The Association Between State-Issued Mask Mandates and County COVID-19 Hospitalization Rates

Abstract

Context: Mask mandates are one form of nonpharmaceutical intervention that has been utilized to combat the spread of SARS-CoV2, the virus that causes COVID-19.

Objective: This study examines the association between state-issued mask mandates and changes in county-level and hospital referral region (HRR)-level COVID-19 hospitalizations across the United States.

Design: Difference-in-difference and event study models were estimated to examine the association between state-issued mask mandates and COVID-19 hospitalization outcomes.

Participants: All analyses were conducted with US county-level data.

Interventions: State-issued mask mandates. County-level data on the mandates were collected from executive orders identified on state government Web sites from April 1, 2020, to December 31, 2020.

Main outcome measures: Daily county-level (and HRR-level) estimates of inpatient beds occupied by patients with confirmed or suspected COVID-19 were collected by the US Department of Health and Human Services.

Results: The state issuing of mask mandates was associated with an average of 3.6 fewer daily COVID-19 hospitalizations per 100 000 people (P < .05) and a 1.2-percentage-point decrease in the percentage of county beds occupied with COVID-19 patients (P < .05) within 70 days of taking effect. Event study results suggest that this association increased the longer mask mandates were in effect. In addition, the results were robust to analyses conducted at the HRR level.

Conclusions: This study demonstrated that state-issued mask mandates were associated with reduction in COVID-19 hospitalizations across the United States during the earlier portion of the pandemic. As new variants of the virus cause spikes in COVID-19 cases, reimposing mask mandates in indoor and congested public areas, as part of a layered approach to community mitigation, may reduce the spread of COVID-19 and lessen the burden on our health care system.

FIGURE 1

Frequency of Dates in Which Counties Were First Subject to a State-Issued Mask Mandate, April 1, 2020, to December 31, 2020^{a a}Policy data were collected from state government Web sites containing executive or administrative orders. State-issued mask mandates were defined as requirements for persons to wear a mask either (1) anywhere outside their home or (2) in both retail businesses and in restaurants/food establishments. This figure is available in color online (www.JPHMP.com).

FIGURE 2

The Association Between State-Issued Mask Mandates and County-Level Changes in Numbers of COVID-19 Hospitalizations per 100 000 People and Percentage of Beds Occupied With COVID-19 Patients, April 1, 2020, to December 31, 2020: Event Studies Results^{a a}Policy data were collected from state government Web sites containing executive or administrative orders. County hospitalization data were collected by the Department of Health and Human Services. All models controlled for other state-issued community prevention policies (ie, stay-at-home orders, gathering bans, restaurant closures, and bar closures) and included county and day fixed effects. Results are displayed as coefficient estimates and 95% confidence intervals. All analyses were weighted by county population and estimated with robust standard errors clustered at the county level.

FIGURE 3

The Association Between State-Issued Mask Mandates and HRR-Level Changes in Numbers of COVID-19 Hospitalizations per 100 000 People and Percentage of Beds Occupied With COVID-19 Patients, April 1, 2020, to December 31, 2020: Event Studies Results^a Abbreviation: HRR, hospital referral region. ^aPolicy data were collected from state government Web sites containing executive or administrative orders. HRR hospitalization data were collected by the Department of Health and Human Services. All models controlled for other state-issued community prevention policies (ie, stay-at-home orders, gathering bans, restaurant closures, and bar closures) and included HRR and day fixed effects. Results are displayed as coefficient estimates and 95% confidence intervals. All analyses were weighted by HRR population and estimated with robust standard errors clustered at the county level.