Your browser doesn't support javascript.
loading
State variation in effects of state social distancing policies on COVID-19 cases.
Kaufman, Brystana G; Whitaker, Rebecca; Mahendraratnam, Nirosha; Hurewitz, Sophie; Yi, Jeremy; Smith, Valerie A; McClellan, Mark.
Afiliação
  • Kaufman BG; Margolis Center for Health Policy, Duke University, 230 Science Drive, Durham, NC, 27705, USA. Brystana.kaufman@duke.edu.
  • Whitaker R; Population Health Sciences, Duke University School of Medicine, Durham, NC, USA. Brystana.kaufman@duke.edu.
  • Mahendraratnam N; Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, NC, USA. Brystana.kaufman@duke.edu.
  • Hurewitz S; Margolis Center for Health Policy, Duke University, 230 Science Drive, Durham, NC, 27705, USA.
  • Yi J; Margolis Center for Health Policy, Duke University, 230 Science Drive, Durham, NC, 27705, USA.
  • Smith VA; Margolis Center for Health Policy, Duke University, 230 Science Drive, Durham, NC, 27705, USA.
  • McClellan M; Margolis Center for Health Policy, Duke University, 230 Science Drive, Durham, NC, 27705, USA.
BMC Public Health ; 21(1): 1239, 2021 06 28.
Article em En | MEDLINE | ID: mdl-34182972
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) sickened over 20 million residents in the United States (US) by January 2021. Our objective was to describe state variation in the effect of initial social distancing policies and non-essential business (NEB) closure on infection rates early in 2020. METHODS: We used an interrupted time series study design to estimate the total effect of all state social distancing orders, including NEB closure, shelter-in-place, and stay-at-home orders, on cumulative COVID-19 cases for each state. Data included the daily number of COVID-19 cases and deaths for all 50 states and Washington, DC from the New York Times database (January 21 to May 7, 2020). We predicted cumulative daily cases and deaths using a generalized linear model with a negative binomial distribution and a log link for two models. RESULTS: Social distancing was associated with a 15.4% daily reduction (Relative Risk = 0.846; Confidence Interval [CI] = 0.832, 0.859) in COVID-19 cases. After 3 weeks, social distancing prevented nearly 33 million cases nationwide, with about half (16.5 million) of those prevented cases among residents of the Mid-Atlantic census division (New York, New Jersey, Pennsylvania). Eleven states prevented more than 10,000 cases per 100,000 residents within 3 weeks. CONCLUSIONS: The effect of social distancing on the infection rate of COVID-19 in the US varied substantially across states, and effects were largest in states with highest community spread.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 2_ODS3 / 4_TD / 6_ODS3_enfermedades_notrasmisibles Base de dados: MEDLINE Assunto principal: Distanciamento Físico / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: BMC Public Health Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 2_ODS3 / 4_TD / 6_ODS3_enfermedades_notrasmisibles Base de dados: MEDLINE Assunto principal: Distanciamento Físico / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: BMC Public Health Ano de publicação: 2021 Tipo de documento: Article