BackgroundWe present a method of randomly drawing a longitudinal sample of Medicare patients to 1) conduct longitudinal analysis of care use and outcomes over a ten-year follow-up; 2) provide a representative cross-sectional sample in each year during this time period; and 3) provide adequate precision for estimates in comparisons that involve minority patients at the county level. This method was applied to patients with diabetes in the Diabetes Belt (a region in the Appalachian and southern

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... with higher rates of diabetes) and surrounding counties.MethodsWe used the Medicare Master Beneficiary Summary Files (A/B/C/D and Chronic Conditions segments) to identify eligible patients for each year. We targeted a sample of just under 900,000 patients per year. The 2006 sample is stratified by county and white/minority status, and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county size with oversampling of the minority population. Patients who were alive, did not move between counties, and stayed enrolled in Medicare fee-for-service (FFS) were retained in the sample for subsequent years. Non-retained patients were replaced with a sample of patients in their first year of Medicare FFS (e.g., new enrollees) or patients who moved into a sampled county during the previous year.ResultsThe resulting sample contains an average of 899,266 patients each year and closely matches population demographics and chronic conditions. For all years in the sample, the weighted sample average age and the population age differ by < 0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. No difference was statistically significant at the α = 0.05 level.ConclusionsThis carefully constructed survey sample will allow us to to perform longitudinal and cross-sectional analysis on healthcare utilization and outcomes. This sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries for longitudinal follow-up with possible oversampling of some sub-populations.