Amanda Norton , Scarlett Rakowska , Tracey Galloway , Kathleen Wilson , Laura Rosella , Matthew Adams
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引用次数: 0
摘要
新冠肺炎对健康的影响和风险在社会、经济和种族梯度上不成比例(Chen等人,2021;Thompson等人,2021年;Mamuji et al.,2021;新冠肺炎和种族,2020)。通过检查安大略省前五波疫情,我们确定了基于前排序区(FSA)的社会人口统计状况及其与新冠肺炎病例的关系是否稳定或随时间变化。新冠肺炎波是使用新冠肺炎病例数的时间序列图定义的。然后,将FSA级别的黑人可见少数民族百分比、东南亚可见少数民族比例和中国可见少数民族%整合到具有其他既定脆弱性特征的空间误差模型中。模型表明,与新冠肺炎感染相关的基于地区的社会人口统计模式随着时间的推移而变化。如果社会人口统计学特征被确定为高风险(新冠肺炎病例率增加),可以增加检测、公共卫生信息和其他预防性护理,以保护人口免受不公平的疾病负担。
Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?
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.