{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:40:16Z","timestamp":1740184816731,"version":"3.37.3"},"reference-count":90,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001160","name":"Health Effects Institute","doi-asserted-by":"publisher","award":["4953-RFA14-3\/16-4"],"award-info":[{"award-number":["4953-RFA14-3\/16-4"]}],"id":[{"id":"10.13039\/100001160","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000139","name":"U.S. Environmental Protection Agency","doi-asserted-by":"publisher","award":["83587201-0"],"award-info":[{"award-number":["83587201-0"]}],"id":[{"id":"10.13039\/100000139","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Climate Change Solutions Fund"},{"name":"2020 Harvard Star-Friedman Award for Promising Scientific Research"},{"name":"NIH","award":["R01 ES024332-01A1, P50MD010428, R01 ES026217, R01 ES028033, R01 ES030616, R01 AG066793-01, R01 MD012769, P30ES000002, R01 MD016054-01A1, and T32ES007142"],"award-info":[{"award-number":["R01 ES024332-01A1, P50MD010428, R01 ES026217, R01 ES028033, R01 ES030616, R01 AG066793-01, R01 MD012769, P30ES000002, R01 MD016054-01A1, and T32ES007142"]}]},{"DOI":"10.13039\/100007229","name":"Harvard University","doi-asserted-by":"publisher","award":["Data Science Methods and Tools for Climate Change Solutions"],"award-info":[{"award-number":["Data Science Methods and Tools for Climate Change Solutions"]}],"id":[{"id":"10.13039\/100007229","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007229","name":"Harvard University","doi-asserted-by":"crossref","award":["Data Science Methods and Tools for Climate Change Solutions"],"award-info":[{"award-number":["Data Science Methods and Tools for Climate Change Solutions"]}],"id":[{"id":"10.13039\/100007229","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fernholz Foundation"}],"content-domain":{"domain":["www.plosglobalpublichealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Glob Public Health"],"abstract":"<jats:p>Imposing stricter regulations for PM<jats:sub>2.5<\/jats:sub>has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM<jats:sub>2.5<\/jats:sub>. However, many studies reporting a relationship between COVID-19 outcomes and PM<jats:sub>2.5<\/jats:sub>have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM<jats:sub>2.5<\/jats:sub>concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM<jats:sub>2.5<\/jats:sub>and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1<jats:italic>\u03bcg<\/jats:italic>\/<jats:italic>m<\/jats:italic><jats:sup>3<\/jats:sup>in long-term PM<jats:sub>2.5<\/jats:sub>exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual\u2019s odds of COVID-19 mortality. Code to reproduce our study is publicly available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/NSAPH\/PM_COVID_ecoinference\" xlink:type=\"simple\">https:\/\/github.com\/NSAPH\/PM_COVID_ecoinference<\/jats:ext-link>. The results confirm previous evidence of an association between long-term exposure to PM<jats:sub>2.5<\/jats:sub>and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.<\/jats:p>","DOI":"10.1371\/journal.pgph.0002178","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T17:28:04Z","timestamp":1690997284000},"page":"e0002178","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgph.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States"],"prefix":"10.1371","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-9082","authenticated-orcid":true,"given":"Sophie M.","family":"Woodward","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7924-0706","authenticated-orcid":true,"given":"Daniel","family":"Mork","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4884-657X","authenticated-orcid":true,"given":"Xiao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhewen","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5177-8598","authenticated-orcid":true,"given":"Danielle","family":"Braun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesca","family":"Dominici","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"pgph.0002178.ref001","unstructured":"US Environmental Protection Agency. 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