Department of Geography, Geomatics & Environment, University of Toronto Mississauga, DV3284, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada.
Department of Anthropology, University of Toronto Mississauga, HSC354, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada.
Spat Spatiotemporal Epidemiol. 2023 Jun;45:100586. doi: 10.1016/j.sste.2023.100586. Epub 2023 Apr 8.
COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.
COVID-19 对健康的影响和风险在社会、经济和种族方面存在不成比例的情况（Chen 等人，2021 年；Thompson 等人，2021 年；Mamuji 等人，2021 年；COVID-19 和种族，2020 年）。通过研究安大略省的前五次疫情浪潮，我们确定了基于邮政编码的社会人口统计学指标及其与 COVID-19 病例的关系是否稳定或随时间变化。使用 COVID-19 病例按 epi 周的时间序列图定义 COVID-19 浪潮。然后，在空间误差模型中整合了邮政编码层面的黑人少数族裔百分比、东南亚少数族裔百分比和华裔少数族裔百分比，以及其他已确定的脆弱性特征。这些模型表明，与 COVID-19 感染相关的基于地区的社会人口统计学模式随时间而变化。如果社会人口统计学特征被确定为高风险（COVID-19 病例率增加），则可能会实施更多的检测、公共卫生宣传和其他预防护理措施，以保护人群免受疾病的不平等负担。
