Socioeconomic disparities in medical provider visits among medicare managed care enrollees.

This study examined socioeconomic disparities in medical provider visits for elderly people enrolled in two Medicare managed care plans. Controlling for health and demographic differences, elderly people of lower income had fewer primary care visits and those with lower education had fewer specialist visits. The number of emergency room visits was not significantly related to socioeconomic status (SES).

Does quality of care for cardiovascular disease and diabetes differ by gender for enrollees in managed care plans?

Purpose: To assess gender differences in the quality of care for cardiovascular disease and diabetes for enrollees in managed care plans.

Methods: We obtained data from 10 commercial and 9 Medicare plans and calculated performance on 6 Health Employer Data and Information Set (HEDIS) measures of quality of care (beta-blocker use after myocardial infarction [MI], low-density lipoprotein cholesterol [LDL-C] check after a cardiac event, and in diabetics, whether glycosylated hemoglobin [HgbA1c], LDL cholesterol, nephropathy, and eyes were checked) and a 7th HEDIS-like measure (angiotensin-converting enzyme [ACE] inhibitor use for congestive heart failure). A smaller number of plans provided HEDIS scores on 4 additional measures that require medical chart abstraction (control of LDL-C after cardiac event, blood pressure control in hypertensive patients, and HgbA1c and LDL-C control in diabetics).

Use of geocoding in managed care settings to identify quality disparities.

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.