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Methods for estimation of disparities in medication use in an observational cohort study: results from the Multi-Ethnic Study of Atherosclerosis

Authors


Correspondence to: R. McClelland, Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Building 29, Suite 310, 6200 NE 74th St, Seattle, WA 98115, USA. E-mail: rmcclell@u.washington.edu

ABSTRACT

Purpose

Evaluating disparities in health care is an important aspect of understanding differences in disease risk. The purpose of this study is to describe the methodology for estimating such disparities, with application to a large multi-ethnic cohort study.

Methods

The Multi-Ethnic Study of Atherosclerosis includes 6814 participants aged 45–84 years free of cardiovascular disease. Prevalence ratio (PR) regression was used to model baseline lipid lowering medication (LLM) or anti-hypertensive medication use at baseline as a function of gender, race, risk factors, and estimated pre-treatment biomarker values.

Results

Hispanics and African Americans had lower prevalence of medication use than did non-Hispanic whites, even at the same risk factor profile. This became non-significant after adjusting for socioeconomic status. Although gender did not influence the prevalence of LLM use (PR = 1.09, 95%CI 0.95–1.25), there were differences in the association of diabetes and HDL with LLM use by gender. Men were significantly less likely to be on anti-hypertensive medications than women (PR = 0.86, 95%CI 0.80–0.92, p < 0.001), and this was not explained by risk factors or socioeconomic status. Lack of health insurance strongly influenced medication use, controlling for risk factors and other markers of socioeconomic status.

Conclusions

Disparities exist in the treatment of cholesterol and hypertension. Hispanics and African Americans had less use of LLM; men had less use of anti-hypertensives. Risk factors have differential associations with medication use depending on gender. Methods described in this paper can provide improved disparity estimation in observational cohort studies. Copyright © 2013 John Wiley & Sons, Ltd.

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