Winston-Salem State University, Winston-Salem, NC, USA.
North Carolina Agricultural and Technical State University, Greensboro, NC, USA.
J Racial Ethn Health Disparities. 2023 Apr;10(2):491-500. doi: 10.1007/s40615-022-01238-1. Epub 2022 Feb 15.
The COVID-19 pandemic and its associated mitigation strategies have significant psychosocial, behavioral, socioeconomic, and health impacts, particularly in vulnerable US populations. Different factors have been identified as influencers of the transmission rate; however, the effects of area deprivation index (as a measure of social determinants of health, SDoH) as a factor on COVID-19 disease early dynamics have not been established. We determined the effects of area deprivation index (ADI) and demographic factors on COVID-19 outcomes in Washington, D.C. This retrospective study used publicly available data on COVID-19 cases and mortality of Washington, D.C., during March 31st-July 4th, 2020. The main predictors included area deprivation index (ADI), age, and race/ethnicity. The ADI of each census block groups in D.C. (n=433) were obtained from Neighborhood Atlas map. Using a machine learning-based algorithm, the outcome variables were partitioned into time intervals: time duration (P, days), rate of change coefficient (E), and time segment load (P×E) for transmission rate and mortality. Correlation analysis and multiple linear regression models were used to determine associations between predictors and outcome variables. COVID-19 early transmission rate (E) was highly correlated with ADI (SDoH; r= 0.88, p=0.0044) of the Washington, D.C. community. We also found positive association between ADI, age (0-17 years, r=0.91, p=0.0019), and race (African American/Black, r=0.86; p=0.0068) and COVID-19 outcomes. There was high variability in early transmission across the geographic regions (i.e., wards) of Washington, D.C., and this variability was driven by race/ethnic composition and ADI. Understanding the association of COVID-19 disease early transmission and mortality dynamics and key socio-demographic risk factors such as age, race, and ADI, as a measure of social determinants, will contribute to health equity/equality and distribution of economic resources/assistance and is essential for future predictive modeling of the COVID-19 pandemic to limit morbidity and mortality.
COVID-19 大流行及其相关的缓解策略对美国弱势群体的心理社会、行为、社会经济和健康都产生了重大影响。已经确定了不同的因素作为传播率的影响因素；然而，作为健康社会决定因素（SDoH）衡量标准的地区贫困指数（ADI）对 COVID-19 疾病早期动态的影响尚未确定。我们确定了华盛顿特区的地区贫困指数（ADI）和人口统计学因素对 COVID-19 结果的影响。这项回顾性研究使用了 2020 年 3 月 31 日至 7 月 4 日期间华盛顿特区 COVID-19 病例和死亡率的公开可用数据。主要预测因素包括地区贫困指数（ADI）、年龄和种族/民族。华盛顿特区每个普查区组的 ADI（n=433）是从邻里地图中获得的。使用基于机器学习的算法，将结果变量分为时间间隔：持续时间（P，天）、变化率系数（E）和传输率和死亡率的时间段负载（P×E）。相关性分析和多元线性回归模型用于确定预测因子与结果变量之间的关联。COVID-19 的早期传播率（E）与华盛顿特区社区的 SDoH（ADI；r=0.88，p=0.0044）高度相关。我们还发现 ADI、年龄（0-17 岁，r=0.91，p=0.0019）和种族（非裔美国人/黑人，r=0.86；p=0.0068）与 COVID-19 结果之间存在正相关关系。华盛顿特区的地理区域（即 wards）之间的早期传播存在很大差异，这种差异是由种族/民族构成和 ADI 驱动的。了解 COVID-19 疾病早期传播和死亡率动态以及关键社会人口学风险因素（如年龄、种族和 ADI）的关联，作为健康社会决定因素的衡量标准，将有助于实现健康公平和平等以及经济资源/援助的分配，这对于未来预测 COVID-19 大流行以限制发病率和死亡率至关重要。
Zotero 插件