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

Niloofar Jalali; N Ken Tran; Anindya Sen; Plinio Pelegrini Morita

doi:10.2196/31813

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

JMIR Infodemiology. 2022 Mar 3;2(1):e31813. doi: 10.2196/31813. eCollection 2022 Jan-Jun.

Authors

Niloofar Jalali¹, N Ken Tran², Anindya Sen³, Plinio Pelegrini Morita^{2

4

5

6}

Affiliations

¹ School of Public Health and Health Systems Faculty of Applied Health Sciences University of Waterloo Waterloo, ON Canada.
² School of Public Health and Health Systems University of Waterloo Waterloo, ON Canada.
³ Department of Economics University of Waterloo Waterloo, ON Canada.
⁴ Department of Systems Design Engineering University of Waterloo Waterloo, ON Canada.
⁵ JW Graham Information Technology Emerging Leader Chair Applied Health Informatics University of Waterloo Waterloo, ON Canada.
⁶ Institute of Health Policy Management and Evaluation University of Toronto Toronto, ON Canada.

PMID: 35287305
PMCID: PMC8900047
DOI: 10.2196/31813

Abstract

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.

Keywords: COVID-19; Google mobility data; cases; clustering; deaths; mobility.

©Niloofar Jalali, N Ken Tran, Anindya Sen, Plinio Pelegrini Morita. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 03.03.2022.