识别社会流动性的社会经济、人口和政治决定因素及其对 COVID-19 病例和死亡的影响:来自美国各县的证据。

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2022-03-03 eCollection Date: 2022-01-01 DOI:10.2196/31813
Niloofar Jalali, N Ken Tran, Anindya Sen, Plinio Pelegrini Morita
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引用次数: 0

摘要

背景:COVID-19 在地方一级的传播受到人口流动的极大影响。美国的人均 COVID-19 感染率和死亡率极高。控制 COVID-19 传播的高效非药物干预措施取决于我们对公众流动性决定因素的了解:本研究利用公开的谷歌数据和机器学习来调查美国各县的人口流动情况。统计分析用于研究人口流动的社会经济、人口和政治决定因素以及人均 COVID-19 病例和死亡率的相应模式:根据流动模式的差异,使用 K-均值聚类方法对 2020 年 3 月 1 日至 12 月 31 日期间美国 1085 个县的每日谷歌人口流动数据进行聚类。比较了不同聚类的社会流动性指标(零售、杂货店和药店、工作场所和居住地)。探讨了不同聚类之间社会经济、人口和政治变量的统计差异,以确定流动性的决定因素。各聚类与每日人均 COVID-19 病例和死亡人数相匹配:结果:我们将美国各县分为 4 个谷歌流动性集群。人口流动性较高的聚类中,65 岁及以上人口比例较高,高中及大学以下学历的白人人口比例较高,大学以下学历人口比例较高,使用公共交通上班的人口比例较低,2016 年总统大选期间投票给克林顿的选民比例较低。此外,从 2020 年 11 月到 12 月,人口流动性较大的集群的人均 COVID-19 病例和死亡率急剧上升:具有某些人口特征的共和党倾向县的社会流动性增加较多,最终在 2020 年下半年 COVID-19 的发病率更为显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties.

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties.

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties.

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties.

Background: The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility.

Objective: This study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates.

Methods: Daily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths.

Results: Our results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020.

Conclusions: Republican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020.

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