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Governor's Party, Policies, and COVID-19 Outcomes: Further Evidence of an Effect
This study connects the aggregate strength of public health policies taken in response to the COVID-19 pandemic in the U.S. states to the governors’ party affiliations and to state-level outcomes. Understanding the relationship between politics and public health measures can better prepare American...
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Published in: | American journal of preventive medicine 2022-03, Vol.62 (3), p.433-437 |
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creator | Shvetsova, Olga Zhirnov, Andrei Giannelli, Frank R. Catalano, Michael A. Catalano, Olivia |
description | This study connects the aggregate strength of public health policies taken in response to the COVID-19 pandemic in the U.S. states to the governors’ party affiliations and to state-level outcomes. Understanding the relationship between politics and public health measures can better prepare American communities for what to expect from their governments in a future crisis and encourage advocacy for delegating public health decisions to medical professionals.
The public health Protective Policy Index captures the strength of policy response to COVID-19 at the state level. The authors estimated a Bayesian model that links the rate of disease spread to Protective Policy Index. The model also accounted for the possible state-specific undercounting of cases and controls for state population density, poverty, number of physicians, cardiovascular disease, asthma, smoking, obesity, age, racial composition, and urbanization. A Bayesian linear model with natural splines of time was employed to link the dynamics of Protective Policy Index to governors’ party affiliations.
A 10–percentage point decrease in Protective Policy Index was associated with an 8% increase in the expected number of new cases. Between late March and November 2020 and at the state-specific peaks of the pandemic, the Protective Policy Index in the states with Democratic governors was about 10‒percentage points higher than in the states with Republican governors.
Public health measures were stricter in the Democrat-led states, and stricter public health measures were associated with a slower growth of COVID-19 cases. The apparent politicization of public health measures suggests that public health decision making by health professionals rather than by political incumbents could be beneficial. |
doi_str_mv | 10.1016/j.amepre.2021.09.003 |
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The public health Protective Policy Index captures the strength of policy response to COVID-19 at the state level. The authors estimated a Bayesian model that links the rate of disease spread to Protective Policy Index. The model also accounted for the possible state-specific undercounting of cases and controls for state population density, poverty, number of physicians, cardiovascular disease, asthma, smoking, obesity, age, racial composition, and urbanization. A Bayesian linear model with natural splines of time was employed to link the dynamics of Protective Policy Index to governors’ party affiliations.
A 10–percentage point decrease in Protective Policy Index was associated with an 8% increase in the expected number of new cases. Between late March and November 2020 and at the state-specific peaks of the pandemic, the Protective Policy Index in the states with Democratic governors was about 10‒percentage points higher than in the states with Republican governors.
Public health measures were stricter in the Democrat-led states, and stricter public health measures were associated with a slower growth of COVID-19 cases. The apparent politicization of public health measures suggests that public health decision making by health professionals rather than by political incumbents could be beneficial.</description><identifier>ISSN: 0749-3797</identifier><identifier>EISSN: 1873-2607</identifier><identifier>DOI: 10.1016/j.amepre.2021.09.003</identifier><identifier>PMID: 34756754</identifier><language>eng</language><publisher>Netherlands: Elsevier Inc</publisher><subject>Advocacy ; Asthma ; Bayes Theorem ; Bayesian analysis ; Cardiovascular diseases ; Coronaviruses ; COVID-19 ; Decision making ; Governors ; Humans ; Linear analysis ; Medical personnel ; Obesity ; Pandemics ; Politicization ; Politics ; Population density ; Poverty ; Public health ; Public Policy ; Research Brief ; SARS-CoV-2 ; Smoking ; United States - epidemiology ; Urbanization</subject><ispartof>American journal of preventive medicine, 2022-03, Vol.62 (3), p.433-437</ispartof><rights>2021 American Journal of Preventive Medicine</rights><rights>Copyright © 2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Science Ltd. Mar 2022</rights><rights>2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved. 2021 American Journal of Preventive Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c491t-9d037f336b89eb538a3898f5a4ad9a083906432e025bb4ee4f251dc9d0f615263</citedby><cites>FETCH-LOGICAL-c491t-9d037f336b89eb538a3898f5a4ad9a083906432e025bb4ee4f251dc9d0f615263</cites><orcidid>0000-0003-0154-0968 ; 0000-0002-3663-6356 ; 0000-0002-4540-3060 ; 0000-0002-2978-8239</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902,30976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34756754$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shvetsova, Olga</creatorcontrib><creatorcontrib>Zhirnov, Andrei</creatorcontrib><creatorcontrib>Giannelli, Frank R.</creatorcontrib><creatorcontrib>Catalano, Michael A.</creatorcontrib><creatorcontrib>Catalano, Olivia</creatorcontrib><title>Governor's Party, Policies, and COVID-19 Outcomes: Further Evidence of an Effect</title><title>American journal of preventive medicine</title><addtitle>Am J Prev Med</addtitle><description>This study connects the aggregate strength of public health policies taken in response to the COVID-19 pandemic in the U.S. states to the governors’ party affiliations and to state-level outcomes. Understanding the relationship between politics and public health measures can better prepare American communities for what to expect from their governments in a future crisis and encourage advocacy for delegating public health decisions to medical professionals.
The public health Protective Policy Index captures the strength of policy response to COVID-19 at the state level. The authors estimated a Bayesian model that links the rate of disease spread to Protective Policy Index. The model also accounted for the possible state-specific undercounting of cases and controls for state population density, poverty, number of physicians, cardiovascular disease, asthma, smoking, obesity, age, racial composition, and urbanization. A Bayesian linear model with natural splines of time was employed to link the dynamics of Protective Policy Index to governors’ party affiliations.
A 10–percentage point decrease in Protective Policy Index was associated with an 8% increase in the expected number of new cases. Between late March and November 2020 and at the state-specific peaks of the pandemic, the Protective Policy Index in the states with Democratic governors was about 10‒percentage points higher than in the states with Republican governors.
Public health measures were stricter in the Democrat-led states, and stricter public health measures were associated with a slower growth of COVID-19 cases. The apparent politicization of public health measures suggests that public health decision making by health professionals rather than by political incumbents could be beneficial.</description><subject>Advocacy</subject><subject>Asthma</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Cardiovascular diseases</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Decision making</subject><subject>Governors</subject><subject>Humans</subject><subject>Linear analysis</subject><subject>Medical personnel</subject><subject>Obesity</subject><subject>Pandemics</subject><subject>Politicization</subject><subject>Politics</subject><subject>Population density</subject><subject>Poverty</subject><subject>Public health</subject><subject>Public Policy</subject><subject>Research Brief</subject><subject>SARS-CoV-2</subject><subject>Smoking</subject><subject>United States - epidemiology</subject><subject>Urbanization</subject><issn>0749-3797</issn><issn>1873-2607</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>7QJ</sourceid><recordid>eNp9kUFv1DAQhS0EosvCP0AoEgc4NGFsx3HMAQkt21Kp0u4BuFpOMqZeJfFiJyv13-PVlrZw4OSD35uZ9z5CXlMoKNDqw64wA-4DFgwYLUAVAPwJWdBa8pxVIJ-SBchS5VwqeUZexLgDAFlT9Zyc8VKKSopyQbaX_oBh9OFdzLYmTLfn2db3rnUYzzMzdtlq8-PqS05Vtpmn1g8YP2YXc5huMGTrg-twbDHzNkmztbXYTi_JM2v6iK_u3iX5frH-tvqaX28ur1afr_O2VHTKVQdcWs6rplbYCF4bXqvaClOaThmouYKq5AyBiaYpEUvLBO3aZLMVFaziS_LpNHc_NwN2LY5TML3eBzeYcKu9cfrvn9Hd6J_-oGsBTKaSluT93YDgf80YJz242GLfmxH9HDUTqoK0Co7St_9Id34OY4qn0yW1lLQSIqnKk6oNPsaA9v4YCvqITO_0CZk-ItOgdEKWbG8eB7k3_WH0kBRTnQeHQceEJ_XeuZAK1513_9_wGzbPp1Y</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Shvetsova, Olga</creator><creator>Zhirnov, Andrei</creator><creator>Giannelli, Frank R.</creator><creator>Catalano, Michael A.</creator><creator>Catalano, Olivia</creator><general>Elsevier Inc</general><general>Elsevier Science Ltd</general><general>American Journal of Preventive Medicine. Published by Elsevier Inc</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>7QJ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-0154-0968</orcidid><orcidid>https://orcid.org/0000-0002-3663-6356</orcidid><orcidid>https://orcid.org/0000-0002-4540-3060</orcidid><orcidid>https://orcid.org/0000-0002-2978-8239</orcidid></search><sort><creationdate>20220301</creationdate><title>Governor's Party, Policies, and COVID-19 Outcomes: Further Evidence of an Effect</title><author>Shvetsova, Olga ; Zhirnov, Andrei ; Giannelli, Frank R. ; Catalano, Michael A. ; Catalano, Olivia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c491t-9d037f336b89eb538a3898f5a4ad9a083906432e025bb4ee4f251dc9d0f615263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Advocacy</topic><topic>Asthma</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Cardiovascular diseases</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Decision making</topic><topic>Governors</topic><topic>Humans</topic><topic>Linear analysis</topic><topic>Medical personnel</topic><topic>Obesity</topic><topic>Pandemics</topic><topic>Politicization</topic><topic>Politics</topic><topic>Population density</topic><topic>Poverty</topic><topic>Public health</topic><topic>Public Policy</topic><topic>Research Brief</topic><topic>SARS-CoV-2</topic><topic>Smoking</topic><topic>United States - epidemiology</topic><topic>Urbanization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shvetsova, Olga</creatorcontrib><creatorcontrib>Zhirnov, Andrei</creatorcontrib><creatorcontrib>Giannelli, Frank R.</creatorcontrib><creatorcontrib>Catalano, Michael A.</creatorcontrib><creatorcontrib>Catalano, Olivia</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>American journal of preventive medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shvetsova, Olga</au><au>Zhirnov, Andrei</au><au>Giannelli, Frank R.</au><au>Catalano, Michael A.</au><au>Catalano, Olivia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Governor's Party, Policies, and COVID-19 Outcomes: Further Evidence of an Effect</atitle><jtitle>American journal of preventive medicine</jtitle><addtitle>Am J Prev Med</addtitle><date>2022-03-01</date><risdate>2022</risdate><volume>62</volume><issue>3</issue><spage>433</spage><epage>437</epage><pages>433-437</pages><issn>0749-3797</issn><eissn>1873-2607</eissn><abstract>This study connects the aggregate strength of public health policies taken in response to the COVID-19 pandemic in the U.S. states to the governors’ party affiliations and to state-level outcomes. Understanding the relationship between politics and public health measures can better prepare American communities for what to expect from their governments in a future crisis and encourage advocacy for delegating public health decisions to medical professionals.
The public health Protective Policy Index captures the strength of policy response to COVID-19 at the state level. The authors estimated a Bayesian model that links the rate of disease spread to Protective Policy Index. The model also accounted for the possible state-specific undercounting of cases and controls for state population density, poverty, number of physicians, cardiovascular disease, asthma, smoking, obesity, age, racial composition, and urbanization. A Bayesian linear model with natural splines of time was employed to link the dynamics of Protective Policy Index to governors’ party affiliations.
A 10–percentage point decrease in Protective Policy Index was associated with an 8% increase in the expected number of new cases. Between late March and November 2020 and at the state-specific peaks of the pandemic, the Protective Policy Index in the states with Democratic governors was about 10‒percentage points higher than in the states with Republican governors.
Public health measures were stricter in the Democrat-led states, and stricter public health measures were associated with a slower growth of COVID-19 cases. The apparent politicization of public health measures suggests that public health decision making by health professionals rather than by political incumbents could be beneficial.</abstract><cop>Netherlands</cop><pub>Elsevier Inc</pub><pmid>34756754</pmid><doi>10.1016/j.amepre.2021.09.003</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-0154-0968</orcidid><orcidid>https://orcid.org/0000-0002-3663-6356</orcidid><orcidid>https://orcid.org/0000-0002-4540-3060</orcidid><orcidid>https://orcid.org/0000-0002-2978-8239</orcidid><oa>free_for_read</oa></addata></record> |
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source | Applied Social Sciences Index & Abstracts (ASSIA); ScienceDirect Freedom Collection |
subjects | Advocacy Asthma Bayes Theorem Bayesian analysis Cardiovascular diseases Coronaviruses COVID-19 Decision making Governors Humans Linear analysis Medical personnel Obesity Pandemics Politicization Politics Population density Poverty Public health Public Policy Research Brief SARS-CoV-2 Smoking United States - epidemiology Urbanization |
title | Governor's Party, Policies, and COVID-19 Outcomes: Further Evidence of an Effect |
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