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Alzheimer’s & Dementia 9 (2013) 30–38

Potentially avoidable hospitalizations among Medicare beneficiaries with Alzheimer’s disease and related disorders Pei-Jung Lina,*, Howard M. Fillitb, Joshua T. Cohena, Peter J. Neumanna a

Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA b Alzheimer’s Drug Discovery Foundation, New York, NY, USA

Abstract

Background: Individuals with Alzheimer’s disease and related disorders (ADRD) have more frequent hospitalizations than individuals without ADRD, and some of these admissions may be preventable with proactive outpatient care. Methods: This study was a cross-sectional analysis of Medicare claims data from 195,024 fee-forservice ADRD beneficiaries aged 65 years and an equal number of matched non-ADRD controls drawn from the 5% random sample of Medicare beneficiaries in 2007–2008. We analyzed the proportion of patients with potentially avoidable hospitalizations (PAHs, as defined by the Medicare Ambulatory Care Indicators for the Elderly) and used logistic regression to examine patient characteristics associated with PAHs. We used paired t tests to compare Medicare expenditures by ADRD status, stratified by whether there were PAHs related to a particular condition. Results: Compared with matched non-ADRD subjects, Medicare beneficiaries with ADRD were significantly more likely to have PAHs for diabetes short-term complications (OR 5 1.43; 95% CI 1.31–1.57), diabetes long-term complications (OR 5 1.08; 95% CI 5 1.02–1.14), and hypertension (OR 5 1.22; 95% CI 1.08–1.38), but less likely to have PAHs for chronic obstructive pulmonary disease (COPD)/asthma (OR 5 0.85; 95% CI 0.82–0.87) and heart failure (OR 5 0.89; 95% CI 0.86–0.92). Risks of PAHs increased significantly with comorbidity burden. Among beneficiaries with a PAH, total Medicare expenditures were significantly higher for those subjects who also had ADRD. Conclusion: Medicare beneficiaries with ADRD were at a higher risk of PAHs for certain uncontrolled comorbidities and incurred higher Medicare expenditures compared with matched controls without dementia. ADRD appears to make the management of some comorbidities more difficult and expensive. Ideally, ADRD programs should involve care management targeting high-risk patients with multiple chronic conditions. Ó 2013 The Alzheimer’s Association. All rights reserved.

Keywords:

Alzheimer’s disease; Hospitalization; Comorbidity; Medicare expenditures; Quality of care; High-risk patients; Diabetes; Hypertension; CDPD/asthma; Heart failure

1. Introduction Managing comorbidities has been a long-standing challenge in the care of patients with Alzheimer’s disease and related disorders (ADRD). Compared with individuals without dementia, Medicare beneficiaries with ADRD are more This work was supported by a grant from Janssen Alzheimer Immunotherapy Research & Development and Pfizer to the Tufts Medical Center. Abstracts of this study were presented at the 2012 annual research meeting of AcademyHealth and the 2012 international conference of the Alzheimer’s Association. *Corresponding author. Tel.: 617-636-4616; Fax: 617-636-5560. E-mail address: plin@tuftsmedicalcenter.org

likely to be hospitalized, have longer hospital stays, and incur higher expenditures for their comorbidities [1–4]. Further, hospital admissions represent the largest component of healthcare expenditures for individuals with ADRD [5,6] and constitute more than half of total expenditures among the most expensive patients with prominent comorbidities [7]. Additional time spent in the hospital also increases suffering and the risk of adverse events. Therefore, reducing unnecessary admissions is clearly an important and underserved need in ADRD care. Some hospital admissions are expected as part of the natural course of treatment, whereas others are considered potentially avoidable and “indicators of poor care or missed

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opportunities to better coordinate care” [8]. Potentially avoidable hospitalization (PAH) is an important quality indicator because, unlike process-of-care indicators, PAH directly measures the end result of care as experienced by the patient. Such outcome measures are a critical component of initiatives to provide quality-of-care information that resonates with patients, providers, and payers [9]. A recent study showed that the incidence of dementia was significantly associated with increased risk of all-cause hospitalizations and PAHs for conditions such as pneumonia and ulcer, based on a longitudinal cohort of 3019 enrollees in a private, integrated health system [10]. Although prior studies have reported more frequent hospitalizations among ADRD patients (and some of these admissions may be preventable with proactive outpatient care) [1,10,11], the resulting expenditures are less clear. Our analysis extends the existing literature by comparing PAHs as well as Medicare expenditures for selected comorbidities in a matched cohort of beneficiaries with and without ADRD, using more recent data from a large, national sample of Medicare enrollees. We also examined patient characteristics associated with PAHs to shed light on the identification of high-risk patients for which policies and disease management may be particularly valuable.

2. Methods 2.1. Data and sample We used the national 5% sample Medicare Beneficiary Annual Summary File (BASF) and corresponding Medicare claims records for 2007–2008. The BASF contains demographic, enrollment, and date-of-death information for each beneficiary, as well as flags for 19 medical conditions defined by the CMS Chronic Condition Data Warehouse (CCW) condition categories. The claims files contain date of service, diagnosis codes, and payment information for all healthcare services (inpatient, physician office, outpatient, postacute skilled nursing facility [SNF], home health, hospice, durable medical equipment) provided to a Medicare beneficiary. This study was approved by the institutional review board of the Tufts Medical Center. Figure 1 describes our sample selection process. From the 2007 Medicare sample (n 5 2,585,349), we excluded beneficiaries who were ,65 years of age (n 5 394,775). We also excluded beneficiaries who died during 2007–2008 or who enrolled in 2008 (n 5 208,488) because they lacked 2 years of claims data, which are needed to construct the PAH indicators. Managed care enrollees (n 5 133,778) were excluded because CMS did not require managed care plans to submit claims with diagnoses. Thus, we retained fee-for-service (FFS) beneficiaries who had 11 or 12 months of both Part A and Part B FFS coverage during a year. Finally, beneficiaries who used hospice care (n 5 14,842) were excluded because, by definition, they opt not to extend medical care

(e.g., hospital admission) and thus are not an appropriate population for studying avoidable hospitalizations. Among these 1,833,466 eligible Medicare beneficiaries, we identified individuals as having ADRD if they had at least one claim as of December 31, 2008 with an ICD-9-CM (ninth edition of the International Classification of Diseases, Clinical Modification) diagnosis code defined by the CCW: 331.0, 331.1, 331.11, 331.19, 331.2, 331.7, 290.0, 290.1, 290.10, 290.11, 290.12, 290.13, 290.20, 290.21, 290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.1, 294.10, 294.11, 294.8, and 797. The final unmatched sample included 198,693 beneficiaries with ADRD (10.8%) and 1,634,773 beneficiaries without ADRD (89.2%). The ADRD prevalence rates in our sample were comparable to other estimates [12–14]. 2.2. Indicators of potentially avoidable hospitalizations PAHs have been defined as hospital admissions preventable by good ambulatory care, or by early intervention to avoid severe disease [15,16]. We classified a hospitalization as a PAH if it was directly related to one of five avoidable adverse events designated by the Medicare Ambulatory Care Indicators for the Elderly (MACIEs) [16,17]: (1) serious short-term complications of diabetes; (2) serious long-term complications of diabetes; (3) chronic obstructive pulmonary disease (COPD) or asthma; (4) hypertension; and (5) heart failure (see Appendix 1). The MACIEs were developed by the Medicare Payment Advisory Commission (MedPAC) to analyze ambulatory care quality through Medicare claims files. To conduct this analysis, we needed 2 years of claims data for each beneficiary so that follow-up clinical information could be checked for the PAH events. For example, among patients with outpatient or inpatient visits for heart failure in 2007, 2008 data were used to see if they had hospital admissions related to heart failure during that year [16]. The MedPAC has used MACIEs to examine quality of care associated with various geographic and socioeconomic factors [17]. Detailed methodology and rationale for developing the MACIEs has been described elsewhere [16,18]. The five PAH conditions designated by MACIEs are also examined in the Prevention Quality Indicators endorsed by the Agency for Healthcare Research and Quality (AHRQ) and validated from the clinician perspective [19]. 2.3. Analytic approach 2.3.1. Unmatched analysis We calculated the proportion of beneficiaries having PAH for each condition of interest, stratified by whether the patient had ADRD. To examine whether ADRD beneficiaries were more likely than their peers without ADRD to have PAHs, we used logistic regression to calculate odds ratios (ORs), controlling for age, gender, race, Medicare and Medicaid dual eligibility, whether an individual lived in a metropolitan

P.-J. Lin et al. / Alzheimer’s & Dementia 9 (2013) 30–38

2007 5% Medicare sample N=2,585,349

Age 65 n=2,190,574

Age < 65 n=394,775

Had both 2007 & 2008 data n=1,982,086

Died during 2007-2008 or newly enrolled in 2008 n=208,488

Full or nearly full FFS* n=1,848,308

Non-FFS n=133,778

Non-hospice beneficiaries n=1,833,466

Hospice beneficiaries n=14,842

Beneficiaries with ADRD† n=198,693 (10.8%)

Beneficiaries without ADRD n=1,634,773 (89.2%)

One-to-one propensity score matching with logistic regression adjusted for age, sex, race, Medicaid eligibility, MSA, and number of CCW conditions Beneficiaries with ADRD n=195,024

Beneficiaries without ADRD n=195,024

Fig. 1. Sample selection flowchart. ADRD, Alzheimer’s disease and related disorders; CCW, Chronic Condition Data Warehouse; FFS, fee-for-service; MSA, metropolitan statistical area. Asterisk (*): full or nearly full fee-for-service (FFS) indicates the beneficiary had 11 or 12 months of both Part A and Part B fee-for service coverage. Dagger (y): beneficiaries were identified as having ADRD or senile dementia (ADRD) if they had at least one inpatient, skilled nursing facility, home health agency, hospice, or physician office claims with the following ICD-9-CM diagnosis codes as of December 31, 2007: 331.0, 331.1, 331.11, 331.19, 331.2, 331.7, 290.0, 290.1, 290.10, 290.11, 290.12, 290.13, 290.20, 290.21, 290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.1, 294.10, 294.11, 294.8, 797, as defined by the CMS CCW Condition Categories.

statistical area (MSA), and the number of chronic conditions defined by the CMS CCW (excluding ADRD). 2.3.2. Propensity score matching To further account for differences between subjects with and without ADRD, we used logistic regression to calculate propensity scores and then used a “greedy algorithm” [20] to match subjects from the two groups. A propensity score represents the probability that a beneficiary with a given set of characteristics will be classified as having ADRD. Selecting beneficiaries matched on propensity score mitigates the possibility that the two groups being compared differ in terms of a confounding factor that could influence the PAH risk independently of ADRD. The propensity score model included age, gender, race, Medicare and Medicaid dual eligibility, residence in an MSA, and the number of CCW conditions (ex-

cluding ADRD) (concordance index 5 0.79). Despite the large sample of non-ADRD beneficiaries (n 5 1,634,773), we used one-to-one matching to avoid the possible bias of many-to-one matching [21]. This process identified 195,024 ADRD patients (of 198,693 possible; 98.2%) and an equal number of non-ADRD controls. 2.3.3. Patient characteristics associated with PAHs In subgroup analysis of beneficiaries with ADRD, we used logistic regression to identify patient characteristics associated with PAHs, controlling for a same set of predictors in our propensity score model. Because the ICD-9-CM codes for ADRD do not denote disease severity, we created an indicator for late-stage disease based on the presence of medical complications, including pressure ulcers, eating disorders and malnutrition, aspiration pneumonia, and

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incontinence. The rationale for selecting these complications as indicative of advanced ADRD has been reported previously [22–26]. All other ADRD beneficiaries without these complications were classified as having earlier stage disease.

3.2. PAHs among ADRD beneficiaries and controls

2.3.4. Medicare expenditures by ADRD and by PAH condition To examine the excess expenditures attributable to ADRD, we used paired t tests to compare total Medicare expenditures for propensity-matched ADRD beneficiaries and non-ADRD subjects, stratified by whether they had PAHs related to a particular condition. Total Medicare expenditures included payments for all medical services in 2008.

As detailed in the portion of Table 2, a substantial proportion of ADRD beneficiaries had PAHs for uncontrolled chronic obstructive pulmonary disease (COPD)/asthma (20.2%) and heart failure (11.7%). Compared with propensity-score-matched subjects, Medicare beneficiaries with ADRD were more likely to have PAHs for diabetes short-term complications (OR 5 1.43; 95% confidence interval [CI] 1.31–1.57), diabetes long-term complications (OR 5 1.08; 95% CI 1.02–1.14), and hypertension (OR 5 1.22; 95% CI 1.08–1.38), but less likely to have PAHs for COPD/asthma (OR 5 0.85; 95% CI 0.82–0.87) and heart failure (OR 5 0.89; 95% CI 0.86–0.92).

3. Results

3.3. Association between patient characteristics and PAHs

3.1. Sample characteristics

Table 3 details the association between various patient characteristics and PAHs among ADRD beneficiaries. The risks of PAHs increased significantly with the number of comorbidities (ORs ranging from 1.25 to 8.72). Compared to those with earlier stage disease, beneficiaries with advanced ADRD (17.4% of the patient cohort) had significantly higher risks of PAHs for diabetes long-term complications (OR 5 1.23; 95% CI 1.13–1.34), hypertension (OR 5 1.23; 95% CI 1.02–1.47), COPD/asthma (OR 5 1.79; 95% CI 1.71– 1.87), and heart failure (OR 5 1.25; 95% CI 1.19–1.32).

In the unmatched sample (left three numerical columns in Table 1), ADRD beneficiaries were significantly older, disproportionately female, non-white, and dually eligible for Medicare and Medicaid services compared with non-ADRD beneficiaries (all P , .001). Comorbidity burden was significantly higher among ADRD beneficiaries: 80.4% had one or more conditions, compared with 54.8% among non-ADRD beneficiaries (P ,.001). The proportion of beneficiaries living in an MSA differed significantly between the two groups, but the magnitude of the difference was ,1%. The right three columns in Table 1, which compare ADRD and non-ADRD beneficiaries matched by propensity score, shows that these two groups had well-balanced characteristics.

3.4. Medicare expenditures by ADRD and by PAH Total Medicare expenditures were 51% higher among ADRD beneficiaries compared with propensity-matched

Table 1 Characteristics of unmatched and propensity-matched ADRD beneficiaries and non-ADRD controls—2008 Medicare claims data Unmatched sample

Age in years (mean 6 SD) 65–69 70–74 75–79 80–84 85 Female Race White Black Hispanic Other/Unknown Medicare and Medicaid dual-eligible Metropolitan statistical area Number of CCW conditions* 0 1–2 3–4 5

Matched sample

ADRD (n 5 198,693)

Non-ADRD (n 5 1,634,773)

82.6 (7.4) 4.6% 11.1% 17.5% 24.7% 42.2% 70.7%

75.8 (7.2) 23.2% 25.9% 21.4% 16.1% 13.3% 61.2%

84.5% 9.8% 2.8% 2.9% 26.7% 77.8%

86.7% 7.9% 1.9% 3.6% 10.6% 78.4%

19.6% 32.3% 29.0% 19.1%

45.2% 33.6% 15.8% 5.4%

P-value ,.001 ,.001

,.001 ,0.001

,.001 ,.001 ,.001

ADRD (n 5 195,024)

Non-ADRD (n 5 195,024)

82.5 (7.4) 4.7% 11.3% 17.8% 25.1% 41.1% 70.4%

82.0 (7.1) 4.7% 11.3% 17.8% 25.2% 41.1% 70.3%

84.6% 9.7% 2.8% 2.9% 25.3% 77.7%

84.4% 9.8% 2.9% 2.9% 25.3% 77.5%

20.0% 32.9% 28.7% 18.4%

20.0% 32.8% 28.8% 18.4%

P-value ,.001 .992

.801 .175

.988 .109 .861

Abbreviations: ADRD, Alzheimer’s disease and related disorders; CCW, Chronic Condition Data Warehouse. *CCW conditions include acute myocardial infarction, atrial fibrillation, cataract, chronic kidney disease, chronic obstructive pulmonary disease, heart failure, diabetes, glaucoma, hip/pelvic fracture, ischemic heart disease, depression, osteoporosis, rheumatoid arthritis/osteoarthritis, stroke/transient ischemic attack, female breast cancer, colorectal cancer, prostate cancer, lung cancer, and endometrial cancer.

P.-J. Lin et al. / Alzheimer’s & Dementia 9 (2013) 30–38

Table 2 Potentially avoidable hospitalizations in unmatched and propensity-score matched cohorts of ADRD beneficiaries and non-ADRD controls—2007–2008 Medicare claims data Unmatched analyses

Diabetes Hospitalization for short-term complications Hospitalization for long-term complications Hypertension Hospitalization for hypertension COPD/Asthma Hospitalization for COPD/asthma Heart failure Hospitalization for heart failure

ADRD (n 5 198,693)

Non-ADRD (n 5 1,634,773)

OR (95% CI)*

(n 5 98,741) 1.31%

(n 5 445,047) 0.86%

1.44 (1.34–1.54)

2.78%

2.13%

(n 5 134,910) 0.46% (n 5 55,681) 20.21% (n 5 72,764) 11.98%

(n 5 696,787) 0.24% (n 5 177,124) 20.75% (n 5 224,642) 8.14%

1.16 (1.11–1.22)

1.34 (1.21–1.48) 0.89 (0.86–0.91) 1.02 (0.99–1.05)

Matched analyses

ADRD (n 5 195,024)

Non-ADRD (n 5 195,024)

OR (95% CI)

(n 5 96,117) 1.31%

(n 5 81,979) 0.92%

1.43 (1.31–1.57)

2.78% (n 5 131,797) 0.46% (n 5 53,955) 20.18% (n 5 70,529) 11.71%

2.58% (n 5 120,283) 0.37% (n 5 38,369) 23.03% (n 5 50,393) 12.97%

1.08 (1.02–1.14)

1.22 (1.08–1.38) 0.85 (0.82–0.87) 0.89 (0.86–0.92)

Abbreviations: ADRD, Alzheimer’s disease and related disorders; CCW, Chronic Condition Data Warehouse; COPD, chronic obstructive pulmonary disease. *Unmatched analyses: Odds ratios were calculated based on logistic regressions adjusted for age, gender, race, Medicaid eligibility, metropolitan statistical area, and number of CMS CCW categories.

subjects without ADRD ($13,949 vs $9237 in 2008, P , .001). The elevated expenditures among the ADRD cohort were also observed when attention was restricted to beneficiaries who had PAHs directly related to their comorbidity diagnoses (Figure 2). For example, expenditures averaged $42,931 for ADRD beneficiaries with PAHs related to diabetes long-term complications. For propensity-matched nonADRD subjects who also had PAHs for diabetes long-term complications, expenditures averaged $35,030. The difference of $7901 (P , .001) represents the excess expenditure associated with ADRD status. For beneficiaries without PAHs related to diabetes long-term complications, but diagnosed with diabetes, excess expenditures attributable to ADRD were $6574 ($24,593 vs. $18,019, P , .001). Figure 2 can also be used to infer the impact of PAHs on total expenditures. For example, total Medicare expenditures were $21,961 (121%) higher (P , .001) for ADRD beneficiaries with a PAH related to heart failure ($40,061) than for other ADRD beneficiaries without a heart failurerelated PAH ($18,100). 4. Discussion ADRD appears to make the management of other comorbidities more difficult and expensive. When compared with

propensity-score-matched subjects without ADRD, Medicare beneficiaries with ADRD were more likely to have PAHs related to diabetes complications and hypertension. Many ADRD beneficiaries had PAHs for COPD/asthma and heart failure, although the PAH risk for these beneficiaries was lower compared with matched subjects without ADRD. Among beneficiaries with a PAH related to a particular condition, total Medicare expenditures were significantly higher for those subjects who also had ADRD. The National Alzheimer’s Project Act (NAPA) was signed into law in 2011 [27]. Central to the NAPA is the goal of providing patients with high-quality and efficient care. For conditions such as diabetes, hypertension, COPD/asthma, and heart failure, high-quality outpatient management reduces the risk of almost all types of serious hospitalizations [28]. Our data show that many ADRD beneficiaries were nonetheless admitted to the hospital for these conditions (e.g., 20% for COPD/asthma and 12% for heart failure). The PAH findings in our study may signal suboptimal use of appropriate ambulatory care [28]. Moreover, current treatment guidelines for the PAH conditions generally emphasize patient selfmanagement, which is especially problematic for ADRD patients. As disease progresses, ADRD patients may lose their self-care skills and have difficulty complying with care management instructions (e.g., adherence to medications) [4],

P.-J. Lin et al. / Alzheimer’s & Dementia 9 (2013) 30–38

Table 3 Odds ratios of patient characteristics associated with potentially avoidable hospitalizations among ADRD beneficiaries

Age in years 65–69 (reference) 70–74 75–79 80–84 85 Female Race White (reference) Black Hispanic Other/unknown Dual eligible MSA Number of CCW conditions 2 (reference) 3–4 5 Late-stage ADRD

Diabetes short-term complications

Diabetes long-term complications

Hypertension

COPD/asthma

Heart failure

— 0.64 (0.51–0.80) 0.63 (0.51–0.78) 0.51 (0.41–0.63) 0.36 (0.29–0.45) 0.99 (0.88–1.12)

— 0.91 (0.77–1.07) 0.83 (0.71–0.97) 0.64 (0.55–0.75) 0.41 (0.35–0.48) 1.21 (1.12–1.31)

— 0.92 (0.61–1.40) 0.78 (0.52–1.17) 0.84 (0.57–1.24) 0.86 (0.59–1.25) 0.87 (0.72–1.05)

— 1.00 (0.90–1.12) 0.89 (0.81–0.99) 0.78 (0.71–0.86) 0.62 (0.56–0.68) 1.17 (1.11–1.22)

— 0.92 (0.80–1.06) 1.05 (0.92–1.20) 1.20 (1.05–1.36) 1.48 (1.30–1.68) 1.08 (1.02–1.14)

— 1.76 (1.51–2.05) 2.16 (1.69–2.76) 1.18 (0.86–1.64) 1.18 (1.04–1.33) 0.94 (0.82–1.08)

— 1.79 (1.61–1.99) 1.51 (1.24–1.84) 1.25 (1.01–1.56) 1.10 (1.01–1.20) 1.06 (0.96–1.16)

— 2.11 (1.71–2.61) 0.79 (0.44–1.41) 0.59 (0.30–1.15) 0.96 (0.79–1.16) 1.08 (0.89–1.33)

— 0.75 (0.69–0.81) 0.84 (0.73–0.96) 0.79 (0.68–0.91) 1.11 (1.06–1.17) 0.89 (0.84–0.94)

— 1.22 (1.13–1.32) 0.86 (0.73–1.01) 0.89 (0.75–1.05) 1.05 (0.99–1.11) 0.86 (0.82–0.92)

— 1.59 (1.36–1.86) 2.38 (2.05–2.77) 0.83 (0.72–0.96)

— 1.29 (1.15–1.45) 2.70 (2.43–2.99) 1.23 (1.13–1.34)

— 1.43 (1.17–1.74) 1.93 (1.58–2.36) 1.23 (1.02–1.47)

— 1.25 (1.18–1.33) 2.01 (1.90–2.12) 1.79 (1.71–1.87)

— 2.58 (2.36–2.81) 8.72 (8.06–9.44) 1.25 (1.19–1.32)

Abbreviations: ADRD, Alzheimer’s disease and related disorders; CCW, Chronic Condition Data Warehouse; COPD, chronic obstructive pulmonary disease; MSA, metropolitan statistical area.

a situation that is particularly challenging for those who lack support networks (family, informal caregiver). Although we used different methodology and criteria to identify PAHs, our results are consistent with previous studies that found ADRD patients were more likely than other Medicare beneficiaries to have PAHs related to diabetes (OR 5 3.61; 95% CI 2.32–5.63) and hypertension (OR 5 2.60; 95% CI 2.16–3.12) [1], and less likely to be admitted to a hospital for heart failure (OR 5 0.80; 95% CI 0.50–1.26) [3]. However, our findings contrast with a recent longitudinal cohort study by Phelan and colleagues, suggesting an increased risk of hospitalization for congestive heart failure exacerbation among dementia patients (OR 5 1.73; 95% CI ADRD with PAH

Non-ADRD with PAH

1.15–2.60) [10]. The difference might be due to cohort characteristics (e.g., older, fee-for-service Medicare beneficiaries in our study vs. younger, healthier enrollees in Group Health Cooperative in the Phelan et al. study), case and control group identification (ADRD diagnosis recorded in claims files in our study vs. incident dementia in Phelan et al.), different levels of ADRD severity, and the PAH measures used. Moreover, some comorbidities (e.g., heart failure, COPD) are more severe than others (e.g., diabetes, hypertension) and commonly reflect “end-of-life conditions” [16,29]. Possibly, less aggressive medical services for end-of-life conditions are given to patients with ADRD. To build on the study presented here, future researchers could assess other end-ofADRD, no PAH

Non-ADRD, no PAH

$42,931 $40,061 $35,088

$35,030

$34,995

$29,911 $24,970

$18,352

$24,593 $18,019

$26,374

$25,889 $19,476 $16,391

$21,911 $21,044 $18,100 $16,081

$13,769

$11,811

Diabetes short-term complications

Diabetes long-term complications

Hypertension

COPD/Asthma

Heart failure

Fig. 2. Overall Medicare expenditures by type of potentially avoidable hospitalization in propensity-matched ADRD beneficiaries and non-ADRD controls (2008$). ADRD, Alzheimer’s disease and related disorders; COPD, chronic obstructive pulmonary disease; PAH, potentially avoidable hospitalization.

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life conditions and explore patient and caregiver preferences for care, as well as physicians’ practice patterns [26]. Our analysis shows that ADRD patients with higher comorbidity burden were at significantly increased risk of PAHs. These findings suggest that complex care management (CCM) is needed to better manage multiple medical conditions among high-risk patients. Examples of CCM strategies include care managers to coordinate care [30,31] and clinical interventions to improve discharge planning and to ensure continuing care [32]. These interventions may improve overall care quality and patients’ quality of life and caregiver support, as well as reduce unnecessary hospitalizations [30,31,33]. Aggregate payments for ADRD are projected to increase from $183 billion in 2011 to $1.1 trillion in 2050 in the U.S. [14]. Curbing these rising costs has become a priority among policymakers and payers. Past findings that hospitalizations contribute substantially to healthcare expenditures among ADRD beneficiaries [1,3,6,7] are consistent with our findings that average Medicare expenditures were significantly higher among those who had PAHs. For example, total expenditures were 121% higher for ADRD beneficiaries who had PAHs related to heart failure than for other ADRD beneficiaries diagnosed with heart failure but no directly related PAH. For the other conditions, the corresponding increases associated with having a related PAH were 25% for COPD/asthma, 41% for diabetes shortterm complications, 58% for hypertension, and 75% for diabetes long-term complications. A sensitivity analysis restricting attention to Medicare inpatient expenditures (instead of overall Medicare expenditures) yielded similar findings (results not shown). Future cost containment efforts should involve effective care coordination and tailored ADRD management strategies to decrease unnecessary inpatient expenditures. However, without changing Medicare’s FFS payment structure, coordinated care programs have had limited success at reducing cost while improving quality [34,35]. Prior CMS demonstrations for disease management also have had limited impact on improving beneficiary knowledge, increasing preventive care, or reducing PAHs, emergency room visits, or mortality [34–36]. During 2011, two Medicare Advantage (MA) Special Needs Plans (SNPs) have been implemented to enroll exclusively beneficiaries with dementia, providing full Medicare coverage (prescription drug coverage and Parts A and B health benefits) as well as extra services to ADRD beneficiaries and their caregivers (such as a paid consultative visit for selecting hospice care) [37]. Currently, these dementia SNPs have enrolled fewer than 200 ADRD patients and the performance evidence for SNPs in general is scant [38]. Studies evaluating whether the enrollees in dementia SNPs obtain higher quality care and have better outcomes than individuals in traditional FFS and regular MA plans will help determine whether these plans should be implemented on a large scale. The CMS Quality Initiatives and new Affordable Care Act initiatives, such as Accountable Care Organizations and retrospective bundling payments, all aim to shift the system

away from paying providers based on volume and instead paying them based on quality and value of care [9,39]. For ADRD care, provision of good ambulatory care that can prevent hospitalizations may reduce expenditures. By allowing providers to design a more integrated healthcare delivery structure and to determine how payments will be allocated among participating providers, these initiatives may lower expenditures and promote better coordination between acute inpatient care and postdischarge recovery. Linking the care quality to payments would further encourage providers to improve provider performance by reducing unnecessary hospitalizations. Our study’s strengths include a large Medicare sample, evaluation of PAH conditions particularly important in older populations, examination of patient characteristics associated with PAHs, and use of a statistical matching approach to assess PAH and expenditure differences in beneficiaries with and without ADRD. Nonetheless, there are limitations that should be considered when interpreting our findings. First, our sample included Medicare fee-for-service beneficiaries, so the results might not generalize to managed care enrollees or nursing home residents. A study analyzing 1991–1993 data demonstrated that nursing home residents with ADRD were more likely to be hospitalized for certain conditions such as gastroenteritis and kidney/urinary track infections [11]. Further, other conditions, such as COPD exacerbation and heart failure, could be treated in nursing homes without prompting transfer to hospital. Second, the use of Medicare claims data has inherent limitations in identifying ADRD patients [40]. Mild cases might be underdiagnosed and uncoded in claims files because of reimbursement issues, failure to recognize symptoms, and resistance to an ADRD diagnosis [41,42]. Nonetheless, resulting misclassifications would likely underestimate the PAH and expenditure differences between ADRD and non-ADRD beneficiaries. Third, Medicare expenditures may be underestimated as a result of the absence in our analysis of Part D drug expenditure data and our exclusion of managed care enrollees. According to MedPAC, Managed Advantage plans were, on average, paid higher per enrollee than traditional FFS plans (e.g., 114% of FFS costs in 2009) [17]. We also lacked data on Medicaid payments for dually eligible beneficiaries. Fourth, our indicator for advanced ADRD was based on the presence of complications commonly seen in late-stage patients. In the future it would be useful to validate this indicator by using other data sources that contain disease staging information, such as electronic health records. Fifth, we could not determine the relationship between provision of ambulatory care and PAHs. Future research should identify ambulatory care elements that may reduce PAHs and expenditures. Finally, because we needed 2 years of claims data to check for PAH events, beneficiaries with partial-year data (e.g., subjects who died during 2007–2008) were excluded from our analysis. The impact is likely to be minimal, however, as a sensitivity analysis including beneficiaries without complete data did not produce notably different findings.

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Our analysis suggests that Medicare beneficiaries with ADRD have substantial PAHs for certain uncontrolled comorbidities. For some conditions, such as diabetes and hypertension, ADRD beneficiaries were at a higher risk of condition-related PAHs compared with matched controls. The resulting Medicare expenditures highlight the opportunity to reduce cost while improving quality. Future ADRD management programs should involve CCM services targeting high-risk patients with multiple chronic conditions. Continued monitoring and reporting of PAHs is essential to help CMS and providers to establish performance benchmarks and identify areas of care in need for quality improvement. By combining PAH outcomes with other quality measures, such as resource use, future research can assess the efficiency of care and guide incentive designs to encourage value-driven care [9]. Acknowledgments This work was supported by a grant from Janssen Alzheimer Immunotherapy R&D and Pfizer to the Tufts Medical Center. Publication was not contingent on Janssen Alzheimer Immunotherapy R&D and Pfizer’s approval. We are grateful to Chi-Hui Fang for assistance with data analysis. RESEARCH IN CONTEXT 1. Systematic review: We searched PubMed for English-language original research that examined potentially avoidable hospitalizations (PAHs) among individuals with Alzheimer’s disease and related disorders (ADRD). Three studies were identified [1,10,11], all measuring PAHs as defined using ambulatory-care sensitive conditions. 2. Interpretation: We analyzed five PAH conditions, as defined by the Medicare Ambulatory Care Indicators for the Elderly. Compared with matched non-ADRD subjects, Medicare beneficiaries with ADRD were significantly more likely to have PAHs for diabetes short-term complications, diabetes long-term complications, and hypertension, but less likely to have PAHs for COPD/asthma and heart failure. Risks of PAHs significantly increased with comorbidity burden. Among beneficiaries with a PAH, total Medicare expenditures were significantly higher for those subjects who also had ADRD. 3. Future directions: Ideally, ADRD programs should involve care management targeting high-risk patients with multiple chronic conditions. Continued monitoring and reporting of PAHs is essential to help CMS and providers to establish performance benchmarks and identify areas of care in need for quality improvement.

References [1] Bynum JP, Rabins PV, Weller W, Niefeld M, Anderson GF, Wu AW. The relationship between a dementia diagnosis, chronic illness, Medicare expenditures, and hospital use. J Am Geriatr Soc 2004;52:187–94. [2] Sloan FA, Taylor DH Jr. Effect of Alzheimer disease on the cost of treating other diseases. Alzheimer Dis Assoc Disord 2002;16:137–43. [3] Zhao Y, Kuo TC, Weir S, Kramer MS, Ash AS. Healthcare costs and utilization for Medicare beneficiaries with Alzheimer’s. BMC Health Serv Res 2008;8:108. [4] Hill JW, Futterman R, Duttagupta S, Mastey V, Lloyd JR, Fillit H. Alzheimer’s disease and related dementias increase costs of comorbidities in managed Medicare. Neurology 2002;58:62–70. [5] Zhu CW, Scarmeas N, Torgan R, Albert M, Brandt J, Blacker D, et al. Clinical features associated with costs in early AD: baseline data from the Predictors Study. Neurology 2006;66:1021–8. [6] Weiner M, Powe NR, Weller WE, Shaffer TJ, Anderson GF. Alzheimer’s disease under managed care: implications from Medicare utilization and expenditure patterns. J Am Geriatr Soc 1998;46:762–70. [7] Lin PJ, Biddle AK, Ganguly R, Kaufer DI, Maciejewski ML. The concentration and persistence of health care expenditures and prescription drug expenditures in Medicare beneficiaries with Alzheimer disease and related dementias. Med Care 2009;47:1174–9. [8] Medicare Payment Advisory Commission. Report to the Congress: promoting greater efficiency in Medicare. Washington, DC: MedPAC; 2007. [9] CMS, roadmap for quality measurement. https://www.cms.gov/ QualityInitiativesGenInfo/downloads/QualityMeasurementRoadmap_ OEA1-16_508.pdf/. Accessed October 1. [10] Phelan EA, Borson S, Grothaus L, Balch S, Larson EB. Association of incident dementia with hospitalizations. JAMA 2012;307:165–72. [11] Carter MW, Porell FW. Vulnerable populations at risk of potentially avoidable hospitalizations: the case of nursing home residents with Alzheimer’s disease. Am J Alzheimers Dis Other Dement 2005; 20:349–58. [12] Hebert LE, Scherr PA, Bienias JL, Bennett DA, Evans DA. Alzheimer disease in the US population: prevalence estimates using the 2000 census. Arch Neurol 2003;60:1119–22. [13] Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al. Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology 2007; 29:125–32. [14] Thies W, Bleiler L. 2011 Alzheimer’s disease facts and figures. Alzheimers Dement 2011;7:208–44. [15] Jiang HJ, Wier LM, Potter DEB, Burgess J. Potentially preventable hospitalizations among Medicare-Medicaid dual eligibles, 2008. Rockville, Maryland: AHRQ; 2010. [16] Westrick E, Kogut S. Medicare ambulatory care indicators for the elderly: Refinement of the access to care for the elderly project indicators: MagnaCare Health Services Improvement, Inc. for MedPAC; 2006. [17] Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy. Washington, DC: MedPAC; 2011. [18] Medicare Payment Advisory Commission. Appendixes: Physician and other health professional services (online-only appendixes for Chapter 4, March 2011 report). http://www.medpac.gov/chapters/Mar11_ Ch04_APPENDIX.pdf/. Accessed October 1. [19] Davies S, McDonald KM, Schmidt E, Schultz E, Geppert J, Romano PS. Expanding the uses of AHRQ’s prevention quality indicators: Validity from the clinician perspective. Med Care 2011;49:679–85. [20] Parsons LS. Performing a 1:N Case-control match on propensity score. Paper presented at: Proceedings of the 29th Annual SAS Users Group International Conference; May 2004; Montreal, Canada. [21] Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 2009;28:3083–107.

P.-J. Lin et al. / Alzheimer’s & Dementia 9 (2013) 30–38

[22] Fillit H, Hill JW, Futterman R. Health care utilization and costs of Alzheimer’s disease: the role of co-morbid conditions, disease stage, and pharmacotherapy. Fam Med 2002;34:528–35. [23] Berlowitz DR, Brandeis GH, Morris JN, Ash AS, Anderson JJ, Kader B, et al. Deriving a risk-adjustment model for pressure ulcer development using the Minimum Data Set. J Am Geriatr Soc 2001; 49:866–71. [24] Andrieu S, Reynish W, Nourhashemi F, Ousset PJ, Grandjean H, Grand A, et al. Nutritional risk factors for institutional placement in Alzheimer’s disease after one year follow-up. J Nutr Health Aging 2001;5:113–7. [25] Horner J, Alberts MJ, Dawson DV, Cook GM. Swallowing in Alzheimer’s disease. Alzheimer Dis Assoc Disord 1994;8:177–89. [26] Mitchell SL, Teno JM, Kiely DK, Shaffer ML, Jones RN, Prigerson HG, et al. The clinical course of advanced dementia. N Engl J Med 2009;361:1529–38. [27] Obama PB. Draft. National plan to address Alzheimer’s disease, http:// aspe.hhs.gov/daltcp/napa/NatlPlan.shtml/. Accessed March 25. [28] Agency for Healthcare Research and Quality. Guide to prevention quality indicators: Hospital admission for ambulatory care sensitive conditions (Revision 3). AHRQ Publication No. 02–R0203. Rockville, MD: AHRQ; January 9, 2004. [29] Lorenz K, Lynn J, Morton SC, Dy S, Mularski R, Shugarman L, et al. End-of-Life care and outcomes: Summary, evidence report/technology assessment no. 110. (prepared by the Southern California Evidencebased Practice Center, under Contract No. 290-02-0003). AHRQ Publication No. 05-E004-1. Rockville, MD: AHRQ; December 2004. [30] Vickrey BG, Mittman BS, Connor KI, Pearson ML, Della Penna RD, Ganiats TG, et al. The effect of a disease management intervention on quality and outcomes of dementia care: a randomized, controlled trial. Ann Intern Med 2006;145:713–26. [31] Callahan CM, Boustani MA, Unverzagt FW, Austrom MG, Damush TM, Perkins AJ, et al. Effectiveness of collaborative care for older adults with Alzheimer disease in primary care: a randomized controlled trial. JAMA 2006;295:2148–57.

[32] Epstein AM, Jha AK, Orav EJ. The relationship between hospital admission rates and rehospitalizations. N Engl J Med 2011; 365:2287–95. [33] Boustani MA, Sachs GA, Alder CA, Munger S, Schubert CC, Guerriero Austrom M, et al. Implementing innovative models of dementia care: the Healthy Aging Brain Center. Aging Ment Health 2011; 15:13–22. [34] McCall N, Cromwell J. Results of the Medicare Health Support disease-management pilot program. N Engl J Med 2011;365:1704–12. [35] Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials. JAMA 2009; 301:603–18. [36] Bott DM, Kapp MC, Johnson LB, Magno LM. Disease management for chronically ill beneficiaries in traditional Medicare. Health Aff (Millwood) 2009;28:86–98. [37] CMS, SNP comprehensive report. www.cms.gov/mcradvpartdenroldata/ snp/List.asp/. Accessed October 1. [38] Maciejewski ML, Wang V, Grabowski DC, Lin PJ. Early evidence on the quality of care provided by special needs plans. Med Care 2011; 49:891–6. [39] U.S. Department of Health and Human Services. Accountable Care Organizations: Improving care coordination for people with Medicare. http://www.healthcare.gov/news/factsheets/2011/03/ accountablecare03312011a.html/. Accessed November 1. [40] Lin PJ, Kaufer DI, Maciejewski ML, Ganguly R, Paul JE, Biddle AK. An examination of Alzheimer’s disease case definitions using Medicare claims and survey data. Alzheimers Dement 2010;6:334–41. [41] Fillit H, Geldmacher DS, Welter RT, Maslow K, Fraser M. Optimizing coding and reimbursement to improve management of Alzheimer’s disease and related dementias. J Am Geriatr Soc 2002; 50:1871–8. [42] Newcomer R, Clay T, Luxenberg JS, Miller RH. Misclassification and selection bias when identifying Alzheimer’s disease solely from Medicare claims records. J Am Geriatr Soc 1999;47:215–9.

Appendix Descriptionof potentially avoidable hospitalization indicators defined by the Medicare Ambulatory Care Indicators for the Elderly (MACIE) Indicator

Description

1. Hospitalizations for serious short term complications of diabetes

Admissions for diabetic, hyperosmolar, and ketotic coma and admissions for uncontrolled diabetes mellitus among patients with 2 outpatient visits or 1 inpatient visit with diabetes mellitus within a calendar year. Hospitalizations for renal, ophthalmologic, neurologic and circulatory complications of diabetes mellitus and non-traumatic lower extremity amputation, in patients with 2 or more out-patient visits or 1 in-patient visit with a diagnosis code for diabetes mellitus within a calendar year. Hospitalizations with hypertension as the primary diagnosis, in patients aged 65 years and older, with 2 or more out- or in-patient visits with a diagnosis code for hypertension in the measurement year. Admissions for respiratory diagnoses among patients with COPD (including asthma) defined as 2 visits (out- or inpatient) with coded for COPD or asthma in the measurement year. Admissions for heart failure in the measurement year among patients with 2 outpatient visits or 1 inpatient visit with heart failure in the year prior to the measurement year.

2. Hospitalizations for serious long term complications of diabetes

3. Hospitalizations for hypertension

4. Hospitalizations for COPD/asthma 5. Hospitalizations for heart failure

Source: Westrick E, Kogut S. Medicare Ambulatory Care Indicators for the Elderly: Refinement of the Access to Care for the Elderly Project Indicators (final report to MedPAC, January 2006).