Use Of Geocoding In Managed Care Settings To Identify Quality Disparities

Abstract

Tracking quality-of-care measures is essential for improving care, particularly for vulnerable populations. Although managed care plans routinely track quality measures, few examine whether their performance differs by enrollee race/ethnicity or socioeconomic status (SES), in part because plans do not collect that information. We show that plans can begin examining and targeting potential disparities using indirect measures of enrollee race/ethnicity and SES based on geocoding. Using such measures, we demonstrate disparities within both Medicare+Choice and commercial plans on Health Plan Employer Data and Information Set (HEDIS) measures of diabetes and cardiovascular care, including instances in which race/ethnicity and SES have distinct effects. D i s pa r i t i e s i n q ua l i t y o f c a r e based on race/ethnicity and socioeconomic status (SES) are well documented. Eliminating these disparities is essential to improving health in the United States. Tracking measures of the quality of care received by different racial/ethnic and SES groups to help target quality improvement efforts and monitor progress represents a crucial step toward this goal. This is especially true for managed care plans, which now serve more than half of all Americans.1 Yet relatively little is known about disparities in managed care settings.Although managed care plans routinely collect and monitor performance on quality measures, such as the National Committee for Quality Assurance (NCQA) Health Employer Data and Information Set (HEDIS), few plans examine these data for possible disparities based on enrollees' race/ethnicity or SES. A key bar-5 1 6 M a r c h /A p r i l 2 0 0 5 D a t a W a t c h

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