Abstract

High-risk medication use is associated with an increased risk of adverse events, but little is known about its chronic utilization by key demographic groups. We aimed to study the associations between age, sex, and race/ethnicity with new chronic use of high-risk medications in older adults. In this retrospective cohort study, we analyzed data from older adults aged ≥65 years enrolled in a national health insurer who started a high-risk medication between 2017 and 2022 across 16 high-risk medication classes. We used generalized estimating equations to estimate the associations between sociodemographic classifications and the onset of chronic high-risk medication use after initiation (≥90 days' supply across ≥2 fills within 180 days). We adjusted the analyses for sociodemographic and clinical patient characteristics and added three-way interaction terms for race/ethnicity, sex, and age to explore whether the outcome varied across different subgroups of race/ethnicity, age, and sex. Across 2,751,069 patients (mean age: 74 years [SD = 7], 72% White, 60% Female), 406,075 (15%) became new chronic users of ≥1 high-risk medication. Compared to White older adults, Asian (RR = 0.81, 95% CI: 0.79-0.84), Black (RR = 0.92, 95% CI: 0.90-0.94), and Hispanic (RR = 0.85, 95% CI: 0.83-0.86) older adults had a lower risk of becoming new chronic users. Men had a higher risk compared to women (RR = 1.09, 95% CI: 1.08-1.10). Age was not significantly associated with new chronic high-risk medication use (≥75 years: RR = 1.00, 95% CI: 1.00-1.01). We observed differences across some medication classes, like benzodiazepines, first-generation antihistamines, and antimuscarinics for which non-White older adults were at a higher risk. The joint presence of specific age, sex, and race/ethnicity characteristics decreased the risk of becoming a new chronic user (e.g., Hispanic/Female/65-74 years: RR = 0.96, 95% CI: 0.94-0.99). New chronic high-risk medication use varied across older adults by sociodemographic characteristics, suggesting the need to individualize medication optimization approaches and better understand how systematic barriers in access to health care may influence differences in high-risk medication use in older adults.

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