COVID-19 Infection and Mortality in State Veterans Homes: A Multilevel Approach.

Objectives: Some state veterans homes (SVHs) received media attention in the wake of the COVID-19 pandemic because of allegations of poor infection control and excess mortality. However, little research has investigated how these facilities differ from community nursing homes (CNHs) and what the geographical trends of these infection and mortality differences are. We aimed to test (1) whether infection was overall lower in SVHs than CNHs, (2) whether mortality was overall lower in SVHs than CNHs, as well as the geographic distribution of nursing home infection and mortality across the United States.

Design: Retrospective nationwide cohort study.

Setting And Participants: Skilled nursing facilities in the United States from May 2020 to July 2022 during the COVID-19 pandemic.

Methods: Using multilevel negative binomial regression, we modeled COVID-19 infection and mortality rates in skilled nursing homes, testing for overall SVH differences from May 2020 to July 2022, placing random effects on counties to calculate adjusted county-level infection and mortality rates.

Results: SVHs experienced 18% fewer cases but 25% more deaths overall compared with CNHs. Counties with the highest levels of facility infection, including counties with SVHs, were situated mainly in Midwestern, Atlantic, and Southern states, with the majority of counties with low infection levels in Central and Western states. Counties with the highest levels of facility mortality emerged in Rust Belt and Midwestern states down to Southern states, with the lowest levels of county-level mortality, particularly among counties containing SVHs, occurring westward to Central and Western states.

Conclusion And Implications: SVHs experienced lower infection levels but higher mortality levels than CNHs, and fewer extremely high infection and mortality rates in counties containing SVHs despite higher mortality risk in SVHs, calling attention to unobserved facility-level differences such as gender and age distributions and future research opportunities using more granular geographical aggregations to better understand facility-level SVH risk within the broader neighborhood context.