Social-Economic Backgrounds to US County-Based COVID-19 Deaths: PLS-SEM Analysis.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-08-01 Epub Date: 2023-08-02 DOI:10.1007/s40615-023-01698-z
Benjamin P Bowser
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引用次数: 0

Abstract

A complex interplay of social, economic, and environmental factors drove the COVID-19 epidemic. Understanding these factors is crucial in explaining the racial disparities observed in COVID-19 deaths. This research investigated various hypotheses, including ecological, racial, demographic, economic, and political party factors, to determine their impact on COVID-19 deaths. The study utilized data from the National Center for Health Statistics (NCHS), specifically focusing on COVID-19 deaths categorized by race and Hispanic origin in US counties, with over 100 recorded deaths as of July 11, 2022.

Method: To analyze the data, the study employed partial least squares (PLS) as the statistical approach, considering the presence of multicollinearity in the county-level socioeconomic data. SmartPLS4 software was utilized to illustrate paths depicting variance and covariance and to conduct significance tests. The analysis encompassed overall COVID-19 deaths and deaths among White, Black, and Hispanic Americans, utilizing the same latent variables and paths.

Results: The results revealed that the number of residents aged 65 years or older in a county was the most influential predictor of COVID-19 deaths, irrespective of race. Economic factors emerged as the second strongest predictors. However, when considering each racial group separately, distinct factors aligned with the five hypotheses emerged as significant contributors to COVID-19 deaths. Furthermore, the diagrams illustrating the relationships between these factors (covariates) varied among racial groups, indicating that the underlying social influences differed across races.

Discussion: In light of these findings, it becomes evident that a "one-size-fits-all" approach to prevention strategies is suboptimal. Instead, targeted prevention efforts tailored to specific racial and social classes at high risk of COVID-19 death could have provided more precise messaging and necessitate direct engagement.

美国县级 COVID-19 死亡的社会经济背景:PLS-SEM 分析。
社会、经济和环境因素的复杂相互作用推动了 COVID-19 的流行。了解这些因素对于解释 COVID-19 死亡的种族差异至关重要。本研究调查了各种假设,包括生态、种族、人口、经济和政党因素,以确定它们对 COVID-19 死亡的影响。研究利用了美国国家卫生统计中心(NCHS)的数据,特别关注美国各县按种族和西班牙裔分类的 COVID-19 死亡病例,截至 2022 年 7 月 11 日,记录的死亡病例超过 100 例:考虑到县级社会经济数据中存在多重共线性,本研究采用偏最小二乘法(PLS)作为统计方法对数据进行分析。利用 SmartPLS4 软件来说明描述方差和协方差的路径,并进行显著性检验。利用相同的潜在变量和路径,分析了 COVID-19 的总体死亡人数以及白人、黑人和西班牙裔美国人的死亡人数:结果显示,一个县 65 岁或以上的居民人数是对 COVID-19 死亡最有影响力的预测因素,与种族无关。经济因素是第二大预测因素。然而,当分别考虑每个种族群体时,与五个假设一致的不同因素成为 COVID-19 死亡的重要因素。此外,说明这些因素(协变量)之间关系的图表在不同种族群体之间也各不相同,这表明不同种族的潜在社会影响因素也不尽相同:鉴于这些发现,"一刀切 "的预防策略显然不是最佳选择。相反,针对 COVID-19 死亡高风险的特定种族和社会阶层开展有针对性的预防工作,可以提供更准确的信息,并有必要直接参与其中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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