Neighborhood Socioeconomic Disadvantage and 30 Day Rehospitalizations: An Analysis of Medicare Data

By A. Kind, S. Jencks, Jane E. Brock, Menggang Yu, Christie M. Bartels, W. Ehlenbach, C. Greenberg, Maureen A. Smith. Annals of Internal Medicine, 2014

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Context The relationship between factors indicative of personal socioeconomic disadvantage, such as where patients live, and readmission risk is unclear. Contribution The authors linked the ZIP+4 codes for Medicare patients' residences to census block groups in the 2000 U.S. census. For each census block group (or neighborhood), they calculated an area deprivation index score. Multivariable models assessed the relationship between area deprivation index score rankings and rehospitalization, controlling for patient and hospital characteristics. Caution The study did not assess the quality of care provided by the hospitals. Implication Medicare patients from disadvantaged neighborhoods have an increased risk for rehospitalization. The Editors Thirty-day rehospitalizations affect 1 in 5 hospitalized Medicare patients, cost more than $17 billion annually, and result in hospital-based Medicare payment penalties for congestive heart failure, pneumonia, and acute myocardial infarction rehospitalizations (1). Most persons believe that all hospitals can prevent at least some rehospitalizations by using a spectrum of programs to better support vulnerable patients during the high-risk period after hospital discharge (13). However, the targeting of these programs has proven challenging, potentially because important factors contributing to rehospitalizations, such as socioeconomic disadvantage, are not well-measured (4, 5). Socioeconomic disadvantage is a complex theoretical concept, which describes the state of being challenged by low income, limited education, and substandard living conditions for both the person and his or her neighborhood or social network (6, 7). Detailed assessment of an individual patient's socioeconomic status is a time-consuming and potentially uncomfortable task to add to a clinical encounter, and because such information is rarely available in the patient's medical record, clinical teams often overlook socioeconomic factors when creating individualized care plans after hospital discharge (8). Measures of neighborhood socioeconomic disadvantage, such as concentration of poverty in the neighborhood surrounding the patient's residence, could be more easily accessed and assigned as a risk factor at the point of patient admission by using the patient's address. However, the association between neighborhood disadvantage and rehospitalization risk has not yet been established. It is plausible that neighborhood socioeconomic disadvantage would influence rehospitalization risk because vulnerable patients depend on neighborhood supports for stability generally (912), and these needs are likely to be increased in the period after hospital discharge (3). Safety-net hospitals in the United States, which serve socioeconomically disadvantaged areas, are more apt to be financially penalized for their rehospitalization rates (1316). Living in a socioeconomically disadvantaged neighborhood has been associated with health behaviors (17); access to food (18, 19) and safety (20); and outcomes, such as mortality (10, 1217), birthweight (21), and rehospitalization risk for heart failure (22). In addition, important health indicators improve with moving persons to areas of less concentrated poverty (23, 24). In 2003, Singh created a composite measure of neighborhood socioeconomic disadvantage for the United States, the area deprivation index (ADI), on the basis of similar measures used in many other countries for resource planning and health policy development (2529). The ADI is a factor-based index that uses 17 U.S. census poverty, education, housing, and employment indicators to characterize census-based regions (25, 2729). It has been correlated with many health outcomes, including all-cause, cardiovascular, cancer, and childhood mortality and cervical cancer prevalence (25, 2732). Socioeconomic disadvantage based on neighborhood risk through a ZIP codelinked ADI does not require a potentially lengthy and intrusive discussion with patients and families and could easily be made available to clinical teams and policymakers. Our objective was to determine whether neighborhood socioeconomic disadvantage could be useful to clinical planning by examining its relevance in a population likely to be targeted by clinical improvement activities designed to reduce readmission risk. We analyzed the association between ADI (defined at the census block group level) and 30-day rehospitalizations for patients discharged with congestive heart failure, pneumonia, or acute myocardial infarction, the clinical conditions used for the current calculation of Medicare's rehospitalization penalties. Methods Data Sources and Study Population We used data from 2004 to 2009 from the Chronic Condition Data Warehouse (33), including Medicare claims and enrollment files prelinked to annual Medicare provider of service files for a 5% random national sample of Medicare beneficiaries. Beneficiaries who received railroad retirement benefits or were in an HMO were excluded because these groups have incomplete data. We identified 307 827 patients who were older than 65 years hospitalized with congestive heart failure, acute myocardial infarction, or pneumonia using Medicare readmission measure definitions (3436). We used the ZIP+4 code listed for the patient's residence within Medicare data to link to the census block group with the same ZIP+4 area in 2000 U.S. census data for the 50 U.S. states and the District of Columbia. Each census block group covers an area of 600 to 3000 persons, averaging 1500 persons per group (37). We excluded 52 083 patients without a ZIP+4 code in their Medicare data (n = 9741) or whose documented ZIP+4 code did not exist in the 2000 census data (n = 42 342). Patients in this latter category may include those who designate a post office box as their primary residence or those who reside in new ZIP+4 areas, U.S. territories, or institutions (such as prisons). Hand-checking of a small random sampling of these patients' ZIP+4 codes suggests that most were assigned to a post office box. The final sample size was 255 744 patients. These patients originated from 4802 unique hospitals (mean patients per hospital, 53.3; range, 1 to 743 patients). The University of Wisconsin Institutional Review Board approved this study. Variables Census Block GroupLevel Variables We calculated ADI scores for each U.S. census block group using the Singh method (14, 1618). This involved summing Singh's 17 census indicators weighted by Singh's factor score coefficients for each indicator (25) (Table 1). The Appendix contains more detail on constructing the ADI. We examined the distribution of ADI values and sorted neighborhoods into percentiles by increasing ADI. Table 1. Census Data Block Group Components and Factor Score Coefficients in the Singh ADI Patient-Level Variables We constructed all-cause rehospitalization within 30 days of discharge from Medicare claims (3436). Other variables drawn from Medicare files included patient age, sex, race, Medicaid status, initial Medicare enrollment due to disability, index hospitalization length of stay, and discharge to a skilled-nursing facility. Race was categorized as white, black, or other based on the beneficiary race code. Each patient's Centers for Medicare & Medicaid Services hierarchical condition category score, calculated from all outpatient and inpatient claims over the 12 months before the index hospitalization, was included as a risk adjustment measure (38). Comorbid conditions were identified using Elixhauser methods, incorporating data from the index hospitalization and from all hospitalizations and physician claims during the year before the index hospitalization (39). Of the comorbid conditions identified using this approach, 17 had frequencies of greater than 5% in the sample and were included as indicators. Comorbid conditions occurring less often were compiled into an other comorbid condition indicator and included alcohol or drug abuse, rheumatoid arthritis, collagen vascular disease, chronic blood loss anemia, liver disease, lymphoma, metastatic cancer, solid tumor without metastases, paralysis, psychoses, and peptic ulcer disease. We assessed the rurality of each patient's ZIP code of residence using the U.S. Department of Agriculture's ruralurban commuting area codes, grouped into categories of urban core areas, suburban areas, large town areas, and small town or isolated rural areas (40, 41). Index hospital characteristics, including Medicare geographic region, for-profit status, and medical school affiliation, were drawn from the Medicare provider of service file corresponding to the patient's index hospitalization date (42). We estimated the annual Medicare discharge volume for each hospital by multiplying the number of claims from each hospital in the 5% national sample by 20. We then grouped hospitals into low-, middle-, and high-volume tertiles. Approximately 0.1% of our sample was missing data on race (n = 291), and less than 0.3% were missing data on hospital medical school affiliation (n = 777) and for-profit status (n = 777). There were no missing data for other patient-level variables. Statistical Analysis We examined the unadjusted relationship between ADI percentile and 30-day rehospitalization, overall and by primary disease. On the basis of the empirical ADI data, the most disadvantaged neighborhoods made up the top 15% of the distribution. To better assess within-group differences, we divided this most disadvantaged 15% into 3 equally sized 5% groupings representing the third-most, second-most, and most disadvantaged 5% of neighborhoods. The remaining neighborhoods (85%) were grouped into a comparator category. We examined frequencies of patient and index hospital characteristics for each grouping. We used logistic regression to assess the relationship between ADI grouping and 30-day rehospitalization. Next, to assess the full spectrum of ADI effect, we divided the

Subfields: Epidemiology, General Health Professions, Health

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