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

CMAJ Open

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.

Published: January 2021

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641155PMC
http://dx.doi.org/10.9778/cmajo.20200100DOI Listing

Publication Analysis

Top Keywords

data set
12
npi implementation
12
federal provincial
8
provincial territorial
8
territorial municipal
8
nonpharmaceutical interventions
8
response covid-19
8
covid-19 pandemic
8
can-npi data
8
npis implemented
8

Similar Publications

Chromosome-scale genome assembly of Korean goosegrass (Eleusine indica).

Sci Data

January 2025

Department of Crop Science, Chungnam National University, Daejeon, 34134, Republic of Korea.

Goosegrass, belonging to the genus Eleusine within the Chloridoideae subfamily, is often one of the problematic weeds with strong invasiveness, competing with crops for essential survival resources. Although a chromosome-level genome assembly of E. indica from China was published last year, the present research focuses on a population of E.

View Article and Find Full Text PDF

Recovery of nearly 3,000 archaeal genomes from 152 terrestrial geothermal spring metagenomes.

Sci Data

January 2025

Chinese Academy of Sciences Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, China.

Terrestrial geothermal springs, reminiscent of early Earth conditions, host diverse and abundant populations of Archaea. In this study, we reconstructed 2,949 metagenome-assembled genomes (MAGs) from 152 metagenomes collected over six years from 48 geothermal springs in Tengchong, China. Among these MAGs, 1,431 (49%) were classified as high-quality, while 1,518 (51%) were considered as medium-quality.

View Article and Find Full Text PDF

Chromosome-level genome assembly and annotation of the gynogenetic large-scale loach (Paramisgurnus dabryanus).

Sci Data

January 2025

Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Hubei Hongshan Laboratory, Key Laboratory of Aquaculture Disease Control, Ministry of Agriculture and Rural Affairs, The Innovation Academy of Seed Design, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.

The large-scale loach (Paramisgurnus dabryanus; Cypriniformes: Cobitidae) is primarily distributed in East Asia. It is an important economic fish species characterized by fast growth, temperature-dependent sex determination and the ability to breathe air. Currently, molecular mechanism studies related to some aspects such as sex determination, toxicology, feed nutrition, growth and genetic evolution have been conducted.

View Article and Find Full Text PDF

Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.

Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).

View Article and Find Full Text PDF

A dataset of forest regrowth in globally key deforestation regions.

Sci Data

January 2025

Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China.

Deforestation-induced forest loss largely affects both the carbon budget and ecosystem services. Subsequent forest regrowth plays a crucial role in ecosystem restoration and carbon replenishment. However, there is an absence of comprehensive datasets explicitly delineating the forest regrowth following deforestation.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!