Cargando…

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 determinant...

Descripción completa

Detalles Bibliográficos
Autores principales: Jalali, Niloofar, Tran, N Ken, Sen, Anindya, Morita, Plinio Pelegrini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900047/
https://www.ncbi.nlm.nih.gov/pubmed/35287305
http://dx.doi.org/10.2196/31813
_version_ 1784664022936715264
author Jalali, Niloofar
Tran, N Ken
Sen, Anindya
Morita, Plinio Pelegrini
author_facet Jalali, Niloofar
Tran, N Ken
Sen, Anindya
Morita, Plinio Pelegrini
author_sort Jalali, Niloofar
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8900047
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-89000472022-03-10 Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties Jalali, Niloofar Tran, N Ken Sen, Anindya Morita, Plinio Pelegrini JMIR Infodemiology Original Paper 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. JMIR Publications 2022-03-03 /pmc/articles/PMC8900047/ /pubmed/35287305 http://dx.doi.org/10.2196/31813 Text en ©Niloofar Jalali, N Ken Tran, Anindya Sen, Plinio Pelegrini Morita. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 03.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Jalali, Niloofar
Tran, N Ken
Sen, Anindya
Morita, Plinio Pelegrini
Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties
title Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties
title_full Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties
title_fullStr Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties
title_full_unstemmed Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties
title_short Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties
title_sort identifying the socioeconomic, demographic, and political determinants of social mobility and their effects on covid-19 cases and deaths: evidence from us counties
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900047/
https://www.ncbi.nlm.nih.gov/pubmed/35287305
http://dx.doi.org/10.2196/31813
work_keys_str_mv AT jalaliniloofar identifyingthesocioeconomicdemographicandpoliticaldeterminantsofsocialmobilityandtheireffectsoncovid19casesanddeathsevidencefromuscounties
AT trannken identifyingthesocioeconomicdemographicandpoliticaldeterminantsofsocialmobilityandtheireffectsoncovid19casesanddeathsevidencefromuscounties
AT senanindya identifyingthesocioeconomicdemographicandpoliticaldeterminantsofsocialmobilityandtheireffectsoncovid19casesanddeathsevidencefromuscounties
AT moritapliniopelegrini identifyingthesocioeconomicdemographicandpoliticaldeterminantsofsocialmobilityandtheireffectsoncovid19casesanddeathsevidencefromuscounties