Social determinants of COVID-19 mortality at the county level
As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available d...
Gespeichert in:
Veröffentlicht in: | PloS one 2020-10, Vol.15 (10), p.e0240151-e0240151 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | e0240151 |
---|---|
container_issue | 10 |
container_start_page | e0240151 |
container_title | PloS one |
container_volume | 15 |
creator | Fielding-Miller, Rebecca K Sundaram, Maria E Brouwer, Kimberly |
description | As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns. |
doi_str_mv | 10.1371/journal.pone.0240151 |
format | Article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2451127216</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A638372477</galeid><doaj_id>oai_doaj_org_article_dcd14aaa3b9d4693917567e001d4c434</doaj_id><sourcerecordid>A638372477</sourcerecordid><originalsourceid>FETCH-LOGICAL-c758t-6ae5cd587945b8d51b8790fbea14a661b6c7e01f47ff59755e82f16855e780623</originalsourceid><addsrcrecordid>eNqNkl1rFDEUhgdRbK3-A9EBQfRi12TyORcKZf1aKCxY7W3IZDK7WTLJNskU--_NutOyI72QXOQkec57cpK3KF5CMIeIwQ9bPwQn7XznnZ6DCgNI4KPiFNaomtEKoMdH8UnxLMYtAARxSp8WJwgBUuXD0-LjpVdG2rLVSYfeOOlSLH1XLlZXy88zWJe9D0lak25Lmcq00aXyg8srq2-0fV486aSN-sU4nxW_vn75ufg-u1h9Wy7OL2aKEZ5mVGqiWsJZjUnDWwKbHIKu0RJiSSlsqGIawA6zriM1I0TzqoOU54BxQCt0Vrw-6O6sj2LsPIoKEwgrVkGaieWBaL3cil0wvQy3wksj_m74sBYyJKOsFq1qc1kpUVO3mNaohozQXB_AFiuMcNb6NFYbml63SrsUpJ2ITk-c2Yi1vxH55hTXPAu8GwWCvx50TKI3UWlrpdN-ONwbccAxy-ibf9CHuxuptcwNGNf5XFftRcU5RRyxCrO91vwBKo9W90Zlm3Qm708S3k8SMpP077SWQ4xiefnj_9nV1ZR9e8RutLRpE70dkvEuTkF8AFXwMQbd3T8yBGLv8rvXEHuXi9HlOe3V8QfdJ93ZGv0BuCX0Nw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2451127216</pqid></control><display><type>article</type><title>Social determinants of COVID-19 mortality at the county level</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Fielding-Miller, Rebecca K ; Sundaram, Maria E ; Brouwer, Kimberly</creator><contributor>Zaller, Nickolas D.</contributor><creatorcontrib>Fielding-Miller, Rebecca K ; Sundaram, Maria E ; Brouwer, Kimberly ; Zaller, Nickolas D.</creatorcontrib><description>As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0240151</identifier><identifier>PMID: 33052932</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Autoregressive models ; Biology and Life Sciences ; Coronavirus Infections - epidemiology ; Coronavirus Infections - mortality ; Coronaviruses ; COVID-19 ; Datasets ; Demographic aspects ; Demography - statistics & numerical data ; Emigrants and Immigrants - statistics & numerical data ; Epidemics ; Farmers - statistics & numerical data ; Farmworkers ; Fatalities ; Generalized method of moments ; Health disparities ; Health risks ; Households ; Humans ; Immigrants ; Insurance ; Insurance Coverage - statistics & numerical data ; Medicine and Health Sciences ; Minority & ethnic groups ; Mortality ; Occupational health ; Pandemics ; Patient outcomes ; People and places ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - mortality ; Population ; Population density ; Poverty ; Public health ; Research and Analysis Methods ; Risk factors ; Severe acute respiratory syndrome coronavirus 2 ; Socioeconomic Factors ; Uninsured people ; United States ; Workers</subject><ispartof>PloS one, 2020-10, Vol.15 (10), p.e0240151-e0240151</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Fielding-Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Fielding-Miller et al 2020 Fielding-Miller et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-6ae5cd587945b8d51b8790fbea14a661b6c7e01f47ff59755e82f16855e780623</citedby><cites>FETCH-LOGICAL-c758t-6ae5cd587945b8d51b8790fbea14a661b6c7e01f47ff59755e82f16855e780623</cites><orcidid>0000-0002-5099-0589</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556498/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556498/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33052932$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Zaller, Nickolas D.</contributor><creatorcontrib>Fielding-Miller, Rebecca K</creatorcontrib><creatorcontrib>Sundaram, Maria E</creatorcontrib><creatorcontrib>Brouwer, Kimberly</creatorcontrib><title>Social determinants of COVID-19 mortality at the county level</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.</description><subject>Age</subject><subject>Autoregressive models</subject><subject>Biology and Life Sciences</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - mortality</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Demographic aspects</subject><subject>Demography - statistics & numerical data</subject><subject>Emigrants and Immigrants - statistics & numerical data</subject><subject>Epidemics</subject><subject>Farmers - statistics & numerical data</subject><subject>Farmworkers</subject><subject>Fatalities</subject><subject>Generalized method of moments</subject><subject>Health disparities</subject><subject>Health risks</subject><subject>Households</subject><subject>Humans</subject><subject>Immigrants</subject><subject>Insurance</subject><subject>Insurance Coverage - statistics & numerical data</subject><subject>Medicine and Health Sciences</subject><subject>Minority & ethnic groups</subject><subject>Mortality</subject><subject>Occupational health</subject><subject>Pandemics</subject><subject>Patient outcomes</subject><subject>People and places</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - mortality</subject><subject>Population</subject><subject>Population density</subject><subject>Poverty</subject><subject>Public health</subject><subject>Research and Analysis Methods</subject><subject>Risk factors</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Socioeconomic Factors</subject><subject>Uninsured people</subject><subject>United States</subject><subject>Workers</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl1rFDEUhgdRbK3-A9EBQfRi12TyORcKZf1aKCxY7W3IZDK7WTLJNskU--_NutOyI72QXOQkec57cpK3KF5CMIeIwQ9bPwQn7XznnZ6DCgNI4KPiFNaomtEKoMdH8UnxLMYtAARxSp8WJwgBUuXD0-LjpVdG2rLVSYfeOOlSLH1XLlZXy88zWJe9D0lak25Lmcq00aXyg8srq2-0fV486aSN-sU4nxW_vn75ufg-u1h9Wy7OL2aKEZ5mVGqiWsJZjUnDWwKbHIKu0RJiSSlsqGIawA6zriM1I0TzqoOU54BxQCt0Vrw-6O6sj2LsPIoKEwgrVkGaieWBaL3cil0wvQy3wksj_m74sBYyJKOsFq1qc1kpUVO3mNaohozQXB_AFiuMcNb6NFYbml63SrsUpJ2ITk-c2Yi1vxH55hTXPAu8GwWCvx50TKI3UWlrpdN-ONwbccAxy-ibf9CHuxuptcwNGNf5XFftRcU5RRyxCrO91vwBKo9W90Zlm3Qm708S3k8SMpP077SWQ4xiefnj_9nV1ZR9e8RutLRpE70dkvEuTkF8AFXwMQbd3T8yBGLv8rvXEHuXi9HlOe3V8QfdJ93ZGv0BuCX0Nw</recordid><startdate>20201014</startdate><enddate>20201014</enddate><creator>Fielding-Miller, Rebecca K</creator><creator>Sundaram, Maria E</creator><creator>Brouwer, Kimberly</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-5099-0589</orcidid></search><sort><creationdate>20201014</creationdate><title>Social determinants of COVID-19 mortality at the county level</title><author>Fielding-Miller, Rebecca K ; Sundaram, Maria E ; Brouwer, Kimberly</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-6ae5cd587945b8d51b8790fbea14a661b6c7e01f47ff59755e82f16855e780623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age</topic><topic>Autoregressive models</topic><topic>Biology and Life Sciences</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - mortality</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Demographic aspects</topic><topic>Demography - statistics & numerical data</topic><topic>Emigrants and Immigrants - statistics & numerical data</topic><topic>Epidemics</topic><topic>Farmers - statistics & numerical data</topic><topic>Farmworkers</topic><topic>Fatalities</topic><topic>Generalized method of moments</topic><topic>Health disparities</topic><topic>Health risks</topic><topic>Households</topic><topic>Humans</topic><topic>Immigrants</topic><topic>Insurance</topic><topic>Insurance Coverage - statistics & numerical data</topic><topic>Medicine and Health Sciences</topic><topic>Minority & ethnic groups</topic><topic>Mortality</topic><topic>Occupational health</topic><topic>Pandemics</topic><topic>Patient outcomes</topic><topic>People and places</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - mortality</topic><topic>Population</topic><topic>Population density</topic><topic>Poverty</topic><topic>Public health</topic><topic>Research and Analysis Methods</topic><topic>Risk factors</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Socioeconomic Factors</topic><topic>Uninsured people</topic><topic>United States</topic><topic>Workers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fielding-Miller, Rebecca K</creatorcontrib><creatorcontrib>Sundaram, Maria E</creatorcontrib><creatorcontrib>Brouwer, Kimberly</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fielding-Miller, Rebecca K</au><au>Sundaram, Maria E</au><au>Brouwer, Kimberly</au><au>Zaller, Nickolas D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Social determinants of COVID-19 mortality at the county level</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-10-14</date><risdate>2020</risdate><volume>15</volume><issue>10</issue><spage>e0240151</spage><epage>e0240151</epage><pages>e0240151-e0240151</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33052932</pmid><doi>10.1371/journal.pone.0240151</doi><tpages>e0240151</tpages><orcidid>https://orcid.org/0000-0002-5099-0589</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2020-10, Vol.15 (10), p.e0240151-e0240151 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_2451127216 |
source | MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
subjects | Age Autoregressive models Biology and Life Sciences Coronavirus Infections - epidemiology Coronavirus Infections - mortality Coronaviruses COVID-19 Datasets Demographic aspects Demography - statistics & numerical data Emigrants and Immigrants - statistics & numerical data Epidemics Farmers - statistics & numerical data Farmworkers Fatalities Generalized method of moments Health disparities Health risks Households Humans Immigrants Insurance Insurance Coverage - statistics & numerical data Medicine and Health Sciences Minority & ethnic groups Mortality Occupational health Pandemics Patient outcomes People and places Pneumonia, Viral - epidemiology Pneumonia, Viral - mortality Population Population density Poverty Public health Research and Analysis Methods Risk factors Severe acute respiratory syndrome coronavirus 2 Socioeconomic Factors Uninsured people United States Workers |
title | Social determinants of COVID-19 mortality at the county level |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T08%3A20%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Social%20determinants%20of%20COVID-19%20mortality%20at%20the%20county%20level&rft.jtitle=PloS%20one&rft.au=Fielding-Miller,%20Rebecca%20K&rft.date=2020-10-14&rft.volume=15&rft.issue=10&rft.spage=e0240151&rft.epage=e0240151&rft.pages=e0240151-e0240151&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0240151&rft_dat=%3Cgale_plos_%3EA638372477%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2451127216&rft_id=info:pmid/33052932&rft_galeid=A638372477&rft_doaj_id=oai_doaj_org_article_dcd14aaa3b9d4693917567e001d4c434&rfr_iscdi=true |