Association Between Population Mobility Reductions and New COVID-19 Diagnoses in the United States Along the Urban–Rural Gradient, February–April, 2020

Xiaojiang Li, PhD, MS; Abby E. Rudolph, MPH, PhD; Jeremy Mennis, MS, PhD

Disclosures

Prev Chronic Dis. 2020;17(10):e118 

In This Article

Data and Methods

County-level daily mobility data for February 15 through April 26, 2020, were obtained from Google's Community Mobility Report, which comprises aggregated and anonymized data from Google users who turned on the "location history" setting on their cellular telephone.[10,11] The data set included 6 location categories, determined by the different types of places encoded within Google Maps: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential. Daily changes in mobility were measured relative to the median value of travel for the corresponding location type and day of the week from January 3, 2020, through February 6, 2020. County-level daily mobility change was correlated with the daily county growth rate of COVID-19 cases[12] 11 days later (to account for the average incubation period[13]) plus the time delay between testing and state reporting,[14] beginning on the day the first confirmed COVID-19 case was reported in each county. A catplot was used to visualize the distribution of the county-level correlation coefficients and their significance for mobility to each location type, stratified by the 6-level urban–rural classification scheme from the National Center for Health Statistics: large central metropolitan, large fringe metropolitan, medium metropolitan, small metropolitan, micropolitan, or noncore county.[15] We repeated the analysis by using a 5-day time lag to test the sensitivity of our results.

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