Department of Epidemiology and Biostatistics (Xia, Buckeridge, Maheu-Giroux), School of Population and Global Health, McGill University, Montréal, Que.; MAP Centre for Urban Health Solutions (Xia, Ma, Moloney, Yiu, Mishra), St. Michael's Hospital, Unity Health Toronto, Toronto, Ont.; School of Population and Public Health (Velásquez García, Janjua), University of British Columbia; British Columbia Centre for Disease Control (Velásquez García, Janjua), Vancouver, BC; Departments of Community Health Sciences and Family Medicine (Sirski, Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Department of Community Health Sciences (Vickers, Williamson) and Centre for Health Informatics (Williamson), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Kustra), University of Toronto, Toronto, Ont.; Département de médecine sociale et préventive (Brisson), Faculté de médecine, Université Laval, Québec, Que.; Department of Epidemiology (Baral), Johns Hopkins School of Public Health, Baltimore, Md.; Division of Infectious Diseases (Mishra), Department of Medicine, University of Toronto, Toronto, Ont.
CMAJ. 2022 Feb 14;194(6):E195-E204. doi: 10.1503/cmaj.211249.
Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec.
We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves.
We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities.
Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.
了解与健康社会决定因素相关的 SARS-CoV-2 传播不平等现象,有助于制定针对当地传播动态的有效缓解策略。本研究旨在量化加拿大 4 个省份(不列颠哥伦比亚省、曼尼托巴省、安大略省和魁北克省)16 个人口普查都会区(以下简称城市)中 SARS-CoV-2 病例的地理集中的社会决定因素。
我们使用了确诊的 SARS-CoV-2 病例的监测数据和传播区域(DA)级别的社会决定因素的人口普查数据。我们计算了基尼系数,以确定每个城市确诊的 SARS-CoV-2 病例的整体地理异质性,并计算了基尼协方差系数,以确定每个城市每个社会决定因素(收入、教育、住房密度以及少数族裔、新移民和必要工人的比例)的异质性。我们使用洛伦兹(集中)曲线可视化了异质性。
我们观察到城市中 SARS-CoV-2 病例的地理集中,因为一半的累计病例集中在包含其人口 21%-35%的 DA 中,安大略省城市的地理异质性最大(基尼系数为 0.32-0.47),其次是不列颠哥伦比亚省(0.23-0.36)、曼尼托巴省(0.32)和魁北克省(0.28-0.37)。病例不成比例地集中在收入和教育程度较低的地区,以及少数族裔、新移民、高密度住房和必要工人比例较高的地区。尽管所有城市都存在以少数族裔比例为特征的集中现象,但社会决定因素的集中程度因城市而异。
在所包括的所有城市都观察到 SARS-CoV-2 病例的地理集中,但社会决定因素的模式有所不同。应根据不平等传播的当地驱动因素,有针对性地在地理上优先分配资源和服务,以应对 SARS-CoV-2 的再次爆发。