ArticleEnvironmental epidemiology (Philadelphia, Pa.)2026
Mortality dynamics of West Nile virus and Lyme disease in the United States: Temporal, spatial, and socioeconomic dimensions (2003-2021).
Article in Environmental epidemiology (Philadelphia, Pa.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: West Nile virus (WNV) and Lyme disease pose a growing public health threat in the United States. This study investigated spatial and temporal mortality trends of WNV and Lyme disease and evaluated associations with individual and county-level factors. Methods: We utilized National Center for Health Statistics data to identify WNV- and Lyme-related deaths (2003-2021), including demographics and the decedent's county of residence. County-level insurance status was sourced from the U.S. Census Bureau, and urbanicity was defined using the 2013 National Center For Health Statistics classification. We applied negative binomial regression models to identify predictors of mortality. Results: Our analysis of 3694 deaths (WNV: 2,628 [71.2%]; Lyme: 1,066 [28.8%]) revealed WNV peaks occurred in 2003 (246 deaths, 9.4% of WNV deaths) and 2012 (281 deaths, 10.7%), and an increasing trend for Lyme (103 deaths, 9.7% in 2021). Overall, deaths were highest in 2012 (343, 9.3%) and lowest in 2009 (105, 2.6%). Age ≥65 (rate ratio [RR] = 1.5, confidence interval [CI] = 1.27, 1.48), male sex (RR = 1.2, CI = 1.04, 1.26), and White race (RR = 1.7, CI = 1.37, 1.73) were associated with increased mortality. Spatial analysis indicated high WNV mortality in California, Colorado, Arizona, and Texas, and states along the Mississippi River. In contrast, Lyme disease deaths were concentrated in the Northeast and the Upper Midwest. Conclusion: Our results suggest that age, sex, race, and urbanicity are important factors influencing mortality from WNV and Lyme disease. More research is needed to link exposures and incidence to mortality of WNV and Lyme to better understand the drivers of regional variation, given the spatial and temporal variability.
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