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# Characterization of non-adopters of COVID-19 non-pharmaceutical interventions through a national cross-sectional survey to assess attitudes and behaviours

## Introduction

Over 1 year following the discovery of SARS-CoV-2 and with over 194 million cases and 4.1 million deaths globally as of July 20211, the COVID-19 pandemic has emerged as one of the greatest public health crises of the last century. The negative impacts of the COVID-19 pandemic have been extensive and broad reaching impacting healthcare, the economy and the general population globally2,3,4,5,6. In an effort to curtail the spread of SARS-CoV-2, medical and public health communities have worked together with governments to provide recommendations and develop policies for promoting uptake of public health behaviors (now commonly referred to as non-pharmaceutical interventions (NPIs)), including limiting social gatherings, physical distancing, using face masks, avoiding public spaces and quarantining when sick7,8,9.

The implementation of NPIs has coincided with a decline in the number of new COVID-19 cases among many different countries8,10,11,12. Using modelling, the number of cases in mainland China at the end of February 2020 were estimated to be 67-fold higher without NPIs than with NPIs8. A model was also used to estimate the effect of NPIs across 11 European countries between February and May 2020. This model estimated that the interventions were sufficient to drive the time-varying reproduction number below one and demonstrated that NPIs had a large effect on reducing transmission of COVID-1910. Unfortunately, these NPIs have also led to high economic, health and social costs globally13,14,15,16.

Distinct attitudes towards NPIs for COVID-19 have emerged with variation in the public’s agreement with and willingness to adopt these recommendations9,17,18,19,20. Prior studies have demonstrated higher adoption for NPIs among individuals who have higher concern about the harms of SARS-CoV-2, a greater knowledge of the pandemic, are older ($$\ge$$ 50 years), are female and are highly educated17,20. Willingness to adopt NPIs during both the COVID-19 pandemic and prior disease outbreaks have also been linked to the level of trust people have in government and institutions21,22. Misinformation and COVID-19 related conspiracy theories contribute towards non-adoption of NPIs23,24. Usage of traditional news media (i.e., print, television or radio) has been typically associated with greater adherence and less conspiracy beliefs compared to people who use social media as an information source23.

With the recent approval of several COVID-19 vaccinations across the globe25, and the initiation of a mass roll-out strategy, many individuals are under false assumptions that NPIs are no longer needed or important in controlling the spread of COVID-19. However, experts are warning that as vaccinations are distributed, the public must continue to adhere to NPIs26,27. Public health messaging will be critical during vaccine roll-out to encourage adoption and continued adherence to NPIs26. Lack of information transparency and failure to customize information to different subpopulations have been cited as major factors contributing to communication ineffectiveness regarding emerging infectious diseases in the past and the COVID-19 pandemic thus far17,28,29,30. Therefore, identifying the characteristics of individuals who are most likely to be non-adopters is crucial to the development of effective communication strategies including; knowledge translation tools, targeted marketing programs, and community engagement.

We aimed to characterize self-reported non-adopters of NPIs compared to people who reported following these behaviors. We wanted to understand the differences in sociodemographic factors, attitudes towards COVID-19 and risk and trust measures across these 2 groups. We also wanted to identify the informational and social media platforms they use to obtain their COVID-19 data. This information will help identify people at highest risk for non-adoption of NPIs to inform targeted marketing interventions, including optimal communication strategies and platforms, to encourage behaviour change among non-adopters and reduce spread of COVID-19.

## Results

The online survey was distributed to 14,887 participants and 5893 respondents (40%) clicked on the survey link, with 1395 (24% of respondents) excluded due to not completing and submitting the survey. A response rate of 40% is typical for the Angus Reid Forum31 as they distribute broadly to their panel to promote quick responses. The proportion of individuals who completed the survey were similar in age, sex, ethnicity, highest level of education, and province of residence to those who did not complete the survey. Overall, 4498 participants completed the survey (Table 1) and were included in the analysis. The majority of participants were female (2298, 51%), Caucasian (3866, 85%) and educated with either an undergraduate or graduate university degree (1314, 29%). There were 1998 (44%) respondents from Alberta. The age of participants ranged from 18 to 94 years old, with a mean of 47 years ($$\pm$$ 16 years). For information on sociodemographic factors associated with each NPI please see Supplemental Table S1. There were 721 (16%) participants who reported that over the last few weeks they maintained physical distancing sometimes/rarely or never, 631 (14%) who masked sometimes/rarely or never, 986 (22%) who avoided crowded spaces sometimes/rarely or never and 646 (14%) who stayed home while sick, sometimes/rarely or never (Fig. 1).

Using a cluster analysis, two distinct non-overlapping clusters were identified: 3504 (78%) were clustered as adopters and 994 (22%) were non-adopters (Supplemental Table S2). Adoption of NPIs differed by age, sex, province of residence, race/ethnicity, annual income, education level, and political leaning (Table 1). A multivariable model, adjusted for all sociodemographic variables identified that annual household income and ethnicity was not associated with differences between adoption clusters and therefore not used in the subsequent adjusted analyses. Compared to those aged 18–34 years, participants age 35–54 years (aOR 0.64, 95% CI 0.53–0.79) and $$\ge$$ 55 years (aOR 0.25, 95% CI 0.20–0.31) and those of female sex (aOR 0.55, 95% CI 0.47–0.65) had lower odds of being in the non-adopter cluster. Albertans had greater odds of being non-adopters compared with all other provinces; however, when adjusted, this association was no longer significant for Quebec participants. Education was negatively associated with non-adoption, with respondents having a university degree (aOR 0.49, 95% CI 0.37–0.63) having lower odds of being non-adopters than respondents who graduated high school or less. People of Middle Eastern/Central Asian/South Asian (OR 0.39, 95% CI 0.18–0.86) and Chinese/Filipino/Other Asian (OR 0.52, 95% CI 0.30–0.88) ethnicity had lower odds of non-adoption compared with Caucasians; however, this association attenuated following adjustment for other demographics.

People reporting slightly liberal (aOR 0.42, 95% CI 0.28–0.62), liberal (OR 0.42, 95% CI 0.30–0.58) or very liberal (aOR 0.23, 95% CI 0.15–0.35) political leaning had lower odds of non-adoption compared to people reporting moderate/middle of the road political leaning. Conversely, compared with moderate/middle of the road, people reporting slightly conservative (aOR 2.02, 95% CI 1.54–2.63), conservative (aOR 4.12 95% CI 3.26–5.21) or very conservative (aOR 7.00, 95% CI 5.19–9.41) political views had greater odds of non-adoption (Table 1).

Respondents who disagreed with the statement that “younger people are mostly to blame for the increase in cases” had greater odds of being non-adopters of NPIs (aOR 4.29, 95% CI 3.53–5.21); however, 36% of people who disagreed with this statement were of the age 18–34 years compared to 27% of people $$\ge$$ 55 years old. Therefore, the youngest age group were more likely to disagree that younger people were to blame for the increase in cases, yet had higher odds of being in the non-adopter cluster.

### Attitudes towards NPIs and reasons for non-adoption

The greatest proportion of respondents in the non-adopter cluster reported that they did not believe that the recommendations work as the reason for having not followed the public health guidelines (333/994, 34%). Whereas the greatest proportion of adopters reported that they had intended to follow the guideline but simply forgot (78/3504, 2%) as the reason for having not followed public health guidelines (Fig. 2). Other main reasons non-adopters reported not following NPIs included: they did not think the recommendations were important for their health (129/994, 13%) or the health of their friends and family (85/994, 9%), or the NPIs were too burdensome to follow (124/994, 12%). Fewer reported their behaviours were influenced by those around them not following recommendations (79/994, 8%) (Fig. 2).

## Discussion

Consistent with other research, males and young adults in our study reported lower adherence to public health recommendations20,21,32. In April 2020, a cross-sectional survey completed in Alberta and Ontario found that the highest non-adoption of NPIs occurred among males, age 16–29 years old, Alberta residents, with low COVID-19 knowledge and low concern20. Despite extensive resources and effort put towards messaging campaigns, this non-adopter population appears to be quite similar in characteristics from this earlier survey in April 202020 to our current survey in November 2020. This demonstrates the challenges of changing individuals’ attitudes and behaviours regarding NPIs.

Political leaning was the strongest sociodemographic predictor of adoption. A recent survey found only very small differences between conservative and liberal supporters in Canada and Republican and Democrats in the US in behavioral responses to the pandemic; however, there were greater differences in confidence in governments and concern about COVID-1933. Using geotracking data in the US with 15 million smartphones per day, Gollwitzer et al. found that Republican-leaning counties exhibited lower physical distancing than Democratic-leaning counties34. In an analysis of tweets, conservatives were more likely than liberals to believe and spread conspiracy theories and misinformation on the COVID-19 pandemic35. Outside of the setting of COVID-19, bipartisan support by government leaders has led to less partisan motivated reasoning. This strategy may be also be effective for combatting COVID-1936,37.

We found that NPI non-adopters were more likely to have little concern about themselves or their friends and family becoming ill from SARS CoV-2. Non-adopters were also less likely to live with someone high risk of the disease or know someone who had COVID-19. They were also much more likely to have multiple regular interactions with people outside their household. Prior studies have demonstrated that the strongest facilitators for adoption of physical distancing are wanting to protect oneself and others and feeling a responsibility to protect the community. Prior barriers identified included needing to help friends or family members with errands, feeling lonely, and not trusting the messages from the government about the pandemic32. Respondents who expressed distrust in government, healthcare, professional services and technology had lower odds of adoption of any of the assessed NPIs. Trust in government institutions and leaders has been shown to be essential if that country chooses to impose restrictions and maintain public support38.

The importance of trust is an intrinsic component to address COVID-19. Dating back to the foundations of modern-day communication, the Aristotelian embodiment of the ethos of the information source as a key component of trust offers important perspectives on the substantive cornerstone for public compliance with the recommended actions39,40. Trust also is linked to clarity in communication. Trusted, known public health sources with evidence-based, up-to-date, valid information allow everyone to obtain, process, and understand this information in order to make appropriate health decisions. Lessons from previous infectious disease outbreaks and public health emergencies including HIV/AIDS, H1N1, SARS, and MERS highlight the importance of clarity in communication41. Trust in government has been correlated with willingness to adopt protective behaviours in the face of other health threats such as the 2009 H1N1 pandemic42,43 and the 2014–2016 West African Ebola epidemic44.

A previous survey conducted in Canada at the end of April 2020 reported respondents from Ontario and Quebec as having the least amount of trust in the Canadian government45; however, in our study we identified highest distrust among Albertans (48%) followed by Saskatchewan and Manitoba (40%). Other studies have also demonstrated that the higher the degree of trust in the health system, the greater the compliance with guidelines30,46,47. We identified a high degree of trust in healthcare among our respondents with 75% reporting they either trusted or completely trusted healthcare; however, there were greater odds of non-adoption in people expressing distrust in the healthcare system.

Belief in the efficacy of NPIs has been found to be critical for compliance with NPI recommendations48. The greatest proportion of people in the non-adopter cluster reported that they did not believe that the recommendations work as the reason for having not followed the public health guidelines. People who felt that COVID-19 restrictions were harming the economy had lower odds of adoption of NPIs. Prior research has found that there is a positive association between risk perception and economic threat perception, meaning that people that perceive a personal economic threat may as a result be less likely to adhere to guidelines30,49.

Individuals have access to a seemingly endless stream of information on COVID-19 through many different informational and social media platforms. One study evaluating the effect of information overload on the intention to self-isolate found that there was a negative impact of information overload on efficacy as information overload often does not allow for accurate understanding and therefore the uncertainty associated lowers efficacy50. Social media misinformation and lack of well-designed education programs without community engagement can impact on compliance and acceptability of NPIs17. Mobilizing an effective public health response during a pandemic requires clear communication and trust48,51. Non-adopters were more likely to report that public health messaging has been unclear and inconsistent. As well, they were more likely to report that messaging has actually made them less likely to adopt NPIs. Our assessment of the trust people have in institutions (Table 4) and the “information sources” (from learned intermediaries (e.g. physicians, health professionals), news readers (celebrities and known people), social media (anonymous sources), and their government offers guidance for future communication and trust building approaches on COVID-19 NPIs and related efforts with vaccination52.

Facebook, YouTube and Instagram were the most commonly used social media platforms. We found that individuals using YouTube and Snapchat for COVID-19 information had higher odds of non-adoption. Several prior studies have found a negative association between COVID-19 conspiracy beliefs, usage and trust in social media networks with adoption of NPIs53,54. One study evaluating conspiracy beliefs associated with COVID-19 on social media found that YouTube had the strongest association with conspiracy beliefs, followed by Facebook53. This suggests that targeting these social media platforms may be an effective option for distributing targeted messaging to promote uptake of NPIs for COVID-19 prevention. Overall, effective, credible, consistent and culturally informed health communication is vital in influencing positive health behaviours and building trust41,55,56, especially in terms of encouraging people to adhere to COVID-19 control measures and NPIs.

There are several strengths and limitations to our work. This was a cross-sectional survey representing persons attitudes and behaviors at the time of this study, which will continue to change over time as the pandemic evolves. The survey recruited participants from an existing voluntary nationwide panel designed to be representative of the Canadian population; however, by using a panel there will be a component of selection bias as participants have volunteered to partake in research surveys and have access to electronic devices to do so. A non-response bias is possible as 1395 participants began but never fully completed the survey and were therefore excluded, however based on age, sex, ethnicity, education level and province of residence, these data were missing at random. Despite having a large sample size and being conducted in both English and French, generalizability may be limited. It is possible that some of the questions in the survey may have been interpreted differently by participants leading to variability in responses. It is also possible that individuals’ motivations towards non-adoption of public health behaviours are complex and intertwined and not easily characterized or captured by survey tools. Future work including repeat surveys and qualitative studies to assess public attitudes and behaviors through this changing pandemic will be key in maintaining effective messaging promoting adoption of NPIs.

Throughout the vaccination roll-out, adherence to and adoption of NPIs to reduce the spread of COVID-19 will remain critical. NPIs are most successful when there is a greater uptake from the public. This work provides a significant contribution to the COVID-19 literature through characterization of individuals more likely to be non-adopters of NPIs. An in-depth review of these individuals’ sociodemographic factors, behaviours and attitudes towards COVID-19 and the barriers for NPI adoption is presented. We deliver a unique perspective though a Canadian national survey at a critical time during the pandemic, just at the initiation of the second wave with rising COVID-19 cases occurring across the country. This information will be useful for developing effective communication strategies including; knowledge translation tools, marketing programs and community engagement, targeted toward non-adopters of NPIs, in both message content and effective platforms for dissemination.

## Methods

### Study design

A cross-sectional survey (Supplementary Fig. S1) was used to assess respondents’ self-reported adherence to NPIs including physical distancing, masking, avoiding public spaces and staying home when sick as methods to reduce spread of COVID-19. This survey was informed by an online survey and focus groups conducted in Alberta, Canada by the research team57,58. Information on sociodemographic factors, attitudes towards COVID-19 and NPIs as well as risk and trust measures were collected. Participants were also asked about their usage and trust in information and social media platforms for COVID-19 information. This study was approved by the University of Calgary Conjoint Health Research Ethics Board (REB20-1228). Informed consent was obtained, and participation was voluntary. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was used to report our findings59.

### Variables and measurement

The main outcome measure was adoption of NPIs assessed by respondents answering the question; over the last few weeks, how often have you been performing each of the behaviors: (1) social/physical distancing-keeping at least 2 m from other people who are not in your social bubble, (2) wearing a mask in public indoor spaces when physical distancing is not possible, (3) avoiding places & activities where you would interact with a large number of people outside your household, and (4) staying home if you were sick with any symptoms, even mild ones. Adoption of these NPIs was assessed on a Likert scale of; all the time, most of the time, sometimes, rarely and never.

Sociodemographic factors including sex, age, province of residence, household income, highest level of education, ethnicity, and political leaning were collected and categorized. Questions identifying attitudes towards COVID-19, public heath recommendations and reasons for non-adoption of recommendations were asked. Likert scales were used to assess agreement with statements on public health messaging and restrictions, how effective people believe public health recommendations are at reducing spread of COVID-19, effectiveness of public messaging thus far, and trust in specific institutions. Respondents’ usage and trust of information platforms and social media platforms were collected.

### Statistical analysis

Descriptive statistics (percentage frequencies) were calculated for all sociodemographic characteristics, attitudes toward COVID-19 and towards NPIs and adoption of NPIs. Categorical variables were compared using chi-squared tests. Respondents who had not completed all survey responses were excluded, therefore there were no missing data.

Cluster analysis was used as a data-driven method to identify the most important and meaningful patterns in the survey. This analysis highlighted how different or similar individuals are in their attitudes toward NPIs such that empirical patterns can be summarized in an insightful and concise manner. The goal of cluster analysis is to estimate a limited number of clusters with the most similarity within clusters but most dissimilarity between clusters. Clustering was based on individuals’ attitudes and behaviors toward NPIs (limiting social gatherings, physical distancing, using face masks, avoiding public spaces and quarantining when sick), this includes reported actions and opinions on effectiveness of NPIs and on clarity of public messaging about NPIs.

A series of statistical tests were conducted to determine the clustering method and the optimal number of clusters to explain the empirical variations in the data. The Kmeans algorithm was used for cluster analysis to partition the dataset into two distinct non-overlapping clusters labeled as adopter and non-adopter clusters. Kmeans is an iterative algorithm that assigns observations or data points to a cluster with the objective to minimize the sum of squared distance between the data points and the cluster’s arithmetic mean of all the data points that belong to that cluster. The output of analysis in this part is a data-driven and detailed way to allocate each observation to their appropriate cluster and generate a clustering indicator variable (adopter and non-adopter cluster) to be used for further investigation.

Logistic regression was used to calculate the odds ratio (OR) with 95% confidence interval (CI) for the non-adoption cluster compared to the adoption cluster as a reference. Logistic regression was used to identify risk factors for non-adoption of NPIs by sociodemographic factors, attitudes towards COVID-19, attitudes towards NPIs and towards public health messaging, and trust in specific institutions. Logistic regression was also used to identify communication channels and social media platforms at higher odds of being used and trusted by non-adopters. Backwards stepwise regression identified sociodemographic factors significantly associated with adoption clusters. These sociodemographic factors were included in multivariable regression models estimating the adjusted odds ratio (aOR) comparing adoption clusters with (1) attitudes towards COVID-19, (2) attitudes towards NPIs and (3) attitudes toward public health messaging. All P-values were two-tailed tests, and the statistical significance level was set at P < 0.05. All statistical analyses were performed using STATA version 15.1 (College Station, TX).

### Declarations

All experiments were performed in accordance with relevant guidelines and regulations.

### Ethics approval

The study was approved by the University of Calgary Conjoint Health Research Ethics Board (REB20-1228).

### Consent for publication

All authors give their consent to publication of this work.

## Data availability

The summary dataset used and or analyzed during the current study are available from the corresponding author on a reasonable request.

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## Funding

This study was funded by a COVID-19 Rapid Response Funding Grant from Alberta Innovates (Grant #202100489).

## Author information

Authors

### Contributions

R.L., J.L.B., O.A., B.S., H.S., M.M., M.F., T.T., B.J.M., D.A.M., J.H. and R.O. were all involved in study design. R.L., J.L.B., B.S., H.S., M.M., M.F., T.T., B.J.M., D.A.M., J.H. and R.O., were involved in the creation of the survey. R.L., O.A., B.S. and R.O. performed the analysis. R.L., J.L.B. and O.A. wrote the initial draft of the paper. All authors made edits and contributions to the final draft of this manuscript.

### Corresponding author

Correspondence to Raynell Lang.

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### Competing interests

The authors declare no competing interests.

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Lang, R., Atabati, O., Oxoby, R.J. et al. Characterization of non-adopters of COVID-19 non-pharmaceutical interventions through a national cross-sectional survey to assess attitudes and behaviours. Sci Rep 11, 21751 (2021). https://doi.org/10.1038/s41598-021-01279-2

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