Original Article

Analysis of the Effects of COVID-19 Mask Mandates on Hospital Resource Consumption and Mortality at the County Level

Authors: Steven G. Schauer, DO, MS, Jason F. Naylor, PA-C, Michael D. April, MD, PhD, Brandon M. Carius, PA-C, Ian L. Hudson, DO, MPH


Objectives: Coronavirus disease 2019 (COVID-19) threatens vulnerable patient populations, resulting in immense pressures at the local, regional, national, and international levels to contain the virus. Laboratory-based studies demonstrate that masks may offer benefit in reducing the spread of droplet-based illnesses, but few data are available to assess mask effects via executive order on a population basis. We assess the effects of a county-wide mask order on per-population mortality, intensive care unit (ICU) utilization, and ventilator utilization in Bexar County, Texas.

Methods: We used publicly reported county-level data to perform a mixed-methods before-and-after analysis along with other sources of public data for analyses of covariance. We used a least-squares regression analysis to adjust for confounders. A Texas state-level mask order was issued on July 3, 2020, followed by a Bexar County–level order on July 15, 2020. We defined the control period as June 2 to July 2 and the postmask order period as July 8, 2020–August 12, 2020, with a 5-day gap to account for the median incubation period for cases; longer periods of 7 and 10 days were used for hospitalization and ICU admission/death, respectively. Data are reported on a per-100,000 population basis using respective US Census Bureau–reported populations.

Results: From June 2, 2020 through August 12, 2020, there were 40,771 reported cases of COVID-19 within Bexar County, with 470 total deaths. The average number of new cases per day within the county was 565.4 (95% confidence interval [CI] 394.6–736.2). The average number of positive hospitalized patients was 754.1 (95% CI 657.2–851.0), in the ICU was 273.1 (95% CI 238.2–308.0), and on a ventilator was 170.5 (95% CI 146.4–194.6). The average deaths per day was 6.5 (95% CI 4.4–8.6). All of the measured outcomes were higher on average in the postmask period as were covariables included in the adjusted model. When adjusting for traffic activity, total statewide caseload, public health complaints, and mean temperature, the daily caseload, hospital bed occupancy, ICU bed occupancy, ventilator occupancy, and daily mortality remained higher in the postmask period.

Conclusions: There was no reduction in per-population daily mortality, hospital bed, ICU bed, or ventilator occupancy of COVID-19-positive patients attributable to the implementation of a mask-wearing mandate.
Posted in: Infectious Disease53

Full Article

Having trouble viewing the article content below? Click here to open it directly.


Table 1. Public data sources

Download Image

Table 2. Unadjusted comparison of the before and after mask order groups on a per-100,000 population basis

Download Image

Table 3. Comparison of covariance data before and after mask order periods on a per-100,000 population basis (based on respective populations)

Download Image

Table 4. Comparison of before and after on least squares regression with model adjustment for statewide caseload, SAPD traffic call volume, SAPD public health complaints, and mean daily temperature

Download Image


1. World Health Organization. Listing of WHO's Response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline. Published June 29, 2020. Accessed August 16, 2020.
2. World Health Organization. Coronavirus disease 2019 (COVID-19) situation report - 79. https://www.who.int/docs/default-source/coronaviruse/situationreports/20200408-sitrep-79-covid-19.pdf?sfvrsn=4796b143_6. Published April 8 2020. Accessed July 5, 2021.
3. Agrawal S, Bhandari S, Bhattacharjee A, et al. City-scale agent-based simulators for the study of non-pharmaceutical interventions in the context of the COVID-19 epidemic. J Indian Inst Sci 2020;Nov 12:1–39.
4. Greenhalgh T, Schmid MB, Czypionka T, et al. Face masks for the public during the covid-19 crisis. BMJ 2020;369:m1435.
5. Chu DK, Akl EA, Duda S, et al. 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;395:1973–1987.
6. Howard J, Huang A, Li Z, et al. An evidence review of face masks against COVID-19. Proc Natl Acad Sci USA 2021;118:e2014564118.
7. Mahase E. Covid-19: what is the evidence for cloth masks? BMJ 2020;369:m1422.
8. Bartoszko JJ, Farooqi MAM, Alhazzani W, et al. Medical masks vs N95 respirators for preventing COVID-19 in healthcare workers: a systematic review and meta-analysis of randomized trials. Influenza Other Respir Viruses 2020;14:365–373.
9. Wang J, Pan L, Tang S, et al. Mask use during COVID-19: a risk adjusted strategy. Environ Pollut 2020;266:115099.
10. Hsiao T-C, Chuang H-C, Griffith SM, et al. COVID-19: an aerosol’s point of view from expiration to transmission to viral-mechanism. Aerosol Air Qual Res 2020;20:905–910.
11. Esposito S, Principi N, Leung CC, et al. Universal use of face masks for success against COVID-19: evidence and implications for prevention policies. Eur Respir J 2020;55, 2001260.
12. Zhou ZG, Yue DS, Mu CL, et al. Mask is the possible key for self-isolation in COVID-19 pandemic. J Med Virol 2020;92:1745–1746.
13. Mitze T, Kosfeld R, Rode J, et al. Face masks considerably reduce COVID-19 cases in Germany: a synthetic control method approach. Proc Natl Acad Sci USA 2020;117:32293–32301.
14. Eikenberry SE, Mancuso M, Iboi E, et al. To mask or not to mask: modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect Dis Model 2020;5, 293–308.
15. Goldberg MH, Gustafson A, Maibach EW, et al. Mask-wearing increased after a government recommendation: a natural experiment in the US during the COVID-19 pandemic. Front Commun 2020;5:44.
16. Elachola H, Ebrahim SH, Gozzer E. COVID-19: facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020. Travel Med Infect Dis 2020;35:101637.
17. Brosseau L, Sietsema M. Commentary: masks-for-all for COVID-19 not based on sound data. https://www.cidrap.umn.edu/news-perspective/2020/ 04/commentary-masks-all-covid-19-not-based-sound-data. Published 2020. Accessed July 5, 2021.
18. Cheng KK, Lam TH, Leung CC. Wearing face masks in the community during the COVID-19 pandemic: altruism and solidarity. Lancet 2020. DOI: 10.1016/S0140-6736(20)30918-1.
19. Feng S, Shen C, Xia N, et al. Rational use of face masks in the COVID-19 pandemic. Lancet Respir Med 2020;8:434–436.
20. Szarpak L, Smereka J, Filipiak KJ, et al. Cloth masks versus medical masks for COVID-19 protection. Cardiol J 2020;27:218–219.
21. Xiao Y, Torok ME. Taking the right measures to control COVID-19. Lancet Infect Dis 2020;20:523–524.
22. Zhai J. Facial mask: a necessity to beat COVID-19. Build Environ 2020;175:106827.
23. Lyu W, Wehby GL. Community use of face masks and COVID-19: evidence from a natural experiment of state mandates in the US: study examines impact on COVID-19 growth rates associated with state government mandates requiring face mask use in public. Health Aff (Millwood) 2020;39:1419–1421.
24. Cheng VC, Wong S-C, Chuang VW, et al. The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J Infect 2020;81:107–114.
25. Leffler CT, Ing EB, Lykins JD, et al. Association of country-wide coronavirus mortality with demographics, testing, lockdowns, and public wearing of masks. Am J Trop Med Hyg 2020;103:2400–2411.
26. Courtemanche CJ, Garuccio J, Le A, et al. Did social-distancing measures in Kentucky help to flatten the COVID-19 curve? https://isfe.uky.edu/research/ 2020/did-social-distancing-measures-kentucky-help-flatten-covid-19-curve. Published April 2020. Accessed September 22, 2020.
27. Kenyon C. Flattening-the-curve associated with reduced COVID-19 case fatality rates- an ecological analysis of 65 countries. J Infect 2020;81: e98–e99.
28. Holshue ML, DeBolt C, Lindquist S, et al. First case of 2019 novel coronavirus in the United States. N Engl J Med 2020;382:929–936.
29. Lauer SA, Grantz KH, Bi Q, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med 2020;172:577–582.
30. Bukhari Q, Massaro JM, D'Agostino RB Sr, et al. Effects of weather on coronavirus pandemic. Int J Environ Res Public Health 2020;17:5399.
31. Lewis EG, Breckons M, Lee RP, et al. Rationing care by frailty during the COVID-19 pandemic. Age Ageing 2021;50:7–10.
32. Rao G, Singh A, Gandhotra P, et al. Paradigm shifts in cardiac care: lessons learned from COVID-19 at a large New York health system. Curr Probl Cardiol 2021;46:100675.
33. Kheyfets VO, Lammers SR, Wagner J, et al. PEEP/FIO2 ARDSNet Scale grouping of a single ventilator for two patients: modeling tidal volume response. Respir Care 2020;65:1094–1103.
34. Burns PB, Rohrich RJ, Chung KC. The levels of evidence and their role in evidence-based medicine. Plast Reconstr Surg 2011;128:305.
35. Leatherdale ST. Natural experiment methodology for research: a review of how different methods can support real-world research. Int J Soc Res Methodol 2019;22:19–35.
36. Gerhardt RT, Koller AR, Rasmussen TE, et al. Analysis of remote trauma transfers in South Central Texas with comparison with current US combat operations: results of the RemTORN-I study. J Trauma Acute Care Surg 2013;75(2 suppl 2):S164–S168.
37. Centers for Disease Control and Prevention. National Center for Chronic Disease Prevention and Health Promotion. Nutrition, physical activity, and obesity: data, trends and maps. https://www.cdc.gov/nccdphp/dnpao/datatrends-maps/index.html. Accessed September 10, 2020.
38. Center for Health Statistics, Texas Behavioral Risk Fedor Surveillance System Survey Data (BRFSS). Austin, Texas: Texas Department of State Health Services, 2013-2014. https://www.dshs.texas.gov/chs/brfss. Accessed September 22, 2020.
39. Xu J, Cheng Y, Yuan X, et al. Trends and prediction in daily incidence of novel coronavirus infection in China, Hubei Province and Wuhan City: an application of Farr's law. Am J Transl Res 2020;12:1355–1361.