Original Research

US trends in mask wearing during the COVID-19 pandemic depend on rurality


name here
George Pro1
PhD, Assistant Professor *

name here
Krista Schumacher2
PhD, Coordinator of Rural Research and Grant Development

name here
Randolph Hubach3
PhD, Associate Professor

name here
Nickolas Zaller4
PhD, Professor

name here
Zachary Giano5
PhD, Postdoctoral Fellow

name here
Ricky Camplain6
PhD, Assistant Professor

name here
Carolyn Camplain7
JD, Senior Program Coordinator

name here
Shane Haberstroh8
EdD, Associate Professor

name here
Julie A Baldwin9
PhD, Regent’s Professor

name here
Denna L Wheeler10
PhD, Clinical Associate Professor


*A/Prof George Pro


1, 4 Department of Health Behavior and Health Education, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 West Markham Street, Little Rock, Arkansas, USA

2, 3, 5, 10 Center for Rural Health, Oklahoma State University Center for Health Sciences, 1111 W. 17th St., Tulsa OK

6, 9 Center for Health Equity Research, Northern Arizona University, 1395 South Knoles Drive, Flagstaff, Arizona, USA; and Department of Health Sciences, Northern Arizona University, 1100 South Beaver Street, Flagstaff, Arizona, USA

7 Center for Health Equity Research, Northern Arizona University, 1395 South Knoles Drive, Flagstaff, Arizona, USA

8 Department of Educational Psychology, Northern Arizona University, 801 South Knoles Drive, Flagstaff, Arizona, USA


12 July 2021 Volume 21 Issue 3


RECEIVED: 21 December 2020

REVISED: 28 April 2021

ACCEPTED: 4 May 2021


Pro G, Schumacher K, Hubach R, Zaller N, Giano Z, Camplain R, Camplain C, Haberstroh S, Baldwin JA, Wheeler DL.  US trends in mask wearing during the COVID-19 pandemic depend on rurality . Rural and Remote Health 2021; 21: 6596. https://doi.org/10.22605/RRH6596


This work is licensed under a Creative Commons Attribution 4.0 International Licence


Introduction:  Face masks are widely recommended as a COVID-19 prevention strategy. State mask mandates have generally reduced the spread of the disease, but decisions to wear a mask depend on many factors. Recent increases in case rates in rural areas following initial outbreaks in more densely populated areas highlight the need to focus on prevention and education. Messaging about disease risk has faced challenges in rural areas in the past. While surges in cases within some communities are likely an impetus for behavior change, rising case rates likely explain only part of mask-wearing decisions. The current study examined the relationship between county-level indicators of rurality and mask wearing in the USA.
Methods:  National data from the New York Times’ COVID-19 cross-sectional mask survey was used to identify the percentage of a county’s residents who reported always/frequently wearing a mask (2–14 July 2020). The New York Times’ COVID-19 data repository was used to calculate county-level daily case rates for the 2 weeks preceding the mask survey (15 June – 1 July 2020), and defined county rurality using the Index of Relative Rurality (n=3103 counties). Multivariate linear regression was used to predict mask wearing across levels of rurality. The model was adjusted for daily case rates and other relevant county-level confounders, including county-level indicators of age, race/ethnicity, gender, political partisanship, income inequality, and whether each county was subject to a statewide mask mandate.
Results:  Large clusters of counties with high rurality and low mask wearing were observed in the Midwest, upper Midwest, and mountainous West. Holding daily case rates and other county characteristics constant, the predicted probability of wearing a mask decreased significantly as counties became more rural (β=–0.560; p<0.0001).
Conclusion:  Upticks in COVID-19 cases and deaths in rural areas are expected to continue, and localized outbreaks will likely occur indefinitely. The present findings highlight the need to better understand the mechanisms underlying perceptions of COVID-19 risk in rural areas. Dissemination of scientifically correct and consistent information is critical during national emergencies.


COVID-19, prevention, disparities, epidemiology, USA.

full article:


The coronavirus disease 2019 (COVID-19) is a global pandemic, with more than 31 million cases reported in the USA and deaths approaching 600 000 by April 2021.1 Case and death rates vary by state and county, as outbreaks and new hotspots have begun shifting from more densely populated urban areas to smaller and more rural municipalities.2,3 Broadly, characteristics associated with poor COVID-19 outcomes, such as older age, fewer health care providers per population, and poorer general health, are more prevalent in rural communities.4-7 Specifically, predicted COVID-19 case fatality rates have been shown to increase as counties become more rural.8,9

The Centers for Disease Control and Prevention and the World Health Organization have recommended the widespread use of face masks in public to limit the spread of COVID-19,10,11 due in part to confirmation of disease transmission by asymptomatic individuals.12,13 Face masks are one part of a broader strategy to curb disease transmission, which also includes physical distancing, rapid testing and contact tracing. Lyu and Wehby recently demonstrated a significant association between state policies mandating the use of face masks and a reduction in daily COVID-19 incidence14. Similarly, a study by Van Dyke and colleagues in Kansas demonstrated that daily case rates decreased in counties that implemented face mask mandates, while daily case rates continued to increase in counties that opted out of the mandate15. Both reports highlight the importance of health policy during pandemics and states of emergency. However, individual-level measures of mask wearing are also needed, as some communities or populations may continue to be reluctant to wear a mask in spite of state laws. For example, the effect of state mandates on COVID-19 incidence rates may vary depending on an individual’s perceived risk. While several studies have investigated perceived risk of COVID-19,16-18 none have incorporated the interaction between perceived risk and state mandates.

Several demographic characteristics have also been shown to be positively associated with mask wearing, including non-White race/ethnicity and older age.19,20 Women have been shown to be more likely to wear masks than men,20 due in part to the role of toxic masculinity and affective responses among some men.21 In addition, an observational study conducted in the summer of 2020 in Wisconsin found that urban/suburban shoppers were much more likely than rural shoppers to wear a mask (48% v 20%, respectively).19

Scientifically correct and consistent health communication during pandemics is critical but faces challenges, including streamlining and expediting communication between government officials, health providers, the scientific community, the media, and the public.22 Other factors that complicate the public’s adherence to public health recommendations include information overload, information uncertainty, and misinformation.23 A high or increasing COVID-19 incidence rate is likely one factor that affects mask-wearing behavior, as individuals in counties with only a few reported cases may have a low sense of personal risk, even in the midst of a global pandemic. Political partisanship has also been shown to be associated with COVID-19 preventive behaviors, such that more right-leaning individuals tend to be less likely to take preventive measures such as quarantining and social distancing.24

Studies have shown that retention of health messaging is lower in rural versus urban or suburban areas,25 and the meaning of health may be socially constructed differently within populations.26 For example, distrust of medical providers and perceived outsiders may be embedded in some rural communities.27 As a result, COVID-19 messaging about masks from federal or state authorities may be viewed cautiously by rural residents.28 Additionally, perceived susceptibility to a disease is a key factor in the success of public health campaigns,29 which highlights the implications for how messages should be prepared and disseminated to subgroups within rural areas.


Data sources and variables

The New York Times’ mask-wearing survey data30 (maintained by the New York Times COVID Data Repository)31 to calculate the percentage of county residents who responded that they always or frequently wear a mask in public when they expect to be within six feet of other people. Survey response options included wearing a mask in public always, frequently, sometimes, rarely, or never. The mask-wearing survey was administered by the global data and survey firm Dynata on behalf of the New York Times. The survey was cross-sectional and completed online by roughly 250 000 participants between 2 July 2020 and 14 July 2020. The survey data were weighted by age and gender, then weighted survey responses were transformed into county-level estimates. The weighting strategy used to derive the final analytic dataset has been described in further detail by the New York Times and Dynata.30

The focal independent variable was a county-level indicator for rurality. Definitions of rurality differ, and it is incumbent on researchers to clearly describe rurality metrics in research, while also considering additional factors related to geography and environment that affect health.32 The Index of Relative Rurality (IRR), which was developed by Waldorf and Kim,33 was used as an alternative to threshold-based or categorical measurements of rurality, like the Rural/Urban Commuting Area Codes,34 for example. The IRR is a continuous measurement and ranges between 0 (very urban) and 1 (very rural). IRR scores are at the county level and account for several geographic and population characteristics, including population size, population density, remoteness, and built-up area. For a county-level map illustration of mask wearing and rurality across the USA, each county was color coded based on nine combinations of low/mid/high mask wearing and low/mid/high rurality. For the map only, low/mid/high were defined by the 16th and 84th percentile distributions of mask wearing and rurality.

Also considered were several county-level variables that likely confound the relationship between rurality and mask wearing. The New York Times’ COVID-19 Data Repository was used to obtain case counts for all counties with at least one case. Dates were restricted to the 2 weeks before the mask survey was administered, or between 15 June 2020 and 1 July 2020. The mean daily case rate per 100 000 county residents was calculated for each county, using total county population estimates from the US Census Bureau’s American Community Survey.35 A continually updated, online report published by the American Association of Retired Persons36 was used to define whether a county was in a state whose governor had issued a statewide mask mandate by 15 July (California, Connecticut, Delaware, Illinois, Maine, Nevada, New Mexico, New York, North Carolina, Oregon, Rhode Island, Virginia, Washington). Presidential election data for 2020 were used to determine the percentage of a county that voted for the Republican candidate, sourced from publicly available data that was aggregated from several national news organizations.37 Additional variables were sourced from the Robert Wood Johnson Foundation’s County Health Rankings dataset.38 Included as covariates were the percentage of a county that is non-White, female, and aged 65 years and older. Also included was a county-level indicator for income inequality, which is the ratio of the household income at the 80th percentile of the distribution of household income values to that at the 20th percentile.

The final sample included counties that had at least one COVID-19 case reported during the study time frame and had complete information for all study variables (n=3103 counties), representing 99% of all 3141 US counties.


All analyses were conducted using SAS software v9.4 (SAS Institute; http://www.sas.com). Descriptive statistics for the sample of 3103 counties were reported, as well as characteristics for five counties with the highest mask-wearing percentages and five counties with the lowest mask-wearing percentages. Always or frequently wearing a mask was modeled using multivariate linear regression, adjusted for the focal independent variable IRR, as well as all confounders. Derived from the output of the multivariate model, the authors then plotted the predicted values for the percentage of a county’s residents wearing masks across levels of IRR, adjusted for daily case rates and confounders.

Ethics approval

This study was determined non-human subjects research by the University of Arkansas for Medical Sciences' Institutional Review Board (approval number 261595).


Across 3103 counties, the majority of county residents reported wearing a mask always or frequently between 2 July and 14 July 2020 (median = 72%; Table 1), and the median IRR score was 0.52. The median daily COVID-19 case rate between 15 June and 1 July 2020 was 243 per 100 000. The five counties with the highest percentages of mask wearing were in California, Virginia, and Florida (average IRR score = 0.42; Table 2). The average IRR score for the bottom five counties was 0.55, or more rural. The most apparent cluster of high rurality and low mask wearing spanned the Midwest, upper Midwest, and mountainous West (Fig1).  

In multivariate regression analyses, an increase in rurality was significantly associated with a decrease in mask wearing (β=–0.560; p<0.0001) (Table 3). In addition, compared to residents in counties without a statewide mandate, those in counties with mandates were more likely to wear a mask (β=0.090; p<0.0001). The percentages of county residents who were non-white (β=0.002; p<0.0001) and aged 65 years and older (β=0.004; p<0.0001) were positively associated with mask wearing. Conversely, higher percentages of county residents who voted for the Republican presidential candidate in 2020 were associated with a decrease in mask wearing (β=–0.084; p=0.013). No association was identified between income inequality and mask wearing. The adjusted predicted values for the percentage of county residents wearing a mask are illustrated in Figure 2.

Table 1:  County characteristics (n=3103 counties)table image

Table 2:  Characteristics of top five and bottom five counties by mask wearingtable image

Table 3:  Multivariate linear regression modeling mask wearing, 2–14 July 2020 (n=3103 counties)table image

table image Figure 1:  County-level map of mask wearing and rurality.

table image Figure 2:  Adjusted predicted probability of mask wearing across levels of rurality.


Face masks are a critical preventive measure to curb the ongoing spread of COVID-19. The present analysis included nearly all US counties and our results are broadly representative of national trends. In the adjusted model, mask wearing became significantly less likely as the level of rurality increased, after holding constant case rates, race/ethnicity, age, gender, political partisanship, and socioeconomic disparities. The study’s national findings are aligned with previous state reports that mask wearing is less common in more rural areas.19 Case rates were hypothesized to have an impact on perceived risk and mask-wearing behavior, because perceived susceptibility is a key cue to action.39 However, case rates during the 2 weeks prior to the mask survey were not associated with subsequent mask wearing. Also identified was a relationship between Republican-leaning counties and lower rates of mask wearing, which is very likely attributable to contradictory messages about masks and the overall severity of the pandemic exhibited by the Republican presidential candidate. For example, perceived susceptibility to COVID-19 may be related to messaging by many elected political leaders and popular media outlets who publicly questioned the importance of mask wearing, which likely decreased many individual’s perceptions of susceptibility.40-42 Other reports have also identified associations between more conservative political ideologies and less mask wearing.43 The present findings highlight the need to better understand the personal, geographic, and political mechanisms underlying health behavior and factors that drive individual perceptions of COVID-19 risk.

Research shows that the small size and tightly knit social structures of rural communities can foster community resilience.44,45 Rural social networks are critical to supporting behavior change and have shown to be influential in a variety of public health areas.46,47 For example the Popular Opinion Leader model, utilized to promote HIV risk reduction, posits that behavior change is achieved when new risk-reducing methods are disseminated by social network opinion leaders through their personal contacts.48 In the COVID-19 pandemic, rural community influencers should be trained and given resources to connect community members with health services, reduce disease stigma, discuss the benefits of mask wearing, and provide accurate, up-to-date epidemiologic information in their area. Faith leaders are one such group that have been shown to effectively disseminate public health information in rural communities.49,50 Adapting the Popular Opinion Leader model to enhance mask wearing is one example of adapting existing interventions to complement other public health efforts during the COVID-19 pandemic.51

Potential limitations

Several additional potential confounders are important to consider, but county-level data were not available and therefore not included in the multivariate model. County-level confounders may include the frequency and style of mask communication campaigns, information about social networks and peer mask-wearing behavior, and an indicator of each county’s enforcement of mask mandates.


Wearing a face mask during a viral disease pandemic is a preventive strategy that reduces transmission and decreases mortality. Despite this, adherence to wearing a face covering in public varies widely by geography and rurality. Holding the case rates and other county-level characteristics constant across all US counties, the present study found that individuals in rural counties were less likely than their more urban counterparts to wear a mask. This is particularly concerning in light of recent surges in cases, hospitalizations, and deaths in rural communities.3 Activities centered around public health prevention and education should be grounded in behavior change theories while focusing on the unique strengths and assets of rural communities.


The authors thank Dr J. William Pro for assistance with creating the US map. This work was partially supported by the Northern Arizona University Southwest Health Equity Research Collaborative (NIH/NIMHD, Grant #U54012388).


1 Centers for Disease Control and Prevention. United States COVID-19 Cases and Deaths by State over Time. 2021. Available: web link (Accessed 15 April 2021).
2 Ahmed R, Williamson M, Akhter Hamid M, Ashraf N. United States county-level COVID-19 death rates and case fatality rates vary by region and urban status. Healthcare 2020; 8: 330. DOI link, PMid:32917009
3 Paul R, Arif A, Adeyemi O, Ghosh S, Han D. Progression of COVID19 from urban to rural areas in the United States: a spatiotemporal analysis of prevalence rates. The Journal of Rural Health 2020; 36: 591-601. DOI link, PMid:32602983
4 Henning-Smith C. The unique impact of COVID-19 on older adults in rural areas. Journal of Aging & Social Policy 2020; 32: 4-5. DOI link, PMid:32475255
5 Melvin S, Wiggins C, Burse N, Thompson E, Monger M. The role of public health in COVID-19 emergency response efforts from a rural health perspectiv. Preventing Chronic Disease 2020; 17(E70): 1-6. DOI link, PMid:32701430
6 Shah P, Owens J, Franklin J, Mehta A, Heymann W, Sewell W, et al. Demographics, comorbidities and outcomes in hospitalized COVID-19 patients in rural southwest Georgia. Annals of Medicine 2020; 52(7): 354-360. DOI link, PMid:32620056
7 Singh G, Siahpush M. Widening rural–urban disparities in all-cause mortality and mortality from major causes of death in the USA, 1969–2009. Journal of Urban Health 2013; 91(2): 272-292. DOI link, PMid:24366854
8 Pro G, Hubach R, Wheeler D, Camplain R, Haberstroh S, Giano Z, et al. Differences in US COVID-19 case rates and case fatality rates across the urban–rural continuum. Rural and Remote Health 2020; 20: 6074. Available: web link, DOI link, PMid:32811154 (Accessed August 2020).
9 Cheng K, Sun Y, Monnat S. COVID-19 death rates are higher in rural counties with larger shares of Blacks and Hispanics. The Journal of Rural Health 2020; 36: 602-608. DOI link, PMid:32894612
10 Centers for Disease Control and Prevention. Use of masks to help slow the spread of COVID-19. 2020. Available: web link (Accessed 28 June 2020).
11 World Health Organization. Coronovirus disease (COVID-19) advice for the public: when and how to use masks. Geneva: World Health Organization, 2020.
12 Furukawa N, Brooks J, Sobel J. Evidence supporting transmission of Severe Acute Respiratory Syndrome Coronavirus 2 while presymptomatic or asymptomatic. Emerging Infectious Diseases 2020; 26(7). DOI link
13 Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveillance 2020; 25(10): 2000180. DOI link, PMid:32183930
14 Lyu W, Wehby G. Community use of face masks and COVID-19: evidence from a natural experiment of state mandates in the US. Health Affairs 2020; 39(8): 1419-1425. DOI link, PMid:32543923
15 Van Dyke M, Rogers T, Pevzner E, Satterwhite CL, Shah HB, Beckman WJ, et al. Trends in county-level COVID-19 incidence in counties with and without a mask mandate – Kansas, June 1 – August 23, 2020. Morbidity and Mortality Weekly Report (MMWR) 2020; 69(47): 1777-1781. DOI link, PMid:33237889
16 Masters N, Shih S, Bukoff A, Akel KB, Kobayashi LC, Miller AL, et al. Social distancing in response to the novel coronavirus (COVID-19) in the United States. PLOS One 2020; 15(9): e0239025. DOI link, PMid:32915884
17 Bruine de Bruin W. Age differences in COVID-19 risk perceptions and mental health: evidence from a national U.S. survey conducted in March 2020. The Journals of Gerontology Series B. 2020; 76(2): e24-e29. DOI link, PMid:32470120
18 Niepel C, Kranz D, Borgonovi F, Emslander V, Greiff S. The coronavirus (COVID-19) fatality risk perception of US adult residents in March and April 2020. British Journal of Health Psychology 2020; 25(4): 883-888. DOI link, PMid:32519364
19 Haischer M, Beilfuss R, Hart M, Opielinski L, Wrucke D, Zirgaitis G, et al. Who is wearing a mask? Gender-, age-, and location-related differences during the COVID-19 pandemic. PLOS One 2020; 15(10): e0240785. DOI link, PMid:33057375
20 Hearne B, Nino M. Understanding how race, ethnicity, and gender shape mask-wearing adherence during the COVID-19 pandemic: evidence from the COVID Impact Survey. Journal of Racial and Ethnic Health Disparities 2020; s40615-020-00941-1. DOI link, PMid:33469866
21 Palmer C, Peterson R. Toxic mask-ulinity: the link between masculine toughness and affective reactions to mask wearing in the COVID-19 era. Politics & Gender 2020; 16(4): 1044-1051. DOI link
22 Wang H, Cleary P, Little J, Auffray C. Communicating in a public health crisis. The Lancet 2020; 2(10): E503. DOI link
23 Vraga E, Jacobsen K. Strategies for effective health communication during the coronavirus pandemic and future emerging infectious disease events. World Medical & Health Policy 2020; 12(3): 233-241. DOI link
24 Kushner Gadarian S, Goodman S, Pepinsky T. Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic. SSRN 2020; 30 March. DOI link
25 Balamurugan A, Rivera M, Sutphin K, Campbell D. Health communications in rural America: lessons learned from an arthritis campaign in rural Arkansas. Journal of Rural Health 2007; 23(3): 270-275. DOI link, PMid:17565529
26 Taylor-Clark K, Koh H, Viswanath K. Perceptions of environmental health risks and communication barriers among low-SEP and racial/ethnic minority communities. Journal of Health Care for the Poor and Underserved 2007; 18: 165-183. DOI link, PMid:18065858
27 Rural Health Information Hub. Health promotion and disease prevention program challenges. Grand Forks, ND: Rural Health Information Hub, 2020.
28 Prusaczyk B. Strategies for disseminating and implementing COVID-19 public health prevention practices in rural areas. Journal of Rural Health 2020; 37(1): 142-144. DOI link, PMid:32246497
29 Jhummon-Mahadnac N, Knott J, Marshall C. A cross-sectional study of pandemic influenze health literacy and the effect of a public health campaign. BMC Research Notes 2012; 5(1): 377. DOI link, PMid:22830499
30 New York Times. Mask-wearing survey data. Available: web link (Accessed 8 September 2020).
31 New York Times. US counties COVID-19 data. 2020. Available: web link (Accessed 10 September 2020).
32 Bennett K, Borders T, Holmes G, Backes Kozhimanni K, Ziller E. What is rural? Challenges and implications of definitions that inadequately encompass rural people and places. Health Affairs 2019; 38(12). DOI link, PMid:31794304
33 Waldorf B, Kim A. Defining and measuring rurality in the US: from typologies to continuous indices. Workshop on Rationalizing Rural Area Classification. Commissioned paper, 16/17 April. Washington, DC: Keek Center, 2015.
34 United States Department of Agriculture. Rural-Urban Commuting Area Codes. Available: web link (Accessed 4 August 2020).
35 US Census Bureau. American Community Survey 1-year estimates. Available: web link 2020. (Accessed 1 September 2020).
36 Markowitz A. State-by-state guide to face mask requirements. 2020. Available: web link (Accessed 11 December 2020).
37 McGovern T, Larson S, Morris B, Ro J, Hodges M. 2020 County level presidential results: United States General Election Presidential Results by County from 2008 to 2020. Available: web link (Accessed 20 April 2021).
38 Robert Wood Johnson Foundation. County health rankings & roadmaps. 2020. Available: web link (Accessed 15 August 2020).
39 Champion V, Skinner C. The Health Belief Model. 4th edn. Jossey-Bass, 2008.
40 Makridis C, Rothwell J. The real cost of political polarization: evidence from the COVID-19 pandemic. SSRN 2020; 29 June. DOI link
41 Gonsalves G, Yamey G. Political interference in public health science during COVID-19. British Medical Journal 2020; 371: m3878. DOI link, PMid:33023874
42 Romer D, Hall Jamieson K. Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Social Science & Medicine 2020; 263: 113356. DOI link, PMid:32967786
43 Xu P, Cheng J. Individual differences in social distancing and mask-wearing in the pandemic of COVID-19: the role of need for cognition, self-control and risk attitude. Personality and Individual Differences 2021; 2021(175): 110706. DOI link, PMid:33551529
44 Kapucu N, Hawkins C, Rivera F. Disaster preparedness and resilience for rural communities. Risk, Hazards & Crisis in Public Policy 2013; 4(4): 215-233. DOI link
45 Taylor B, Croff J, Story C, Hubach R. Recovering from an epidemic of teen pregnancy: the role of rural faith leaders in building community resilience. Journal of Religion and Health 2019; 60: 311-325. DOI link, PMid:31190274
46 Li L, Guan J, Liang L, Lin C, Wu Z. Popular opinion leader intervention for HIV stigma reduction in health care settings. AIDS Education and Prevention 2013; 25(4): 327-335. DOI link, PMid:23837810
47 Kelly J. Popular opinion leaders and HIV prevention peer education: resolving discrepant findings, and implications for the development of effective community programmes. AIDS Care 2004; 16(2): 139-150. DOI link, PMid:14676020
48 Kelly J, Murphy D, Sikkema K, McAuliffe TL, Roffman RA, Solomon LJ, et al. Randomised, controlled, community-level HIV-prevention intervention for sexual-risk behaviour among homosexual men in US cities: Community HIV Prevention Research Collaborative. The Lancet 1997; 350(9090): 1500-1505. DOI link
49 Sullivan G, Hunt J, Haynes T, Bryant K, Cheney AM, Pyne JM, et al. Building partnerships with rural Arkansas faith communities to promote veterans' mental health: lessons learned. Progress in Community Health Partnerships 2014; 8(1): 11-19. DOI link, PMid:24859098
50 Baruth M, Bopp M, Webb B, Peterson J. The role and influence of faith leaders on health-related issues and programs in their congregation. Journal of Religion and Health 2015; 54: 1747-1759. DOI link, PMid:25119627
51 Quinn K. Applying the popular Opinion Leader Intervention for HIV to COVID-19. AIDS and Behavior 2020; 24: 3291–3294. DOI link, PMid:32588259