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Estimating Excess Mortality Among People Living with HIV/AIDS During the COVID-19 Pandemic in the USA

Hall, L.; Chowell, G.

2025-06-10 hiv aids
10.1101/2025.06.09.25329225 medRxiv
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ObjectivesTo quantify the all-cause excess death rate of people living with HIV/AIDS (PWHA) during the multi-year 2020-2022 COVID-19 pandemic in the United States (U.S.), including stratifications by sex, age, race/ethnicity, and region. DesignUsing publicly available data from the CDC NCHHSTP AtlasPlus dashboard, we employed the ensemble n-subepidemic modeling framework (SubEpiPredict toolbox). This dynamic, uncertainty-aware approach was used to generate counterfactual forecasts of U.S. deaths among PWHA for 2020-2022. MethodsThe models were calibrated using 12 years of pre-pandemic mortality trends (2008-2019), with the median excess death rate calculated as the difference between forecasted and observed death rates. Results were stratified by age, sex, race/ethnicity, and U.S. region. ResultsOverall excess mortality among PWHA was estimated at 7,783 crude excess deaths (95% prediction interval [PI]: 5,098-10,525), corresponding to 2.77 excess deaths per 100,000 people (95% PI: 1.81-3.75), with the largest burden observed in 2021. Excess death rates were highest among males (3.39), individuals aged 55-64 years (4.94), multiracial populations (12.82), and residents of the Northeast U.S. (4.12). In contrast, the largest absolute number of excess deaths occurred among males (4,692), adults aged 65 years and older (2,560), Black/African American individuals (3,969), and residents of the Southern U.S. (4,025). ConclusionsThese systematic, model-based results reveal stark heterogeneities among PWHA by exposing recent mortality patterns that may not be captured by disease-specific mortality reporting alone. These heterogeneous findings can inform future public health programming and resource allocation and support tailored interventions for vulnerable populations.

Published in Sexually Transmitted Diseases · not in our set (fewer than 10 published preprints to learn from) · training set

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