amphetamine.

Double arrow indicates a two-step process. Notice the general structural similarity among the compounds.

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whereas the NE system mediates attention processes such as selective attention, arousal, and perception [6].

D- and L-amphetamines do not directly act as neurotransmitters but rather function by promoting the action of the DA and NE system. Amphetamine is known to facilitate the release of DA and NE into the synaptic cleft, and D-amphetamine is known to be much more potent than the L- compound [6]. In addition, because amphetamine is structurally related to DA and NE, it competes with the endogenous neurotransmitters for both reuptake and breakdown [4]. Amphetamine causes an increase in concentration of dopamine and norepinephrine in the synaptic cleft, and therefore, the receiving neurons are more frequently activated for a longer duration. As NE and DA regulate attention and preparedness for response, it can be suggested that amphetamine’s stimulating effect on these systems eventually leads to enhanced attentionrelated processes among those treated with MAS. Abuse and Its Effects Abuse of Prescription Stimulants on College Campuses Amphetamines such as Adderall are an important topic in contemporary culture because the illicit use of prescription medication contributes a significant portion to the substance abuse problem widely prevalent in American colleges. Adderall, in line with similar drugs like Ritalin and other methylphenidates, are part of the social stigma associated with pressure-driven college students who need an ‘edge’ in fulfilling their academic goals. The use of amphetamines such as Adderall is widespread because of its known effects as a performance enhancer.

Its effects of increased wakefulness and enhanced focus can be seen as easy temptation within the college environment, whether it be for recreational stimulation (achieving a ‘high’) or for periods of academic stress. A study at the University of Michigan conducted in 2007 found that 69% of students reported prescription stimulants like Adderall and Ritalin improved concentration and 67% said that use of the drugs helped them study [1]. One web-based survey of 4580 college students conducted in 2006 elucidates the actual reach of such abuse. The data showed 8.3% of students (382) had used prescription drugs illicitly in their life and 5.9% (269) had used prescription drugs illicitly in the past year. 75.8% of these 269 students who reported recent illicit use of prescription drugs had taken an amphetamine-dextroamphetamine agent (i.e. Adderall) while 24.5% had taken a methylphenidate (i.e. Ritalin, Concerta) [2]. Interestingly, another survey taken at the University of Michigan asked undergraduates how many of their peers they perceived

to be using prescription drugs for non-medical purposes. Students reported perceived usage rates of 70.2% for prescription stimulants and 69.9% for prescription opioids. This shows a gross exaggeration in comparison to actual usage rates (5.9% for recent use and 8.3% for lifetime use) suggesting a pervasive belief that prescription medicine abuse is common and normal behavior [3]. However, data on actual usage rates show that a significant portion of the college population nevertheless abuses prescription medication. Though these estimates are well below perceived usage rates, this openness to misuse of prescription medication in ‘Generation Rx’ may pose a problem for future years as perceptions of heightened drug abuse can signal a relaxed context in determination of a norm which could potentially bring actual usage rates closer to the ones perceived. Furthermore, studies have shown a growing trend in prescription drug abuse. The Substance Abuse and Mental Health Services Administration (SAMHSA) and the National Survey on Drug Use and Health (NSDUH) have reported an increase in non-medical use of pain relievers among 18-25 year-olds from 4.1 percent in 2002 to 4.6 percent in 2007 [4]. Data from the National Center for Addiction and Substance Abuse (CASA) suggests that non-medical use of stimulants and painkillers has tripled from 1992 to 2009 [5]. While these studies reveal the trends in abuse of performance enhancers such as Adderall across American colleges, they do not highlight the secondary effects resulting from misuse of prescription medication and there is little data to show a clear formulation of the consequences for ‘Generation Rx’.

Side Effects of Adderall The increasing rates of nonmedical use of prescription stimulants such as Adderall represent an important public health concern. Due to their mechanism of action, amphetamine stimulants like Adderall can cause rapid development of tolerance. Increased tolerance leads to a compensating increase in dosage rates leading to greater difficulty in user withdrawal [6] Other known side effects have also been published. Most of these represent shortterm effects though, if persistent, they can require a visit to the physician’s office. Regarding Adderall and similar stimulants, there is a wide spectrum of typical effects.

Affecting the GI tract are dry mouth, loss of appetite, nausea, diarrhea, and weight loss. Affecting the nervous system are insomnia, agitation, anxiety, dizziness, depression, seizures, stroke, euphoria, tics, and headaches.

Finally, tachycardia (fast heart rate), palpitations, elevated blood pressure, myocardial infarction (heart attack),

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Spring 2012 and sudden death may also result in the cardiovascular system. Other significant side effects include impotence, changes in libido, and psychotic episodes [7]. The risk and significance of these side effects increase when looking at the subset of the population taking Adderall.

Among full-time college students, nonmedical Adderall users were more than 1.5 times as likely as their peers to have used alcohol in the past month (95.4% vs. 63.0%).

They were also more than twice as likely to have been binge drinkers (89.5% vs. 41.4%) and more than 3 times as likely to have been heavy alcohol users (55.2% vs.

15.6%). Adderall abusers were also more likely to have used illicit drugs in contrast to nonusers. Abusers were at least twice as likely to have used marijuana, cocaine, hallucinogens, LSD, ecstasy, inhalants, tranquilizers, or methamphetamine than nonusers [8]. Thus, there is a significant trend of polydrug usage among Adderall abusers; the complex interactions of varying drugs can increase the chance of deleterious side effects.

A Corporate Background In 1996, Shire Pharmaceuticals, then based in the U.K., introduced Adderall IR which became a very successful drug for ADHD patients, especially children. The drug quickly became Shire’s best-selling product and source of revenue. However, towards the end of the 1990s, Shire became increasingly worried about its dependence on Adderall IR and the imminent rise of generic versions and in late 2001 released a new once-daily extended-release formulation called Adderall XR in hopes of switching Adderall IR patients to XR in light of impending generic competition [1]. Competition with generic Adderall began unexpectedly early in February 2002 when an American company Barr Pharmaceuticals began selling the first generic Adderall IR in the US [2]. Shire sued Barr over the similarities of the appearance of its generic but did not win the suit. Furthermore, despite Shire’s efforts to remain competitive with its new XR brand, the company still suffered significant losses due to its dependency on Adderall (which accounted for almost half of the company’s sales) and lack of a broader product base. Despite this significant setback, Shire reestablished proprietary market dominance by July 2002 when Adderall XR became more popular than Adderall IR and its many generic formulations, and Shire’s profits were once again on the rise. In November 2001, a 17 year patent for Adderall XR use in children was approved in the US by the FDA [3]. Although the formulation was protected until 2018, Shire’s market exclusivity expired in 2004 permitting the entrance of generic extended release formulations much

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earlier than the patent expiration. In February 2003, Barr filed for permission to sell a generic version of Adderall XR by submitting an Abbreviated New Drug Application (ANDA). Shire again filed suit against Barr claiming infringement of Adderall XR’s patent, though Barr refuted the allegations. Ultimately, Shire hoped to delay Barr’s entry into the extended-release market since, regardless of outcome, US law dictates that the court must be given sufficient time to reconcile the dispute before the generic drug in question can be launched. Consequently, Barr was not permitted to sell its product until 30 months after the filing date [4]. Later that year in August, Shire won another patent for Adderall XR, strengthening its suit against Barr. However, Barr again challenged the ruling, delaying its settlement until January 2006. Due to postponement of the trial with Barr Pharmaceuticals, Shire and Barr did not settle on an agreement until August 2006; the delays were an economic benefit for Shire and the final settlement also landed in Shire’s favor. Under this deal, Barr was prohibited from producing a generic Adderall XR for three more years but as part of the agreement was to purchase Adderall IR from Shire for $63 million [7]. Similarly, in November of 2003, Impax Laboratories, another US company, announced plans to launch a generic Adderall XR, further increasing the pressure of competition on Shire. A month later, Shire filed a lawsuit against Impax on the same premises as the Barr suit, preventing Impax from marketing its drug for 30 months [5]. Shire’s decision to challenge both Barr and Impax constituted an advantageous economic strategy that ultimately delayed the onset of generic competition until 2006 and gave Shire needed time to develop new pipeline drugs. In January 2006, Shire won the case against Impax and the two companies agreed upon a settlement that prohibited Impax from selling generic versions of Adderall until 2010 and required Impax to pay royalties. Although Shire defended its prize drug from Barr and Impax, it allowed smaller companies such as Teva Pharmaceuticals and Colony Pharmaceuticals to produce generic Adderall and did not take legal action against them [6]. The settlements with both Impax and Barr, however, ended with major victories for Shire. In addition, Shire won FDA approval for Adderall XR for treatment of adult ADHD in August 2004. With this step, Adderall XR became the first stimulant drug approved for adult ADHD in the US and a new adult market at least twice the size of the pediatric market opened up greater opportunities for Shire. This expansion was compounded with the discovery that around 65% of children with ADHD continued to have symptoms as adults and hence

continued to rely on stimulants well past childhood.

Shire was able to use the additional revenue from the Adderall XR sales to broaden its drug portfolio and reduce its heavy reliance on Adderall [8]. However, despite its efforts, Adderall XR remained Shire’s top-selling medication throughout the decade and Shire allotted much of its energy and resources towards protecting Adderall XR’s monopoly. Knowing full well that generic versions of XR were bound to enter the market by 2009, Shire developed a new and improved version of XR and intended it to potentially replace Adderall XR as the top-selling ADHD medication. This new drug, called NRP104, was approved in October 2006 by the FDA [9]. To further secure its presence in the ADHD market and limit growing competition, Shire purchased New River Pharmaceuticals for $2.6 billion to gain control of Vyvanse, a potential successor of Adderall XR which boasted a longer effect and was approved by the FDA in February 2007 [10]. However, Vyvanse’s performance on the market proved to be disappointing and Adderall XR remained Shire’s best-selling drug even as generic versions began to crop up in April 2009. In February 2005, Shire’s Adderall XR was banned by the Canadian health regulatory organization Health Canada for six months after international reports questioned the safety of the drug. These reports highlighted the deaths of 14 children and 5 adults after taking Adderall or Adderall XR, though none of the deaths occurred in Canada. At that time, the Canadian market contributed to around $10 million, or less than 2% of Shire’s annual sales [11].

Adderall XR was still permitted in the US since the FDA believed there was little evidence link the drug to the reported deaths and that the potential risks of XR needed to be further investigated. The contrasting actions between the FDA and Health Canada highlight the differences between healthcare regulation laws between the two countries. In Canada, regulators have the power to ban a drug from the market while the safety of the drug is being investigated whereas, in the US, such an action is usually not permitted [12]. The ban was highly criticized by healthcare professionals and patients who depended on Adderall. Subsequently in August 2005, Health Canada lifted the ban on Adderall after they declared

that it did not pose a greater health risk than the other comparable ADHD medications on the market such as Ritalin; a new black box warning was added, however, reflecting the potential dangers of the drug [13].

Legality and Legal Status Attention Deficit Disorder (ADD) and Attention Deficit Hyperactivity Disorder (ADHD) were first clinically described in 1902 at which time the leading prescription for these conditions was Benzedrine. Stimulants such as Benzedrine were shown to be effective in treating indicative symptoms of the disorder (at the time both ADD and ADHD were lumped together as a single disorder), but Benzedrine was eventually abandoned because of its adverse effects compared to other treatments. It was believed, for instance, that the use of Benzedrine resulted in a higher instance of cancer, though testing on this topic has proven inconclusive [1]. The story of Adderall, or dextroamphetamine saccharate, begins in 1960 when this new stimulant was approved by the US Food and Drug Administration (FDA) to treat ADD and ADHD. The patent initially belonged to Shire Pharmaceuticals, the developers of this new drug, but the production rights were subsequently acquired by Barr Pharmaceuticals, and Barr was in turn bought by Teva Pharmaceuticals (2-3). Since then, the legality of Adderall has changed rather significantly within the United States and other countries. In the early 1990s when the instance of ADD and ADHD in children was discovered to be on the rise, the production of amphetamines and methylphenidates ballooned in the U.S. from 2000kg to over 14000kg by the end of the decade (4-8). This sharp increase in overall use was acutely felt in the U.S. as opposed to other countries where the recreational use of amphetamines was not as popular due to the wider availability of stimulants such as cocaine and MDMA, as in eastern European countries [4]. It was this dramatic spike in usage of amphetamines that brought the legality of these drugs in the United States to the forefront of debate on prescription drugs, especially given the widespread concern that children are overdiagnosed with ADHD (9-14). The Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV)

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Spring 2012 lists the symptoms of ADHD as “lack of attention to details/careless mistakes; lack of sustained attention; poor listener; failure to follow through on tasks; poor organization; avoids tasks requiring sustained mental effort; loses things; easily distracted; forgetful”. At least six of these symptoms must have persisted for more than six months in individuals older than seven years old, and these symptoms must result in clinically significant impairment in two different settings (e.g. school and home) (13-16). The subjectivity of these symptoms combined with the requirement of symptoms to persist in two settings is often seen as problematic because this means that, in the case of children and adolescents, teachers and other educators are responsible for a large part of the diagnosis process (9-16). Besides the fact that these individuals are untrained in medical diagnoses, several studies have indicated a large bias as to which children or adolescents educators identify as symptomatic or having impairment (9-16). Noting that Adderall is scheduled in line with other amphetamines in many countries and citing statistics that the majority of Adderall usage is prescribed for children, the Food and Drug Administration required Shire to seek reapproval for their Adderall production in 2003 when the company attempted to expand its production to new doses. The FDA approved the addition of Adderall XR 5, Adderall XR 10, Adderall XR 15, Adderall XR 20, Adderall XR 25, and Adderall XR 30 (increasing variations in mg per capsule) to Shire’s repertoire of ADHD and ADD treatments (17-22), a dramatic increase in dosage which further called into question the ethics of prescribing strong stimulants to children. It is also worth noting that the FDA approved the Adderall XR series for use in adults relatively readily in 2004, requiring only a brief series of Phase 4 trials before the various dosages were approved for adult consumption (17-22).

This quick approval of an adult Adderall by the FDA after Shire’s Supplemental New Drug Application (SNDA) highlights two things: first, the fact that ADHD and ADD were initially seen as being syndromes which almost exclusively affected adolescents, and second, that the concerns of the FDA regarding the increased dosages of Adderall in adolescents had relatively little to do with the actual action of the drug, but rather with the safety of adolescents in general and the unknown effects of this particular drug on adolescent development, for example. In a similar instance, Shire was denied approval to produce and market a once-daily methylphenidate transdermal patch marketed for adolescents known as Methypatch.

Shire acquired the rights to market Methypatch from Noven Pharmaceuticals in 2003, and having failed to

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obtain approval, Noven and Shire were asked to carry out further testing on the Methypatch in response to the concerns of the FDA, especially considering that the patch would be predominately prescribed and marketed to adolescents (17-22). The FDA is mainly concerned with the long-term effects these drugs might have on adolescent development, the same concerns raised upon the reapproval of Adderall; however, Shire faced a relatively greater challenge with the Methypatch because the widespread popularity and usage of Adderall allowed much of the data regarding its acute and longitudinal effects to be more readily available. For the very reason that these drugs are more often prescribed to children, in addition to the high risk for abuse like other Schedule II drugs, the Drug Enforcement Administration (DEA) urges “the proactive effort of many groups including physicians, parents, school officials, and law enforcement to evaluate the use of these drugs in their communities” [5].

However, since there is currently no intention of rescheduling the drug, the government continues its control over the production of the substance though it may not be banned outright due to the high level of medical use and necessity. This control may cause friction between the pharmaceutical companies which produce the drugs and enforcement agencies who monitor it, as recent Adderall shortages have indicated, though the situation is not likely to change soon.

Financial and Strategic Considerations Ownership Adderall, in both its formulations and many generic iterations, is one of the world’s best selling ADHD drugs.

Because of its popularity, a rush to expand its access to markets across the world has bestowed the drug with a somewhat fragmented history. The original producer, Shire Pharmaceuticals, would eventually sell production rights to DuraMed Pharmaceuticals which was subsequently purchased by Teva Pharmaceuticals through its Barr Pharmaceuticals acquisition. This history has situated the generic and commercial versions of the drug in various divisions of these and other companies through other contractual agreements. As of 2011, a generic version of Adderall Instant Release (IR) is sold

by Teva Pharmaceuticals under its Barr Division and a brand-name version is sold under its DuraMed Division, while Adderall Extended Release (XR) has continued to be sold by Shire. CorePharma, Sandoz, and Ranbaxy, now owned by the Japanese pharmaceutical company Daiichi Sankyo, all have a stake in the production and distribution of generic iterations of Adderall.

Generic and Brand Name Market Interaction The series of acquisitions undertaken by Teva and its subsidiaries helping it become the world’s largest producer of generic medicines has resulted in the unusual situation of a company producing both the generic (Barr) and brand name (Duramed) versions of a drug. The resulting product positioning carries implications for supply and demand as well as strategy. Primarily, this case of ‘corporate cannibalism’, or sale of multiple competing products by the same company in a given market, may have helped broaden market access for the parent company Teva even though generic medicines typically have lower margins and may be limited by law to certain markets. Though this generic production does reinforce socially responsible behavior to increase the availability of cheap, effective drugs especially to elderly patients and those that rely on government-subsidized healthcare, recent supply droughts have particularly harmed this channel since doctors have limited range of prescription alternatives and frustrated patients are often obliged to purchase modified or brand name counterparts at full price.

Support Systems Another important outcome of this structure of ownership is the question of financial and social onus for support programs, especially regarding abuse which has been progressively increasing in college-aged youth and students. Shire’s anti-abuse campaigns have been quite active though it stands to reason this may only be an artifact of the legal repercussions faced by the original producer of the drug. The company offers Continuing Medical Education (CME) and non-CME educational activities in the field of ADHD for healthcare professionals, the AWARE (Act, Watch, Ask, Regulate, and Educate) brochure program to communicate information related to appropriate stimulant use for ADHD, a comprehensive education program designed specifically to reach health care professionals who serve college students through the ACHA (American College Health Association), support of the prescription stimulant section of MyStudentBody (MSB), the largest and most extensive online suite of college health education in the U.S., and InsideADHD.org, an interactive health education program targeted at adults with ADHD [1]. Such an extensive list of resources aimed at preventing prescription drug abuse from a private sector source have been rare and relatively sparse in the past, though much more could be achieved through collaboration between the full gamut of firms responsible for producing the medicine as well as governmental and

non-governmental sponsors. Due to this urgent need of prescription drug abuse prevention resources, the question of corporate responsibility again comes into play when a company transfers rights of a drug at some part of its production. Here, the creation of a public good (the educational and supportive materials made open to all) is directly commissioned by Shire, the company originally responsible for formulating the medicine though often in collaboration with governmental organizations such as the National Institutes of Health. The corporate standard for the organization and financial support of anti-abuse campaigns should be re-evaluated not only because the new production rights claimant of the drug, Teva, has become its pre-eminent producer, but also because it stands to gain directly from the continued support sponsored by Shire. A legal redefinition of the rights and duties connected with intellectual property will most likely offer the greatest chance for progress, though corporate attitudes toward social responsibility must also be tempered by contextual understanding of these roles. It is worth noting, however, that Shire’s reliance on ADHD drugs, which by as early as 2005 (before the release of sister drugs such as Vyvanse, Daytrana, and Intuniv) accounted for 58% or $1.33 billion of Shire’s revenue, may explain its interest and considerable long term investment in anti-abuse campaigns. Shire has recently been interested in diversifying its product base, and in 2006, secured two separate deals with Barr Pharmaceuticals to give distribution rights for its commercial Adderall and receive usage of its transvaginal ring technology in drugs such as the oral contraceptive Seasonique.

Supply Disruptions and Customer Adaptation From the late 2000s to early 2010s, there has been a widely-reported shortage of all Adderall formulations on the prescription market. Shire has cited “API {active pharmaceutical ingredient} supply issues and uneven product distribution patterns” while Teva, the largest producer, also cites “API issues” as the key cause of the shortage on the FDA’s Current Drug Shortages list [2].

Since the DEA fulfills quotas for the usage of amphetamines in pharmaceutical production as in the case of Adderall and other mixed amphetamine salts (MAS), the industry continues to point out the possibility of bureaucratic misjudgment or interference though the DEA maintains that quotas are currently satisfying drug development needs. “We’ve given them quota sufficient to meet the needs and then it’s up to them how they manufacture their product,” said Gary Boggs, a supervisory special agent for the Office of Diversion for the Drug

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Spring 2012 Enforcement Administration. “Company business decisions surrounding competition, marketing -- and profit margins -- are behind many of the troubles that patients have encountered. Manufacturers might make more of an expensive brand-name drug and not enough of a generic version. Or they may distribute too much product in one place, causing a shortage somewhere else” [3].

As children and increasingly adults find themselves having to purchase proprietary formulations due to waning generic supply, questions of corporate motivation and regulatory efficacy come to mind. Though the companies maintain that a lack of pharmaceutical ingredients is to blame, a temporary drought in the market has

turned greater attention to the DEA’s quota system.

“Officials at the Food and Drug Administration say the shortages are a result of overly strict quotas set by the Drug Enforcement Administration, which, for its part, questions whether there really are shortages or whether manufacturers are simply choosing to make more of the expensive pills than the generics” writes Gardiner Harris in the New York Times [4]. The DEA, however, is pursuing a precarious balance to curb abuse, especially among college-aged adolescents who may fall victim to longer term effects; “We see people abuse it in college and then continue to abuse it nonmedically once they leave” [5].

References

The New York Times. 11 Feb 2005.

13. Branswell H. ADHD drug allowed back on market. The Globe and Mail. 25 Aug 2005.

Legality and Legal Status 1. Beach SA, et al. Benzedrine in Refraction: Preliminary Report. Transactions of the American Ophthalmology Society. 1937; 35 221-226 2. http://westpalmbeach.injuryboard.com/fda-and-prescription-drugs/ adderall-ir-adderall-xr-are-they-from-shire-barr-teva-or-ranbaxy. aspx?googleid=269158 3. http://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=638 4020&KC=&FT=E&locale=en_EP 4. http://www.fda.gov/AboutFDA/CentersOffices/CDER/ ManualofPoliciesProcedures/default.htm 5. http://www.justice.gov/dea/pubs/cngrtest/ct051600.htm 6. http://www.wolframalpha.com/input/?i=ADHD 7. http://www.cdc.gov/ncbddd/adhd/data.html 8. http://www.cdc.gov/nchs/fastats/adhd.htm 9. Miranda G, et al. Gender Differences in ADHD: A Meta-Analysis and Critical Review. Journal of the American Academy of Child and Adolescent Psychiatry.

2009; 36, 1036-1045 10. Singh, I. Biology in Context: Social and Cultural Perspectives. Children and Society. 2002; 16, 360-367 11. Andrew S, et al. Prevalence of Medication Treatment for Attention Deficit – Hyperactivity Disorder Among Elementary School Children in Johnston County, North Carolina. American Journal of Public Health. 2002; 92 12. Singh, I, et al. Boys Will be Boys: Fathers’ Perspectives on ADHD Symptoms, Diagnosis, and Drug Treatment. Harvard Review of Psychiatry.

2003; 11, 308-316 13. Larry S., et al. Diagnosis and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. The Journal of the American Medical Association. 1998; 279, 1100-1107 14. Burns, G, et al. Understanding Source Effects in ADHD Rating Scales: Reply to DuPaul. Psychological Assessment. 2003; 15, 118-119 15. http://www.drugs.com/pro/adderall-xr.html 16. http://www.drugs.com/pro/adderall.html 17. http://www.shire.com/shireplc/financialreports/AR2003/cns.html 18. http://findarticles.com/p/articles/mi_m0NKV/is_1_4/ai_97592342/ 19. http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index. cfm?fuseaction=Search.DrugDetails 20. http://www.fda.gov/Drugs/DevelopmentApprovalProcess/ FormsSubmissionRequirements/ElectronicSubmissions/default.htm 21. http://www.fda.gov/Drugs/DevelopmentApprovalProcess/ HowDrugsareDevelopedandApproved/ApprovalApplications/Over-theCounterDrugs/default.htm 22. http://www.fda.gov/downloads/AboutFDA/CentersOffices/CDER/ UCM183605.pdf 23. http://www.drugs.com/imprints.php?action=search&drugname=adderall Financial and Strategic Considerations 1. http://today.msnbc.msn.com/id/43049351/ns/today/t/statement-shireregarding-adderall-abuse/#.Tw9A7kq7XNt 2. http://www.fda.gov/Drugs/DrugSafety/DrugShortages/ucm050792.htm 3. http://reason.com/blog/2011/12/28/ why-is-there-an-adderall-shortage-becaus 4. http://www.nytimes.com/2012/01/01/health/policy/fda-is-findingattention-drugs-in-short-supply.html?_r=3 5. http://www.nytimes.com/2012/01/01/health/policy/fda-is-findingattention-drugs-in-short-supply.html?pagewanted=2&_r=3 6. http://www.rxlist.com/script/main/hp.asp 7. http://www.drugs.com/imprints.php?action=search&drugname=amphetam ine&maxrows=50

Scientific Background 1. Findling, R. L. (2008). Evolution of the treatment of ADHD in children: a review. Clin. Ther. 30(5), 942-957.

2. Millichap, J. G. (2009). Attention deficit hyperactivity disorder handbook: a physician’s guide to ADHD. New York, NY: Springer.

3. CDC. (2005). Mental health in the United States: prevalence of diagnosis and medication treatment for attention-deficit/hyperactivity disorder---United States, 2003. MMWR. 54:842-847.

4. Heal, D. J., Smith, S. L. & Findling, R. L. (2011). ADHD: current and future therapeutics. Curr. Top. Behav. Neurosci.

5. Bradley, C. (1937). The Behavior of children receiving Benzedrine. Am. J. Psychiatry, 94, 577-85.

6. Solanto, M. V. (1998). Neuropsychopharmacological mechanisms of stimulant drug action in attention-deficit hyperactivity disorder: a review and integration. Behav. Brain Res. 94, 127-152.

7. Churchland, P. S. (2002). Brain-Wise: Studies in neurophilosophy, Cambridge, MA: MIT University Press. Chapter 1 (pp. 1-34).

Abuse and its Effects 1. Teter C, McCabe S, Cranford J, Boyd C, Guthrie S. Prevalence and motives for illicit use of prescription stimulants in an undergraduate student sample.

Journal of American College Health. 2005; 53 (6): 253-262.

2. Teter C, McCabe S, LaGrange K, Cranford J, Boyd C. Illicit use of specific prescription stimulants among college students: prevalence, motives, and routes of administration. Pharmacotherapy. 2006;26 (10): 1501-1510.

3. McCabe SE. Misperceptions of non-medical prescription drug use: A web survey of college students. Addictive Behaviors. 2008; 33 (5): 713-724.

4. The NSDUH Report: Trends in nonmedical use of prescription pain relievers: 2002-2007. Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies; February 2009.

5. Under the counter: The diversion and abuse of prescription drugs in the U.S.: The National Center on Addiction and Substance Abuse at Columbia University; July 2005.

6. Amphetamines: Drug use and abuse: Merck Manual Home Health Handbook. Merck. 2007. <http://www.merckmanuals.com/home/special_ subjects/drug_use_and_abuse/amphetamines.html> 7. Side effects of Adderall. Drugs.com. 2012. <http://www.drugs.com/sfx/ adderall-side-effects.html>.

8. The NSDUH Report: Nonmedical use of Adderall among full-time college students. Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies; April 2009.

A Corporate Background 1. Rodgers S. Shire wins approval of U.S. regulators to sell Adderall XR – Refined version of drug aims to fend off generic threat. The Wall Street Journal.

2001 Oct 15; 27.

2. Generic Adderall approved for Barr. The Record. 12 Feb 2002; L08.

3. Shire gets US patent. Western Mail. 29 Nov 2001; 15.

4. Jenkins P. Shire files US lawsuit against Barr. Financial Times. 26 Feb 2003; 23.

5. Suit over generic medication. The New York Times. 2003 Dec 31.

6. Irving R. Shire wins deal to hold off copycats on Adderall. The Times. 20 Jan 2006; 56.

7. Condie B. Huge boost for Shire in US patent case win. Evening Standard. 15 Aug 2006; 26.

8. FDA approves Adderall XR ® to treat ADHD in adults. Medical News Today.

14 Aug 2004.

9. Drug approval drives up Shire. The Times. 10 Oct 2006; 50.

10. FDA approves new ADHD drug. Beaumont Enterprise. 24 Feb 2007; A2.

11. Canada stops sales of hyperactivity drug. The New York Times. 10 Feb 2005.

12. Harris G, Carey B. Senator says F.D.A. asked Canada not to suspend drug.

27

Meet the Staff The Triple Helix Co-Presidents Benjamin Dauber Tae Yeon Kim

Print Editor-in-Chief Jonathan Gutman

Print Managing Editors

Events Coordinator Catherine Castro Rachel Kulikoff Tim Rudnicki

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Public Relations

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Urveel Shah