Abstract
Objectives
This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto’s neighbourhoods during five distinct time periods of the pandemic.
Methods
Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device–derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well.
Results
The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns.
Conclusion
Toronto’s neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.
Résumé
Objectifs
Cette étude a deux grands objectifs de recherche : 1) examiner les schémas d’agrégation spatiale des baisses de mobilité et des cas de COVID-19 à Toronto et leurs liens avec les populations marginalisées; et 2) cerner les caractéristiques socioéconomiques les plus pertinentes liées à la mobilité humaine et aux taux de cas de COVID-19 dans les quartiers de Toronto au cours de cinq périodes distinctes de la pandémie.
Méthode
À l’aide d’une approche spatio-quantitative, nous avons combiné des analyses de points chauds, des analyses de corrélation de Pearson et des tests de Wilcoxon à deux échantillons pour analyser des ensembles de données incluant : les cas de COVID-19, un indicateur dérivé d’appareils mobiles pour mesurer le temps passé à l’extérieur du domicile au niveau du quartier (c.-à-d. la mobilité), ainsi que les données socioéconomiques du recensement de 2016 et de l’indice de marginalisation ontarien. Nous avons aussi examiné les variations temporelles entre les phases de la pandémie.
Résultats
Nous avons repéré d’importants schémas d’agrégation spatiale des baisses de mobilité et des cas de COVID-19 à Toronto, ainsi que leurs liens avec les populations marginalisées. Les points chauds de la COVID-19 se trouvaient dans des grappes de quartiers plus défavorisés sur le plan matériel, où il y avait davantage de travailleurs essentiels et de personnes passant du temps à l’extérieur de leur domicile. La structure spatiale des grappes de cas de COVID-19 et de la mobilité a légèrement changé au fil du temps, mais les caractéristiques des groupes socioéconomiques communes à toutes les grappes sont restées semblables durant toutes les périodes sauf la première. Nous avons créé une série de cartes et de visualisations pour faire ressortir les schémas spatio-temporels dynamiques.
Conclusion
Les quartiers de Toronto ont vécu la pandémie de COVID-19 de façons très différentes : les points chauds des cas de COVID-19 sont survenus dans des communautés plus marginalisées sur le plan matériel et racial et moins susceptibles de réduire leur mobilité. L’étude fournit des preuves solides dans un contexte canadien pour améliorer l’élaboration des politiques et approfondir la compréhension des déterminants sociaux de la santé à Toronto pendant la pandémie de COVID-19.
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Availability of data and material
Ontario Marginalization data are publicly available at https://www.publichealthontario.ca/en/data-and-analysis/health-equity/ontario-marginalization-index.
Code availability
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Acknowledgements
The authors would like to thank the two anonymous reviewers whose insightful and critical comments greatly improved the paper. Support from CIHR Operating Grant: Canadian 2019 Novel Coronavirus (COVID-19) Rapid Research Funding Opportunity (grant number OV7-170378) is gratefully acknowledged. The paper included materials from a graduate Major Research Paper –Forsyth, Jack. 2021. Spatiotemporal sociodemographic analysis of covid-19 case rates and mobility in Toronto neighbourhoods, Master of Spatial Analysis, Toronto Metropolitan University (formerly Ryerson University).
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Forsyth: Developing the topic, conducting the literature review, data cleaning and analysis and writing of the paper. Wang: Developing the topic, providing guidance in all stages and aspects of the study, revising the paper. Thomas-Bachli: Providing helpful insights and advice on the topic, data analysis and interpretation. All the authors read and approved the final manuscript for publication.
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Forsyth, J., Wang, L. & Thomas-Bachli, A. COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial–temporal analysis. Can J Public Health 114, 806–822 (2023). https://doi.org/10.17269/s41997-023-00791-4
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DOI: https://doi.org/10.17269/s41997-023-00791-4