Efforts to reduce racial disparities in Medicare managed care must consider the disproportionate effects of geography

Am J Manag Care. 2007 Jan;13(1):51-6.


Objective: To examine the impact of geographic variation on racial differences in 7 of 15 Health Plan Employer Data and Information Set (HEDIS) measures that assess the quality of the Medicare managed care program (also known as Medicare+Choice).

Study design: Cross-sectional analysis using the 2004 individual-level HEDIS for Medicare managed care plans and 2003 Medicare enrollment and demographic (ie, denominator) data for more than 5.1 million Medicare+Choice enrollees.

Methods: Individual-level HEDIS data were linked with Medicare enrollment data. Hierarchical generalized linear models were used to assess statistical significance of region and race. Direct standardization was used to estimate the rate of meeting each HEDIS standard while controlling for differences in age and sex.

Results: Quality of care for white Medicare+Choice enrollees was strongly correlated with the racial composition of the geographic area. Except for cholesterol management after an acute cardiac event, between-region racial variation was consistently greater than within-region racial variation.

Conclusion: Removing within-region racial variation while ignoring geographic differences will not equalize the experiences of black and white elders. Rather, both racial and geographic components of healthcare quality must be addressed if the Medicare managed care program is to provide care of equal quality to all elders regardless of race.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • African Americans / statistics & numerical data*
  • Age Factors
  • Catchment Area, Health*
  • Cross-Sectional Studies
  • Female
  • Geography*
  • Humans
  • Male
  • Managed Care Programs / standards*
  • Managed Care Programs / trends
  • Medicare Part C / standards*
  • Medicare Part C / trends
  • Minority Groups
  • Multivariate Analysis
  • Poverty
  • Prejudice*
  • Quality Indicators, Health Care*
  • Regression Analysis
  • Sex Factors
  • Socioeconomic Factors
  • United States
  • Whites / statistics & numerical data*