Association of County-Wide Mask Ordinances with Reductions in Daily CoVID-19 Incident Case Growth in a Midwestern Region Over 12 Weeks
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Abstract
Importance
This study assessed the longitudinal impact of new COVID-19 cases when a mask ordinance was implemented in 2 of a 5-county Midwestern U.S. metropolitan region over a 3-month period of time. Reduction in case growth was significant and reduced infection inequities by race and population density.
Objective
The objective of this study was to assess the impact that a mandatory mask wearing requirement had on the rate of COVID-19 infections by comparing counties with a mandatory policy with those neighboring counties without a mandatory masking policy.
Design
This was a quasi-experimental longitudinal study conducted over the period of June 12-September 25, 2020.
Setting
This study was a population-based study. Data were abstracted from local health department reports of COVID-19 cases.
Participants
Raw cases reported to the county health departments and abstracted for this study; census-level data were synthesized to address county-level population, income and race.
Intervention(s) (for clinical trials) or Exposure(s) (for observational studies)
The essential features of this intervention was an instituted mask mandate that occurred in St. Louis City and St. Louis County over a 12 week period.
Main Outcome(s) and Measure(s)
The primary study outcome measurement was daily COVID-19 infection growth rate. The mask mandate was hypothesized to lower daily infection growth rate.
Results
Over the 15-week period, the average daily percent growth of reported COVID-19 cases across all five counties was 1.81% (±1.62%). The average daily percent growth in incident COVID-19 cases was similar between M+ and M- counties in the 3 weeks prior to implementation of mandatory mask policies (0.90% [±0.68] vs. 1.27% [±1.23%], respectively, p=0.269). Crude modeling with a difference-in-difference indicator showed that after 3 weeks of mask mandate implementation, M+ counties had a daily percent COVID-19 growth rate that was 1.32 times lower, or a 32% decrease. At 12 weeks post-mask policy implementation, the average daily COVID-19 case growth among M- was 2.42% (±1.92), and was significantly higher than the average daily COVID case growth among M+ counties (1.36% (±0.96%)) (p<0.001). A significant negative association was identified among counties between percent growth of COVID-19 cases and percent racial minorities per county (p<0.001), as well as population density (p<0.001).
Conclusions and Relevance
These data demonstrate that county-level mask mandates were associated with significantly lower incident COVID-19 case growth over time, compared to neighboring counties that did not implement a mask mandate. The results highlight the swiftness of how a mask ordinance can impact the trajectory of infection rate growth. Another notable finding was that following implementation of mask mandates, the disparity of infection rate by race and population density was no longer significant, suggesting that regional-level policies can not only slow the spread of COVID-19, but simultaneously create more equal environment.
Key Points
Question
How are local mask ordinances associated with growth of COVID-19 cases among adjacent counties?
Findings
Ecological longitudinal analysis reveals a significant slowing of daily COVID-19 case growth after mask ordinance implementation among counties.
Meaning
Local-level policy of mask ordinances are shown to be an effective COVID-19 mitigation strategy even within locations of diverse populations.
Article activity feed
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Our take
This study, available as a preprint and thus not yet peer reviewed, found that the implementation of a mandatory mask mandate in two metropolitan U.S. counties was significantly associated with a 44% lower COVID-19 case growth in the twelve-week period following the mandate’s introduction. However, because it is likely that other policies and behaviors were occurring concurrently with the policies, it is unclear to what extent these mandates contributed to observed changes in COVID-19 growth rates.
Study design
ecological
Study population and setting
The authors used daily confirmed COVID-19 case counts for five counties in the St. Louis, Missouri, metropolitan area over a 15-week period. Case counts were tracked for the three-week period preceding the implementation of a mandatory mask mandate …
Our take
This study, available as a preprint and thus not yet peer reviewed, found that the implementation of a mandatory mask mandate in two metropolitan U.S. counties was significantly associated with a 44% lower COVID-19 case growth in the twelve-week period following the mandate’s introduction. However, because it is likely that other policies and behaviors were occurring concurrently with the policies, it is unclear to what extent these mandates contributed to observed changes in COVID-19 growth rates.
Study design
ecological
Study population and setting
The authors used daily confirmed COVID-19 case counts for five counties in the St. Louis, Missouri, metropolitan area over a 15-week period. Case counts were tracked for the three-week period preceding the implementation of a mandatory mask mandate in St. Louis City and St. Louis County (July 3, 2020) and for twelve weeks after. The authors compared daily percent (%) changes in COVID-19 cases prior to and proceeding the implementation of mask mandates, comparing counties with and without mask mandates.
Summary of main findings
Daily COVID-19 case growth in the three weeks preceding mask ordinances was similar in counties with and without these mandates. Three weeks after mask mandates were introduced in St. Louis City and St. Louis County, COVID-19 growth rates were 44% lower in jurisdictions with the mask mandates compared to those without mandates. This disparity in COVID-19 case growth continued to widen between counties with and without mask mandates in the 12 weeks following implementation: counties with a mask mandate reported a 78% lower daily case growth rate compared to counties without mandates.
Study strengths
The authors compare daily COVID-19 case growth in a small sample of geographically proximal counties, some of which enacted mask mandates, and others did not. The comparison of case growth rates before and after these counties helps establish a different pattern of case growth that occurred before and after the mandates were enacted.
Limitations
Implementation of other non-pharmaceutical measures and behavior change in response to COVID-19 case growth could explain subsequent changes in epidemic trajectories beyond the implementation of the mask mandates. Because policy introduction may not correspond to subsequent behavior change (i.e., increased mask use), the plausibility of a policy introduction alone reducing COVID-19 case growth is questionable. Additionally, the counties included in the analysis may differ, in both measured and unmeasured ways, that could explain changes in daily COVID-19 case growth beyond mask mandates. Moreover, these counties very likely had many social, economic, and political factors, which could have influenced case rates concurrently with mask mandates. Finally, because mask mandates were introduced in these select counties in response to exponential COVID-19 case growth, the measured impact of this policy on COVID-19 transmission may not be consistent if implemented in other settings, including jurisdictions with lower COVID-19 case burdens or non-exponential case growth.
Value added
This is among the first studies to attempt to quantify the impact of a mandatory mask mandate on COVID-19 transmission in metropolitan U.S. counties.
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SciScore for 10.1101/2020.10.28.20221705: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar …
SciScore for 10.1101/2020.10.28.20221705: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
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