Evidence map›Paper›PMID 32873583›Full record

ArticleCMAJ open

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

Liam G McCoy, Jonathan Smith, Kavya Anchuri, Isha Berry, Joanna Pineda, Vinyas Harish, Andrew T Lam, Seung Eun Yi, Sophie Hu, Laura Rosella and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CMAJ open. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. Influence of non-pharmaceutical COVID-19 interventions on speed-related and alcohol-related traffic injuries in five cities of the province of Québec, Canada.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2026
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  13. Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
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  19. Proposed framework for a national set of reporting measures in Canada in response to the COVID-19 pandemic.Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Liam G McCoyFaculty 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 SmithFaculty 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 AnchuriFaculty 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 BerryFaculty 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 PinedaFaculty 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 HarishFaculty 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 LamFaculty 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 YiFaculty 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 HuFaculty 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 RosellaFaculty 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 FineFaculty 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.
COVID-19 Canada Open Data Working Group: Non-Pharmaceutical Interventions

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNonpharmaceutical 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.

methodsWe 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.

resultsWe 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.

interpretationThere 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.

Indexed as

CanadaCOVID-19COVID-19 TestingGeographyGovernmentHumansInfection ControlPandemicsPhysical DistancingPolicySARS-CoV-2Social MediaTime Factors

Identifiers

PMID32873583
PMCPMC7641155

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.