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Association of State Stay-at-Home Orders and State-Level African American Population With COVID-19 Case Rates

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To identify the key insights or developments described in this article
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Key Points

Question  Are stay-at-home orders and state-level proportion of African American residents associated with coronavirus 2019 (COVID-19) infection rates?

Findings  In this cross-sectional study including 3023 daily state-level observations from March to May 2020, results from multivariate regression models indicated that stay-at-home orders were associated with reductions in cumulative COVID-19 case rates. States with larger African American populations had higher COVID-19 case rates.

Meaning  These findings underscore the importance of stay-at-home orders in addressing the COVID-19 pandemic and the need to address racial disparities in rates of infection.

Abstract

Importance  To cope with the continuing coronavirus disease 2019 (COVID-19) pandemic, state and local officials need information on the effectiveness of policies aimed at curbing disease spread, as well as state-specific characteristics, like the racial mix, associated with increased risks related to the disease.

Objective  To investigate whether state-imposed stay-at-home orders (SAHOs) and the proportion of African American population in a state were associated with the state-level COVID-19 cases.

Design, Setting, and Participants  This cross-sectional study used daily, state-level data on COVID-19 cases, tests, and fatalities from the COVID Tracking Project. Data from March 1 to May 4, 2020, for all states (except Washington state) as well as the District of Columbia were used.

Exposures  The key exposure variables were state-level SAHO (1 if in place, 0 otherwise), and proportion of state population who are African American.

Main Outcomes and Measures  The primary outcome was daily cumulative COVID-19 case rates. A secondary outcome was subsequent COVID-19 fatality rates, derived using mean cumulative fatality rates 21 to 28 days after each date. Multivariate regression models were estimated.

Results  The final sample included 3023 pooled state- and day-level observations. The mean (SD) cumulative positive case rate was 103.186 (200.067) cases per 100 000 state population, the mean (SD) cumulative test rate was 744.23 (894.944) tests per 100 000 state population, and the mean (SD) subsequent cumulative fatality rate was 12.923 (21.737) deaths per 100 000 state population. There was a negative association of SAHOs with cumulative case rates (β = −1.166; 95% CI, −1.484 to −0.847; P < .001) and subsequent fatality rates (β = −0.204; 95% CI, −0.294 to −0.113; P < .001). Estimation analyses indicated that expected cumulative case rates would have been more than 200% higher and fatality rates approximately 22% higher if there were no SAHOs, as compared with SAHOs fully in place. A higher proportion of African American population was associated with higher case rates (β = 0.045; 95% CI, 0.014 to 0.077; P = .001) and fatality rates (β = 0.068; 95% CI, 0.044 to 0.091; P < .001).

Conclusions and Relevance  In this cross-sectional study, SAHOs were associated with reductions in COVID-19 case rates. These findings could help inform policy makers to address the continued COVID-19 pandemic in the US. The proportion of African American population was positively associated with COVID-19 case rates, and this state-level finding adds to evidence from existing ecological studies using county-level data on racial disparities in COVID-19 infection rates and underlines the urgency of better understanding and addressing these disparities.

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Article Information

Accepted for Publication: September 9, 2020.

Published: October 23, 2020. doi:10.1001/jamanetworkopen.2020.26010

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Padalabalanarayanan S et al. JAMA Network Open.

Corresponding Author: Bisakha Sen, PhD, University of Alabama at Birmingham, 1665 University Blvd, RPHB 330, Birmingham, AL 35294 (bsen@uab.edu).

Author Contributions: Dr Sen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ms Padalabalanarayanan and Mr Hanumanthu contributed equally to the work and are co–first authors.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: Sen.

Statistical analysis: All authors.

Supervision: Sen.

Conflict of Interest Disclosures: None reported.

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