The Effectiveness and Durability of Stay‐at‐Home Orders on Reducing the Spread of COVID‐19 in Rural and Urban America
Authors: Jiang David , Roy Darius , Pollock Benjamin , Shah Nilay , McCoy Rozalina 
Published: September 2021

Research Objective

Throughout the spring of 2020, stay‐at‐home orders were imposed to curb the spread of the coronavirus disease 2019 (COVID‐19) in the United States. However, there is limited data and evidence on their effectiveness, especially in rural areas as compared to urban areas. Therefore, we examined the association between stay‐at‐home order implementation and the incidence of COVID‐19 cases in rural and urban counties.

Study Design

We conducted an interrupted time series analysis using a mixed effects zero‐inflated Poisson model with random intercept by county and standardized by population, which examined the association between stay‐at‐home orders and 14‐day lagged county‐level counts of daily new COVID‐19 cases in rural vs. urban counties between 1/22/2020 and 6/10/2020. To further bolster our findings, the association between stay‐at‐home orders and mobility in rural vs. urban counties was assessed using Google Community Mobility Reports.

Population Studied

We examined the COVID‐19 daily incident and stay‐at‐home orders for all 3142 United States counties. Each county was categorized as urban or rural using the 2013 county‐level Rural–Urban Continuum Codes per federal standards. There are 1976 rural and 1166 urban counties in the United States.

Principal Findings

As of June 10, 2020, there were 1,786,886 cases of COVID‐19 in the U.S., of which 9.0% (N = 161,452) were in rural counties. During the study period, 1854 (93.8%) rural counties and 1075 (92.2%) urban counties were covered by a stay‐at‐home order. Stay‐at‐home orders were implemented later (median March 30 vs. March 28) and were shorter (median 35 vs. 54 days) in rural than urban counties. Indoor mobility was, on average, 2.6–6.9% higher in rural than urban counties during and after stay‐at‐home orders. Compared to the pre‐stay‐at‐home baseline period, the number of new COVID‐19 cases increased under stay‐at‐home by IRR 1.60 (95% CI, 1.57–1.64) in rural and 1.36 (95% CI, 1.30–1.42) in urban counties. For each day under stay‐at‐home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981–0.982) in rural and 0.952 (95% CI, 0.951–0.953) in urban counties. Each day after stay‐at‐home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994–0.995) in rural and 0.997 (95% CI, 0.995–0.999) in urban counties.

Conclusions

Stay‐at‐home orders decreased mobility and slowed the spread of COVID‐19 less effectively in rural than urban counties.

Implications for Policy or Practice

As states are considering implementing a new round of stay‐at‐home orders, our findings reveal several ways to improve the implementation, enforcement, and adherence. First, they should be implemented earlier and maintained longer for optimal effectiveness. There was marked heterogeneity in the timing and duration of stay‐at‐home orders and our data reinforces the need for multi‐jurisdictional, ideally federal, infection control mandates. Any new stay‐at‐home orders should be gradually implemented to avoid the pre‐stay‐at‐home surge of mobility and subsequent spike in COVID‐19 cases. To better encourage and facilitate compliance, leaders at all levels need to use scientific evidence to advocate for the importance of stay‐at‐home orders, set personal examples, and develop employment, housing, educational, and healthcare assistance for the most vulnerable.

Primary Funding Source

National Institutes of Health.