Heterogeneity in the Effectiveness of Non-pharmaceutical Interventions During the First SARS-CoV2 Wave in the United States.

Abstract

Background: Attempts to quantify effect sizes of non-pharmaceutical interventions (NPI) to control COVID-19 in the US have not accounted for heterogeneity in social or environmental factors that may influence NPI effectiveness. This study quantifies national and sub-national effect sizes of NPIs during the early months of the pandemic in the US. Methods: Daily county-level COVID-19 cases and deaths during the first wave (January 2020 through phased removal of interventions) were obtained. County-level cases, doubling times, and death rates were compared to four increasingly restrictive NPI levels. Socio-demographic, climate and mobility factors were analyzed to explain and evaluate NPI heterogeneity, with mobility used to approximate NPI compliance. Analyses were conducted separately for the US and for each Census regions (Pacific, Mountain, east/West North Central, East/West South Central, South Atlantic, Middle Atlantic and New England). A stepped-wedge cluster-randomized trial analysis was used, leveraging the phased implementation of policies. Results: Aggressive (level 4) NPIs were associated with slower COVID-19 propagation, particularly in high compliance counties. Longer duration of level 4 NPIs was associated with lower case rates (log beta -0.028, 95% CI -0.04 to -0.02) and longer doubling times (log beta 0.02, 95% CI 0.01-0.03). Effects varied by Census region, for example, level 4 effects on doubling time in Pacific states were opposite to those in Middle Atlantic and New England states. NPI heterogeneity can be explained by differential timing of policy initiation and by variable socio-demographic county characteristics that predict compliance, particularly poverty and racial/ethnic population. Climate exhibits relatively consistent relationships across Census regions, for example, higher minimum temperature and specific humidity were associated with lower doubling times and higher death rates for this period of analysis in South Central, South Atlantic, Middle Atlantic, and New England states. Conclusion and Relevance: Heterogeneity exists in both the effectiveness of NPIs across US Census regions and policy compliance. This county-level variability indicates that control strategies are best designed at community-levels where policies can be tuned based on knowledge of local disparities and compliance with public health ordinances.

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Citation

Published Version (Please cite this version)

10.3389/fpubh.2021.754696

Publication Info

Pan, William K, Daniel Fernández, Stefanos Tyrovolas, Giné-Vázquez Iago, Rishav Raj Dasgupta, Benjamin F Zaitchik, Paul M Lantos, Christopher W Woods, et al. (2021). Heterogeneity in the Effectiveness of Non-pharmaceutical Interventions During the First SARS-CoV2 Wave in the United States. Frontiers in public health, 9. p. 754696. 10.3389/fpubh.2021.754696 Retrieved from https://hdl.handle.net/10161/24163.

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Scholars@Duke

Pan

William Kuang-Yao Pan

Elizabeth Brooks Reid and Whitelaw Reid Associate Professor

William Pan, DrPH, Elizabeth Brooks Reid and Whitelaw Reid Professor of Population Studies and Global Environmental Health, joined the faculty at Duke in 2011. He holds a joint appointment at DGHI and the Nicholas School of Environment, and is Adjunct Professor in the Department of International Health at Johns Hopkins Bloomberg School of Public Health. He is a biostatistician with expertise in spatial analysis, demography land use science, infectious disease epidemiology and environmental health.  He has over 20-years of experience leading large, multi-institutional and interdisciplinary research teams to study the impact of human-environment dynamics influencing human health in low- and middle-income countries (LMICs).  His work is primarily focused in Latin America, particularly the Amazon region.  His current research focuses on: (1) studying the health effects of mercury and other chemical exposures from artisanal and small-scale gold mining (ASGM); (2) developing tools for forecasting vector-borne disease risk, focusing particularly on the integration and modeling of climate, land use, population and malaria surveillance data; (3) studying the role of migration and social network connectivity influencing infectious disease transmission; (4) understanding the risk of lead exposure among hunters and their families, and to identify solutions to mitigate that risk; and (5) evaluating multi-faceted benefits of nature-based solutions related to agroforestry, climate resilience, livelihoods, and disease mitigation.

Lantos

Paul Michael Lantos

Professor of Medicine

I am interested in the spatial epidemiology of infectious diseases. My research utilizes geographic information systems (GIS) and geostatistical analyses to understand the spatial and spatiotemporal distribution of diseases, and their relationship with environmental and demographic factors. I currently have active studies evaluating the spatial distribution of numerous domestic and international infectious diseases, including SARS-CoV-2 (COVID-19), cytomegalovirus, influenza, and Lyme disease. Additionally I am interested in maternal-child health, and I have a number of ongoing studies of neighborhood health disparities in obstetrical care and birth outcomes. I am interested in GIS education and have conducted workshops on public health GIS in Mongolia and China.


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