• Yunyu Xiao.
• School of Social Work, Indiana University-Purdue University Indianapolis, 902 W. New York StreetEducation/Social Work Building, ES 4119, Indianapolis, US.
• JMIR Public Health Surveill. 2020 Nov 16.

BackgroundNon-pharmaceutical interventions (NPIs) have been implemented in the New York State since the COVID-19 outbreak on March 1, 2020 to control the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Socioeconomic heterogeneity across counties closely manifests differences in the post-NPIs growth rate of incidence, which is a crucial indicator to guide future infectious control policy making. Few studies, however, examined the geospatial and sociological variations in the epidemic growth across different time points of NPIs.ObjectiveTo guide a more effective reopening plan while controlling the transmission, the current study aims at 1) identifying hotspots of the growth rate of COVID-19 incidence among the 57 counties and New York City in NYS over time, and 2) examining the association of COVID-19 growth rates after eight critical NPIs time points and most relevant county-level sociological predictors.MethodsCounty-level COVID-19 incidence rates were retrieved from the Social Explorer Website between March 7, 2020 to June 22, 2020. 5-day moving average growth rates of COVID-19 incidence were calculated for 16 selected time points, including the dates of eight NPIs and their respective 14-day-lag-behind time points. A total of 36 county-level indicators were extracted from multiple public datasets. Geospatial mapping and heatmap were used to analyze spatial and temporal heterogeneity of county-level COVID-19 outbreak over selected NPIs-related dates. Generalized mixed effect least absolute shrinkage and selection operator (LASSO) regression, controlling for the 5-day moving average growth rates of COVID-19 testing rates, was employed to identify significant county-level predictors related to the changes of county-level COVID-19 growth rates over time.ResultsCOVID-19 infection increased and peaked by the end of March (η=22.50%). Growth rates of COVID-19 decreased by 50.48% after implementing NPIs such as closures of schools, non-essential businesses, parks, and subways. There was a geospatial shift in the region with the highest growth rates from New York metropolitan area towards Western and Northern regions over time. Proportions of population aged 45 years and above (β=3.25 [0.17-6.32]), living alone at residential houses (β=3.31 [0.39--6.22]), and proportion of crowd residential houses (β=6.15 [2.15-10.14]) were positively associated with the growth rate of COVID-19 infection. In contrast, living alone at rental houses (β=-2.47 [-4.83--0.12]) and rate of mental health providers (β=-1.11 [-1.95--0.28]) were negatively associated with COVID-19 growth rate across all 16 time points.ConclusionsThere are geospatial differences in COVID-19 incidence after implementing different NPIs. Socioeconomic, racial/ethnic, and healthcare resource disparities at the structural and historical levels across counties need to be considered in infection control policymaking to narrow the unequal health impact on vulnerable populations effectively.Clinicaltrial

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