Assessing the impact of mask mandates on SARS-CoV-2 transmission: a case study of Utah

Throughout the COVID-19 pandemic, the effectiveness of face masks mandates has been intensely debated. Many methods have been used to demonstrate mask effectiveness, including one that compares the change in reproduction number following implementing and removing face mask mandates1. Using data from Utah, we calculated the effect of mask mandates (EFm) in each local health district from before and after three key mandates: the Salt Lake and Summit County (SLSC) mask mandates enacted; the Utah statewide mask mandate enacted; and the Utah statewide mandate was lifted. We found that most counties had a reduction in the growth rate of cases following the mandates. There were reductions in EFm in many counties after the introduction of the SLSC mask mandates and a more widespread reduction in EFm across the state following the statewide mandate. Lifting the mandates, many counties across the states saw an increase in EFm. Our data shows mask mandates were an effective way to reduce transmission both within the jurisdiction they were enacted and in neighboring jurisdictions. We provide evidence to support mask mandates as a way to prevent transmission to be better equipped to respond to future pandemics.

ABSTRACT Throughout the COVID-19 pandemic, the effectiveness of face masks mandates has been intensely debated. Many methods have been used to demonstrate mask effectiveness, including one that compares the change in reproduction number following implementing and removing face mask mandates1. Using data from Utah, we calculated the effect of mask mandates (EFm) in each local health district from before and after three key mandates: the Salt Lake and Summit County (SLSC) mask mandates enacted; the Utah statewide mask mandate enacted; and the Utah statewide mandate was lifted. We found that most counties had a reduction in the growth rate of cases following the mandates. There were reductions in EFm in many counties after the introduction of the SLSC mask mandates and a more widespread reduction in EFm across the state following the statewide mandate. Lifting the mandates, many counties across the states saw an increase in EFm. Our data shows mask mandates were an effective way to reduce transmission both within the jurisdiction they were enacted and in neighboring jurisdictions. We provide evidence to support mask mandates as a way to prevent transmission to be better equipped to respond to future pandemics. ii TABLE OF CONTENTS ABSTRACT ii ACKNOWLEDGEMENTS 1 INTRODUCTION 2 METHODS 5 RESULTS 7 DISCUSSION 11 CONCLUSION 15 REFERENCES 16 iii 1 ACKNOWLEDGEMENTS A special thank you to the Undergraduate Research Opportunities Program at the University of Utah and the Centers for Disease Control and Prevention (75D30121F00003 and 1U01CK000585-01) for funding and research support. I would also like to thank Dr. Lindsay Keegan and Holly Shoemaker for their guidance and support throughout this process. 2 INTRODUCTION When vaccines and other therapeutics are not available, public health decision makers must rely on non-pharmaceutical interventions (NPI) as community mitigation strategies such as school closures, social distancing, and face mask mandates to reduce disease transmission and severe outcomes2. During the COVID-19 pandemic, vaccines against SARS-CoV-2 did not become available until December 20203 consequently, for the first year of the pandemic, all personal and community mitigation efforts came in the form of NPIs. While numerous NPIs were considered, mask mandates became the primary approach to slowing the spread of COVID-194. Face masks have been utilized as a method to slow transmission before the start of the COVID-19 pandemic, especially in other countries during flu season5. However, masks have been the catalysts of intense debates fueled by politics, with different states choosing to implement and enforce mandates often based on their political leanings6,7. The deployment of mask mandates in Utah is representative of the fragmented nature of the way that interventions were employed throughout the state (Figure 1). The first mandates applied in the state occurred in both Salt Lake and Summit counties (SLSC), which received approval on June 27th, 2020. The next mandate was put in place in Grand County on July 7th, 2020. As cases began to spike in the fall of 2020, there were scattered attempts from different health departments to deal with the rising cases, creating a mosaic of interventions throughout the state ranging from recommendations to actual mandates. Throughout this period, there were special mandates for schools and public transportation8,9. A statewide mandate was issued on November 9th, 2020, for all 3 counties until April 10th, 2021. After the statewide mandate ended, Salt Lake City opted to keep their mandates longer. We show a labeled map of the Local Health Districts (LHDs) in the supplement (Figure SI1). Figure 1: Schematic of the date of each mask mandate by jurisdiction in Utah. The plot shows each local health district (LHD) in Utah (in bold) and the timing of the mask mandate within that jurisdiction. Jurisdictions that only implemented the statewide mandate are shown in blue, jurisdictions that implemented a different mandate at the LHD level are shown in pink, and mandates that were implemented at a smaller jurisdictional level (i.e., a city or county) are shown in yellow. These smaller jurisdictional levels are indicated in italics and are indented below the LHD that the reside within. In some instances, mandates from an LHD or city/county overlap with the statewide mandate and while these are shown in the color of the initial issuing jurisdiction, a transparent blue box is shown behind the mandates to indicate that these mandates overlapped with the statewide mandate (e.g., Davis County LHD). In other instances, the LHD or city/county end before the start of the statewide mandate so while the LHD or city/county mandate are shown in the color that matches the issuing jurisdiction, the statewide mandate is indicated in blue because these jurisdictions did not have their own mandate at this time (e.g., Logan [city]). Many studies measure the efficacy of mask mandates on the spread of airborne diseases which show masks to be effective at protecting individuals from infection1,10-20. One method, described by Britton et al.1 estimates the effect of mask mandates at the population level by comparing the change in the growth rate of cases before and after a 4 mandate is implemented or lifted. One benefit of the fragmented system of mandates in Utah is that it has created a natural experiment to compare the efficacy of these different interventions within the same state by comparing the data between health districts. Here, we show how the state, county, and local mandates differed in their effectiveness in reducing the number of COVID-19 cases within the jurisdiction where the mandate was implemented as well as across the state of Utah. 5 METHODS The data for this project is from the Utah Department of Health and Human Services COVID-19 surveillance dashboard21. We employ the method outlined in Britton et al.1 to calculate the Face Mask Effect (EFm). EFm is calculated by comparing the effective reproduction number (Re) preand post-intervention, and is given by the following equation: 𝐸!" = 1 − 𝑅!" 𝑅#$ Where RFm and Rno are calculated by: 𝑅 = (𝑐 % 𝑟𝜇 + 1)&'% Additionally, 𝑟 is calculated before (𝑟#$ ) or after (𝑟!" ) a mask mandate is implemented, and is given by: 𝑙𝑛( 𝑟!" = 𝑙𝑛( 𝑟#$ = 𝑖(𝑡 + 𝑑) ) 𝑖(𝑡) 𝑑 𝑖(𝑡) ) (𝑖(𝑡 − 𝑑) 𝑑 In this study, we consider 28 days before and after the mask mandate to account for the variabilities in Utah’s reported cases with respect to day of the week due to weekend lab closures. Parameter values and meaning in Table 1. 6 Table 1: Tables of variables, their meaning, and if applicable the value we used for this study Variable Meaning Value EFm Effectiveness of mask mandates Estimated RFm Effective reproduction number following the implementation of a mask mandate Estimated Rno the effective reproduction number prior to the implementation of a mask mandate Estimated 𝜇 Mean generation time 6.5 days22 𝜎 Standard deviation of the generation time distribution 4 days22 Coefficient of variation of the generation time distribution 1.62522 𝑟 The growth rate of cases Estimated 𝑑 Number of days before or after a mandate that we consider. 28 days 𝑟#$ the growth rate of cases 𝑑 days before a mask mandate is implemented Estimated 𝑟!" the growth rate of cases 𝑑 days after a mask mandate is implemented Estimated i(t) The incidence at the time of the mandate (t) Estimated 𝜎 𝜇 𝑐 = We calculated the EFm at the at the LHD level across the state following three dates: June 28th, 2020 the date of the SLSC mask mandates23,24 November 9th, 2020 the date of the Utah State-wide mask mandate25,26 and April 10th, 2021 the date when all mandates were lifted except in Salt Lake City27. All analyses were conducted in R28, all code is available on github29. 7 RESULTS Mask mandates were implemented in Utah at times when cases were trending upwards and there was concern for healthcare infrastructure (Figure 2). Following the SLSC mask mandate on June 28, 2020, Salt Lake County Health District (HD) saw a 22.4% reduction in Re in the 28 days following the mandate compared to the 28 days preceding the mandate (Table 2, Figure 3). Conversely, Summit County HD saw a 6.7% increase in Re following the mandate (Table 2, Figure 3). After the statewide mandate was implemented on November 9, 2020, all of the LHDs across the state except San Juan HD saw reductions in Re following the mandate (Table 2, Figure 3). After the statewide mandate was lifted on April 10, 2021 six LHDs exhibited a rise in cases, with some more gradual than others (Table 2, Figure 3). Southeast Utah HD had the largest increase in Re (26.2%) and Salt Lake County HD had the smallest increase of 1.9% following lifting the mandate. Figure 2: Plot of incident cases of SARS-CoV-2 by county health district over the course of the COVID-19 pandemic with vertical lines indicating the implementation and lifting of the different mask mandates as labeled. A) Incident cases of SARS-CoV-2 in SLSC and B) Incident cases of SARS-CoV-2 in all Health Districts in Utah. 8 In addition to the mandates impacting the jurisdictions for which they were levied, the SLSC mandates had an impact on other LHDs across the state (Table 2, Figure 3). The largest effect was in the Central Utah HD which had a 42.4% reduction in Re, but LHDs along the interstate highway I-15 corridor all saw a reduction in Re. However, not all LHDs saw a reduction following the SLSC mask mandates, 5 counties saw an increase in Re and the LHD with the highest increase was San Juan HD which had a 104.5% increase in Re following the mandate (Table 2). 9 Table 2: Table of the effectiveness of mandates implementing or lifting masking requirements by LHD in Utah for the SLSC mask mandate on 6/28/2020, the statewide mask mandate on 11/9/2020, and the lifting of the statewide mask mandate on 4/10/2020. Health District EFm for SLSC mask mandate EFm for statewide Mask Mandate EFm for the lifting of the statewide Mask Mandate* Salt Lake County Health District* -22.4% -17.2% 1.9% Summit County Health District 6.68% -14.2% -2.9% Wasatch County Health District 22.0% -7.0% -17.8% Weber-Morgan Health District -30.2% -16.2% -17.1% TriCounty Health District -9.9% -30.8% 9.5% Southwest Utah Health District -24.3% -7.2% -1.4% Southeast Utah Health District 6.9% -17.4% 26.2% San Juan Health District 104.5% 11.2% -11.0% Central Utah Health District -42.4% -2.4% 15.6% Tooele County Health District 9.1% -14.4% -0.04% Davis County Health District -24.6% -12.5% -5.7% Utah County Health District -17.1% -12.7% 11.0% Bear River Health District -11.9% -9.3% 14.4% *Salt Lake City still had a mandate but the county as a whole did not 10 Figure 3: Plot of the Effect of Mask Mandates (EFm) following the implementation or removal of mask mandates. Where a negative EFm corresponds to a reduction in transmission while a positive EFm corresponds to an increase in transmission following the mandate change. A) Shows the effect following the implementation of the SLSC mask Mandates on June 27, 2020, B) shows the effect following the implementation of the statewide mandate on November 9, 2020, and C) shows effect of lifting the statewide mandate on April 10th, 2021. Interstate highways are shown in red with I-15 annotated. 11 DISCUSSION Overall, we found that the majority of counties experienced a reduction in transmission after mask mandates were implemented. The most comprehensive reductions were shown after the statewide mandate was implemented with nearly all health districts exhibiting some decrease (except San Juan HD). After the statewide mandate was lifted, most LHDs exhibited an increase in transmission. While the statewide mandate was lifted in April 2020, Salt Lake City maintained a city-wide mask mandate until 2022, which may partially explain why Salt Lake County HD only saw a modest increase in transmission. Although it is not surprising that mask mandates resulted in reductions in transmission within the jurisdiction that they were issued, we found a surprising trend that many additional LHDs exhibited a reduction in transmission following a mask mandate in a different jurisdiction. In particular, we found that LHDs that run along the interstate highway I-15 corridor all exhibited a reduction in transmission following the SLSC mask mandate. This suggests that mandates in large population hubs like Salt Lake County can have far reaching impacts. This could indicate that people may be changing individual behavioral patterns in response to other jurisdictions mandates and rising case counts. Another interesting finding was that the largest reductions in transmission occurred after the implementation of the June 2020 mandates with most LHDs exhibiting a larger decrease following these mandates than after the statewide mandate. The statewide mandate reduced transmission across all LHDs (except one) including counties 12 that did not exhibit a decrease following the SLSC mandates. One potential explanation for this reduced impact could be that counties throughout the state had already begun to apply their own mask restrictions in the weeks leading up to the statewide mandate or that individuals had already started to change their own behavior in the absence of mandates as cases of COVID-19 were increasing at that time. Exploring the patterns of counties that did not see an impact of mask mandates can also be illuminating. There was an increase in transmission in Summit County following the SLSC mandate, which could be attributed to the fact that their case levels were very low when they instituted the mandate, averaging 39.1 cases per week prior to the mandate and 112.7 cases per week after. Additionally, San Juan HD saw a 104.5% increase in Re following the SLSC mask mandates, and a 11.2% increase in Re following the statewide mandate. One potential explanation for this is that 21% of San Juan HD’s population is self-identified as Navajo30 and the Navajo nation was experiencing a major outbreak of SARS-CoV-2 during this time30,31. Our findings are particularly interesting when taken in the broader context of mandates. Seegert et al.32 explored the economic impacts of mask mandates and found that statewide mandates stimulate economic activity while county-wide mandates depress activity, suggesting that this could be a result of risk perception32. Taken with our findings that mask mandates generally reduced transmission within the jurisdiction where levied as well as in connected jurisdictions, it suggests that in future disease outbreak scenarios, swift, statewide action may be equally effective at reducing transmission and more effective at reducing economic harms than a piecemeal, local approach. 13 A number of studies have attempted to estimate the effectiveness of face mask mandates1,10-20. One randomized control trial concluded that wearing masks reduced the risk of infection for the wearer by 18%; while another found the symptomatic incidence was reduced 9.5%10,11. Observational studies that directly estimate the effect of mask mandates on Re, estimated a 29% reduction in Kansas1,15, a 21–43% reduction in Jena, Germany1,17, and a 15.1% reduction when averaged over 190 countries and adjusted for confounders16. Further, a simulation-based study found masking equated to an average 25-35% reduction in infectious contacts13. Overall, our study shows a similar median effectiveness of 12.7% reduction following the implementation of the statewide mandate across all LHDs (a 13.5% reduction when excluding San Juan HD) and a similar 11.9% reduction across all LHDs following the implementation of the SLSC mandate (a 7.9% reduction when just considering the LHDs with mandates). Our study attempts to evaluate the effectiveness of mask mandates as they are implemented. However, it has several limitations. We use government mask mandates as a proxy for mask wearing. Unfortunately, the relationship between mandates and mask use is relatively weak, with one study showing no statistically significant relationships and other studies showing between a 13% – 23% relationship between mandates and mask use in the United States18,19,33. Consequently, just because mandates were in place, doesn’t mean that there was full compliance. Many mandates were designed to be lightly enforced, the tense political climate surrounding mask use reduced willingness to enforce mask mandates, potentially decreasing the efficacy of those mandates34. Further, some people may have worn a mask regardless of mandate status. Our study does not attempt 14 to link actual mask usage to the implementation of masking mandates. Here, we examine mask mandates and their impact at the population-level. While mask mandates dictate that individuals must wear a mask, many people used ineffective masks35. Our study does not attempt to provide evidence on the individual effectiveness of using a mask. Another potential limitation of our study is the timing of vaccine availability. Vaccines first started becoming available to healthcare workers in Utah in December 2020, becoming widely available to the general public by March 20213,36,37. Given the timing of the statewide mask mandate on November 9, 2020, we do not expect that vaccination impacted estimates of the face mask effectiveness. However, with widespread vaccination access beginning in March 2021, it is possible that vaccination may have impacted estimates of the effectiveness of removing the statewide mandate in April 2021. However, we believe that this impact was limited as only 32% of people in Utah were fully vaccinated by May 8th, 2021 which is when our study’s time frame ends38. 15 CONCLUSION Our study shows that the implementation of mask regulations in Utah played a role in reducing SARS-CoV-2 transmission throughout the state. We found that implementing and lifting mask mandates influenced both the jurisdictions under which the mandate was levied as well as connected jurisdictions. 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