Grekousis George, Lu Yi, Wang Ruoyu
School of Geography and Planning Department of Urban and Regional Planning Sun Yat-Sen University Guangzhou China.
Guangdong Key Laboratory for Urbanization and Geo-simulation Guangdong China.
Geogr J. 2022 Jun;188(2):245-260. doi: 10.1111/geoj.12436. Epub 2022 Mar 28.
Identifying the socioeconomic drivers of COVID-19 deaths is essential for designing effective policies and health interventions. However, how the significance and impact of these factors varies across different spatial regimes has been scantly explored. In this ecological cross-sectional study, we apply the spatial lag by regimes regression model to examine how the socioeconomic and health determinants of COVID-19 death rate vary across (a) metropolitan vs. non-metropolitan, (b) shelter-in-place vs. no-shelter-in-place order, and (c) Democratic vs. Republican US counties. A total of 20 variables were studied across 3108 counties in the contiguous US for the first year of the pandemic (6 February 2020 to 5 February 2021). The results show that the COVID-19 death rate not only depends on a complex interplay of the population demographic, socioeconomic and health-related characteristics, but also on the spatial regime that the residents live, work and play. Household median income, household size, percentage of African Americans, percentage aged 40-59 and heart disease mortality are significant to metropolitan but not to non-metropolitan counties. We identified lack of insurance access as a significant driver across all regimes except for Democratic. We also showed that the political orientation of the governor might have impacted COVID-19 death rates due to the public response (i.e., shelter-in-place vs. no-shelter-in-place order). The proposed analysis allows for understanding the socioeconomic context in which public health policies can be applied, and importantly, it presents how COVID-19 death related factors vary across different spatial regimes.
确定新冠疫情死亡病例的社会经济驱动因素对于制定有效的政策和卫生干预措施至关重要。然而，这些因素的重要性和影响如何在不同空间区域中变化，目前鲜有研究。在这项生态横断面研究中，我们应用按区域回归的空间滞后模型，以检验新冠死亡率的社会经济和健康决定因素如何在以下方面存在差异：（a）大都市与非大都市地区；（b）实施就地避难令与未实施就地避难令的地区；（c）美国民主党控制的县与共和党控制的县。在疫情的第一年（2020年2月6日至2021年2月5日），我们对美国本土3108个县的20个变量进行了研究。结果表明，新冠死亡率不仅取决于人口统计学、社会经济和健康相关特征的复杂相互作用，还取决于居民生活、工作和娱乐的空间区域。家庭收入中位数、家庭规模、非裔美国人百分比、40-59岁人口百分比以及心脏病死亡率在大都市县具有显著影响，但在非大都市县则不然。我们发现，除民主党控制的县外，缺乏保险覆盖是所有区域的一个重要驱动因素。我们还表明，由于公众响应（即就地避难令与未实施就地避难令），州长的政治倾向可能影响了新冠死亡率。所提出的分析有助于理解可应用公共卫生政策的社会经济背景，重要的是，它展示了与新冠死亡相关的因素在不同空间区域中的差异。
