Regional Variations in Diagnostic PracticesSong, Yunjie; Skinner, Jonathan; Bynum, Julie; Sutherland, Jason; Wennberg, John E.; Fisher, Elliott S.
doi: 10.1056/NEJMsa0910881pmid: 20463332
BackgroundCurrent methods of risk adjustment rely on diagnoses recorded in clinical and administrative records. Differences among providers in diagnostic practices could lead to bias.MethodsWe used Medicare claims data from 1999 through 2006 to measure trends in diagnostic practices for Medicare beneficiaries. Regions were grouped into five quintiles according to the intensity of hospital and physician services that beneficiaries in the region received. We compared trends with respect to diagnoses, laboratory testing, imaging, and the assignment of Hierarchical Condition Categories (HCCs) among beneficiaries who moved to regions with a higher or lower intensity of practice.ResultsBeneficiaries within each quintile who moved during the study period to regions with a higher or lower intensity of practice had similar numbers of diagnoses and similar HCC risk scores (as derived from HCC coding algorithms) before their move. The number of diagnoses and the HCC measures increased as the cohort aged, but they increased to a greater extent among beneficiaries who moved to regions with a higher intensity of practice than among those who moved to regions with the same or lower intensity of practice. For example, among beneficiaries who lived initially in regions in the lowest quintile, there was a greater increase in the average number of diagnoses among those who moved to regions in a higher quintile than among those who moved to regions within the lowest quintile (increase of 100.8%; 95% confidence interval [CI], 89.6 to 112.1; vs. increase of 61.7%; 95% CI, 55.8 to 67.4). Moving to each higher quintile of intensity was associated with an additional 5.9% increase (95% CI, 5.2 to 6.7) in HCC scores, and results were similar with respect to laboratory testing and imaging.ConclusionsSubstantial differences in diagnostic practices that are unlikely to be related to patient characteristics are observed across U.S. regions. The use of clinical or claims-based diagnoses in risk adjustment may introduce important biases in comparative-effectiveness studies, public reporting, and payment reforms.
Clarifying Sources of Geographic Differences in Medicare SpendingZuckerman, Stephen; Waidmann, Timothy; Berenson, Robert; Hadley, Jack
doi: 10.1056/NEJMsa0909253pmid: 20463333
BackgroundAlthough geographic differences in Medicare spending are widely considered to be evidence of program inefficiency, policymakers need to understand how differences in beneficiaries' health and personal characteristics and specific geographic factors affect the amount of Medicare spending per beneficiary before formulating policies to reduce geographic differences in spending.MethodsWe used Medicare Current Beneficiary Surveys from 2000 through 2002 to examine differences across geographic areas (grouped into quintiles on the basis of Medicare spending per beneficiary over the same period). We estimated multivariate-regression models of individual spending that included demographic and baseline health characteristics, changes in health status, other individual determinants of demand, and area-level measures of the supply of health care resources. Each group of variables was entered into the model sequentially to assess the effect on geographic differences in spending.ResultsUnadjusted Medicare spending per beneficiary was 52% higher in geographic regions in the highest spending quintile than in regions in the lowest quintile. After adjustment for demographic and baseline health characteristics and changes in health status, the difference in spending between the highest and lowest quintiles was reduced to 33%. Health status accounted for 29% of the unadjusted geographic difference in per-beneficiary spending; additional adjustment for area-level differences in the supply of medical resources did not further reduce the observed differences between the top and bottom quintiles.ConclusionsPolicymakers attempting to control Medicare costs by reducing differences in Medicare spending across geographic areas need better information about the specific source of the differences, as well as better methods for adjusting spending levels to account for underlying differences in beneficiaries' health measures.