Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis

Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions-a delayed public-health response, past epidemic experience, proportion of elderly in population, population density,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one 2023-03, Vol.18 (3), p.e0282617-e0282617
Hauptverfasser: Chen, Bin, Liu, Yao, Yan, Bo, Wu, Long, Zhang, Xiaomin
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page e0282617
container_issue 3
container_start_page e0282617
container_title PloS one
container_volume 18
creator Chen, Bin
Liu, Yao
Yan, Bo
Wu, Long
Zhang, Xiaomin
description Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions-a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita-influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular "playbook"-a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country's past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.
doi_str_mv 10.1371/journal.pone.0282617
format Article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2784817757</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A740104911</galeid><doaj_id>oai_doaj_org_article_154f661872f14944a4467cf1b3017bbe</doaj_id><sourcerecordid>A740104911</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-6ecf3168475fbcc28a0b8ba8476e7be499846a2d719bd06353f5399676e64d053</originalsourceid><addsrcrecordid>eNqNk92O1CAUxxujcdfVNzBKYmL0omMpFMqNZjJ-TbLJJH6sl4RS2rJpyyxQtW_gY0t3upup2QvThMLhd_7ncOBE0VOYrCCi8M2lGWwv2tXe9GqVpHlKIL0XnUKG0pikCbp_ND-JHjl3mSQZygl5GJ0gkuc5Qeg0-vOjGcEvZRVwplNAmqH3VisHOjPZBimVc9XQAt-IHhjfKOuA7oEcbKH7Gihh2xFsdhfb9zFkk5cXrfYj0N1eSP8OrIG0xrn4oDyGCH2l68EKr01IH4gwjE67x9GDSrROPZn_Z9H3jx--bT7H57tP2836PJaEpT4mSlYIkhzTrCqkTHORFHkhwpooWijMWI6JSEsKWVEmBGWoyhBjJGwTXIYCnEXPD7r71jg-F9HxlOY4h5RmNBDbA1Eaccn3VnfCjtwIza8NxtZcWK9lqzjMcEUIzGlaQcwwFhgTKitYoATSolBB6-0cbSg6VUoVaiDahehyp9cNr81PzhjDh2RezQLWXA3Ked5pJ1Xbil6ZYc4bkSxBAX3xD3r36WaqFuEAuq9MiCsnUb6mOIEJZhAGanUHFb5SdTpcoap0sC8cXi8cAuPVb1-LwTm-_frl_9ndxZJ9ecQ2SrS-caYdptfjliA-gNfPzarqtsgw4VPD3FSDTw3D54YJbs-OL-jW6aZD0F911RD5</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2784817757</pqid></control><display><type>article</type><title>Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS)</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Chen, Bin ; Liu, Yao ; Yan, Bo ; Wu, Long ; Zhang, Xiaomin</creator><contributor>Cheong, Siew Ann</contributor><creatorcontrib>Chen, Bin ; Liu, Yao ; Yan, Bo ; Wu, Long ; Zhang, Xiaomin ; Cheong, Siew Ann</creatorcontrib><description>Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions-a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita-influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular "playbook"-a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country's past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0282617</identifier><identifier>PMID: 36888633</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Biology and Life Sciences ; China ; Comparative analysis ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Epidemics ; Fatalities ; Forecasts and trends ; Fuzzy sets ; Humans ; Impact analysis ; Income ; Medicine and Health Sciences ; Mortality ; Older people ; Pathways ; People and Places ; Population density ; Prevention ; Public health ; Qualitative analysis ; Social Sciences</subject><ispartof>PloS one, 2023-03, Vol.18 (3), p.e0282617-e0282617</ispartof><rights>Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Chen 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>2023 Chen et al 2023 Chen et al</rights><rights>2023 Chen 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6ecf3168475fbcc28a0b8ba8476e7be499846a2d719bd06353f5399676e64d053</citedby><cites>FETCH-LOGICAL-c692t-6ecf3168475fbcc28a0b8ba8476e7be499846a2d719bd06353f5399676e64d053</cites><orcidid>0000-0003-0527-2235</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/PMC9994757/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994757/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2101,2927,23865,27923,27924,53790,53792,79471,79472</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36888633$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Cheong, Siew Ann</contributor><creatorcontrib>Chen, Bin</creatorcontrib><creatorcontrib>Liu, Yao</creatorcontrib><creatorcontrib>Yan, Bo</creatorcontrib><creatorcontrib>Wu, Long</creatorcontrib><creatorcontrib>Zhang, Xiaomin</creatorcontrib><title>Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions-a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita-influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular "playbook"-a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country's past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.</description><subject>Aged</subject><subject>Biology and Life Sciences</subject><subject>China</subject><subject>Comparative analysis</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Epidemics</subject><subject>Fatalities</subject><subject>Forecasts and trends</subject><subject>Fuzzy sets</subject><subject>Humans</subject><subject>Impact analysis</subject><subject>Income</subject><subject>Medicine and Health Sciences</subject><subject>Mortality</subject><subject>Older people</subject><subject>Pathways</subject><subject>People and Places</subject><subject>Population density</subject><subject>Prevention</subject><subject>Public health</subject><subject>Qualitative analysis</subject><subject>Social Sciences</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk92O1CAUxxujcdfVNzBKYmL0omMpFMqNZjJ-TbLJJH6sl4RS2rJpyyxQtW_gY0t3upup2QvThMLhd_7ncOBE0VOYrCCi8M2lGWwv2tXe9GqVpHlKIL0XnUKG0pikCbp_ND-JHjl3mSQZygl5GJ0gkuc5Qeg0-vOjGcEvZRVwplNAmqH3VisHOjPZBimVc9XQAt-IHhjfKOuA7oEcbKH7Gihh2xFsdhfb9zFkk5cXrfYj0N1eSP8OrIG0xrn4oDyGCH2l68EKr01IH4gwjE67x9GDSrROPZn_Z9H3jx--bT7H57tP2836PJaEpT4mSlYIkhzTrCqkTHORFHkhwpooWijMWI6JSEsKWVEmBGWoyhBjJGwTXIYCnEXPD7r71jg-F9HxlOY4h5RmNBDbA1Eaccn3VnfCjtwIza8NxtZcWK9lqzjMcEUIzGlaQcwwFhgTKitYoATSolBB6-0cbSg6VUoVaiDahehyp9cNr81PzhjDh2RezQLWXA3Ked5pJ1Xbil6ZYc4bkSxBAX3xD3r36WaqFuEAuq9MiCsnUb6mOIEJZhAGanUHFb5SdTpcoap0sC8cXi8cAuPVb1-LwTm-_frl_9ndxZJ9ecQ2SrS-caYdptfjliA-gNfPzarqtsgw4VPD3FSDTw3D54YJbs-OL-jW6aZD0F911RD5</recordid><startdate>20230308</startdate><enddate>20230308</enddate><creator>Chen, Bin</creator><creator>Liu, Yao</creator><creator>Yan, Bo</creator><creator>Wu, Long</creator><creator>Zhang, Xiaomin</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>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>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0527-2235</orcidid></search><sort><creationdate>20230308</creationdate><title>Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis</title><author>Chen, Bin ; Liu, Yao ; Yan, Bo ; Wu, Long ; Zhang, Xiaomin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-6ecf3168475fbcc28a0b8ba8476e7be499846a2d719bd06353f5399676e64d053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aged</topic><topic>Biology and Life Sciences</topic><topic>China</topic><topic>Comparative analysis</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>Epidemics</topic><topic>Fatalities</topic><topic>Forecasts and trends</topic><topic>Fuzzy sets</topic><topic>Humans</topic><topic>Impact analysis</topic><topic>Income</topic><topic>Medicine and Health Sciences</topic><topic>Mortality</topic><topic>Older people</topic><topic>Pathways</topic><topic>People and Places</topic><topic>Population density</topic><topic>Prevention</topic><topic>Public health</topic><topic>Qualitative analysis</topic><topic>Social Sciences</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Bin</creatorcontrib><creatorcontrib>Liu, Yao</creatorcontrib><creatorcontrib>Yan, Bo</creatorcontrib><creatorcontrib>Wu, Long</creatorcontrib><creatorcontrib>Zhang, Xiaomin</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 &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; 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 &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; 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>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 &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; 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 &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; 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>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>Chen, Bin</au><au>Liu, Yao</au><au>Yan, Bo</au><au>Wu, Long</au><au>Zhang, Xiaomin</au><au>Cheong, Siew Ann</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-03-08</date><risdate>2023</risdate><volume>18</volume><issue>3</issue><spage>e0282617</spage><epage>e0282617</epage><pages>e0282617-e0282617</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions-a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita-influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular "playbook"-a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country's past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36888633</pmid><doi>10.1371/journal.pone.0282617</doi><tpages>e0282617</tpages><orcidid>https://orcid.org/0000-0003-0527-2235</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2023-03, Vol.18 (3), p.e0282617-e0282617
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_2784817757
source MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry
subjects Aged
Biology and Life Sciences
China
Comparative analysis
Coronaviruses
COVID-19
COVID-19 - epidemiology
Epidemics
Fatalities
Forecasts and trends
Fuzzy sets
Humans
Impact analysis
Income
Medicine and Health Sciences
Mortality
Older people
Pathways
People and Places
Population density
Prevention
Public health
Qualitative analysis
Social Sciences
title Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T10%3A43%3A07IST&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=Why%20were%20some%20countries%20more%20successful%20than%20others%20in%20curbing%20early%20COVID-19%20mortality%20impact?%20A%20cross-country%20configurational%20analysis&rft.jtitle=PloS%20one&rft.au=Chen,%20Bin&rft.date=2023-03-08&rft.volume=18&rft.issue=3&rft.spage=e0282617&rft.epage=e0282617&rft.pages=e0282617-e0282617&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0282617&rft_dat=%3Cgale_plos_%3EA740104911%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=2784817757&rft_id=info:pmid/36888633&rft_galeid=A740104911&rft_doaj_id=oai_doaj_org_article_154f661872f14944a4467cf1b3017bbe&rfr_iscdi=true