Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.
Identifieur interne : 000369 ( Main/Exploration ); précédent : 000368; suivant : 000370Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.
Auteurs : Elizabeth T. Chin [États-Unis] ; Benjamin Q. Huynh [États-Unis] ; Nathan C. Lo [États-Unis] ; Trevor Hastie [États-Unis] ; Sanjay Basu [États-Unis, Royaume-Uni]Source :
- BMC medicine [ 1741-7015 ] ; 2020.
Descripteurs français
- KwdFr :
- Absentéisme (MeSH), Betacoronavirus (MeSH), Enfant (MeSH), Géographie (MeSH), Humains (MeSH), Infections à coronavirus (épidémiologie), Main-d'oeuvre en santé (MeSH), Pandémies (MeSH), Personnel de santé (statistiques et données numériques), Pneumopathie virale (épidémiologie), Prévision (MeSH), Simulation numérique (MeSH), Soins de l'enfant (économie), Unités de soins intensifs (MeSH), Établissements scolaires (MeSH), États-Unis (épidémiologie), Études de faisabilité (MeSH), Évaluation des besoins (MeSH).
- MESH :
- statistiques et données numériques : Personnel de santé.
- économie : Soins de l'enfant.
- épidémiologie : Infections à coronavirus, Pneumopathie virale, États-Unis.
- Absentéisme, Betacoronavirus, Enfant, Géographie, Humains, Main-d'oeuvre en santé, Pandémies, Prévision, Simulation numérique, Unités de soins intensifs, Établissements scolaires, Études de faisabilité, Évaluation des besoins.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Absenteeism (MeSH), Betacoronavirus (MeSH), COVID-19 (MeSH), Child (MeSH), Child Care (economics), Computer Simulation (MeSH), Coronavirus Infections (epidemiology), Feasibility Studies (MeSH), Forecasting (MeSH), Geography (MeSH), Health Personnel (statistics & numerical data), Health Workforce (MeSH), Humans (MeSH), Intensive Care Units (MeSH), Needs Assessment (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), SARS-CoV-2 (MeSH), Schools (MeSH), United States (epidemiology).
- MESH :
- geographic , epidemiology : United States.
- economics : Child Care.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- statistics & numerical data : Health Personnel.
- Absenteeism, Betacoronavirus, COVID-19, Child, Computer Simulation, Feasibility Studies, Forecasting, Geography, Health Workforce, Humans, Intensive Care Units, Needs Assessment, Pandemics, SARS-CoV-2, Schools.
Abstract
BACKGROUND
School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.
METHODS
We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.
RESULTS
At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.
CONCLUSIONS
School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.
DOI: 10.1186/s12916-020-01692-w
PubMed: 32664927
PubMed Central: PMC7360472
Affiliations:
- Royaume-Uni, États-Unis
- Angleterre, Californie, Grand Londres, Massachusetts
- Londres, Stanford (Californie)
- Université Stanford
Links toward previous steps (curation, corpus...)
Le document en format XML
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type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Absenteeism (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>COVID-19 (MeSH)</term><term>Child (MeSH)</term><term>Child Care (economics)</term><term>Computer Simulation (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Feasibility Studies (MeSH)</term><term>Forecasting (MeSH)</term><term>Geography (MeSH)</term><term>Health Personnel (statistics & numerical data)</term><term>Health Workforce (MeSH)</term><term>Humans (MeSH)</term><term>Intensive Care Units (MeSH)</term><term>Needs Assessment (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>SARS-CoV-2 (MeSH)</term><term>Schools (MeSH)</term><term>United States (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Absentéisme (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Enfant (MeSH)</term><term>Géographie (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (épidémiologie)</term><term>Main-d'oeuvre en santé (MeSH)</term><term>Pandémies (MeSH)</term><term>Personnel de santé (statistiques et données numériques)</term><term>Pneumopathie virale (épidémiologie)</term><term>Prévision (MeSH)</term><term>Simulation numérique (MeSH)</term><term>Soins de l'enfant (économie)</term><term>Unités de soins intensifs (MeSH)</term><term>Établissements scolaires (MeSH)</term><term>États-Unis (épidémiologie)</term><term>Études de faisabilité (MeSH)</term><term>Évaluation des besoins (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>United States</term></keywords><keywords scheme="MESH" qualifier="economics" xml:lang="en"><term>Child Care</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Health Personnel</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Personnel de santé</term></keywords><keywords scheme="MESH" qualifier="économie" xml:lang="fr"><term>Soins de l'enfant</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>États-Unis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Absenteeism</term><term>Betacoronavirus</term><term>COVID-19</term><term>Child</term><term>Computer Simulation</term><term>Feasibility Studies</term><term>Forecasting</term><term>Geography</term><term>Health Workforce</term><term>Humans</term><term>Intensive Care Units</term><term>Needs Assessment</term><term>Pandemics</term><term>SARS-CoV-2</term><term>Schools</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Absentéisme</term><term>Betacoronavirus</term><term>Enfant</term><term>Géographie</term><term>Humains</term><term>Main-d'oeuvre en santé</term><term>Pandémies</term><term>Prévision</term><term>Simulation numérique</term><term>Unités de soins intensifs</term><term>Établissements scolaires</term><term>Études de faisabilité</term><term>Évaluation des besoins</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32664927</PMID><DateCompleted><Year>2020</Year><Month>07</Month><Day>21</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1741-7015</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>1</Issue><PubDate><Year>2020</Year><Month>07</Month><Day>15</Day></PubDate></JournalIssue><Title>BMC medicine</Title><ISOAbbreviation>BMC Med</ISOAbbreviation></Journal><ArticleTitle>Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.</ArticleTitle><Pagination><MedlinePgn>218</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12916-020-01692-w</ELocationID><Abstract><AbstractText Label="BACKGROUND">School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.</AbstractText><AbstractText Label="METHODS">We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.</AbstractText><AbstractText Label="RESULTS">At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.</AbstractText><AbstractText Label="CONCLUSIONS">School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chin</LastName><ForeName>Elizabeth T</ForeName><Initials>ET</Initials><Identifier Source="ORCID">0000-0001-5774-4236</Identifier><AffiliationInfo><Affiliation>Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. etchin@stanford.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huynh</LastName><ForeName>Benjamin Q</ForeName><Initials>BQ</Initials><AffiliationInfo><Affiliation>Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lo</LastName><ForeName>Nathan C</ForeName><Initials>NC</Initials><AffiliationInfo><Affiliation>Department of Medicine, University of California San Francisco, San Francisco, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hastie</LastName><ForeName>Trevor</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Statistics, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Basu</LastName><ForeName>Sanjay</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Center for Primary Care, Harvard Medical School, Boston, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Research and Public Health, Collective Health, San Francisco, CA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Public Health, Imperial College, London, UK.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>T15 LM007033</GrantID><Acronym>LM</Acronym><Agency>NLM NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Med</MedlineTA><NlmUniqueID>101190723</NlmUniqueID><ISSNLinking>1741-7015</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="UpdateOf"><RefSource>medRxiv. 2020 Apr 16;:</RefSource><PMID Version="1">32511455</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000041" 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MajorTopicYN="N">Forecasting</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005843" MajorTopicYN="N">Geography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006282" MajorTopicYN="N">Health Personnel</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006280" MajorTopicYN="N">Health Workforce</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007362" MajorTopicYN="N">Intensive Care Units</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020380" MajorTopicYN="N">Needs Assessment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" 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PubStatus="received"><Year>2020</Year><Month>04</Month><Day>08</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>7</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>7</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>7</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32664927</ArticleId><ArticleId IdType="doi">10.1186/s12916-020-01692-w</ArticleId><ArticleId IdType="pii">10.1186/s12916-020-01692-w</ArticleId><ArticleId IdType="pmc">PMC7360472</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>BMC Public Health. 2008 Apr 24;8:135</Citation><ArticleIdList><ArticleId 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IdType="pubmed">18366252</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Royaume-Uni</li><li>États-Unis</li></country><region><li>Angleterre</li><li>Californie</li><li>Grand Londres</li><li>Massachusetts</li></region><settlement><li>Londres</li><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Chin, Elizabeth T" sort="Chin, Elizabeth T" uniqKey="Chin E" first="Elizabeth T" last="Chin">Elizabeth T. Chin</name></region><name sortKey="Basu, Sanjay" sort="Basu, Sanjay" uniqKey="Basu S" first="Sanjay" last="Basu">Sanjay Basu</name><name sortKey="Basu, Sanjay" sort="Basu, Sanjay" uniqKey="Basu S" first="Sanjay" last="Basu">Sanjay Basu</name><name sortKey="Hastie, Trevor" sort="Hastie, Trevor" uniqKey="Hastie T" first="Trevor" last="Hastie">Trevor Hastie</name><name sortKey="Hastie, Trevor" sort="Hastie, Trevor" uniqKey="Hastie T" first="Trevor" last="Hastie">Trevor Hastie</name><name sortKey="Huynh, Benjamin Q" sort="Huynh, Benjamin Q" uniqKey="Huynh B" first="Benjamin Q" last="Huynh">Benjamin Q. Huynh</name><name sortKey="Lo, Nathan C" sort="Lo, Nathan C" uniqKey="Lo N" first="Nathan C" last="Lo">Nathan C. Lo</name></country><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Basu, Sanjay" sort="Basu, Sanjay" uniqKey="Basu S" first="Sanjay" last="Basu">Sanjay Basu</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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