tetano
Editor, Senior Moderator
J Med Internet Res
. 2020 Dec 8.
doi: 10.2196/24614. Online ahead of print.
Generation and analysis of U.S. county-level policy dataset demonstrates correlations of COVID-19 policies with reduced incidence
Senan Ebrahim[SUP] 1 2 [/SUP], Henry Ashworth[SUP] 1 2 [/SUP], Cray Noah[SUP] 1 2 [/SUP], Adesh Kadambi[SUP] 2 3 [/SUP], Asmae Toumi[SUP] 4 [/SUP], Jagpreet Chhatwal[SUP] 4 [/SUP]
Affiliations
Abstract
Background: Worldwide, non-pharmacologic interventions (NPIs) have been the main tool used to mitigate the Coronavirus Disease (COVID-19) pandemic. While preliminary research across the globe has shown NPI policy to be effective, there is currently a lack of information on NPI effectiveness in the United States.
Objective: The purpose of this study was to create a granular NPI dataset at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases.
Methods: Using a standardized crowdsourcing methodology, we collected time series data on seven key NPIs for 1,320 U.S. counties.
Results: This open source dataset is the largest and most comprehensive county NPI policy dataset and meets the need for higher resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P < .001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P = .004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R = 0.21) and elected leadership (R = 0.22).
Conclusions: This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this dataset will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.
. 2020 Dec 8.
doi: 10.2196/24614. Online ahead of print.
Generation and analysis of U.S. county-level policy dataset demonstrates correlations of COVID-19 policies with reduced incidence
Senan Ebrahim[SUP] 1 2 [/SUP], Henry Ashworth[SUP] 1 2 [/SUP], Cray Noah[SUP] 1 2 [/SUP], Adesh Kadambi[SUP] 2 3 [/SUP], Asmae Toumi[SUP] 4 [/SUP], Jagpreet Chhatwal[SUP] 4 [/SUP]
Affiliations
- PMID: 33302253
- DOI: 10.2196/24614
Abstract
Background: Worldwide, non-pharmacologic interventions (NPIs) have been the main tool used to mitigate the Coronavirus Disease (COVID-19) pandemic. While preliminary research across the globe has shown NPI policy to be effective, there is currently a lack of information on NPI effectiveness in the United States.
Objective: The purpose of this study was to create a granular NPI dataset at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases.
Methods: Using a standardized crowdsourcing methodology, we collected time series data on seven key NPIs for 1,320 U.S. counties.
Results: This open source dataset is the largest and most comprehensive county NPI policy dataset and meets the need for higher resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P < .001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P = .004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R = 0.21) and elected leadership (R = 0.22).
Conclusions: This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this dataset will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.