Spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases across the contiguous United States, 2020–2022
Abstract
BackgroundCoronavirus disease 2019 (COVID-19) is a highly transmissible respiratory infection that has caused a global pandemic. Evidence for the effect of short-term PM2.5 exposure on COVID-19 incidence varied across regions, and few studies have used advanced spatial analysis methods to examine spatial autocorrelation or clustering in these associations to improve estimation accuracy and identify highly susceptible geographic regions.MethodsIn this study, we used a novel three-stage strategy, including generalized additive model (GAM) and the newly developed estimation-error-based scan statistic (EESS) and multivariate conditional meta-autoregression (MCMAR), to fill the gap across the contiguous United States.ResultsWe found there was significant spatial heterogeneity (I2=67.416%,P<0.001), autocorrelation (ρ=0.972,P<0.001), and clustering (four clusters were discovered, P≤0.011) in the associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases. Considering the spatial autocorrelation and the spatial clustering effectively improved the fitting results of meta-regression. The pooled relative risk (RR) and 95% confidence intervals (95% CI) for per 10 μg/m3 increase in PM2.5 were 1.0341 and (1.0308, 1.0375). The RRs (95% CIs) for per 10 μg/m3 increase in PM2.5 were 1.0107 (1.0074, 1.0140), 1.1770 (1.1551, 1.1992), 1.0005 (0.9984, 1.0026), 1.0719 (1.0566, 1.0875), and 1.1032 (1.0798, 1.1270) in baseline and clusters 1–4, respectively. By 30 June 2022, a total of 1,761,726 (95% CI: 1,583,970–1,916,779; accounting for 2.06%, 95% CI: 1.85–2.24%) COVID-19 cases were found to be related to elevated PM2.5 exposure relative to the WHO annual mean PM2.5 guideline threshold of 5 μg/m3 across the contiguous United States. The eastern region and central region exhibited greater RR, greater attributable risk percentage, and more attributable cases than the western coastal region.ConclusionThere was significant spatial heterogeneity, autocorrelation, and clustering in the associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases across the contiguous United States.
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BibTeX
@article{li2026spatially,
title = {{Spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases across the contiguous United States, 2020–2022}},
author = {Sheng Li and Chenglin Tao and Wei Wang and Jiayi Chen and Junyu Wang and Caiying Luo and Jian Qian and Nan Wang and Menglu Jiang and Xingyu Zhang and Tao Zhang and Fei Yin and Yue Ma},
journal = {Frontiers in Public Health},
year = {2026},
month = nov,
doi = {10.3389/fpubh.2026.1911782},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1911782},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases across the contiguous United States, 2020–2022
AU - Sheng Li
AU - Chenglin Tao
AU - Wei Wang
AU - Jiayi Chen
AU - Junyu Wang
AU - Caiying Luo
AU - Jian Qian
AU - Nan Wang
AU - Menglu Jiang
AU - Xingyu Zhang
AU - Tao Zhang
AU - Fei Yin
AU - Yue Ma
PY - 2026
DA - 2026/11/04
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1911782
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1911782
AB - BackgroundCoronavirus disease 2019 (COVID-19) is a highly transmissible respiratory infection that has caused a global pandemic. Evidence for the effect of short-term PM2.5 exposure on COVID-19 incidence varied across regions, and few studies have used advanced spatial analysis methods to examine spatial autocorrelation or clustering in these associations to improve estimation accuracy and identify highly susceptible geographic regions.MethodsIn this study, we used a novel three-stage strategy, including generalized additive model (GAM) and the newly developed estimation-error-based scan statistic (EESS) and multivariate conditional meta-autoregression (MCMAR), to fill the gap across the contiguous United States.ResultsWe found there was significant spatial heterogeneity (I2=67.416%,P<0.001), autocorrelation (ρ=0.972,P<0.001), and clustering (four clusters were discovered, P≤0.011) in the associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases. Considering the spatial autocorrelation and the spatial clustering effectively improved the fitting results of meta-regression. The pooled relative risk (RR) and 95% confidence intervals (95% CI) for per 10 μg/m3 increase in PM2.5 were 1.0341 and (1.0308, 1.0375). The RRs (95% CIs) for per 10 μg/m3 increase in PM2.5 were 1.0107 (1.0074, 1.0140), 1.1770 (1.1551, 1.1992), 1.0005 (0.9984, 1.0026), 1.0719 (1.0566, 1.0875), and 1.1032 (1.0798, 1.1270) in baseline and clusters 1–4, respectively. By 30 June 2022, a total of 1,761,726 (95% CI: 1,583,970–1,916,779; accounting for 2.06%, 95% CI: 1.85–2.24%) COVID-19 cases were found to be related to elevated PM2.5 exposure relative to the WHO annual mean PM2.5 guideline threshold of 5 μg/m3 across the contiguous United States. The eastern region and central region exhibited greater RR, greater attributable risk percentage, and more attributable cases than the western coastal region.ConclusionThere was significant spatial heterogeneity, autocorrelation, and clustering in the associations between short-term exposure to PM2.5 and daily reported confirmed COVID-19 cases across the contiguous United States.
SN - 2296-2565
ER -