Abstract
BackgroundIn recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states “re-open.”MethodsWe developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0–5 for each domain.ResultsWe found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding.ConclusionThis detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.
Highlights
In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses
The inclusion criteria for policies included in the analysis were: (1) directive issued by the Governor or state agency lead regarding COVID-19 documented in an Executive Order or state government website, including but not limited to Department of Health; (2) the primary purpose of the policy is to reduce social density with the goal of reducing community transmission of COVID-19; and (3) policies issued between March 11, 2020 and June 19, 2020
COVID-19 has presented one of the most rapid and intense phases of public health policy development and implementation in the last century. Often these policy decisions have had to be made with imperfect evidence under very tight timelines
Summary
Multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states “re-open.”. As confirmed case counts climbed, state, county, and municipal governments adopted policies recommending or requiring actions to reduce social density and slow the progression of the outbreak. Multiple efforts sought to rapidly code these social distancing policy responses for analysis [2,3,4,5,6,7,8,9,10,11].
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