Widening Racial Disparities in the US Overdose Epidemic

Introduction: More Americans died in 2021 from drug overdose than from vehicle accidents and firearms combined. Unlike earlier phases, the current epidemic is marked by its disproportionate impact on communities of color. This report investigates regional and substancespecific variations in racial disparities to generate possible insights into the various forces shaping these trends. Methods: This report used data from 1999 to 2022 on opioid-related overdose deaths from the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research database. Racial disparities at the national, state, and substance levels were compared to describe heterogeneities in disparities trends. Data were analyzed in 2024. Results: Overall age-adjusted overdose mortality in the U.S. increased from 6.2 to 32.7 deaths per 100,000 between 1999 and 2022. In this same time period, mortality has increased most rapidly in Black, Native, and Hispanic/Latino Americans at 249.3%, 166.3%, and 171.8%, respectively. Disparities with White populations vary regionally. The upper Midwest (i.e., Minnesota, Wisconsin) and Washington state rank highest in excessive Native overdose death; the upper Midwest and Washington, DC rank highest as Black overdose deaths. In terms of substances, deaths from polyuse of methamphetamines and opioids have been highest among Native Americans over time, whereas deaths from cocaine and opioids disproportionately Conclusions: The opioid epidemic continues to expand, with particularly rapid acceleration in racially minoritized communities. The growing role of stimulants in opioid overdose deaths is a racialized phenomenon disproportionately impacting Black and Native Americans. Wide variation in state-level disparities suggest that structural racism impacts health in regionally specific ways, highlighting the need for regional solutions. Am J Prev Med 2025;68(4):745-753. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.