The COVID-19-wildfire smoke paradox: Reduced risk of all-cause mortality due to wildfire smoke in Colorado during the first year of the COVID-19 pandemic

Martenies, Sheena E.; Wilson, Ander; Hoskovec, Lauren; Bol, Kirk A.; Burket, Tori L.; Podewils, Laura Jean; Magzamen, Sheryl

doi:10.1016/j.envres.2023.115591

Cited by 2 publications

(1 citation statement)

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“…Recently, several methodological approaches have been proposed to address the independent and joint contribution of environmental exposures and social factors to health outcomes (Martenies, Hoskovec, et al., 2022 ; Martenies, Zhang, et al., 2022 ; Martenies et al., 2023 ). Identification of relevant social or environmental factors associated with disease outcomes are an important pathway to identify effective intervention and mediation strategies to improve health.…”

Section: Introductionmentioning

confidence: 99%

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

Kodros,

Carter,

Oke

et al. 2024

GeoHealth

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The environmental justice literature demonstrates consistently that low‐income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K‐means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non‐Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county‐level blood lead levels, 67th percentile in county‐level NO2, 79th percentile in county‐level CO, and 78th percentile in county‐level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county‐level unemployment, 70th percentile in county‐level population rate lacking a high school diploma, 73rd percentile in county‐level poverty rate, and 28th percentile in county‐level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.

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Section: Introductionmentioning

confidence: 99%