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Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study
Sharmistha Mishra; Huiting Ma; Gary Moloney; Kristy CY Yiu; Dariya Darvin; David Landsman; Jeff Kwong; Andrew Calzavara; Sharon Straus; Adrienne K Chan; Effie Gournis; Heather Rilkoff; Yiqing Xia; Alan Katz; Tyler Williamson; Kamil Malikov; Rafal Kustra; Mathieu Maheu-Giroux; Beate Sander; Stefan Baral.
Afiliación
  • Sharmistha Mishra; University of Toronto
  • Huiting Ma; St. Michael's Hospital, Unity Health Toronto
  • Gary Moloney; St. Michael's Hospital, Unity Health Toronto
  • Kristy CY Yiu; St. Michael's Hospital, Unity Health Toronto
  • Dariya Darvin; St. Michael's Hospital, Unity Health Toronto
  • David Landsman; St. Michael's Hospital, Unity Health Toronto
  • Jeff Kwong; ICES
  • Andrew Calzavara; ICES
  • Sharon Straus; University of Toronto
  • Adrienne K Chan; University of Toronto
  • Effie Gournis; Toronto Public Health
  • Heather Rilkoff; Public Health Ontario
  • Yiqing Xia; St. Michael's Hospital, Unity Health Toronto
  • Alan Katz; University of Manitoba
  • Tyler Williamson; University of Calgary
  • Kamil Malikov; Ontario Ministry of Health
  • Rafal Kustra; University of Toronto
  • Mathieu Maheu-Giroux; McGill University
  • Beate Sander; University Health Network
  • Stefan Baral; JHSPH
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21254585
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ABSTRACT
BackgroundInequities in the burden of COVID-19 observed across Canada suggest heterogeneity within community transmission. ObjectivesTo quantify the magnitude of heterogeneity in the wider community (outside of long-term care homes) in Toronto, Canada and assess how the magnitude in concentration evolved over time (January 21 to November 21, 2020). DesignRetrospective, population-based observational study using surveillance data from Ontarios Case and Contact Management system. SettingToronto, Canada. ParticipantsLaboratory-confirmed cases of COVID-19 (N=33,992). MeasurementsWe generated epidemic curves by SDOH and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 cases by social determinants of health (SDOH) and estimated the crude Gini coefficient. We examined the correlation between SDOH using Pearson correlation coefficients. ResultsThe Gini coefficient of cumulative cases by population size was 0.41 (95% CI 0.36-0.47) and were estimated for household income (0.20, 95%CI 0.14-0.28); visible minority (0.21, 95%CI 0.16-0.28); recent immigration (0.12, 95%CI 0.09-0.16); suitable housing (0.21, 95%CI 0.14-0.30); multi-generational households (0.19, 95%CI 0.15-0.23); and essential workers (0.28, 95% CI 0.23-0.34). Most SDOH were highly correlated. Locally acquired cases were concentrated in higher income neighbourhoods in the early phase of the epidemic, and then concentrated in lower income neighbourhoods. Mirroring the trajectory of epidemic curves by income, the Lorenz curve shifted over time from below to above the line of equality with a similar pattern across SDOH. LimitationsStudy relied on area-based measures of the SDOH and individual case counts of COVID-19. We cannot infer concentration of cases by specific occupational exposures given limitation to broad occupational categories. ConclusionCOVID-19 is increasingly concentrated by SDOH given socioeconomic inequities and structural racism. Primary Funding SourceCanadian Institutes of Health Research.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint