Abstract
Rural populations are more vulnerable to the impacts of COVID-19 compared to their urban counterparts as they are more likely to be older, uninsured, to have more underlying medical conditions, and live further from medical care facilities. We engaged the Southeastern MN (SEMN) community (N = 7,781, 51% rural) to conduct a survey of motivators and barriers to masking to prevent COVID-19. We also assessed preferences for types of and modalities to receive education/intervention, exploring both individual and environmental factors primarily consistent with Social Cognitive Theory. Our results indicated rural compared to urban residents performed fewer COVID-19 prevention behaviors (e.g. 62% rural vs. 77% urban residents reported wearing a mask all of the time in public, p<0.001), had more negative outcome expectations for wearing a mask (e.g. 50% rural vs. 66% urban residents thought wearing a mask would help businesses stay open, p<0.001), more concerns about wearing a mask (e.g. 23% rural vs. 14% urban were very concerned about being ‘too hot’, p<0.001) and lower levels of self-efficacy for masking (e.g. 13.9±3.4 vs. 14.9±2.8, p<0.001). It appears that masking has not become a social norm in rural SEMN, with almost 50% (vs. 24% in urban residents) disagreeing with the expectation ’others in my community will wear a mask to stop the spread of Coronavirus’. Except for people (both rural and urban) who reported not being at all willing to wear a mask (7%), all others expressed interest in future education/interventions to help reduce masking barriers that utilized email and social media for delivery. Creative public health messaging consistent with SCT tailored to rural culture and norms is needed, using emails and social media with pictures and videos from role models they trust, and emphasizing education about when masks are necessary.
Introduction
Living in a rural area has long been recognized as a source of health disparities, with higher levels of morbidity and mortality from certain chronic diseases [1–3]. Rural populations are also more vulnerable to the impacts of COVID-19 compared to their urban counterparts [4, 5]. Those living in rural communities have a higher proportion of risk factors that make them more vulnerable to the effects of COVID-19 such as: less insurance coverage, less healthcare facilities and those that include intensive care, more co-morbidities and disability, and more residents age 65 or older. Furthermore, COVID-19 incidence is higher in rural vs. urban counties since June 2021 [6, 7]. While the COVID-19 vaccine is disseminated across the U.S., as new variants emerge there remains a need to focus on maintenance and adoption of primary prevention measures, including the correct and consistent use of a 2-layer mask—a novel behavior for Americans [8]. This need is especially salient in rural communities, as shown by a poll released by the Kaiser Family Foundation (KFF) [9]. Specifically, this poll demonstrated significant hesitancy to vaccination among rural dwellers, with 35% reporting they did not intend to get a COVID-19 vaccine, even if it were deemed safe, effective, and free, as compared to 26–27% of urban and suburban dwellers [9]. As new variants emerge and spread, COVID-19 vaccination rates in rural counties lag behind those in urban counties [10, 11], with both rates being suboptimal. According to the CDC as of January 31, 2022, vaccination coverage with the first dose of the primary series was lower in rural counties compared to urban counties (58.5% vs 75.4%) [11]. Masking mandates have been used by several states in an effort to curb the spread of the virus, especially in light of evolving knowledge that vaccinated individuals can spread COVID-19 and its variants [12]. Because mask-wearing is a complex behavior that requires both individual and collective cooperation to be effective (e.g. making sure masks are worn properly), there remains a need for social and behavioral science research drawing on behavioral change principles to guide intervention development [13].
Social cognitive theory (SCT) SCT [14] provides a useful framework for understanding both individual and social-environmental level determinants of mask use. SCT emphasizes the concept of reciprocal determinism, whereby individual behavior, such as the choice to wear a mask correctly over nose and mouth in, occurs within the context and interplay of a person with their environment [14–19]. Masking behavior, collective in nature, occurs in the continually changing environment of a worldwide pandemic. To further complicate matters, government and public health leaders have not been united on the recommendation to wear a mask and further confusion is perpetuated by influential voices on social media, television, and print media [20]. The SCT construct, self-efficacy is the most proximal predictor of mask-wearing behavior, whereby those with the highest degree of confidence in their ability to perform a behavior are the ones most likely to adopt the behavior. Other key constructs within the theory include behavioral capability (having the knowledge and skill to perform the behavior), outcome expectations, a person’s understanding of what consequences will follow a behavior, social norms, or the perception that most are performing the desired behavior, and observational learning, or the ability to observe and see others perform the desired behavior [14].
Previously we conducted a community-based survey in a mixed rural-urban, southeastern Minnesota (SEMN) community (N = 7,781, 51% rural) [21]. We found that willingness to wear a mask was lower among rural residents compared to urban residents (88% vs. 95%, p<0.001). Using an SCT framework, the current study explored rural vs. urban differences in barriers to mask wearing and, among those willing to wear a mask, preferences for potential COVID-19 educational interventions. We hypothesized that rural residents would report lower levels of other COVID-19 prevention behaviors, less knowledge about COVID-19 transmission, more negative outcome expectations for wearing a mask, less distress about COVID-19, lower self-efficacy, political beliefs inconsistent with wearing a mask, and higher levels of barriers to wearing a mask compared to their urban counterparts. These data will help to tailor future education/interventions to promote masking to stop the spread of COVID-19.
Methods
Overview
Ethics approval was obtained and the study was deemed exempt (“minimal risk”) by the Mayo Clinic Institutional Review Board. The 26–39 question survey (length determined by branching logic) was developed using or adapting existing items from the literature, including the NIH Phen-X toolkit [22] with feedback from Mayo’s Survey Research Center (SRC) and Community Engagement in Research Advisory Board (CERAB). All study materials, including the survey questions and recruitment materials, were designed to be non-directive about masking to reduce bias related to social desirability and to be inclusive. Following the principles of community-engaged research [23], CERAB was involved at all phases of the study. Specifically, they provided feedback on the overall study design and survey (content and pilot testing) and helped promote the survey to our community. The survey, designed to meet a 6th-grade reading level as determined by Microsoft Word whose scale is consistent with Flesch-Kincaid reading scale [24], was programmed into Research Electronic Data Capture (RedCAP) [25] for participant ease of use. The final survey took about 15–20 minutes to complete with the following sections: 1) about wearing a mask, 2) about coronavirus, 3) about you, 4) intervention questions, and 5) final thoughts. The terms ‘mask’ and ‘Coronavirus’ were defined in the opening of the survey. Participants were not offered any incentives to complete the survey. The survey was conducted between August 4-September 4, 2020, a time when there was a state mask mandate requiring residents to wear a mask whenever in indoor public spaces and for workers who were unable to socially distance outside.
Study design and population
To develop a survey and recruitment plan that would be inclusive and draw in widespread participation, we worked closely with CERAB and the SRC. The survey was anonymous to encourage those who might be concerned about privacy and social desirability to participate. Given the potential controversial nature of masking, we wanted respondents to feel comfortable expressing views that might contradict recommendations and/or mandates. The survey was offered to Southeastern Minnesota residents aged 18 and over from August 4-September 4, 2020. During this time, there was a state mandate to wear masks in indoor spaces and outside when social distancing was not possible. Based on rural-urban commuting area (RUCA) classification [26] using RUCA 1 and 1.1 for urban areas, SEMN is 32% urban and 68% rural.
Outreach/Recruitment
A multifaceted approach including direct community partnerships, social media announcements, and email communications was utilized. Direct community partnerships involved Mayo Clinic internal outreach organizations and established community-based participatory research (CBPR) partnerships (Public Affairs, Employee Resource Groups, CERAB, FAITH! (Fostering African-American Improvement in Total Health) [27] as well as external community partnerships (Rochester Ready and The Center Clinic). Social media advertising was provided via posts shared on Mayo Clinic’s Facebook and Twitter pages. Extensive email outreach included contacting more than 500 businesses/groups in SEMN. All contacted organizations received an outreach email explaining the nature of the research and instructions regarding how to forward survey announcements with their respective communities. Phone and email support were provided as needed while the survey was open to public input. News and media interviews were granted upon request with approval per Mayo Clinic Public Affairs policies.
Measures
Socio-demographics
Gender identity, age, ethnicity/race, zip code, education level, employment status, occupation, political affiliation [28], and rural status as defined by the Rural-Urban RUCA classification [26] were assessed.
Social cognitive theory measures
To assess COVID-19 prevention behaviors [22] (e.g. wearing a mask when out in public, staying six (6) feet away from others) we asked “In the past seven (7) days, how often did you do the following?” with 9 item measure and a 4-point Likert response from ‘none of the time’ to ‘all of the time.’ We assessed COVID-19 knowledge by asking “how does Coronavirus spread?” with participants being able to select all that apply [29]. We developed a study-specific, 10-item measure, composed of 4 attitude items (i.e. wearing a mask … makes me look weak) and 6 outcome expectations (i.e. wearing a mask… will help businesses stay open), to assess the perceived impacts of wearing a mask with a 4-point Likert response from ‘strongly disagree’ to ‘strongly agree.’ We adapted a previous measure [30] to assess masking norms (e.g. ‘I expect that most people in my community will wear a mask to stop Coronavirus’) measured by a 4-point Likert response from ‘strongly disagree’ to ‘strongly agree.’ We adapted the widely-used IES-6 [31], a brief, validated 6-item screening tool with a high degree of reliability used to assess posttraumatic stress reactions, to specifically apply to COVID-19. Respondents were asked how often they experienced distress or felt bothered about COVID-19 using a 4-point Likert response from “not at all” to “often.” To assess perceived likelihood and severity of getting COVID-19, we asked “How likely do you think it is that you could get Coronavirus?” measured by a 4-point Likert response from ‘not at all likely’ to ‘very likely” and “If you got Coronavirus, how serious do you think it would be for you?” measured by a 4-point Likert response from ‘not at all serious’ to ‘very serious.’ To assess self-efficacy, respondents were asked, “How sure are you that you could do the following skills?” (e.g., wear your mask so it covers your nose) measured by a 4-point Likert response from ‘not at all sure’ to ‘very sure.’
To assess information-seeking practices, we asked participants to select what sources (e.g., friends or family members, healthcare providers/institutions, social media platforms, news/TV/radio stations, and Government/politics) they have looked for information about Coronavirus during the previous seven days. We also assessed perceived level of trust for accurate COVID-19 information from the CDC, doctors or other healthcare providers, WHO, State/County/City Health Department, Governor/Mayor, President Trump, and official government websites using a 4-point Likert response from ‘not at all’ to ‘completely’[22]. Barriers and concerns to wearing a mask were assessed by a 12-item measure asking the level of concern about barriers to wearing a mask (e.g. foggy glasses, too hot, trouble understanding what people are saying) with a 4-point Likert response from ‘not at all concerned’ to ‘very concerned.’
Preferences for potential education/interventions
Those who reported being somewhat willing to very willing to wear a mask were asked about their preferences for potential mask education and intervention. Respondents were asked for their topic preferences (e.g., when masks are or aren’t needed, how to care for your mask(s)…) and top choices for receiving information (e.g., by email, social media…) and were able to select all that applied. We defined a strong preference if greater than 50% of the overall sample chose an option as one of their top preference as it does not seem prudent to allocate resources to developing education or platforms for receiving interventions if it was preferred by only half or less of respondents [32].
Statistical analysis
Data were summarized using number, percent for categorical variables; and for continuous variables we used mean, and select percentiles. Responses were compared by rural/urban status using chi-square tests (Fisher’s exact) and two-sample t-tests (rank sum) as appropriate. Missing data were excluded from analyses for the given questions. P-values <0.05 were considered statistically significant.
Geospatial analysis
Geocoding: The zip codes of all persons surveyed during the study period were recorded as part of the survey data. Using the Census Bureau’s Zip Code Tabulation Area map [33], we were able to map other survey variables by Zip Code. Geospatial analysis was performed using ArcMap 10.4.1 [34] (produced by ESRI).
Results
Rural vs. urban socio-demographics
Our survey had 7,781 respondents from SEMN, of which 6100 (78%) identified as female, 1521 (20%) identified as male, and 160 (2%) identified as other genders. Of these respondents, 3963 (51%) lived in a rural area while the remaining 3,818 (49%) lived in an urban area (Fig 1). Table 1 provides socio-demographics overall and by rural and urban status. Fewer rural residents reported a college degree or higher education compared to urban residents (54% vs. 74%, p<0.001) and a lower percentage of rural residents reported Democrat party affiliation (33% vs. 47%, p<0.001).
Fig 1. A total of 7,781 respondents across SEMN answered the online, anonymous, community-based survey.
All counties eligible for the survey were represented. Of these respondents, 3963 (51%) lived in a rural area and 3,818 (49%) lived in an urban area.
Table 1. Demographic characteristics: Southeastern MN voluntary survey sample by rural vs. urban status, N (%).
| Characteristic | Rural N = 3963 | Urban N = 3818 | Total | P* |
|---|---|---|---|---|
| Gender | 0.002 | |||
| Female | 3167 (80) | 2933 (77) | 6100 (78) | |
| Male | 713 (18) | 808 (21) | 1521 (20) | |
| Other | 83 (2) | 77 (2) | 160 (2) | |
| Age | <0.001 | |||
| 18–29 | 504 (13) | 603 (16) | 1107 (14) | |
| 30–39 | 944 (24) | 1032 (27) | 1976 (25) | |
| 40–49 | 958 (24) | 838 (22) | 1796 (23) | |
| 50–59 | 755 (19) | 633 (17) | 1388 (18) | |
| 60–69 | 563 (14) | 500 (13) | 1063 (14) | |
| 70–79 | 213 (5) | 190 (5) | 403 (5) | |
| 80+ | 26 (1) | 22 (1) | 48 (1) | |
| Ethnicity | 0.003 | |||
| Non-Hispanic | 3864 (98) | 3692 (97) | 7556 (98) | |
| Hispanic | 74 (2) | 111 (3) | 185 (2) | |
| Race | ||||
| White | 3855 (97.) | 3605 (94) | 7460 (96) | <0.001 |
| Black or African American | 12 (0.3) | 44 (1.2) | 56 (0.7) | <0.001 |
| American Indian or Alaska Native | 25 (0.6) | 33 (0.9) | 58 (0.7) | 0.23 |
| Asian | 29 (19) | 122 (3.2) | 151 (1.9) | <0.001 |
| Other | 75 (1.9) | 74 (1.9) | 149 (1.9) | |
| Employment Status | <0.001 | |||
| Employed | 3228 (82) | 3103 (81) | 1832 (24) | |
| Employed and working outside home | 2387 (60) | 2098 (55) | 4485 (58) | |
| Unemployed | 729 (18) | 711 (19) | 1440 (19) | |
| College Degree | 2139 (54) | 2782 (74) | 4921 (64) | <0.001 |
| In politics today, what do you consider your political affiliation? | <0.001 | |||
| Prefer not to share | 752 (19) | 500 (13) | 1252 (16) | |
| Democrat | 1283 (33) | 1796 (47) | 3079 (40) | |
| Independent | 660 (17) | 705 (19) | 1365 (18) | |
| Republican | 1002 (25) | 581 (15) | 1583 (20) | |
| Something else | 243 (6) | 223 (6) | 466 (6) |
*Chi-square test (exact) or two-sample t-test (rank sum) as appropriate
Rural vs. urban SCT-based COVID-19 behaviors and experiences
Table 2 summarizes differences in rural vs. urban COVID-19 behaviors and experiences. When asked about COVID-19 prevention behaviors in the past 7 days, 63% of rural residents reported wearing a mask ‘all of the time’ compared to 77% of urban residents (p<0.001). Rural residents had a lower percentage of respondents answering all COVID-19 knowledge questions correctly (62% vs. 65%, p = 0.007). Rural residents had fewer respondents who ‘strongly agreed’ that masking will help businesses stay open compared to urban residents (50% vs. 66%, p<0.001). More rural residents ‘strongly agreed’ that masking should be their choice compared to urban residents (30% vs. 17%, p<0.001). Expectations that people in their community will wear a mask to stop the spread of Coronavirus varied as 11% of rural residents responded ‘strongly agree’ while 21% of urban residents responded ‘strongly agree’ (p<0.001). Rural residents reported lower levels of distress about COVID-19 compared to their urban counterparts (12.5±8.0 vs. 13.2±7.7, <0.001). Rural residents reported lower levels of self-efficacy for mask wearing behaviors (13.9±3.4 vs. 14.9±2.8, p<0.001). Both rural and urban residents reported low levels of being ‘very sure’ that they could ask someone they don’t know to put on a mask (11% and 15% respectively), and more rural residents responded ‘not at all sure’ compared to urban residents (46% vs. 34%), p<0.001.
Table 2. Social cognitive and behavioral factors related to COVID-19 prevention by rural vs. urban status in southeastern, Minnesota, N (%).
| Characteristic | Rural N = 3963 | Urban N = 3818 | P* |
|---|---|---|---|
| COVID-19 Prevention behaviors | |||
| Wearing a mask when out in public | <0.001 | ||
| None of the time | 169 (4) | 85 (2) | |
| Some of the time | 517 (13) | 218 (6) | |
| Most of the time | 801 (20) | 585 (15) | |
| All of the time | 2460 (62) | 2901 (77) | |
| Staying six (6) feet away from others | <0.001 | ||
| None of the time | 223 (6) | 87 (2) | |
| Some of the time | 955 (24) | 679 (18) | |
| Most of the time | 1952 (49) | 2066 (54) | |
| All of the time | 820 (21) | 972 (26) | |
| Hand washing or hand sanitizing | <0.001 | ||
| None of the time | 43 (1) | 22 (1) | |
| Some of the time | 225 (6) | 136 (4) | |
| Most of the time | 883 (22) | 804 (21) | |
| All of the time | 2784 (71) | 2843 (75) | |
| Covering coughs and sneezes | <0.001 | ||
| None of the time | 38 (1) | 27 (1) | |
| Some of the time | 84 (2) | 40 (1) | |
| Most of the time | 437 (11) | 364 (10) | |
| All of the time | 3378 (86) | 3345 (89) | |
| Not touching my face | <0.001 | ||
| None of the time | 262 (7) | 179 (5) | |
| Some of the time | 1225 (31) | 1032 (27) | |
| Most of the time | 1844 (47) | 1857 (49) | |
| All of the time | 600 (15) | 722 (19) | |
| Not touching surfaces in public places | <0.001 | ||
| None of the time | 484 (12) | 302 (8) | |
| Some of the time | 1328 (34) | 1300 (34) | |
| Most of the time | 1543 (39) | 1556 (41) | |
| All of the time | 592 (15) | 651 (17) | |
| Wearing gloves | <0.001 | ||
| None of the time | 3332 (85) | 3075 (81) | |
| Some of the time | 469 (12) | 578 (15) | |
| Most of the time | 93 (2) | 79 (2) | |
| All of the time | 47 (1) | 71 (2) | |
| Staying at home | <0.001 | ||
| None of the time | 714 (18) | 391 (10) | |
| Some of the time | 1491 (38) | 1307 (34) | |
| Most of the time | 1525 (39) | 1859 (49) | |
| All of the time | 216 (5) | 250 (7) | |
| Praying for Coronavirus to go away | <0.001 | ||
| None of the time | 1429 (36) | 1713 (45) | |
| Some of the time | 897 (23) | 868 (23) | |
| Most of the time | 494 (13) | 391 (10) | |
| All of the time | 1104 (28) | 816 (22) | |
| Knowledge about COVID-19 transmission (percent that marked true) | |||
| Through respiratory droplets | 3788 (96) | 3710 (97) | <0.001 |
| Through close contact | 3481 (88) | 3497 (92) | <0.001 |
| Through a contaminated surface | 3497 (88) | 3504 (92) | <0.001 |
| Through the air | 2680 (68) | 2642 (69) | 0.14 |
| The virus is a hoax | 153 (4) | 71 (2) | <0.001 |
| All knowledge questions correct | 2456 (62) | 2479 (65) | 0.007 |
| Mask Impact Scale (Wearing a mask …) | |||
| Mean (SD) | 21.0 (6.6) | 23.7 (5.7) | <0.001 |
| Will help businesses stay open | <0.001 | ||
| Strongly Disagree | 397 (10) | 192 (5) | |
| Disagree | 528 (13) | 265 (7) | |
| Agree | 1044 (27) | 821 (22) | |
| Strongly Agree | 1932 (50) | 2510 (66) | |
| Should be my choice | <0.001 | ||
| Strongly Disagree | 921 (24) | 1385 (37) | |
| Disagree | 1056 (27) | 1215 (33) | |
| Agree | 749 (19) | 501 (13) | |
| Strongly Agree | 1146 (30) | 637 (17) | |
| Should be required | <0.001 | ||
| Strongly Disagree | 900 (23) | 468 (12) | |
| Disagree | 560 (14) | 286 (8) | |
| Agree | 726 (19) | 658 (17) | |
| Strongly Agree | 1733 (44) | 2381 (63) | |
| Isn’t needed | <0.001 | ||
| Strongly Disagree | 2100 (55) | 2745 (74) | |
| Disagree | 805 (21) | 550 (15) | |
| Agree | 582 (15) | 262 (7) | |
| Strongly Agree | 345 (9) | 166 (4) | |
| Shows respect for others | <0.001 | ||
| Strongly Disagree | 439 (11) | 243 (6) | |
| Disagree | 468 (12) | 209 (6) | |
| Agree | 901 (23) | 602 (16) | |
| Strongly Agree | 2101 (54) | 2731 (72) | |
| When others aren’t makes me feel embarrassed | 0.044 | ||
| Strongly Disagree | 1530 (40) | 1528 (41) | |
| Disagree | 1366 (35) | 1232 (33) | |
| Agree | 747 (19) | 726 (19) | |
| Strongly Agree | 226 (6) | 263 (7) | |
| When others aren’t makes me feel disrespected | <0.001 | ||
| Strongly Disagree | 1112 (29) | 688 (18) | |
| Disagree | 907 (23) | 652 (17) | |
| Agree | 1052 (27) | 1212 (32) | |
| Strongly Agree | 835 (21) | 1233 (33) | |
| Makes me look weak | <0.001 | ||
| Strongly Disagree | 2508 (65) | 2915 (78) | |
| Disagree | 1124 (29) | 713 (19) | |
| Agree | 151 (4) | 87 (2) | |
| Strongly Agree | 90 (2) | 40 (1) | |
| Makes me look threatening | <0.001 | ||
| Strongly Disagree | 2377 (61) | 2744 (73) | |
| Disagree | 1145 (30) | 797 (21) | |
| Agree | 238 (6) | 147 (4) | |
| Strongly Agree | 113 (3) | 65 (2) | |
| Makes me a target for security/police | <0.001 | ||
| Strongly Disagree | 2470 (64) | 2742 (73) | |
| Disagree | 1146 (30) | 840 (22) | |
| Agree | 159 (4) | 106 (3) | |
| Strongly Agree | 88 (2) | 60 (2) | |
| Social Norm | |||
| I expect that most people in my community will wear a mask to stop the spread of Coronavirus | <0.001 | ||
| Strongly Disagree | 499 (13) | 228 (6) | |
| Disagree | 1219 (31) | 683 (18) | |
| Agree | 1785 (45) | 2095 (55) | |
| Strongly Agree | 425 (11) | 787 (21) | |
| Impact of Events (stress about coronavirus) | |||
| Mean (SD) | 12.5 (8.0) | 13.2 (7.7) | <0.001 |
| I thought about Coronavirus when I didn’t mean to | <0.001 | ||
| Not at all | 811 (21) | 618 (16) | |
| Rarely | 770 (20) | 766 (20) | |
| Sometimes | 1400 (36) | 1419 (37) | |
| Often | 964 (24) | 994 (26) | |
| I felt watchful or on guard | <0.001 | ||
| Not at all | 909 (23) | 693 (18) | |
| Rarely | 786 (20) | 676 (18) | |
| Sometimes | 1324 (34) | 1423 (38) | |
| Often | 914 (23) | 1003 (26) | |
| Other things kept making me think about Coronavirus | <0.001 | ||
| Not at all | 784 (20) | 605 (16) | |
| Rarely | 734 (19) | 736 (20) | |
| Sometimes | 1431 (36) | 1425 (38) | |
| Often | 977 (25) | 1017 (27) | |
| I was aware that I still had feelings about Coronavirus, but I didn’t deal with them | <0.001 | ||
| Not at all | 1523 (39) | 1292 (34) | |
| Rarely | 1183 (30) | 1266 (33) | |
| Sometimes | 904 (23) | 917 (24) | |
| Often | 309 (8) | 317 (8) | |
| I tried not to think about Coronavirus | <0.001 | ||
| Not at all | 899 (23) | 824 (22) | |
| Rarely | 792 [31] | 878 (23) | |
| Sometimes | 1436 (37) | 1434 (38) | |
| Often | 800 (20) | 660 (17) | |
| I had trouble concentrating | 0.006 | ||
| Not at all | 1597 (41) | 1411 (37) | |
| Rarely | 1050 (27) | 1075 (28) | |
| Sometimes | 871 (22) | 927 (24) | |
| Often | 410 (10) | 379 (10) | |
| Perceived Likelihood of getting Coronavirus | <0.001 | ||
| I have had Coronavirus | 38 (1) | 18 (1) | |
| Not at all likely | 461 (12) | 372 (10) | |
| Somewhat likely | 2118 (54) | 2105 (55) | |
| Likely | 892 (23) | 955 (25) | |
| Very likely | 447 (11) | 362 (10) | |
| Perceived Severity of getting Coronavirus | <0.001 | ||
| Not at all serious | 1221 (31) | 890 (24) | |
| Somewhat serious | 1687 (43) | 1796 (48) | |
| Serious | 620 (16) | 704 (19) | |
| Very serious | 391 (10) | 392 (10) | |
| Self-efficacy (How sure are you that you could…)** | |||
| Mean (SD) | 13.9 (3.4) | 14.9 (2.8) | <0.001 |
| Wear your mask so it covers your nose | <0.001 | ||
| Not at all sure | 42 (1) | 21 (1) | |
| Somewhat sure | 100 (3) | 47 (1) | |
| Sure | 411 (12) | 282 (8) | |
| Very sure | 2900 (84) | 3243 (90) | |
| Wear your mask so it covers your mouth | <0.001 | ||
| Not at all sure | 16 (1) | 10 (0) | |
| Somewhat sure | 59 (2) | 25 (1) | |
| Sure | 428 (12) | 279 (8) | |
| Very sure | 2950 (85) | 3273 (91) | |
| Wear your mask even when people around you aren’t wearing theirs | <0.001 | ||
| Not at all sure | 228 (7) | 118 (3) | |
| Somewhat sure | 315 (9) | 205 (6) | |
| Sure | 687 (20) | 586 (16) | |
| Very sure | 2220 (64) | 2664 (75) | |
| Wear your mask for less than an hour | <0.001 | ||
| Not at all sure | 160 (5) | 139 (4) | |
| Somewhat sure | 138 (4) | 77 (2) | |
| Sure | 560 (17) | 375 (11) | |
| Very sure | 2545 (75) | 2961 (83) | |
| Wear your mask for more than an hour | <0.001 | ||
| Not at all sure | 276 (8) | 143 (4) | |
| Somewhat sure | 311 (9) | 209 (6) | |
| Sure | 639 (19) | 510 (14) | |
| Very sure | 2204 (64) | 2714 (76) | |
| How sure are you that you could ask someone you don’t know to put on their mask when they are around you | <0.001 | ||
| Not at all sure | 1578 (46) | 1224 (34) | |
| Somewhat sure | 983 (29) | 1207 (34) | |
| Sure | 498 (14) | 610 (17) | |
| Very sure | 395 (11) | 550 (15) | |
| Where people got information about COVID-19 in the past seven days | |||
| Center for Disease Control (CDC) | 2065 (52) | 2242 (59) | <0.001 |
| Internet | 1800 (45) | 1895 (50) | <0.001 |
| Healthcare providers | 1585 (40) | 1806 (47) | <0.001 |
| News/TV/radio | 1581 (40) | 1729 (45) | <0.001 |
| Healthcare organizations | 1398 (35) | 1813 (48) | <0.001 |
| World Health Organization (WHO) | 958 (24) | 1175 (31) | <0.001 |
| Coworkers or classmates | 713 (18) | 667 (18) | 0.55 |
| Government/politics | 553 (14) | 579 (15) | 0.13 |
| My religious/spiritual leader | 89 (2) | 74 (2) | 0.34 |
| Perceived Trust for COVID-19 information | |||
| Centers for Disease Control (CDC) | <0.001 | ||
| Not at all | 402 (10) | 192 (5) | |
| Somewhat | 772 (20) | 519 (14) | |
| Mostly | 1466 (37) | 1600 (42) | |
| Completely | 1306 (33) | 1494 (39) | |
| Doctors or other healthcare providers | <0.001 | ||
| Not at all | 110 (3) | 52 (1) | |
| Somewhat | 616 (16) | 286 (8) | |
| Mostly | 1699 (43) | 1604 (42) | |
| Completely | 1511 (38) | 1864 (49) | |
| World Health Organizations (WHO) | <0.001 | ||
| Not at all | 745 (19) | 374 (10) | |
| Somewhat | 798 (20) | 624 (17) | |
| Mostly | 1320 (34) | 1489 (39) | |
| Completely | 1056 (27) | 1300 (34) | |
| State, County, or City Health Department | <0.001 | ||
| Not at all | 323 (8) | 186 (5) | |
| Somewhat | 865 (22) | 523 (14) | |
| Mostly | 1595 (41) | 1626 (43) | |
| Completely | 1158 (29) | 1461 (38) | |
| Governor/Mayor | <0.001 | ||
| Not at all | 1047 (27) | 545 (14) | |
| Somewhat | 942 (24) | 798 (21) | |
| Mostly | 1218 (31) | 1539 (41) | |
| Completely | 725 (18) | 905 (24) | |
| President Trump | <0.001 | ||
| Not at all | 2387 (61) | 2901 (76) | |
| Somewhat | 814 (21) | 518 (14) | |
| Mostly | 537 (14) | 271 (7) | |
| Completely | 197 (5) | 102 (3) | |
| Official Government Websites | <0.001 | ||
| Not at all | 853 (22) | 613 (16) | |
| Somewhat | 1700 (43) | 1771 (47) | |
| Mostly | 1120 (29) | 1163 (31) | |
| Completely | 238 (6) | 240 (6) |
*Chi-square test (exact) or two-sample t-test (rank sum) as appropriate
** these questions were only answered by those who responded they were ‘somewhat’ to ‘very’ willing to wear a mask.
When asked where respondents received information about COVID-19 in the past 7 days, rural respondents were most likely to receive their information from the CDC, the internet, or from healthcare providers. A smaller percentage of rural residents compared to urban residents reported seeking information from healthcare organizations (35% vs. 48%, p<0.001), healthcare providers (40% vs. 47%, p<0.001), CDC (52% vs. 59%, p<0.001), WHO (24% vs. 31%, p<0.001), the internet (45% vs. 50%, p<0.001), and news/TV/radio (40% vs. 45%, p<0.001). Rural residents reported lower levels of trust in all information sources except for President Trump (39% vs 24%, p<0.001).
Rural vs. urban concerns about wearing a mask
Among both rural and urban respondents, the top three concerns about wearing a mask included feeling ‘too hot,’ ‘foggy glasses,’ and ‘trouble understanding what people are saying.’ 23% of rural residents reported being very concerned about being ‘too hot,’ 23% were very concerned about ‘foggy glasses,’ and 26% were very concerned about ‘trouble understanding what people are saying’ compared to 14%, 17% and 15% in urban respondents (p<0.001).
Rural vs. urban preferences for potential education/interventions
Table 3 summarizes preferences for potential education/intervention topics, information delivery, and learning by rural vs. urban status among those willing to wear a mask. Rural residents reported interest in fewer topics compared to their urban counterparts. Top choices included ‘how to care for your mask(s)’ (62% rural vs 69% urban, p<0.001), ‘how to wear a mask (e.g., for fit, comfort…)’ (58% vs. 65%, p<0.001), and ‘how masks work’ (54% vs. 58%, p = 0.001). Top choices for receiving information were similar between the two groups, with the top two learning preferences for both rural and urban residents being email and social media. Most rural residents selected email as their top choice (59%), but social media was a close second (58%). Most urban residents selected social media as their top choice (63%), closely followed by email (62%).
Table 3. Preferences for potential COVID-19 education/intervention topics and delivery by rural vs. urban status in southeastern, Minnesota, N (%).
| Characteristic | Rural N = 3483 | Urban N = 3615 | P* |
|---|---|---|---|
| Topic preferences ** | |||
| When masks are or aren’t needed | 2317 (67) | 2480 (69) | 0.061 |
| How to care for your mask(s) | 2173 (62) | 2478 (69) | <0.001 |
| How to wear a mask (e.g. for fit, comfort. . .) | 2028 (58) | 2331 (65) | <0.001 |
| How masks work | 1886 (54) | 2093 (58) | 0.001 |
| How wearing a mask will affect your medical condition | 1742 (50) | 1826 (51) | 0.68 |
| How to talk with others about wearing a mask | 1691 (49) | 2151 (60) | <0.001 |
| Where to use a mask | 1540 (44) | 1730 (48) | 0.002 |
| Top choice for receiving info ** | |||
| 2046 (59) | 2222 (62) | 0.019 | |
| Social media (e.g. Facebook, Instagram, TikTok, Twitter. . .) | 2022 (58) | 2277 (63) | <0.001 |
| 990 (28) | 975 (27) | 0.17 | |
| In person | 689 (20) | 699 (19) | 0.64 |
| At places I visit (e.g. church, grocery store, gym…) | 679 (20) | 811 (22) | 0.002 |
| Video conference (e.g. Zoom, FaceTime, WhatsApp. . .) | 446 (13) | 520 (14) | 0.052 |
| Phone | 193 (6) | 190 (5) | 0.60 |
*Chi-square test (exact) or two-sample t-test (rank sum) as appropriate
**these questions were only answered by those who responded they were ‘somewhat’ to ‘very’ willing to wear a mask.
Discussion
We previously reported that urban residents were more likely to be ‘very willing’ to wear a mask to stop the spread of COVID-19 compared to rural residents (OR = 1.23, 95% CI 1.05–1.44 [21]. The current report adds knowledge on rural-urban differences on SCT-based constructs related to mask wearing. Rural residents reported more negative outcome expectations for wearing a mask (i.e. wearing a mask will not help businesses stay open) and more concerns about wearing a mask compared to their urban counterparts. Results suggest opportunities for tailoring future educational interventions. Creative messaging on a public health level is needed to reach rural residents, using emails and social media with pictures and videos relevant to rural culture and norms, and emphasizing a variety of topics about masks including when masks are necessary or not needed.
Our results indicate rural residents had lower levels of self-efficacy for masking. Self-efficacy, according to Bandura, is the single most important proximal predictor of behavior [14]. There were also differences in outcome expectations for wearing masks, i.e., fewer rural residents thought that wearing a mask would help businesses stay open (50% vs. 66%). Therefore, there are opportunities for future educational interventions among rural populations focused on how mask-wearing prevents the spread of the virus and can keep businesses open, and also on how to wear a mask to improve its effectiveness (i.e., keep the nose covered). It appears that masking has not become a social norm in the United States, especially among rural residents with almost 50% disagreeing with the expectation ’others in my community will wear a mask to stop the spread of Coronavirus’ (vs. 24% in urban residents). Despite more isolation, social influence is an important factor in rural communities, whereby members are more likely to know each other and to exert behavioral expectations or pressure to conform [35]. Therefore, if a behavior, such as masking is not perceived as a behavioral norm in a community, it may be more difficult to adopt the behavior or adoption of the behavior could result in stress and worry about being the subject of undue attention. Potential education/interventions [36] include providing free masks, providing information, and effective modeling by community or political figures, to normalize masking in both rural and urban communities.
It appears that it may be difficult for respondents to ask others to put on their masks, as seen by both rural and urban residents being ‘not at all sure’ about asking someone they don’t know to put on their mask (46% and 34%, respectively). Rural residents expressed less interest in learning how to talk to others about wearing a mask (49% vs. 60%). While both groups have that concern, rural residents express lower levels of interest in education to mitigate that concern, possibly due to interactions occurring in a less populous area (i.e., more room for social distancing). This suggests that the suboptimal mask use in rural residents might not only be due to outcome expectations but also due to behavioral capability for wearing a mask. Interventions on both the benefits of wearing a mask for the community and how to wear a mask while minimizing physical concerns will be important, especially for rural residents.
While the CDC was most widely listed for providing COVID-19 information by rural residents, only a small percentage of rural residents said that they trust the CDC completely for information about COVID-19. This finding suggests that being a reputable governmental source for healthcare information does not equate to being a trusted source. Healthcare providers were also high on the list, and though rural residents reported more trust in healthcare providers, there was also a low percentage that responded they trust healthcare workers ’completely’ for COVID-19 information. Given the mismatch between information seeking and level of trust, interventions to increase trust in these sources of information could be beneficial for rural residents. This finding is also relevant to COVID vaccination uptake. A study by Viskupič et al found that those who do not trust the CDC or healthcare providers are less likely to get vaccinated [37]. Given many of the current health interventions/marketing comes from the healthcare sector, a different approach may be more suited for rural residents. It is important to note that rural residents reported more trust in Trump compared to urban residents. This is also consistent with the fact that more rural residents reported being Republican. For this reason, we need to recognize that rural residents may be less likely to trust information coming from the Democratic administration. In other words, interventions to normalize masking that utilize relatable role models that rural residents trust may be a more effective strategy.
Improving knowledge about COVID-19 transmission might be helpful for rural residents, and increase their likelihood of wearing a mask or getting vaccinated. Top choices (>50%) for receiving information included email and social media for both rural and urban residents, which highlights the importance of focusing our efforts on online interventions.
Our work contributes to the body of research about COVID-19 prevention and masking to prevent respiratory illness and provides direction for developing interventions, especially for rural populations. This work will be especially important as we continue to promote vaccinations, which is happening more slowly in rural areas, and with less uptake than in urban areas [10], as spread of the virus must still be prevented using simple and effective measures such as hand washing, masking, and social distancing.
Limitations
Our study has some limitations. First, our response rates for racial and ethnic minority groups (including Black/African Americans and Hispanics) were quite low despite extensive community outreach. While we met repeatedly with our CERAB from the inception of the study idea onward, involvement of even more community leaders and key community-based organizations serving these communities could have enhanced our community outreach efforts and diversified our sample. This might also be due, in part, to the constraints on our ability to engage with underserved communities through in-person outreach due to social distancing measures to prevent COVID-19 transmission. Further efforts are needed to encourage participation from these members of our community to ensure that their specific needs are met by public health strategies to prevent the spread of COVID-19.
Second, while we tried to encourage all respondents to share their beliefs about masking, those who were less willing to wear a mask might have been less interested in participating, thus potentially leading to inclusion bias. Because of this, the results should be interpreted with caution as the actual willingness to wear a mask could be lower than what we reported. We attempted to minimize inclusion bias by 1) developing the survey with continual feedback from CERAB, with the goal of including non-directive language, 2) utilizing a wide range of community contacts from across the region to ensure representation across our survey area (Fig 1), and 3) making the survey anonymous to ensure respondents would feel comfortable being honest about mask wearing beliefs regardless of willingness to wear a mask. We recognize that having an online survey limits participation to those who have access to technology. And while we reported a larger response from those identifying as female, previous research [38] suggests that females are more responsive to surveys. Despite the potential for bias, our large sample size included adequate numbers in all groups analyzed.
Lastly, while we used existing measures from the Phen-X database, we also created new measures with little time to develop and test them, due to the rapidly changing circumstances of the pandemic. Despite this, we modeled our measures after existing ones, used sound survey design principles and incorporated our theoretical framework to help guide the questions with feedback from the Survey Research Center and our Community-engaged partners.
In conclusion, creating public health messaging that utilizes images and videos from role models and organizations that rural people trust and identify with and delivery those messages via email and social media will be critical to improving mask utilization as we continue to face the COVID-19 pandemic.
Supporting information
(XLSX)
Acknowledgments
We thank members of the Community Engagement in Research Advisory Board (CERAB) for their input into the design, implementation, and dissemination of the study. We thank Ms. Angelita Falla from the Center Clinic for providing feedback on survey development and community outreach. We would like to thank members of the FAITH! (Fostering African American Improvement in Total Health) Community Steering Committee COVID-19 Task Force, Mr. Clarence Jones, Mrs. Monisha Richard, and Mrs. Jamia Erickson for their support in distributing the survey. We thank Ms. Kimberly Kinnoin and Ms. Michelle Pearson for manuscript assistance. We appreciate the contributions of Dr. Kathleen Yost, Ph.D. and Ms. Ann Harris from the Mayo Clinic Survey Research Center who provided feedback on survey development and community outreach. We appreciate the contribution of Mr. Derrick Lewis, who provided feedback from the beginning of the study and helped with paper editing. We are grateful to Mayo Clinic, Rochester Division of Gastroenterology, and their COVID-19 Focus Group for providing support for the development of this study.
Data Availability
All relevant data are within the paper and its Supporting Information files.
Funding Statement
The study was supported by grant UL1 TR002377 from the National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. The funding source had no role in the study design in the collection, analysis, and interpretation of data; in writing the manuscript; or in the decision to submit the article for publication.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
(XLSX)
Data Availability Statement
All relevant data are within the paper and its Supporting Information files.