Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study

Abstract

We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States.

Design

We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports.

Interventions

Issuance of stay-at-home orders.

Primary and secondary outcome measures

Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility.

Results

Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%–6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) 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 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties.

Conclusion

Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

SciScore for 10.1101/2021.07.13.21260476: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Ethics	not detected.
Sex as a biological variable	not detected.
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

While our study is the first to examine the effects of stay-at-home orders on rural and urban areas, it has limitations. We focused on stay-at-home orders without considering the heterogeneity of what constituted these orders on the local level and did not separately weigh the impacts of additional measures such as school closures, nonessential business closures, prohibition of large gatherings, and mandatory masking. However, it would be difficult to separate these effects as most restrictions were implemented concurrent with, and worked in tandem to, stay-at-home orders. COVID-19 case data may be biased by differences in testing availability. However, because testing was more limited in rural than urban areas,57 our findings are likely an underestimate of the true difference in infection rates between urban and rural counties. We were not able to examine hospitalization and ICU utilization rates in rural and urban counties as these data are not uniformly available at the county-level. Finally, our study does not take into account potential residual or unmeasured confounders that may explain the difference in infection rates between rural and urban counties outside of stay-at-home order implementation. We accounted for this by using a random intercept in our analysis, but due to the absence of granular data at the county-level, potential confounding would be impossible to eliminate completely. High rates of COVID-19 in rural counties, along with the suppressed effect of stay...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

Read the original source