key: cord-0850757-jmyq14vg
authors: Fischer, C. B.; Adrien, N.; Silguero, J. J.; Hopper, J. J.; Chowdhury, A. I.; Werler, M. M.
title: Mask adherence and rate of COVID-19 across the United States
date: 2021-01-25
journal: nan
DOI: 10.1101/2021.01.18.21250029
sha: 395c01eb5ddc89251de8957ad93ade8b6dd9aa32
doc_id: 850757
cord_uid: jmyq14vg

Mask wearing has been advocated by public health officials as a way to reduce the spread of COVID-19. In the United States, policies on mask wearing have varied from state to state over the course of the pandemic. Even as more and more government leaders encourage or even mandate mask wearing, many citizens still resist the notion. Our research examines mask-wearing policy and adherence in association with COVID-19 case rates. We used state-level data on mask-wearing policy for the general public and on proportion of residents who stated they always wear masks in public. For all 50 states and the District of Columbia (DC), these data were abstracted by month for April[boxh]September 2020 to measure their impact on COVID-19 rates in the subsequent month (May[boxh]October 2020). Monthly COVID-19 case rates (number of cases per capita over two weeks) >200 per 100,000 residents were considered high. Fourteen of the 15 states with no mask-wearing policy for the general public reported a high COVID-19 rate. Of the 8 states with at least 75% mask adherence, none reported a high COVID-19 rate. States with the lowest levels of mask adherence were most likely to have high COVID-19 rates in the subsequent month, independent of mask policy or demographic factors. Our analysis suggests high adherence to mask wearing could be a key factor in reducing the spread of COVID-19. This association between high mask adherence and reduced COVID-19 rates should influence policy makers and public health officials to focus on ways to improve mask adherence across the population in order to mitigate the spread of COVID-19.

Mask wearing has been advocated by public health officials as a way to reduce the spread of COVID-19(1, 2, 3). In the United States, policies on mask wearing have varied from state to state over the course of the pandemic (4). For the period of April 1 through October 31, 2020, less than half of states had issued a mandate for mask wearing in public and nearly a third had not made any recommendation. Even as more and more government leaders encourage mask-wearing (5), many citizens still resist the notion. Individuals' mask-wearing behaviors are not only influenced by recommendations and mandates issued by state leaders, but also by print, televised, and social media. Thus, adherence to mask wearing in public remains a challenge for mitigating the spread of COVID-19.

Public health policy-making requires navigating the balance of public good and individual rights (6). The adoption of universal masking policies is increasingly polarized and politicized, demanding that public health authorities balance the values of health and individual liberty. Adherence to public policy is influenced by a complex interplay of factors such as public opinion, cultural practices, individual perceptions and behaviors (7), which are difficult to quantify. The politicization of COVID-19 epidemiology (8) has further complicated policy-making, messaging, and uptake. Nevertheless, adherence is essential for policy effectiveness. Research on lax public health policies and lack of adherence is warranted because they can carry real risks to health, with myriad downstream effects including increased death, stressed health care systems, and economic instability (9) . We examined the impact of state-based mask-wearing policy and adherence on COVID-19 case rates during the summer and early fall of 2020 in order to quantify this effect.

For all 50 states and D.C., data on mask wearing and physical distance policies, mask adherence, COVID-19 cases, and demographics were abstracted from publicly available sources. We utilized the COVID-19 US State Policy Database, created by Dr. Julia Raifman at Boston University School of Public Health (10) , for policy and demographic information. We abstracted data on whether the state issued a mandate of mask use by all individuals in public spaces, and if so, the dates of implementation and whether the mandate was enforced by fines or criminal charge/citation(s). For policies on physical distancing, we recorded whether a stay-at-home order was issued and, if so, when. For mask adherence levels, we utilized the Institute of Health Metrics and Evaluation (IHME) COVID-19 Projections online database (11) , and abstracted daily percentages of the population who say they always wear a mask in public. To calculate monthly COVID-19 case rates, we abstracted the number of new cases reported by the U.S. Centers for Disease Control and Prevention (CDC) (12) and state population sizes in 2019 (13) .

Mask-wearing policy: We categorized the existence of a mask policy as "None" if there was no requirement for face coverings in public spaces, "Recommended" if required in all public spaces without consequences, and "Strict" if required in all public spaces with consequences in the form of fine(s) or . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; citation(s). States and D.C. were categorized as having no policy or having any policy for at least one day of a given month.

Mask-wearing adherence: We calculated the average mask use percentage by month for April-September, 2020. For each month, the distribution of mask adherence across all 50 states and D.C. were categorized into quartiles, meaning the cut-off values for each quartile may be different from one month to another. Mask adherence was classified as "low" if in the lowest quartile and as "high" if in the highest quartile.

COVID-19 rates: We calculated the number of new cases in each month, for each state and D.C. Rates were the number of new cases divided by the population in 2019. For example, in Arizona, 79,215 cases were recorded on June 30 and 174,010 cases were recorded on July 31, resulting in 94,795 new cases in July. We divided the monthly number by 2.2 to obtain the number in a two-week period (43,088). The 2-week rate in July in Arizona = 43,088 cases / 7,278,717 population in 2019 = 0.00592 or 592 per 100,000. We classified a state and D.C. as having a high case rate in a given month if a 2-week rate was >200 cases per 100,000 people, per CDC classifications of highest risk of transmission (14) .

Covariates: Based on CDC at-risk guidelines for COVID-19 (15), we considered non-Hispanic Black, Hispanic, age, and population density as potential confounders. Data on population distributions from the COVID-19 US State Policy Database (10) came from the US Census. Demographic data were dichotomized at the following cut-points: >15% non-Hispanic Black, >15% Hispanic, median age >40 years, and population density >200 people per square mile. Policy data on physical distancing were dichotomized as any versus no stay-at-home order during the April 1 to October 31, 2020 interval.

Statistical analysis: Our analyses took into consideration the delayed effect of mask wearing and policies on COVID-19 health outcomes. Thus, policy and adherence levels in a given month were contrasted with lagged COVID-19 case rates in the subsequent month. Both mask policy and mask adherence for states and D.C. were cross-tabulated with high case rates in the subsequent month. Logistic regression models were used to estimate the odds ratio and 95% confidence intervals for high case rates in the subsequent month associated with average mask adherence (as a continuous variable). Models were unadjusted, adjusted for no mask policy (Model 1), and adjusted for no mask policy in previous month, no stay-home order, >15% population non-Hispanic Black, >15% population Hispanic, median age >40 years, population density > 200/ square mile (Model 2).

. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. Table 1 shows the states in COVID-19 high-risk categories. Because stay-at-home order, mask-wearing policy, mask adherence, and COVID-rates can vary from month to month, we listed those states with consistent classifications across the period April through September (or May through October for COVID-19 rates). Fifteen states had no mask policy and four states had low adherence throughout this period of months. Our comparison of states with high COVID-19 rates by month focused on July, August, September and October. For the low and high mask adherence categories, cut-off values for the low and high quartiles were 31% and 46% in June, 53% and 72% in July, 55% and 71% in August, and . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. ; 55% and 68% in September. Only six states in May and one state in June had rates in the high category, which limited our ability to compare mask adherence data. In the subsequent four months, 16 (31%), 18 (35%), 16 (31%), and 30 (59%) states had high rates of adherence. Figure 1 shows the proportions of states with high COVID-19 rates among those with low and high mask adherence in the preceding month. States with low mask adherence had high COVID-19 rates in the subsequent month. In contrast, for the states with high mask adherence, just one state in July, August, and September and three in October had high COVID-19 rates in the subsequent month. Of the 8 states and D.C. with at least 75% mask adherence (Arizona, Connecticut, Hawaii, Massachusetts, Michigan, New York, Rhode Island, Vermont), none state experienced a high COVID-19 rate in the subsequent month. Figure 2 shows the proportions of states and D.C. with high COVID-19 rates among with those with no and any mask-wearing policy for the general public in the preceding month. Among states and D.C. with no mask-wearing policy, 40 to 73% had high COVID-19 rates in the subsequent month. In contrast, less than 20% of states with a mask-wearing policy had high COVID-19 rates, except in September when over half experienced high rates. Fourteen of the 15 states with no mask-wearing policy for the general public from June through September reported a high COVID-19 rate. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. ; Table 2 shows odds ratios and 95% confidence intervals for average mask adherence and mask policy for the general public in relation to high COVID-19 rates in the subsequent month. Mask adherence was associated with decreased odds of high COVID-19 rates. For every 1% increase in average adherence in June, the fully adjusted odds of high COVID-19 in July decreased by 5%. Similar decreases were observed for July and August mask adherence in relation to COVID-19 rates in August and September, respectively. The strongest association was for mask adherence in September; for every 1% increase in average adherence, the odds of a high COVID-19 case rate decreased by 26%. Crude and adjusted odds ratios for any mask policy in relation to high COVID-19 rates in the subsequent month were decreased; but confidence intervals were wide. For mask policy and adherence in September in relation to high COVID-19 rates in October, collinearity caused the odds ratio to flip.

We show supporting evidence for reducing the spread of COVID-19 through mask wearing. This protective effect of mask wearing was evident across four months of the pandemic, even after adjusting the associations for mask policy, distance policy, and demographic factors. We observed some benefit of mask policy on COVID-19 rates, but the findings were unstable. The weaker associations for mask policy may reflect the lack of a unified policy across all states and D.C. and the inconsistent messaging by the media and government leaders. Our observed associations should influence policy-makers and contribute to public health messaging by government officials and the media that mask wearing is a key component of COVID-19 mitigation.

Our study accounted for temporality by staggering COVID-19 outcome data after adherence measures. Nevertheless, it is possible that average mask adherence in a given month does not capture the most effective time period before increases in COVID-19 rates. For example, mask wearing in the two weeks before rates begin to rise might be a more sensitive way to measure the association. If this is true, we would expect associations between mask adherence and high COVID-19 rates to be . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. ; even stronger. It is also possible that survey respondents misreported their mask-wearing adherence; whether they would be more less likely to over or under-report is open to speculation. The lag between mask adherence measures and COVID-19 rates should reduce reverse causation, but high COVID-19 rates early in a month could affect mask adherence levels later in that month.

Our adjustment for demographic factors at the state population level may not represent the true underlying forces that put individuals at greater risk of contracting COVID-19. Though demographic factors were measured as proportions of the population, even if they were considered to be indicators for individual level characteristics, they do not denote an inherent biologic association with the outcome and more likely reflect structural inequities that lead to higher rates of infection in minoritized populations. Another consideration is that access to COVID-19 testing appears to vary from state to state (16) . If states with less accessible COVID-19 testing also have less mask adherence, the associations we report here may be under-estimates.

Our analysis of state and D.C.-level data does not account for variations in policy, adherence, and demographic factors at smaller geographic levels, such as county-levels. Further analyses of more granular geographic regions would be a logical next step. Indeed, the mix of very rural and many densely populated cities in California may explain why it was the only state to have high mask adherence and high COVID-19 rates in the subsequent month.

In conclusion, we show that mask-wearing adherence, regardless of mask-wearing policy, may curb the spread of COVID-19 infections. We recommend renewed efforts be employed to improve adherence to mask wearing. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.18.21250029 doi: medRxiv preprint 5. Hubbard K. These states have COVID-19 mask mandates. US News & World Report; Dec. 2, 2020. https://www.usnews.com/news/best-states/articles/these-are-the-states-with-mask-mandates, accessed December 28, 2020. 

Masking lack of evidence with politics

The Fragmented United States of America: The impact of scattered lockdown policies on country-wide infections

COVID-19 US state policy database. 2020. Available at: www.tinyurl.com/statepolicies

The Institute for Health Metrics and Evaluation (IHME)

United States COVID-19 Cases and Deaths by State over Time

Population estimates for the U.S., States, and counties

Centers for Disease Control and Prevention. CDC indicators and thresholds for risk of introduction and transmission of COVID-19 in schools

Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates