Racial and Ethnic Differences in Antiseizure Medications Among People With Epilepsy on Medicaid: A Case of Potential Inequities

Abstract

Background and Objectives: Being on a newer, second-, and third-generation antiseizure medication (ASM) may represent an important marker of quality of care for people with epilepsy. We sought to examine whether there were racial/ethnic differences in their use. Methods: Using Medicaid claims data, we identified the type and number of ASMs, as well as the adherence, for people with epilepsy over a 5-year period (2010–2014). We used multilevel logistic regression models to examine the association between newer-generation ASMs and adherence. We then examined whether there were racial/ethnic differences in ASM use in models adjusted for demographics, utilization, year, and comorbidities. Results: Among 78,534 adults with epilepsy, 17,729 were Black, and 9,376 were Hispanic. Overall, 25.6% were on older ASMs, and being solely on second-generation ASMs during the study period was associated with better adherence (adjusted odds ratio: 1.17, 95% confidence interval [CI]: 1.11–1.23). Those who saw a neurologist (3.26, 95% CI: 3.13–3.41) or who were newly diagnosed (1.29, 95% CI: 1.16–1.42) had higher odds of being on newer ASMs. Importantly, Black (0.71, 95% CI: 0.68–0.75), Hispanic (0.93, 95% CI: 0.88–0.99), and Native Hawaiian and Other Pacific Island individuals (0.77, 95% CI: 0.67–0.88) had lower odds of being on newer ASMs when compared with White individuals. Discussion: Generally, racial and ethnic minoritized people with epilepsy have lower odds of being on newer-generation ASMs. Greater adherence by people who were only on newer ASMs, their greater use among people seeing a neurologist, and the opportunity of a new diagnosis point to actionable leverage points for reducing inequities in epilepsy care.

Publication
Neurology: Clinical Practice
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Wyatt P. Bensken, PhD
Research Investigator & Adjunct Assistant Professor of Population and Quantitative Health Sciences

My expertise is in the use of complex health care data, paired with traditional statistical and novel machine learning approaches, to identify opportunities to improve health, health care, and health outcomes for all.