Identifying diabetics in Medicare claims and survey data: implications for health services research
1 Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI, 48104, USA
2 Institute for Employment Research, Regensburger Str. 104, Nuremberg, 90478, Germany
3 Health Policy & Management, Johns Hopkins School of Public Health, 624 N. Broadway, Hampton House 450, Baltimore, MD, 21205, USA
BMC Health Services Research 2014, 14:150 doi:10.1186/1472-6963-14-150Published: 3 April 2014
Diabetes health services research often utilizes secondary data sources, including survey self-report and Medicare claims, to identify and study the diabetic population, but disagreement exists between these two data sources. We assessed agreement between the Chronic Condition Warehouse diabetes algorithm for Medicare claims and self-report measures of diabetes. Differences in healthcare utilization outcomes under each diabetes definition were also explored.
Claims data from the Medicare Beneficiary Annual Summary File were linked to survey and blood data collected from the 2006 Health and Retirement Study. A Hemoglobin A1c reading, collected on 2,028 respondents, was used to reconcile discrepancies between the self-report and Medicare claims measures of diabetes. T-tests were used to assess differences in healthcare utilization outcomes for each diabetes measure.
The Chronic Condition Warehouse (CCW) algorithm yielded a higher rate of diabetes than respondent self-reports (27.3 vs. 21.2, p < 0.05). A1c levels of discordant claims-based diabetics suggest that these patients are not diabetic, however, they have high rates of healthcare spending and utilization similar to diabetics.
Concordance between A1c and self-reports was higher than for A1c and the CCW algorithm. Accuracy of self-reports was superior to the CCW algorithm. False positives in the claims data have similar utilization profiles to diabetics, suggesting minimal bias in some types of claims-based analyses, though researchers should consider sensitivity analysis across definitions for health services research.