Your browser doesn't support javascript.
Outcomes Associated With Social Distancing Policies in St Louis, Missouri, During the Early Phase of the COVID-19 Pandemic.
Geng, Elvin H; Schwab, Joshua; Foraker, Randi; Fox, Branson; Hoehner, Christine M; Schootman, Mario; Mody, Aaloke; Powderly, William; Yount, Byron; Woeltje, Keith; Petersen, Maya.
  • Geng EH; Division of Infectious Diseases, Department of Medicine, Washington University in St Louis, St Louis, Missouri.
  • Schwab J; Institute for Public Health, Washington University in St Louis.
  • Foraker R; Division Biostatistics, School of Public Health, University of California, Berkely.
  • Fox B; Division of General Internal Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri.
  • Hoehner CM; Division of Infectious Diseases, Department of Medicine, Washington University in St Louis, St Louis, Missouri.
  • Schootman M; BJC HealthCare, St Louis, Missouri.
  • Mody A; SSM Health Saint Louis University Hospital, St Louis, Missouri.
  • Powderly W; Division of Infectious Diseases, Department of Medicine, Washington University in St Louis, St Louis, Missouri.
  • Yount B; Division of Infectious Diseases, Department of Medicine, Washington University in St Louis, St Louis, Missouri.
  • Woeltje K; Institute for Public Health, Washington University in St Louis.
  • Petersen M; Mercy Health, St Louis, Missouri.
JAMA Netw Open ; 4(9): e2123374, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1380357
ABSTRACT
Importance In the absence of a national strategy in response to the COVID-19 pandemic, many public health decisions fell to local elected officials and agencies. Outcomes of such policies depend on a complex combination of local epidemic conditions and demographic features as well as the intensity and timing of such policies and are therefore unclear.

Objective:

To use a decision analytical model of the COVID-19 epidemic to investigate potential outcomes if actual policies enacted in March 2020 (during the first wave of the epidemic) in the St Louis region of Missouri had been delayed. Design, Setting, and

Participants:

A previously developed, publicly available, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1) designed to enable localized COVID-19 epidemic projections was used. The compartmental epidemic model is programmed in R and Stan, uses bayesian inference, and accepts user-supplied demographic, epidemiologic, and policy inputs. Hospital census data for 1.3 million people from St Louis City and County from March 14, 2020, through July 15, 2020, were used to calibrate the model. Exposures Hypothetical delays in actual social distancing policies (which began on March 13, 2020) by 1, 2, or 4 weeks. Sensitivity analyses were conducted that explored plausible spontaneous behavior change in the absence of social distancing policies. Main Outcomes and

Measures:

Hospitalizations and deaths.

Results:

A model of 1.3 million residents of the greater St Louis, Missouri, area found an initial reproductive number (indicating transmissibility of an infectious agent) of 3.9 (95% credible interval [CrI], 3.1-4.5) in the St Louis region before March 15, 2020, which fell to 0.93 (95% CrI, 0.88-0.98) after social distancing policies were implemented between March 15 and March 21, 2020. By June 15, a 1-week delay in policies would have increased cumulative hospitalizations from an observed actual number of 2246 hospitalizations to 8005 hospitalizations (75% CrI 3973-15 236 hospitalizations) and increased deaths from an observed actual number of 482 deaths to a projected 1304 deaths (75% CrI, 656-2428 deaths). By June 15, a 2-week delay would have yielded 3292 deaths (75% CrI, 2104-4905 deaths)-an additional 2810 deaths or a 583% increase beyond what was actually observed. Sensitivity analyses incorporating a range of spontaneous behavior changes did not avert severe epidemic projections. Conclusions and Relevance The results of this decision analytical model study suggest that, in the St Louis region, timely social distancing policies were associated with improved population health outcomes, and small delays may likely have led to a COVID-19 epidemic similar to the most heavily affected areas in the US. These findings indicate that an open-source modeling platform designed to accept user-supplied local and regional data may provide projections tailored to, and more relevant for, local settings.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Physical Distancing / COVID-19 / Health Policy / Hospitalization Type of study: Prognostic study Limits: Female / Humans / Male Country/Region as subject: North America Language: English Journal: JAMA Netw Open Year: 2021 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Physical Distancing / COVID-19 / Health Policy / Hospitalization Type of study: Prognostic study Limits: Female / Humans / Male Country/Region as subject: North America Language: English Journal: JAMA Netw Open Year: 2021 Document Type: Article