New Mexico Republicans have to hold their convention in Texas because their Democratic governor doesn't allow superspreader event. New Mexico Republicans moving state convention to Texas https://www.santafenewmexican.com/n...cle_58fad98c-a11c-11eb-b297-7bfe76e9d619.html
Let's take a look at actual science and facts on mask mandates... Association of State-Issued Mask Mandates and Allowing On-Premises Restaurant Dining with County-Level COVID-19 Case and Death Growth Rates — United States, March 1–December 31, 2020 Weekly / March 12, 2021 / 70(10);350–354 https://www.cdc.gov/mmwr/volumes/70/wr/mm7010e3.htm On March 5, 2021, this report was posted online as an MMWR Early Release. Gery P. Guy Jr., PhD1; Florence C. Lee, MPH1; Gregory Sunshine, JD1; Russell McCord, JD1; Mara Howard-Williams, JD2; Lyudmyla Kompaniyets, PhD1; Christopher Dunphy, PhD1; Maxim Gakh, JD3; Regen Weber1; Erin Sauber-Schatz, PhD1; John D. Omura, MD1; Greta M. Massetti, PhD1; CDC COVID-19 Response Team, Mitigation Policy Analysis Unit; CDC Public Health Law Program (View author affiliations) Summary What is already known about this topic? Universal masking and avoiding nonessential indoor spaces are recommended to mitigate the spread of COVID-19. What is added by this report? Mandating masks was associated with a decrease in daily COVID-19 case and death growth rates within 20 days of implementation. Allowing on-premises restaurant dining was associated with an increase in daily COVID-19 case growth rates 41–100 days after implementation and an increase in daily death growth rates 61–100 days after implementation. What are the implications for public health practice? Mask mandates and restricting any on-premises dining at restaurants can help limit community transmission of COVID-19 and reduce case and death growth rates. These findings can inform public policies to reduce community spread of COVID-19. =========================================================== CDC recommends a combination of evidence-based strategies to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 (1). Because the virus is transmitted predominantly by inhaling respiratory droplets from infected persons, universal mask use can help reduce transmission (1). Starting in April, 39 states and the District of Columbia (DC) issued mask mandates in 2020. Reducing person-to-person interactions by avoiding nonessential shared spaces, such as restaurants, where interactions are typically unmasked and physical distancing (≥6 ft) is difficult to maintain, can also decrease transmission (2). In March and April 2020, 49 states and DC prohibited any on-premises dining at restaurants, but by mid-June, all states and DC had lifted these restrictions. To examine the association of state-issued mask mandates and allowing on-premises restaurant dining with COVID-19 cases and deaths during March 1–December 31, 2020, county-level data on mask mandates and restaurant reopenings were compared with county-level changes in COVID-19 case and death growth rates relative to the mandate implementation and reopening dates. Mask mandates were associated with decreases in daily COVID-19 case and death growth rates 1–20, 21–40, 41–60, 61–80, and 81–100 days after implementation. Allowing any on-premises dining at restaurants was associated with increases in daily COVID-19 case growth rates 41–60, 61–80, and 81–100 days after reopening, and increases in daily COVID-19 death growth rates 61–80 and 81–100 days after reopening. Implementing mask mandates was associated with reduced SARS-CoV-2 transmission, whereas reopening restaurants for on-premises dining was associated with increased transmission. Policies that require universal mask use and restrict any on-premises restaurant dining are important components of a comprehensive strategy to reduce exposure to and transmission of SARS-CoV-2 (1). Such efforts are increasingly important given the emergence of highly transmissible SARS-CoV-2 variants in the United States (3,4). County-level data on state-issued mask mandates and restaurant closures were obtained from executive and administrative orders identified on state government websites. Orders were analyzed and coded to extract mitigation policy variables for mask mandates and restaurant closures, their effective dates and expiration dates, and the counties to which they applied. State-issued mask mandates were defined as requirements for persons to wear a mask 1) anywhere outside their home or 2) in retail businesses and in restaurants or food establishments. State-issued restaurant closures were defined as prohibitions on restaurants operating or limiting service to takeout, curbside pickup, or delivery. Allowing restaurants to provide indoor or outdoor on-premises dining was defined as the state lifting a state-issued restaurant closure.* All coding underwent secondary review and quality assurance checks by two or more raters; upon agreement among all raters, coding and analyses were published in freely available data sets.†,§ Two outcomes were examined: the daily percentage point growth rate of county-level COVID-19 cases and county-level COVID-19 deaths. The daily growth rate was defined as the difference between the natural log of cumulative cases or deaths on a given day and the natural log of cumulative cases or deaths on the previous day, multiplied by 100. Data on cumulative county-level COVID-19 cases and deaths were collected from state and local health department websites and accessed through U.S. Department of Health and Human Services Protect.¶ Associations between the policies and COVID-19 outcomes were measured using a reference period (1–20 days before implementation) compared with seven mutually exclusive time ranges relative to implementation (i.e., the effective date of the mask mandate or the date restaurants were permitted to allow on-premises dining). The association was examined over two preimplementation periods (60–41 and 40–21 days before implementation) and five postimplementation periods (1–20, 21–40, 41–60, 61–80, and 81–100 days after implementation). Weighted least-squares regression with county and day fixed effects was used to compare COVID-19 case and death growth rates before and after 1) implementing mask mandates and 2) allowing on-premises dining at restaurants. Because state-issued policies often applied to specific counties, particularly when states began allowing on-premises dining, all analyses were conducted at the county level. Four regression models were used to assess the association between each policy and each COVID-19 outcome. The regression models controlled for several covariates: restaurant closures in the mask mandate models and mask mandates in the restaurant reopening models, as well as bar closures,** stay-at-home orders,†† bans on gatherings of ≥10 persons,§§ daily COVID-19 tests per 100,000 persons, county, and time (day). P-values <0.05 were considered statistically significant. All analyses were weighted by county population with standard errors robust to heteroscedasticity and clustered by state. Analyses were performed using Stata software (version 14.2; StataCorp). This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy.¶¶ During March 1–December 31, 2020, state-issued mask mandates applied in 2,313 (73.6%) of the 3,142 U.S. counties. Mask mandates were associated with a 0.5 percentage point decrease (p = 0.02) in daily COVID-19 case growth rates 1–20 days after implementation and decreases of 1.1, 1.5, 1.7, and 1.8 percentage points 21–40, 41–60, 61–80, and 81–100 days, respectively, after implementation (p<0.01 for all) (Table 1) (Figure). Mask mandates were associated with a 0.7 percentage point decrease (p = 0.03) in daily COVID-19 death growth rates 1–20 days after implementation and decreases of 1.0, 1.4, 1.6, and 1.9 percentage points 21–40, 41–60, 61–80, and 81–100 days, respectively, after implementation (p<0.01 for all). Daily case and death growth rates before implementation of mask mandates were not statistically different from the reference period. During the study period, states allowed restaurants to reopen for on-premises dining in 3,076 (97.9%) U.S. counties. Changes in daily COVID-19 case and death growth rates were not statistically significant 1–20 and 21–40 days after restrictions were lifted. Allowing on-premises dining at restaurants was associated with 0.9 (p = 0.02), 1.2 (p<0.01), and 1.1 (p = 0.04) percentage point increases in the case growth rate 41–60, 61–80, and 81–100 days, respectively, after restrictions were lifted (Table 2) (Figure). Allowing on-premises dining at restaurants was associated with 2.2 and 3.0 percentage point increases in the death growth rate 61–80 and 81–100 days, respectively, after restrictions were lifted (p<0.01 for both). Daily death growth rates before restrictions were lifted were not statistically different from those during the reference period, whereas significant differences in daily case growth rates were observed 41–60 days before restrictions were lifted. Discussion Mask mandates were associated with statistically significant decreases in county-level daily COVID-19 case and death growth rates within 20 days of implementation. Allowing on-premises restaurant dining was associated with increases in county-level case and death growth rates within 41–80 days after reopening. State mask mandates and prohibiting on-premises dining at restaurants help limit potential exposure to SARS-CoV-2, reducing community transmission of COVID-19. Studies have confirmed the effectiveness of community mitigation measures in reducing the prevalence of COVID-19 (5–8). Mask mandates are associated with reductions in COVID-19 case and hospitalization growth rates (6,7), whereas reopening on-premises dining at restaurants, a known risk factor associated with SARS-CoV-2 infection (2), is associated with increased COVID-19 cases and deaths, particularly in the absence of mask mandates (8). The current study builds upon this evidence by accounting for county-level variation in state-issued mitigation measures and highlights the importance of a comprehensive strategy to decrease exposure to and transmission of SARS-CoV-2. Prohibiting on-premises restaurant dining might assist in limiting potential exposure to SARS-CoV-2; however, such orders might disrupt daily life and have an adverse impact on the economy and the food services industry (9). If on-premises restaurant dining options are not prohibited, CDC offers considerations for operators and customers which can reduce the risk of spreading COVID-19 in restaurant settings.*** COVID-19 case and death growth rates might also have increased because of persons engaging in close contact activities other than or in addition to on-premises restaurant dining in response to perceived reduced risk as a result of states allowing restaurants to reopen. Further studies are needed to assess the effect of a multicomponent community mitigation strategy on economic activity. Increases in COVID-19 case and death growth rates were significantly associated with on-premises dining at restaurants after indoor or outdoor on-premises dining was allowed by the state for >40 days. Several factors might explain this observation. Even though prohibition of on-premises restaurant dining was lifted, restaurants were not required to open and might have delayed reopening. In addition, potential restaurant patrons might have been more cautious when restaurants initially reopened for on-premises dining but might have been more likely to dine at restaurants as time passed. Further analyses are necessary to evaluate the delayed increase in case and death growth rates. The findings in this report are subject to at least three limitations. First, although models controlled for mask mandates, restaurant and bar closures, stay-at-home orders, and gathering bans, the models did not control for other policies that might affect case and death rates, including other types of business closures, physical distancing recommendations, policies issued by localities, and variances granted by states to certain counties if variances were not made publicly available. Second, compliance with and enforcement of policies were not measured. Finally, the analysis did not differentiate between indoor and outdoor dining, adequacy of ventilation, and adherence to physical distancing and occupancy requirements. Community mitigation measures can help reduce the transmission of SARS-CoV-2. In this study, mask mandates were associated with reductions in COVID-19 case and death growth rates within 20 days, whereas allowing on-premises dining at restaurants was associated with increases in COVID-19 case and death growth rates after 40 days. With the emergence of more transmissible COVID-19 variants, community mitigation measures are increasingly important as part of a larger strategy to decrease exposure to and reduce transmission of SARS-CoV-2 (3,4). Community mitigation policies, such as state-issued mask mandates and prohibition of on-premises restaurant dining, have the potential to slow the spread of COVID-19, especially if implemented with other public health strategies (1,10). Acknowledgments Angela Werner; Timmy Pierce; Nicholas Skaff; Matthew Penn. CDC COVID-19 Response Team, Mitigation Policy Analysis Unit Moriah Bailey, CDC; Amanda Brown, CDC; Ryan Cramer, CDC; Catherine Clodfelter, CDC; Robin Davison, CDC; Sebnem Dugmeoglu, CDC; Arriana Fitts, CDC; Siobhan Gilchrist, CDC; Rachel Hulkower, CDC; Alexa Limeres, CDC; Dawn Pepin, CDC; Adebola Popoola, CDC; Morgan Schroeder, CDC; Michael A. Tynan, CDC; Chelsea Ukoha, CDC; Michael Williams, CDC; Christopher D. Whitson, CDC. CDC Public Health Law Program Gi Jeong, CDC; Lisa Landsman, CDC; Amanda Moreland, CDC; Julia Shelburne, CDC. Corresponding author: Gery P. Guy Jr., irm2@cdc.gov. 1CDC COVID-19 Response Team; 2CDC Public Health Law Program; 3University of Nevada, Las Vegas. All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed. * For the purposes of this analysis, no distinction was made based on whether reopened restaurants were subject to state requirements to implement safety measures, such as limit dining to outdoor service, reduce capacity, enhance sanitation, or physically distance, or if no mandatory restrictions applied. When states differentiated between bars that serve food and bars that do not serve food, restrictions for bars that serve food were coded as restaurants and restrictions for bars that do not serve food were coded as bars. † https://ephtracking.cdc.gov/DataExplorer/?c=33&i=165 (accessed February 24, 2021) § https://ephtracking.cdc.gov/DataExplorer/?c=33&i=162 (accessed February 24, 2021) ¶ https://protect-public.hhs.govexternal icon (accessed February 3, 2021) ** https://data.cdc.gov/Policy-Surveil...erritorial-Orders-Closing-and-Reope/9kjw-3miq (accessed February 24, 2021) †† https://data.cdc.gov/Policy-Surveil...erritorial-Stay-At-Home-Orders-Marc/y2iy-8irm (accessed February 24, 2021) §§ https://data.cdc.gov/Policy-Surveil...erritorial-Gathering-Bans-March-11-/7xvh-y5vh (accessed February 24, 2021) ¶¶ 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq. *** https://www.cdc.gov/coronavirus/201...ions/business-employers/bars-restaurants.html References Honein MA, Christie A, Rose DA, et al.; CDC COVID-19 Response Team. Summary of guidance for public health strategies to address high levels of community transmission of SARS-CoV-2 and related deaths, December 2020. MMWR Morb Mortal Wkly Rep 2020;69:1860–7. https://doi.org/10.15585/mmwr.mm6949e2external icon PMID:33301434external icon Fisher KA, Tenforde MW, Feldstein LR, et al.; IVY Network Investigators; CDC COVID-19 Response Team. Community and close contact exposures associated with COVID-19 among symptomatic adults ≥18 years in 11 outpatient health care facilities—United States, July 2020. MMWR Morb Mortal Wkly Rep 2020;69:1258–64. https://doi.org/10.15585/mmwr.mm6936a5external icon PMID:32915165external icon Galloway SE, Paul P, MacCannell DR, et al. Emergence of SARS-CoV-2 B.1.1.7 lineage—United States, December 29, 2020–January 12, 2021. MMWR Morb Mortal Wkly Rep 2021;70:95–9. https://doi.org/10.15585/mmwr.mm7003e2external icon PMID:33476315external icon CDC. COVID-19: variants of the virus that causes COVID-19. Atlanta, GA: US Department of Health and Human Services, CDC; 2021. https://www.cdc.gov/coronavirus/2019-ncov/variants/index.html Courtemanche C, Garuccio J, Le A, Pinkston J, Yelowitz A. Strong social distancing measures in the United States reduced the COVID-19 growth rate. Health Aff (Millwood) 2020;39:1237–46. https://doi.org/10.1377/hlthaff.2020.00608external icon PMID:32407171external icon Lyu W, Wehby GL. Community use of face masks and COVID-19: evidence from a natural experiment of state mandates in the US. Health Aff (Millwood) 2020;39:1419–25. https://doi.org/10.1377/hlthaff.2020.00818external icon PMID:32543923external icon Joo H, Miller GF, Sunshine G, et al. Decline in COVID-19 hospitalization growth rates associated with statewide mask mandates—10 states, March–October 2020. MMWR Morb Mortal Wkly Rep 2021;70:212–6. https://doi.org/10.15585/mmwr.mm7006e2external icon PMID:33571176external icon Kaufman BG, Whitaker R, Mahendraratnam N, Smith VA, McClellan MB. Comparing associations of state reopening strategies with COVID-19 burden. J Gen Intern Med 2020;35:3627–34. https://doi.org/10.1007/s11606-020-06277-0external icon PMID:33021717external icon Nicola M, Alsafi Z, Sohrabi C, et al. The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int J Surg 2020;78:185–93. https://doi.org/10.1016/j.ijsu.2020.04.018external icon PMID:32305533external icon Fuller JA, Hakim A, Victory KR, et al.; CDC COVID-19 Response Team. Mitigation policies and COVID-19–associated mortality—37 European countries, January 23–June 30, 2020. MMWR Morb Mortal Wkly Rep 2021;70:58–62. https://doi.org/10.15585/mmwr.mm7002e4external icon PMID:33443494external icon FIGURE. Association between changes in COVID-19 case and death growth rates* and implementation of state mask mandates† (A) and states allowing any on-premises restaurant dining§ (B) — United States, March 1–December 31, 2020 * With 95% confidence intervals indicated with error bars. † A state-issued mask mandate was defined as the requirement that persons operating in a personal capacity (i.e., not limited to specific professions or employees) wear a mask 1) anywhere outside their home or 2) in retail businesses and in restaurants or food establishments. § The effective date of the state order allowing restaurants to conduct any on-premises dining or the date a state-issued restaurant closure expired. Suggested citation for this article: Guy GP Jr., Lee FC, Sunshine G, et al. Association of State-Issued Mask Mandates and Allowing On-Premises Restaurant Dining with County-Level COVID-19 Case and Death Growth Rates — United States, March 1–December 31, 2020. MMWR Morb Mortal Wkly Rep 2021;70:350–354. DOI: http://dx.doi.org/10.15585/mmwr.mm7010e3external icon. MMWR and Morbidity and Mortality Weekly Report are service marks of the U.S. Department of Health and Human Services. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services. References to non-CDC sites on the Internet are provided as a service to MMWR readers and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at these sites. URL addresses listed in MMWR were current as of the date of publication. All HTML versions of MMWR articles are generated from final proofs through an automated process. This conversion might result in character translation or format errors in the HTML version. Users are referred to the electronic PDF version (https://www.cdc.gov/mmwr) and/or the original MMWR paper copy for printable versions of official text, figures, and tables. Questions or messages regarding errors in formatting should be addressed to mmwrq@cdc.gov.
The proper way to look at mask mandates is the reduction of COVID cases, hospitalizations, and deaths in the direct time frame following the implementation of the mask mandate. Let's take a look at what occurred in Kansas based on proper charts and science -- -rather than nonsense charts pushed by a COVDI-denier on Twitter. Trends in County-Level COVID-19 Incidence in Counties With and Without a Mask Mandate — Kansas, June 1–August 23, 2020 https://www.cdc.gov/mmwr/volumes/69/wr/mm6947e2.htm Weekly / November 27, 2020 / 69(47);1777-1781 On November 20, 2020, this report was posted online as an MMWR Early Release. Please note: This report has been corrected. An erratum has been published. Miriam E. Van Dyke, PhD1; Tia M. Rogers, PhD1; Eric Pevzner, PhD2; Catherine L. Satterwhite, PhD3; Hina B. Shah, MPH4; Wyatt J. Beckman, MPH4; Farah Ahmed, PhD5; D. Charles Hunt, MPH4; John Rule6 (View author affiliations) View suggested citation Summary What is already known about this this topic? Wearing face masks in public spaces reduces the spread of SARS-CoV-2. What is added by this report? The governor of Kansas issued an executive order requiring wearing masks in public spaces, effective July 3, 2020, which was subject to county authority to opt out. After July 3, COVID-19 incidence decreased in 24 counties with mask mandates but continued to increase in 81 counties without mask mandates. What are the implications for public health practice? Countywide mask mandates appear to have contributed to the mitigation of COVID-19 transmission in mandated counties. Community-level mitigation strategies emphasizing use of masks, physical distancing, staying at home when ill, and enhanced hygiene practices can help reduce the transmission of SARS-CoV-2. Wearing masks is a CDC-recommended* approach to reduce the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), by reducing the spread of respiratory droplets into the air when a person coughs, sneezes, or talks and by reducing the inhalation of these droplets by the wearer. On July 2, 2020, the governor of Kansas issued an executive order† (state mandate), effective July 3, requiring masks or other face coverings in public spaces. CDC and the Kansas Department of Health and Environment analyzed trends in county-level COVID-19 incidence before (June 1–July 2) and after (July 3–August 23) the governor’s executive order among counties that ultimately had a mask mandate in place and those that did not. As of August 11, 24 of Kansas’s 105 counties did not opt out of the state mandate§ or adopted their own mask mandate shortly before or after the state mandate was issued; 81 counties opted out of the state mandate, as permitted by state law, and did not adopt their own mask mandate. After the governor’s executive order, COVID-19 incidence (calculated as the 7-day rolling average number of new daily cases per 100,000 population) decreased (mean decrease of 0.08 cases per 100,000 per day; net decrease of 6%) among counties with a mask mandate (mandated counties) but continued to increase (mean increase of 0.11 cases per 100,000 per day; net increase of 100%) among counties without a mask mandate (nonmandated counties). The decrease in cases among mandated counties and the continued increase in cases in nonmandated counties adds to the evidence supporting the importance of wearing masks and implementing policies requiring their use to mitigate the spread of SARS-CoV-2 (1–6). Community-level mitigation strategies emphasizing wearing masks, maintaining physical distance, staying at home when ill, and enhancing hygiene practices can help reduce transmission of SARS-CoV-2. The Kansas mandate requiring the wearing of face coverings in public spaces became effective July 3, 2020. Data on county mask mandates were obtained from the Kansas Health Institute.¶ A Kansas state law** enacted on June 9, 2020, authorizes counties to issue public health orders that are less stringent than the provisions of statewide executive orders issued by the governor, which allowed counties to opt out of the state mask mandate. For this study, counties in Kansas that, as of August 11, 2020, did not opt out of the state mandate or adopted their own mask mandate were considered to have a mask mandate in place; those that opted out of the state mandate and did not adopt their own mask mandate were considered to not have a mask mandate in place. Daily county-level COVID-19 incidence (cases per 100,000 population) was calculated using case and population counts accessed from USAFacts†† for Kansas counties during June 1–August 23.§§ Rates were calculated as 7-day rolling averages. Segmented regression¶¶ was used to examine changes in COVID-19 incidence before and after July 3, 2020, among mandated and nonmandated counties. Mandated and nonmandated counties were compared to themselves over time, allowing for the control of constant county-related characteristics (e.g., urbanicity or rurality) that might otherwise confound a comparison between mandated and nonmandated counties. Sensitivity analyses were also conducted by 1) examining incidence trends after July 3 separately among mandated counties with and without other public health mitigation strategies and 2) recategorizing nonmandated counties that included cities mandating masks (n=6) as mandated counties. Analyses were conducted using SAS software (version 9.4; SAS Institute). As of August 11, 24 (23%) Kansas counties had a mask mandate in place, and 81 did not. Mandated counties accounted for two thirds of the Kansas population (1,960,703 persons; 67.3%)*** and were spread throughout the state, although they tended to cluster together. Six (25%) mandated and 13 (16%) nonmandated counties were metropolitan areas.††† Thirteen (54%) mandated counties and seven (9%) nonmandated counties had implemented at least one other public health mitigation strategy not related to the use of masks (e.g., limits on size of gatherings and occupancy for restaurants). During June 1–7, 2020, the 7-day rolling average of daily COVID-19 incidence among counties that ultimately had a mask mandate was three cases per 100,000, and among counties that did not, was four per 100,000 (Table). By the week of the governor’s executive order requiring masks (July 3–9), COVID-19 incidence had increased 467% to 17 per 100,000 in mandated counties and 50% to six per 100,000 among nonmandated counties. By August 17–23, 2020, the 7-day rolling average COVID-19 incidence had decreased by 6% to 16 cases per 100,000 among mandated counties and increased by 100% to 12 per 100,000 among nonmandated counties. Trend analyses using segmented regression (Figure) indicated that during June 1–July 2, 2020, the COVID-19 7-day rolling average incidence increased each day in both counties that ultimately had mask mandates in place (mean increase = 0.25 cases per 100,000 per day; 95% confidence interval [CI] = 0.17–0.33) and counties that did not (mean increase = 0.08 cases per 100,000 per day; 95% CI = 0.01–0.14). After the governor’s executive order, COVID-19 incidence decreased each day in mandated counties (mean decrease = 0.08 cases per 100,000 per day; 95% CI = –0.14 to –0.03); in nonmandated counties, incidence continued to increase each day (mean increase = 0.11 cases per 100,000 per day; 95% CI = 0.01–0.21). Discussion After implementation of mask mandates in 24 Kansas counties, the increasing trend in COVID-19 incidence reversed. Although rates were considerably higher in mandated counties than in nonmandated counties by the executive order, rates in mandated counties declined markedly after July 3, compared with those in nonmandated counties. Kansas counties that had mask mandates in place appear to have mitigated the transmission of COVID-19, whereas counties that did not have mask mandates continued to experience increases in cases. The findings in this report are consistent with declines in COVID-19 cases observed in 15 states and the District of Columbia, which mandated masks, compared with states that did not have mask mandates (7). Mask requirements were also implemented as part of a multicomponent approach in Arizona, where COVID-19 incidence stabilized and then decreased after implementation of a combination of voluntary and enforceable community-level mitigation strategies, including mask requirements, limitations on public events, enhanced sanitation practices, and closures of certain services and businesses (8). The combining of community-level mitigation strategies including physical distancing and enhanced hygiene practices, in addition to consistent and correct use of masks, is a CDC-recommended approach.§§§ The decreased COVID-19 incidence among mask-mandated counties in Kansas occurred during a time when the only other state mandates issued were focused on mitigation strategies for schools as they reopened in mid-August. In at least 13 (54%) of the 24 mandated counties, the mask mandates occurred alongside other county-level recommended or mandated mitigation strategies (e.g., limits on size of gatherings and occupancy for restaurants), facilitating a potential synergistic effect resulting from combining community mitigation strategies. However, in sensitivity analyses, similar decreases in COVID-19 incidence after July 3 were observed among mandated counties with and without other mitigation strategies. Therefore, although implementing multiple mitigation strategies is the recommended approach, strategies related to mask use mandates appear to be important. Additional information on the utility and acceptability of mask mandates in public settings could help further inform health education campaigns aimed at increasing proper use of masks and strengthening mandate adherence. The findings in this report are subject to at least four limitations. First, the ecologic design of this study and limited information on community mask-wearing behaviors and county implementation and enforcement provisions of mask mandates limit the ability to determine the extent to which the countywide mask mandates accounted for the observed declines in COVID-19 incidence in mandated counties. Second, this analysis did not account for mask ordinances in six cities in non–mask-mandated counties. However, in sensitivity analyses recategorizing nonmandated counties that included cities mandating masks as mandated counties, results were consistent with those in primary analyses, although they were attenuated. In those analyses, after the governor’s executive order, COVID-19 incidence among mandated counties stabilized rather than decreased, and incidence continued to increase among nonmandated counties. Third, although the design of this study limits potential confounding from constant county-related characteristics, the findings in this report are conditional on the absence of any time-varying factors (e.g., mobility patterns, changes in other community-level mitigation strategies, and access to testing) within counties before and after July 3. Nonetheless, in additional analyses examining testing data among Kansas counties during the study period, testing rates were observed to increase overall over time. Therefore, despite increases in testing during this period, decreases in COVID-19 incidence were observed in mandated counties after July 3. Finally, counties in Kansas with a mask mandate might not be representative of other U.S. counties. However, the findings are consistent with observations from other states that mask mandates are associated with declines in COVID-19 cases (7). Masks are an important intervention for mitigating the transmission of SARS-CoV-2 (1–6), and countywide mask mandates appear to have contributed to the mitigation of COVID-19 spread in Kansas counties that had them in place. Community-level mitigation strategies emphasizing use of masks, physical distancing, staying at home when ill, and enhanced hygiene practices can help reduce the transmission of SARS-CoV-2. Acknowledgments Melanie Firestone, Epidemic Intelligence Service, CDC; Laura Gieraltowski, Jamie Perniciaro, CDC COVID-19 Response Team. Corresponding author: Miriam Van Dyke, mpy4@cdc.gov. 1Epidemic Intelligence Service, CDC; 2CDC COVID-19 Response Team; 3Office of the Assistant Secretary for Health, U.S. Department of Health and Human Services; 4Kansas Health Institute, Topeka, Kansas; 5Kansas Department of Health and Environment; 6Kansas Army National Guard. All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. D. Charles Hunt, Wyatt J. Beckham, and Hina B. Shah report a grant from the Kansas Health Foundation to the Kansas Health Institute. No other potential conflicts of interest were disclosed. * https://www.cdc.gov/coronavirus/201...wO8Z2baHM0KHS4JXx0inzzMQs3zRHV1qql_0a8mxZfpCw. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cloth-face-cover-guidance.html. † https://governor.kansas.gov/wp-content/uploads/2020/07/20200702093130003.pdfpdf iconexternal icon. § Allen, Atchison, Bourbon, Crawford, Dickinson, Douglas, Franklin, Geary, Gove, Harvey, Jewell, Johnson, Mitchell, Montgomery, Morris, Pratt, Reno, Republic, Saline, Scott, Sedgwick, Shawnee, Stanton, and Wyandotte counties. Data on county orders were collected through point-in-time surveys of local health department and other county officials and were supplemented with online searches for published orders and announcements on social media and local news sites. Text in the county orders was analyzed to determine whether mask mandates were in place as of August 11, 2020. Counties that took no official action to opt out of the state mask mandate or adopted their own mask mandate shortly before or after the state mandate were considered to have a mask mandate in place. Counties were considered to not have a mask mandate in place if they took official action to opt out of the state mask mandate and did not adopt their own mask mandate or if their official action used only the language of guidance (e.g., “should” or “recommend”). ¶ https://www.khi.org/policy/article/20-25external icon. https://www.khi.org/assets/uploads/news/15015/august_11_update1105.pdfpdf iconexternal icon. ** https://ag.ks.gov/docs/default-source/documents/addendum-3-to-march-24-law-enforcement-duties-and-authorities-memo.pdf?sfvrsnpdf iconexternal icon = d088af1a_3. start highlight†† https://usafacts.org/visualizations/coronavirus-covid-19-spread-mapexternal icon. Accessed August 31, 2020.end highlight §§ August 23, 2020, was selected as the study end date because most Kansas counties had already started or were about to begin school the week of August 24, 2020. The implementation of in-person schooling would have signified an important change in events influencing COVID-19 incidence rates after the executive order. ¶¶ Generalized estimating equation regression modeling with an autoregressive correlation variance structure was used to estimate trends over time within counties. Trends in 7-day rolling average of daily COVID-19 incidence among mask mandated counties and among non–mask-mandated counties were analyzed separately before (June 1–July 2, 2020) and after (July 3–August 23, 2020) the governor’s executive order requiring masks, effective July 3. *** Total population in mask-mandated counties = 1,960,703; total population in non–mask-mandated counties = 952,611; based on 2019 U.S. Census data. ††† As designated by the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. https://www.cdc.gov/nchs/data_access/urban_rural.htm#Data_Files_and_Documentation. §§§ https://www.cdc.gov/coronavirus/2019-ncov/community/community-mitigation.html. References Brooks JT, Butler JC, Redfield RR. Universal masking to prevent SARS-CoV-2 transmission–the time is now. JAMA 2020;324:635–7. CrossRefexternal icon PubMedexternal icon Hendrix MJ, Walde C, Findley K, Trotman R. Absence of apparent transmission of SARS-CoV-2 from two stylists after exposure at a hair salon with a universal face covering policy—Springfield, Missouri, May 2020. MMWR Morb Mortal Wkly Rep 2020;69:930–2. CrossRefexternal icon PubMedexternal icon Wang X, Ferro EG, Zhou G, Hashimoto D, Bhatt DL. Association between universal masking in a health care system and SARS-CoV-2 positivity among health care workers. JAMA 2020;324:703–4. CrossRefexternal icon PubMedexternal icon Chu DK, Akl EA, Duda S, et al.; COVID-19 Systematic Urgent Review Group Effort (SURGE) study authors. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet 2020;395:1973–87. CrossRefexternal icon PubMedexternal icon Lerner AM, Folkers GK, Fauci AS. Preventing the spread of SARS-CoV-2 with masks and other “low-tech” interventions. JAMA 2020;324:1935–6. CrossRefexternal icon PubMedexternal icon Kanu FA, Smith EE, Offutt-Powell T, Hong R, Dinh TH, Pevzner E; Delaware Case Investigation and Contact Tracing Teams. Declines in SARS-CoV-2 transmission, hospitalizations, and mortality after implementation of mitigation measures—Delaware, March–June 2020. MMWR Morb Mortal Wkly Rep 2020;69:1691–4. CrossRefexternal icon PubMedexternal icon Lyu W, Wehby GL. Community use of face masks and COVID-19: evidence from a natural experiment of state mandates in the US. Health Aff (Millwood) 2020;39:1419–25. CrossRefexternal icon PubMedexternal icon Gallaway MS, Rigler J, Robinson S, et al. Trends in COVID-19 incidence after implementation of mitigation measures—Arizona, January 22–August 7, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1460–3. CrossRefexternal icon PubMedexternal icon † Kansas counties (n = 24) that as of August 11 did not opt out of the state mandate effective July 3, 2020, or adopted their own mask mandate shortly before or after the state mandate include Allen, Atchison, Bourbon, Crawford, Dickinson, Douglas, Franklin, Geary, Gove, Harvey, Jewell, Johnson, Mitchell, Montgomery, Morris, Pratt, Reno, Republic, Saline, Scott, Sedgwick, Shawnee, Stanton and Wyandotte. Data on county orders were collected through point-in-time surveys of local health department and other county officials and were supplemented with online searches for published orders and announcements on social media and local news sites. Text in the county orders was analyzed to determine whether mask mandates were in place as of August 11, 2020. Counties that took no official action to opt out of the state mask mandate or adopted their own mask mandate shortly before or after the state mandate were considered to have a mask mandate in place. Counties were considered to not have a mask mandate in place if they took official action to opt out of the state mask mandate and did not adopt their own mask mandate or if their official action used only the language of guidance (e.g., “should” or “recommend”). § Before the mask mandate (June 1–July 2), 7-day rolling average COVID-19 incidence increased each day (mean increase = 0.25 cases per 100,000 persons per day; 95% confidence interval [CI] = 0.17–0.33) in mask-mandated counties and increased each day (mean increase = 0.08 cases per 100,000 per day; 95% CI = 0.01–0.14) in nonmandated counties. ¶ After the mask mandate (July 3–August 23), 7-day rolling average COVID-19 incidence decreased each day (mean decrease = 0.08 cases per 100,000 persons per day; 95% CI = –0.14 to –0.03) in mask-mandated counties and increased each day (mean increase = 0.11 cases per 100,000 per day; 95% CI = 0.01–0.21) in nonmandated counties. Suggested citation for this article: Van Dyke ME, Rogers TM, Pevzner E, et al. Trends in County-Level COVID-19 Incidence in Counties With and Without a Mask Mandate — Kansas, June 1–August 23, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1777-1781. DOI: http://dx.doi.org/10.15585/mmwr.mm6947e2external icon. MMWR and Morbidity and Mortality Weekly Report are service marks of the U.S. Department of Health and Human Services. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services. References to non-CDC sites on the Internet are provided as a service to MMWR readers and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at these sites. URL addresses listed in MMWR were current as of the date of publication. All HTML versions of MMWR articles are generated from final proofs through an automated process. This conversion might result in character translation or format errors in the HTML version. Users are referred to the electronic PDF version (https://www.cdc.gov/mmwr) and/or the original MMWR paper copy for printable versions of official text, figures, and tables. Questions or messages regarding errors in formatting should be addressed to mmwrq@cdc.gov.
The amusing part of this Reuters fact check article is that it points to the fabricated mask mandate charts -- regularly posted here by Tsing Tao -- as FALSE INFORMATION on Facebook. One example below. Fact check: Mask mandates are not causing a spike in COVID-19 infections https://www.reuters.com/article/uk-...-a-spike-in-covid-19-infections-idUSKBN2802W4 Posts on Facebook showing a graph plotting daily new coronavirus cases along with the claim that “mask mandates may have actually spread COVID-19.” This claim is false. Mask mandates are not responsible for spikes in COVID-19 cases, and studies have proven that they reduce the spread of the novel coronavirus. Examples of posts making this claim can be found here , here , and here . Dr. Anthony Fauci, the nation's top infectious disease expert, said in an Oct. 28 interview with CNBC’s Shepard Smith that he supported a national mask mandate (here) . On Nov. 10, the Centers for Disease Control and Prevention (CDC) announced that “experimental and epidemiological data support community masking to reduce the spread of SARS-CoV-2" and that “the prevention benefit of masking is derived from the combination of source control and personal protection for the mask wearer” (here) . The image used in the Facebook posts credits “Swiss Policy Research” as the source of the graph, which shows the number of novel coronavirus daily cases in France with a red arrow labeled “Indoor Mask Mandate” pointing to mid-July. The graphic can be found on the organization’s website under the “Overview Diagrams” section of its “Facts About Covid-19" page ( here ) . On its website (swprs.org/), Swiss Policy Research describes itself as “an independent, nonpartisan and nonprofit research group investigating geopolitical propaganda in Swiss and international media.” When reached by Reuters for comment, Swiss Policy Research said that the social media posts misrepresented their analysis and that they “do not claim or suggest that masks increase cases, nor do we suggest people shouldn’t wear masks.” French President Emmanuel Macron announced on July 14 that masks would be required in enclosed public spaces because of concerns about renewed flare-ups of COVID-19 (here). It is also true that recorded daily new cases in France have been climbing since late this summer (here). The correlation in timing, however, does not indicate causation. The social media posts’ claim that wearing masks contributes to the spread of COVID-19 contradicts the findings of several scientific studies. For example, a study published by the University of Kansas on Oct. 25 (here) found that Kansas counties with mask mandates avoided a major surge in COVID-19 infections, while counties without such requirements saw a steady climb (here) . Meanwhile, an Oct. 27 analysis from Vanderbilt University School of Medicine (here) found that Tennessee COVID-19 hospitalizations have been rising at a much lower rate in areas that have mask mandates than in those that do not. On the national scale, the University of Washington's Institute for Health Metrics and Evaluation (IHME) has projected that if 95% of U.S. residents wore masks in public, an additional 129,574 lives could be saved between Sept. 22, 2020 and the end of February 2021 (here) . A study published by Duke University in September found that of the 14 different kinds of face coverings it tested, a fitted N95 surgical mask provided the most effective protection against respiratory droplets while a neck gaiter proved least effective ( here ). Cotton masks did result in lower droplet transmission than not wearing a mask (see Fig. 3). After a national lockdown was effective at containing the epidemic in France, the virus started spreading again after rules were relaxed starting May 11 ( here ). On Oct. 17, France reported 523 new deaths from coronavirus over the previous 24 hours, the highest daily toll since April, when the virus was at its most severe. In anticipating of a second wave of the respiratory disease, France has been back in lockdown since Oct. 30. Less stringent than in March, the latest restrictions have succeeded in lowering daily new infections and eased pressure on the French health system. The number of people hospitalized for COVID-19 sharply declined for a third day running on Nov. 19 (here). As a new wave of the pandemic washes over parts of the Northern Hemisphere, many countries are setting records for infections. In the United States, the number of patients hospitalized with COVID-19 has jumped nearly 50% in the past two weeks (here). With the third coronavirus wave bringing a fresh surge in infections and putting immense strain on the healthcare system, the United States has continued to set records for daily coronavirus infections, with more than 187,000 cases reported on Nov. 19 ( here , here ). As stated by Vox here, more testing for COVID-19 cannot be “the full explanation” for the surge “because hospitalizations (here) and the overall rate of positive tests (here) are trending up.” The New York Times explains that the latest surge has come “as cooler weather is forcing people indoors and as many Americans report feeling exhausted by months of restrictions. Unlike earlier waves, which were met with shutdown orders and mask mandates, the country has shown little appetite for widespread new restrictions” (here). VERDICT False. Reliable scientific findings do not indicate that wearing masks contributes to the increased spread of COVID-19. This article was produced by the Reuters Fact Check team. Read more about our fact-checking work here .
A powerful argument for wearing a mask, in visual form Real-time pandemic data paints a vivid picture of the relationship between mask-wearing and the prevalence of covid-19 symptoms https://www.washingtonpost.com/business/2020/10/23/pandemic-data-chart-masks/ Oct. 23, 2020 Despite the clear opposition to masks within the Trump White House and among its allies, Americans of all political stripes overwhelmingly support their use as a public health measure and say they wear them whenever they’re in public. Still, there are significant differences in mask-use rates at the state level. And data from Carnegie Mellon’s CovidCast, an academic project tracking real-time coronavirus statistics, yields a particularly vivid illustration of how mask usage influences the prevalence of covid-19 symptoms in a given area. Take a look. For all 50 states plus D.C., this chart plots the percentage of state residents who say they wear a mask in public all or most of the time (on the horizontal axis) and the percentage who say they know someone in their community with virus symptoms (on the vertical axis). If you’re curious about the exact numbers for your state, there’s a table at the bottom of this article. Take Wyoming and South Dakota, for instance, in the upper left-hand corner of the chart. Roughly 60 to 70 percent of state residents report frequent mask use, as shown on the bottom axis, which puts them at the bottom for mask rates. They also have some of the highest levels of observed covid-19 symptoms, approaching 40 and 50 percent. Now, note what happens as you move across the chart. States farther to the right have higher rates of mask use. And as mask use increases, the frequency of observed covid-19 symptoms decreases: More masks, less covid-19. This relationship is called a correlation, and it’s a strikingly tight one. Often in these types of plots you have to squint really hard to suss out such a relationship, and researchers occasionally go to comical lengthst o divine the presence of a correlation where none really exists. But there’s no need for that here. There’s a simple statistical measure of correlation intensity called “R-squared,” which goes from zero (absolutely no relationship between the two variables) to 1 (the variables move perfectly in tandem). The R-squared of CovidCast’s mask and symptom data is 0.73, meaning that you can predict about 73 percent of the variability in state-level covid-19 symptom prevalence simply by knowing how often people wear their masks. One other observation to note is that almost without exception, the states with the highest rates of mask-wearing were won by Hillary Clinton in 2016. In other words, more Democrats, more masking — a vivid reflection of how partisanship has been a factor in much of the response to the pandemic. Let’s pause a minute to talk about where exactly this data comes from. Ideally you would want it to be from something like a random-digit-dial survey, the type typically used in public opinion polling, which with enough participants would produce a sample of each state that’s representative of its population and demographics. But the cost of running one such survey for all 50 states plus D.C. would be enormously prohibitive — to say nothing of doing so on a daily basis, which is necessary to produce the kind of real-time data of interest to epidemiologists. So the CovidCast team partnered with Facebook, which is used by 70 percent of U.S. adults and has the ability to survey tens of thousands of them every day at relatively low cost. While the resulting state-level samples aren’t perfect representations of the general population, the researchers weight the responses using Census Bureau demographic datat o ensure they’re a good approximation. “If Facebook’s users are different from the U.S. population generally in a way that the survey weighting process doesn’t account for, then our estimates could be biased,” cautioned Alex Reinhart, a Carnegie Mellon professor of statistics and data science who works on CovidCast and wrote a book on statistical methods. “But if that bias doesn’t change much over time, then we can still use the survey to detect trends and changes.” He also cautioned that the old saw of “correlation doesn’t equal causation” applies here as well. “There could be other explanations for the correlation,” he said. “For example, states that had worse outbreaks earlier in the pandemic both have higher mask usage now and more immunity.” And, he added, “if people say they’re not wearing masks, they may not be taking other protective measures either. So perhaps what we see is a combination of mask usage, other social distancing behaviors and perhaps other factors we haven’t measured.” Nevertheless, the chart is particularly useful in the context of all the other high-quality evidence showing that masks reduce the transmission of the coronavirus and other respiratory diseases. There’s good reason to suspect, in other words, that rates of mask use are driving at least part of the relationship seen in the chart above, even if the data can’t prove that definitively. For people living in states that are driving the latest spike in coronavirus cases, the takeaway is clear: Wear a mask when you go out in public. (Article includes chart data at the bottom)
For every 1% increase in average mask adherence, the odds of a high COVID-19 case rate decreased by 26%. Mask adherence and rate of COVID-19 across the United States https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249891 Published: April 14, 2021 Abstract 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 states 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 ─ September 2020 to measure their impact on COVID-19 rates in the subsequent month (May ─ 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 through September 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. Mean COVID-19 rates for states with at least 75% mask adherence in the preceding month was 109.26 per 100,000 compared to 249.99 per 100,000 for those with less adherence. 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. Figures (Study at url includes all the figures) Introduction The global pandemic of SARS-CoV-2 has overwhelmed health care systems, marked by peak numbers of hospital and intensive care unit admissions and deaths [1,2]. Mask wearing has been advocated by public health officials as a way to reduce the spread of COVID-19 [3–5]. In the United States, policies on mask wearing have varied from state to state over the course of the pandemic [6]. 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 states encourage mask wearing [7], many citizens still resist the notion [8]. Individuals’ mask wearing behaviors are not only influenced by recommendations and mandates issued by state leaders, but also by print, televised, and social media [9]. Thus, adherence to mask wearing in public remains a challenge for mitigating the spread of COVID-19. 10]. 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 [11], which are difficult to quantify. The politicization of COVID-19 epidemiology [9,12] 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 [13]. 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. Methods 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 [14], 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 [15], which holds data collected by Facebook Global in partnership with the University of Maryland Social Data Science Center [16]. We 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) [17] and state population sizes in 2019 [18]. 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 citation(s). We combined the Recommended and Strict groups into “Any” policy. States and D.C. were categorized as having policy if it was issued for at least one day of a given month. Although Hawaii’s governor did not issue a mask wearing policy until after October 2020, we considered that state to have a policy because mayors of the four populous counties had mandated mask wearing earlier in the pandemic. Mask wearing adherence We calculated the average mask use percentage by month for April–September, 2020. For each month, the distributions 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. We also identified states with average mask adherence ≥75% in a given month. 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 [19]. Covariates 20], 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 [13] came from the US Census. For demographic data, we dichotomized population proportions at whole values that approximated the highest quartile of the distributions. Specifically, we created the following categories: >15% non-Hispanic Black, >15% Hispanic, median age >40 years, and population density >200 people per square mile, which corresponded to 74.5%, 78.4%, 82.4%, and 78.4% of the distributions, respectively. 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). Table 1. Because stay-at-home order, mask wearing policy, mask adherence, and COVID-19 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). Eleven states had no stay-at-home order, 15 had no mask policy, and four states had low adherence throughout this six-month period. Table 1. States with high COVID-19 population risk characteristics. Table 1 url - https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0249891.t001 The list of states with high COVID-19 rates by month shows the initial wave in northeastern states in May, followed by a wave in southern states, and then spreading across the U.S. over the next four months (Table 2). Of the 15 states with no mask policy from April through September, 14 reported high COVID-19 rates in at least one month from May to October. Because high COVID rates were reported by only eight states in May and four states in June, we did not examine mask adherence or policy in the preceding April or May. Thus, subsequent comparisons of states with high COVID-19 rates by month focused on July, August, September and October. Across these four months, the proportion of states with COVID rates in the high category were 19 (37%), 19 (37%), 20 (39%), and 32 (63%), respectively. Eight states were reported to have at least 75% mask adherence in any month between June and September (AZ, CT, HI, MA, NY, RI, VT, VA); none reported a high COVID-19 rate in the subsequent month. Table 2. States with high COVID-19 rates. Table 2 url - https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0249891.t002 For mask adherence, the 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 55% and 68% in September. The proportions of states with high COVID-19 rates are shown for those in the low and high quartiles of mask adherence in the preceding month (Fig 1). Most states in the low quartile had high COVID-19 rates in the subsequent month. Indeed all 13 states in the low mask adherence group in September had high COVID-19 rates in October. In contrast, just one state in July, August, and September and three in October in the high quartile had high COVID-19 rates in the subsequent month. When we looked at states with ≥75% mask adherence (Arizona, Connecticut, Hawaii, Massachusetts, Michigan, New York, Rhode Island, Vermont), we found none had experienced a high COVID-19 rate in the subsequent month. Mean COVID-19 rates for states with ≥75% mask adherence in the preceding month was 109.26 per 100,000 compared to 249.99 per 100,000 for those with less adherence. Fig 1. Proportion of states with high COVID-19 rates among those in the low and high mask adherence quartiles in the preceding month. Figure 1 url - https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0249891.g001 The proportions of states and D.C. with high COVID-19 rates were greatest for those with no mask wearing policy for the general public in the preceding month (Fig 2). Among states and D.C. with no mask wearing policy, 50 to 73% had high COVID-19 rates in the subsequent month. In contrast, 25% or fewer 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 for the entire four month period (June through September) reported a high COVID-19 rate. High COVID-rates were less frequent in states and D.C. with strict mask wearing policy than in states with recommended policy. Fig 2. Proportion of states with high COVID-19 rates among those no, any, strict, and recommended mask wearing policy in the preceding month. Figure 2 url - https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0249891.g002 Looking more closely at October when COVID-19 rates increased across the US, we found average adherence was only 47% in September for the 11 states without a mask policy and high October COVID-19 rates. In contrast, average adherence was 68% in the 15 states with lower COVID-19 rates in October and any mask policy in September. Of note, there were no states with ≥75% in September. Odds ratios and 95% confidence intervals for average mask adherence and mask policy for the general public are associated with high COVID-19 rates in the subsequent month (Table 3). Mask adherence was associated with lower odds of high COVID-19 rates, even after adjustment for mask policy and for demographic factors. For every 1% increase in average adherence in June, the fully adjusted odds ratios for high COVID-19 in July was 0.95, indicating a protective effect against high COVID-19 rates. Similar reductions in odds of high COVID-19 rates in August and September were observed for July and August mask adherence, 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%. Table 3. State-level odds ratios and 95% confidence intervals (CI) for high versus lower COVID-19 rates in the subsequent month. Table 3 url - https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0249891.t003 Crude and adjusted odds ratios for any mask policy in relation to high COVID-19 rates in the subsequent month were below 1.0; 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 were not able to measure statistical interactions between mask policy and adherence due to instability arising from small numbers. We did estimate odds ratios for mask adherence within subgroups of states with and without mask policy. Odds ratios indicating protection against high COVID-19 rates remained for all months and policy subgroups, ranging from 0.82 to 0.93 for states with any policy and from 0.60 to 0.95 for states with no policy. Interpretation 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. Indeed, issuing such a policy is not the same as successfully implementing it. 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 observation that states with mask adherence by ≥75% of the population was associated with lower COVID-19 rates in the subsequent month suggests that states should strive to meet this threshold. The difference in mean COVID-19 rates between states with ≥75% and <75% mask adherence was 140 cases per 100,000. It is worth noting that no states achieved this level of mask adherence in September, which might account in part for the spike in COVID-19 rates in October. Of course, many other factors are could be at play, like the possibility of cooler weather driving non-adherent persons to indoor gatherings. 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 that influences 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 even stronger. It is also possible that survey respondents misreported their mask wearing adherence; whether they would be more or less likely to over or under-report is open to speculation, but residents in states with mask wearing policy might over-report adherence to appear compliant. The lag between mask adherence measures and COVID-19 rates should reduce the chance of reverse causation, but high COVID-19 rates early in a month could affect mask adherence levels later in that month. It is important to note that state level distributions of demographic factors do not account for concentrations or sparsity of populations within a given state. Further, 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 [21]. Our study was also limited by the lack of information on accessibility of COVID-19 testing; if less accessible testing is associated with 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, associations between mask policy, adherence and other factors may be obscured in states with many high density and low density areas. Conclusions 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. Supporting information (See study url for supporting information)