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      Duration of Behavioral Policy Interventions and Incidence of COVID-19 by Social Vulnerability of US Counties, April–December 2020

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          Abstract

          Objective:

          State-issued behavioral policy interventions (BPIs) can limit community spread of COVID-19, but their effects on COVID-19 transmission may vary by level of social vulnerability in the community. We examined the association between the duration of BPIs and the incidence of COVID-19 across levels of social vulnerability in US counties.

          Methods:

          We used COVID-19 case counts from USAFacts and policy data on BPIs (face mask mandates, stay-at-home orders, gathering bans) in place from April through December 2020 and the 2018 Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention. We conducted multilevel linear regression to estimate the associations between duration of each BPI and monthly incidence of COVID-19 (cases per 100 000 population) by SVI quartiles (grouped as low, moderate low, moderate high, and high social vulnerability) for 3141 US counties.

          Results:

          Having a BPI in place for longer durations (ie, ≥2 months) was associated with lower incidence of COVID-19 compared with having a BPI in place for <1 month. Compared with having no BPI in place or a BPI in place for <1 month, differences in marginal mean monthly incidence of COVID-19 per 100 000 population for a BPI in place for ≥2 months ranged from –4 cases in counties with low SVI to –401 cases in counties with high SVI for face mask mandates, from –31 cases in counties with low SVI to –208 cases in counties with high SVI for stay-at-home orders, and from –227 cases in counties with low SVI to –628 cases in counties with high SVI for gathering bans.

          Conclusions:

          Establishing COVID-19 prevention measures for longer durations may help reduce COVID-19 transmission, especially in communities with high levels of social vulnerability.

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          Most cited references33

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Inferring the effectiveness of government interventions against COVID-19

            Governments are attempting to control the COVID-19 pandemic with nonpharmaceutical interventions (NPIs). However, the effectiveness of different NPIs at reducing transmission is poorly understood. We gathered chronological data on the implementation of NPIs for several European, and other, countries between January and the end of May 2020. We estimate the effectiveness of NPIs, ranging from limiting gathering sizes, business closures, and closure of educational institutions to stay-at-home orders. To do so, we used a Bayesian hierarchical model that links NPI implementation dates to national case and death counts and supported the results with extensive empirical validation. Closing all educational institutions, limiting gatherings to 10 people or less, and closing face-to-face businesses each reduced transmission considerably. The additional effect of stay-at-home orders was comparatively small.
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              Applied Longitudinal Data Analysis : Modeling Change and Event Occurrence

              Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, and the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. This book is concerned with behavioral, social, and biomedical sciences. It offers a presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using data sets from published studies, the book takes you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.
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                Author and article information

                Journal
                Public Health Rep
                Public Health Rep
                PHR
                spphr
                Public Health Reports
                SAGE Publications (Sage CA: Los Angeles, CA )
                0033-3549
                1468-2877
                6 October 2022
                Jan-Feb 2023
                : 138
                : 1
                : 190-199
                Affiliations
                [1 ]CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
                Author notes
                [*]Donatus U. Ekwueme, PhD, MS, Centers for Disease Control and Prevention, CDC COVID-19 Response, 4770 Buford Hwy NE, MS S107-4, Chamblee, GA 30341, USA. Email: dce3@ 123456cdc.gov
                Author information
                https://orcid.org/0000-0002-4987-3983
                https://orcid.org/0000-0003-1898-6202
                https://orcid.org/0000-0001-9425-915X
                Article
                10.1177_00333549221125202
                10.1177/00333549221125202
                9729715
                36200805
                a208b35d-59da-49c7-8a1e-1382db2483db
                © 2022, Association of Schools and Programs of Public Health
                History
                Categories
                Research
                Custom metadata
                ts1
                January/February 2023

                covid-19,social vulnerability,face mask mandates,stay-at-home orders,gathering bans

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