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Abstract
The 5% Medicare Standard Analytic Files (SAF) are random samples used to analyze national trends in medical treatments, expenditures, and outcomes. Their utility in small-area or multilevel analyses is unknown. To demonstrate possible limitations of the 5% SAF for analysis of health behaviors in small areas. We use descriptive Chi-square goodness-of-fit tests and mapping to explore consistency in the 5% representation of the 100% population in states and counties. We conduct multilevel modeling of individual utilization of mammography or endoscopy services for cancer screening and contrast findings across the 5% and 100% files. Subjects are enrolled in both parts A and B Medicare coverage and ages 65–104, alive and residing in the same state, with no gaps in coverage during the study period. Identically defined groups are drawn from the 5% SAF and 100% population claims and denominator files. The Chi-square tests of homogeneous population subgroups in 5% and 100% files exhibit significant differences in 7 of 8 states. Maps confirm this among states' counties and find that one state is generally under-represented by the 5% SAF, while others show areas with variable representation. Multilevel modeling results are largely consistent across the partitions of the data, but 5% sample models have much lower statistical power. Area-level covariate effect estimates show some differences across the two datasets. Multilevel modeling with contextual variables may be misleading in small area analyses conducted using 5% Medicare SAFs. Provider supply and market characteristics show inconsistent results. Disparities research may benefit from 100% files to provide statistical power needed to detect meaningful differences. This is significant because the Centers for Medicare and Medicaid Services have recently curtailed permissions to use the 100% files. These 100% files are one of few sources of population data available in the U.S. that are representative of small areas in the U.S.. In times of constrained budgets, using population data files is essential so that resources can be targeted to areas robustly identified as having greatest need or gaps in outcomes.

The authors examine trends over 1997-2001 in racial or ethnic disparities in the utilization of three costly, referral-sensitive procedures among the elderly—coronary artery bypass grafting (CABG), percutaneous transluminal coronary angioplasty (PTCA), and hip/joint replacement. Using a multivariate framework, they undertake a simultaneous examination of the relationships between patient, local area context, and health systems on these admission types after comparing them to a control group. This period spans the implementation of the Balanced Budget Act and a major Department of Health and Human Services initiative to reduce disparities in cardiovascular and other diseases. Findings suggest increasing disparities for African Americans relative to Whites in their lower utilization of CABG and PTCA over time, and increasing disparities in the utilization of hip/joint replacement among other races' relative to Whites. The authors find that racial or ethnic disparities in use of referral-sensitive procedures did not narrow over 1997-2001.

This study assesses the association of HMO enrollment with preventable hospitalizations among the elderly in four states. Using 2001 hospital discharge abstracts for elderly Medicare enrollees (age 65 and above) residing in four states (New York, Pennsylvania, Florida, and California), from the Healthcare Cost and Utilization Project (HCUP-SID) database of the Agency for Healthcare Research and Quality, we use a multivariate cross-sectional design with patient-level data for each state. Holding other factors such as demographics and illness severity constant, we find that in three out of four states, Medicare HMO patients had lower odds of a preventable admission versus marker admission than Medicare fee-for-service (FFS) patients. Moreover, in the two states with longest tenure and greatest Medicare HMO penetration, California and Florida, the reduction in preventable admissions among Medicare HMO patients was mainly concentrated among more ill patients. These findings add to the evidence that managed care outperforms traditional care among the elderly, rather than simply skimming off the healthiest populations.

The authors develop a hybrid model of health care use that blends features of the traditional Aday—Andersen behavioral model with the socioecological modeling perspective. They use the model to conceptualize the various levels of influence expected from socioecological variables in individuals' mammography use decisions, build contextual variables from fine-grained data into four different types of geographic areas, and then use two- and three-level modeling of personal and area-level contextual factors to explain observed behavior. The central focus is on whether differentiating the conceptualized levels of influence seems to materially affect regression findings. The test could conceivably be confounded by the modifiable areal unit problem, but little evidence for this is found. Findings for California women suggest that distinctions do matter in how the levels of influence are defined for local neighborhood contextual factors. Studies using only county-level contextual factors will miss some meaningful associations related to interpersonal/proximate-level factors.

Each state is autonomous in its comprehensive cancer control (CCC) program, and considerable heterogeneity exists in the program plans. However, researchers often focus on the concept of nationally representative data and pool observations across states using regression analysis to come up with average effects when interpreting results. Due to considerable state autonomy and heterogeneity in various dimensions—including culture, politics, historical precedent, regulatory environment, and CCC efforts—it is important to examine states separately and to use geographic analysis to translate findings in place and time.