Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.
Identifieur interne : 000068 ( Main/Exploration ); précédent : 000067; suivant : 000069Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.
Auteurs : Jeff Lane [États-Unis] ; Michelle M. Garrison [États-Unis] ; James Kelley [États-Unis] ; Priya Sarma [États-Unis] ; Aaron Katz [États-Unis]Source :
- BMC medical research methodology [ 1471-2288 ] ; 2020.
Abstract
BACKGROUND
In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states "re-open."
METHODS
We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain.
RESULTS
We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding.
CONCLUSION
This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.
DOI: 10.1186/s12874-020-01174-w
PubMed: 33292170
PubMed Central: PMC7721792
Affiliations:
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Garrison</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Kelley, James" sort="Kelley, James" uniqKey="Kelley J" first="James" last="Kelley">James Kelley</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Sarma, Priya" sort="Sarma, Priya" uniqKey="Sarma P" first="Priya" last="Sarma">Priya Sarma</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Katz, Aaron" sort="Katz, Aaron" uniqKey="Katz A" first="Aaron" last="Katz">Aaron Katz</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33292170</idno><idno type="pmid">33292170</idno><idno type="doi">10.1186/s12874-020-01174-w</idno><idno type="pmc">PMC7721792</idno><idno type="wicri:Area/Main/Corpus">000003</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000003</idno><idno type="wicri:Area/Main/Curation">000003</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000003</idno><idno type="wicri:Area/Main/Exploration">000003</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.</title><author><name sortKey="Lane, Jeff" sort="Lane, Jeff" uniqKey="Lane J" first="Jeff" last="Lane">Jeff Lane</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA. lanej3@uw.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Garrison, Michelle M" sort="Garrison, Michelle M" uniqKey="Garrison M" first="Michelle M" last="Garrison">Michelle M. Garrison</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Kelley, James" sort="Kelley, James" uniqKey="Kelley J" first="James" last="Kelley">James Kelley</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Sarma, Priya" sort="Sarma, Priya" uniqKey="Sarma P" first="Priya" last="Sarma">Priya Sarma</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Katz, Aaron" sort="Katz, Aaron" uniqKey="Katz A" first="Aaron" last="Katz">Aaron Katz</name><affiliation wicri:level="2"><nlm:affiliation>University of Washington School of Public Health, Seattle, WA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Washington School of Public Health, Seattle, WA</wicri:regionArea><placeName><region type="state">Washington (État)</region></placeName></affiliation></author></analytic><series><title level="j">BMC medical research methodology</title><idno type="eISSN">1471-2288</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states "re-open."</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.</p></div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">33292170</PMID><DateRevised><Year>2020</Year><Month>12</Month><Day>12</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2288</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>1</Issue><PubDate><Year>2020</Year><Month>12</Month><Day>08</Day></PubDate></JournalIssue><Title>BMC medical research methodology</Title><ISOAbbreviation>BMC Med Res Methodol</ISOAbbreviation></Journal><ArticleTitle>Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.</ArticleTitle><Pagination><MedlinePgn>298</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12874-020-01174-w</ELocationID><Abstract><AbstractText Label="BACKGROUND">In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states "re-open."</AbstractText><AbstractText Label="METHODS">We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain.</AbstractText><AbstractText Label="RESULTS">We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding.</AbstractText><AbstractText Label="CONCLUSION">This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. 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Garrison</name><name sortKey="Katz, Aaron" sort="Katz, Aaron" uniqKey="Katz A" first="Aaron" last="Katz">Aaron Katz</name><name sortKey="Kelley, James" sort="Kelley, James" uniqKey="Kelley J" first="James" last="Kelley">James Kelley</name><name sortKey="Sarma, Priya" sort="Sarma, Priya" uniqKey="Sarma P" first="Priya" last="Sarma">Priya Sarma</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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