Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Spatial and Spatio-Temporal Epidemiology Pub Date : 2023-11-18 DOI:10.1016/j.sste.2023.100623
Daniel P. Johnson , Claudio Owusu
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Abstract

This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (p ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 – 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.

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利用时空贝叶斯模型研究社会脆弱性指数与COVID-19发病率和死亡率之间的关系
本研究比较了两种社会脆弱性指数,美国CDC的SVI和SoVI(社会脆弱性指数)。南卡罗来纳大学复原力研究所(Resilience Institute)的研究人员,他们预测COVID-19病例和死亡风险的能力。我们使用印第安纳州印第安纳波利斯注册管理研究所2020年3月1日至2021年3月31日的印第安纳州COVID-19病例和死亡数据。然后,我们将COVID-19数据汇总到普查区水平,获得CDC SVI和SoVI数据的输入变量、域(成分)和复合测度,建立贝叶斯时空生态回归模型。我们比较了结果的时空模式和SARS-CoV-2感染(COVID-19病例)和相关死亡的相对风险(RR)。结果表明,SARS-CoV-2感染和死亡具有明显的时空格局,其中最大的连续感染热点位于印第安纳波利斯大都市区西南部。我们还观察到一个巨大的连续死亡热点,从东南的辛辛那提地区延伸到特雷霍特的东部和北部(东南到中西部)。时空贝叶斯模型显示,CDC SVI每增加1个百分位数与SARS-CoV-2感染风险增加6%显著相关(p≤0.05)(RR = 1.06, 95% CI = 1.04 ~ 1.08)。然而,据预测,SoVI每增加1个百分点,COVID-19死亡风险就会增加45% (RR = 1.45, 95% CI =1.38 - 1.53)。与社会经济地位、年龄和种族/民族相关的特定领域变量被证明会增加SARS-CoV-2感染和死亡的风险。当将这两个指标纳入模型时,SARS-CoV-2感染和死亡的相对风险估计值存在显著差异。观察到的两种社会脆弱性指数以及感染和死亡之间的差异可能是由于不同的形成方法和输入变量的差异造成的。这一发现为越来越多的关于社会脆弱性与COVID-19之间关系的文献提供了补充,并通过说明局部时空分析的实用性,进一步开发了针对COVID-19的脆弱性指数。
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来源期刊
Spatial and Spatio-Temporal Epidemiology
Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
5.10
自引率
8.80%
发文量
63
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