Evaluating the effectiveness of countywide mask mandates at reducing SARS-CoV-2 infection in the United States

Methods

Institutional Review Board approval was not required for this study, because it did not collect individual patient data, and informed consent was not necessary, because all information accessed is publicly available. No funding was obtained for this study. The counties included are those with populations between 40,000 and 105,000 individuals. The purpose of this population limit was due to the lack of control samples for populations with over 105,000 individuals, because nearly all of these counties had mask mandates in effect during preliminary analysis. Counties with less than 40,000 individuals were excluded because smaller counties tended not to have enough daily infections to analyze. Data were gathered utilizing the US Census Bureau’s 2019 population estimates [15]. Demographic data were also found utilizing the US Census Bureau’s data [16]. The demographic information recorded includes the total estimated population and the average age of the test and control counties. Whether or not a county had a mask mandate was determined utilizing local, online news reports announcing the start times of the mandates. States excluded from the study were those with statewide mask mandates as of August 17, 2020. Four states were randomly selected from a pool of states without mask mandates. This was done by assigning each state without a mask mandate a number and then selecting the four states via Microsoft Excel’s random number generator function (Version 2018). Missouri, Iowa, Tennessee, and Florida were the four states chosen via this selection process.

Counties within these states that met the inclusion criteria and had a mask mandate were labeled as test counties. If a county was within the same state as the test county, had a similar population within 10,000 people, and did not have a mask mandate, this county would be labeled as a control county (Table 1). Each test county’s SARS-CoV-2 daily infection rates were followed for 30 days after the start date of their mask mandate, as well as for 10 days before the mandate. If a county had multiple times that a mask mandate was passed, the first time the mask mandate was passed was utilized for data analysis. The selected control counties were observed for the same 30 days after and 10 days before the test county’s mask mandate. Daily COVID-19 transmission data per county were collected utilizing USAfacts.org [17].

Statistical analysis was performed via a two-tailed, unpaired t-test comparing new daily SARS-CoV-2 infections of the test counties and control counties. A p value <0.05 will be considered statistically significant. To further evaluate the effectiveness of mask mandates, difference-in-difference analysis was performed comparing test counties and control counties 10 days before the mask mandate vs. 30 days after the mandate. This was done to show the trend of COVID-19 infections before and after the mask mandates. Statistical software utilized was SPSS (Version 28.0.0.0). A total of 19 counties that met the inclusion criteria were found to have mask mandates, and 19 controls were also selected utilizing the requirements listed above.

Results

The average population for the test counties was 71,316, and the control county average was 72,158 (p=0.89). The average age for the test counties was 40.5 years old, and the average age for the control counties was 41.8 (p=0.37). Data were collected from July 2020 to October 2020.

After following each county for 30 days after mask mandates were passed, the test counties had an average of 19.63 new COVID-19 infections per day, and the control counties had an average of 23.34 new COVID-19 infections per day. T-test analysis revealed a p value of 0.009 (Figure 1).

Figure 1:

The 30-day average COVID-19 cases of counties with and without mask mandates.

Difference-in-difference analysis revealed that test counties had a similar average COVID-19 case rate 10 days before the mask mandate was passed compared to the controls (16.05 average cases and 14.01 average cases, respectively). After 30 days of the mask mandate, the test counties had a lower average of COVID-19 cases than the controls. The average treatment effect reduced COVID-19 cases by 4.22 cases per day, or 16.9% when utilizing the difference-in-difference analysis (p=0.01) (Figure 2).

Figure 2:

Daily COVID-19 cases in test counties vs. control counties 10 days before the mask mandate was passed and 30 days after the mask mandate was passed.

Discussion

Mask mandates to prevent the spread of SARS-CoV-2 transmission are controversial primarily due to political pressures. Prior studies and this study suggest that masking is effective at reducing SARS-CoV-2 transmission. This study evaluated mask mandates in small US counties to determine their effectiveness in regions where populations may not be as densely packed. Based on our preliminary findings, smaller counties were less likely to pass mask mandates to reduce the spread of COVID-19 [18]. The purpose of this study was to evaluate the utility and effectiveness of mask mandates in small counties. Based on our results, counties that passed mask mandates showed significantly lower average daily COVID-19 transmission rates when compared to other similar counties in the states that did not pass mask mandates. Our data also show that test counties had a lower incidence rate of COVID-19 cases than controls. The differences between the population and age in test counties, and the population and age in control counties, were not statistically significant, indicating that the populations of the control counties are similar to the populations of the test counties. With these data, we conclude that mask mandates reduce SARS-CoV-2 transmission among the general population. Physicians who live in communities with low mask compliance can utilize these data to inform patients of the ability of masks to reduce the risk of SARS-CoV-2 infection. They can also utilize these data to pressure local government officials to mandate mask use in public spaces. With the rise of the Delta variant of SARS-CoV-2 in the United States and the relatively low vaccination rate among the population both in the United States and globally, it is essential to utilize multiple methods to reduce the spread of COVID-19. The data analyzed in this study suggest that mask mandates are a simple yet effective way to reduce transmission of the SARS-CoV-2 virus.

Our study did have some limitations. We did not record compliance with mask mandates and did not actively pursue other factors known to prevent virus spread, such as lockdowns and social distancing. Nevertheless, our study reinforces the CDC guidelines regarding the efficacy of face masks in controlling the spread of the SARS-CoV-2 pandemic.

Conclusions

The use of mask mandates among the general population has been shown to reduce the incidence of SARS-CoV-2 infection. Masking is an effective public health measure that local governments can implement to mitigate SARS-CoV-2 infection. In small US counties where the population density is less than it is in larger urban areas, mask mandates still appear to be effective at reducing COVID-19 transmission. Public health officials and local governments can utilize these data to provide further evidence on the effectiveness of mask mandates and guide their decision-making regarding passing local mandates. With the 5 model approach to osteopathic holistic medical practice, the behavioral model is an important aspect of patient care. Osteopathic physicians can utilize these data to encourage and support mask use among their patients in the United States and abroad to help reduce COVID-19 transmission. In future pandemics with respiratory transmission, these data can also be utilized by physicians to be proactive about changing the behaviors of patients and encouraging mask use, thereby incorporating a holistic and evidence-based approach to preventative care.