title: Racial and ethnic disparities in the financial burden of prescription drugs among older Americans.
abstract: This study examines racial and ethnic disparities in the financial burden of prescription drugs among older Americans using a market and an egalitarian model. A nationally representative data set, the Medical Expenditure Panel Survey 2002, was used. The financial burden of prescription drugs was measured by the out-of-pocket expenditure and proportion. In the market model (utilization adjustment)., utilization was measured at the annual aggregate level by the total number of prescription drugs, the average refills and the average quantity per prescription drug. In the egalitarian model (need adjustment)., health was measured by 15 chronic and costly diseases and the SF-12. Individuals 65 years or older were included. Nationally representative estimates were calculated. Raw racial and ethnic disparities were observed in the bivariate analyses between non-Hispanic whites and Hispanics in the out-of-pocket expenditure and proportion, and between non-Hispanic whites and non-Hispanic blacks in the out-of-pocket proportion. However, these disparities disappeared after controlling for utilization or health needs. Insurance status contributed the most to the disparities in the financial burden of prescription drugs. In conclusion, The disparities in the financial burden of prescription drugs between non-Hispanic elderly whites and Hispanics may be attributable to differences in utilization patterns. However, whether health disparities contribute to disparities in the financial burden of prescription drugs requires studies of specific diseases.
authors.affiliation: School of Medicine, Texas Tech University Health Sciences Center, USA.
authors.forename: K Tom
authors.initials: KT
authors.lastname: Xu
authors.forename: Tyrone F
authors.initials: TF
authors.lastname: Borders
mesh_terms: D000208:Acute Disease / Q000191:economics; D001741:Black or African American / Q000706:statistics & numerical data*; D000368:Aged; D002908:Chronic Disease / Q000191:economics; D017281:Cost of Illness; D016527:Drug Costs / Q000706:statistics & numerical data*; D004363:Drug Utilization / Q000191:economics*; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006297:Health Services Accessibility / Q000191:economics*; D006301:Health Services Needs and Demand / Q000191:economics; D006302:Health Services Research; D006305:Health Status Indicators; D006630:Hispanic or Latino / Q000706:statistics & numerical data*; D006801:Humans; D007356:Insurance, Pharmaceutical Services; D008297:Male; D008484:Medicaid; D006278:Medicare; D017059:Models, Econometric; D011306:Prescription Fees / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D014481:United States; D044465:White People / Q000706:statistics & numerical data*