Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis.


Journal

CMAJ open
ISSN: 2291-0026
Titre abrégé: CMAJ Open
Pays: Canada
ID NLM: 101620603

Informations de publication

Date de publication:
Historique:
entrez: 3 9 2020
pubmed: 3 9 2020
medline: 3 9 2020
Statut: epublish

Résumé

Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity. We performed an environmental scan of government websites, news media and verified government social media accounts to identify NPIs implemented in Canada between Jan. 1 and Apr. 19, 2020. The CAN-NPI data set contains information about each intervention's timing, location, type, target population and alignment with a response stringency measure. We conducted descriptive analyses to characterize the temporal and geographic variation in early NPI implementation. We recorded 2517 NPIs grouped in 63 distinct categories during this period. The median date of NPI implementation in Canada was Mar. 24, 2020. Most jurisdictions heightened the stringency of their response following the World Health Organization's global pandemic declaration on Mar. 11, 2020. However, there was variation among provinces or territories in the timing and stringency of NPI implementation, with 8 out of 13 provinces or territories declaring a state of emergency by Mar. 18, and all by Mar. 22, 2020. There was substantial geographic and temporal heterogeneity in NPI implementation across Canada, highlighting the importance of a subnational lens in evaluating the COVID-19 pandemic response. Our comprehensive open-access data set will enable researchers to conduct robust interjurisdictional analyses of NPI impact in curtailing COVID-19 transmission.

Sections du résumé

BACKGROUND
Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity.
METHODS
We performed an environmental scan of government websites, news media and verified government social media accounts to identify NPIs implemented in Canada between Jan. 1 and Apr. 19, 2020. The CAN-NPI data set contains information about each intervention's timing, location, type, target population and alignment with a response stringency measure. We conducted descriptive analyses to characterize the temporal and geographic variation in early NPI implementation.
RESULTS
We recorded 2517 NPIs grouped in 63 distinct categories during this period. The median date of NPI implementation in Canada was Mar. 24, 2020. Most jurisdictions heightened the stringency of their response following the World Health Organization's global pandemic declaration on Mar. 11, 2020. However, there was variation among provinces or territories in the timing and stringency of NPI implementation, with 8 out of 13 provinces or territories declaring a state of emergency by Mar. 18, and all by Mar. 22, 2020.
INTERPRETATION
There was substantial geographic and temporal heterogeneity in NPI implementation across Canada, highlighting the importance of a subnational lens in evaluating the COVID-19 pandemic response. Our comprehensive open-access data set will enable researchers to conduct robust interjurisdictional analyses of NPI impact in curtailing COVID-19 transmission.

Identifiants

pubmed: 32873583
pii: 8/3/E545
doi: 10.9778/cmajo.20200100
pmc: PMC7641155
doi:

Types de publication

Journal Article

Langues

eng

Pagination

E545-E553

Informations de copyright

Copyright 2020, Joule Inc. or its licensors.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Références

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Auteurs

Liam G McCoy (LG)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont. liam.mccoy@mail.utoronto.ca.

Jonathan Smith (J)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Kavya Anchuri (K)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Isha Berry (I)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Joanna Pineda (J)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Vinyas Harish (V)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Andrew T Lam (AT)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Seung Eun Yi (SE)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Sophie Hu (S)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Laura Rosella (L)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

Benjamin Fine (B)

Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.

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