Exploratory study to characterise the individual types of health literacy and beliefs and their associations with infection prevention behaviours amid the COVID-19 pandemic in Japan: a longitudinal study
- Published
- Accepted
- Received
- Academic Editor
- Shailendra Verma
- Subject Areas
- Infectious Diseases, Psychiatry and Psychology, Public Health, COVID-19
- Keywords
- Individual type, Health literacy, Risk communication, Infection prevention behaviour, Cluster
- Copyright
- © 2024 Yagihashi et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
- Cite this article
- 2024. Exploratory study to characterise the individual types of health literacy and beliefs and their associations with infection prevention behaviours amid the COVID-19 pandemic in Japan: a longitudinal study. PeerJ 12:e16905 https://doi.org/10.7717/peerj.16905
Abstract
Background
During a global infectious disease pandemic such as the coronavirus disease 2019 (COVID-19), individuals’ infection prevention/risk-taking behaviours are likely to differ depending on their health literacy and beliefs regarding the disease. To effectively promote infection prevention behaviours, it is necessary to enable information dissemination and risk communication that consider individuals’ health literacy and beliefs. In this study, we exploratorily characterised segments based on individual health literacy and beliefs regarding COVID-19 among the Japanese during the early stage of the COVID-19 pandemic, and investigated whether infection prevention/risk-taking behaviours and fear of COVID-19 differed among these segments.
Methods
In this study, we conducted two web-based longitudinal surveys in Japan (PHASE 1, 1–30 November 2020, 6,000 participants; PHASE 2, 1–31 December 2020, 3,800 participants). We characterised segments of the target population using cluster analysis on health literacy and beliefs regarding COVID-19 obtained in PHASE 1. We further investigated the associations between the clusters and infection prevention/risk-taking behaviours and fear of COVID-19, obtained from PHASE 2.
Results
Five clusters were identified: ‘Calm/hoax denial’, ‘Hoax affinity/threat denial’, ‘Minority/indifference’, ‘Over vigilance’, and ‘Optimism’. There were significant differences in infection prevention/risk-taking behaviours and fear of COVID-19 among the five clusters. The belief in susceptibility to infection, rather than affinity for hoaxes and conspiracy theories, was coherently associated with infection prevention/risk-taking behaviours and fear of infection across clusters. This study provides foundational knowledge for creating segment-specific public messages and developing interactive risk communication to encourage infection prevention behaviours.
Introduction
The coronavirus disease 2019 (COVID-19) pandemic, which began in 2020, has led to the promotion of behavioural regulations and infection prevention actions worldwide owing to its high infectivity and fatality rate. In the early stage of the COVID-19 pandemic, laws and penalties were often used to regulate behaviour among citizens in many countries; however, as of January 2023, there has been a shift away from the mandatory regulation of behaviours and more toward citizens’ autonomy in infection control. As COVID-19 continues to spread worldwide, it is important to identify strategies that effectively promote these voluntary prevention measures. In contrast, the Japanese government has requested citizens to adopt prevention behaviours and refrain from economic activities as from the early stage of the pandemic. These requests are voluntary rather than legal obligations of citizens, and it is up to individuals to decide what actions they take in response to the government’s call. Therefore, risk communication that encourages infection control based on autonomy, as Japan has been promoting, is becoming increasingly important worldwide.
Individuals’ thoughts on infectious diseases are related to the ideas and beliefs that arise from health literacy. Health literacy refers to an individual’s skill in health information and services needed to make appropriate health decisions (Weiss, 2007; World Health Organization, 1998). In addition, it has recently been attributed not only to individual skills but also to the interaction between the individual and their surrounding environment. In other words, health literacy regarding the COVID-19 pandemic refers to individuals’ skills in obtaining health information and services and making behavioural decisions; these skills are influenced by the social context. Furthermore, individuals’ thoughts on infectious diseases and health beliefs vary from individual to individual. Since COVID-19 is a new and unknown disease, its transmission mechanism and characteristics have not been clarified. Therefore, how individuals obtain information and make decisions about infection prevention or risk-taking behaviours is likely to be mediated by their health beliefs.
The health belief model, one of the leading health behaviour theories, can provide important insight into people’s prevention/risk-taking behaviour during the COVID-19 pandemic. It states that the drivers of health behaviour include one’s perception of threat and the balance of advantages and disadvantages (Rosenstock, 1974); this perception of threat consists of susceptibility (i.e., a feeling that there is a high probability of being infected with COVID-19) and severity (i.e., how serious the consequences would be if the individuals were infected with COVID-19). The balance of advantages and disadvantages is then tempered by the disadvantages (costs and barriers) of performing the behaviour. These are heavily influenced by individuals’ thoughts and beliefs.
Segment-specific risk communication about health literacy and beliefs is known to be effective in promoting health-related behaviour. Ishikawa et al. (2012) reported that participation rates in breast cancer screening increased by providing segment-specific information after categorising the target population into three segments based on their beliefs about cancer and its screening. Takemura et al. (2022) conducted a survey of optimistic or pessimistic perceptions about the probability of contracting COVID-19 and emphasized the importance of segment-specific and tailor-made risk communication amid the pandemic. To promote infection prevention behaviours during COVID-19, segments should be characterised based on individuals’ health literacy and beliefs regarding COVID-19, implementing risk communication according to these segments. Amid an infectious pandemic, risk communication aims to change behaviour by providing information. Even in one-way information provision aimed at behaviour change during a pandemic (United States Department of Health and Human Services, and Prevention, 2018), understanding the characteristics of the segments would be useful in constructing public messages based on the diversity of the target population. Furthermore, understanding these characteristics would be helpful in developing interactive risk communication tailored to relevant sub-groups. However, while previous studies have reported the factors associated with COVID-19 infection prevention behaviours, such as demographic factors (e.g., age, gender) (Muto et al., 2020; Pampel, Krueger & Denney, 2010), sociodemographic factors (e.g., perception of infection risk, personality, and norms) (Bruine de Bruin & Bennett, 2020; Nakayachi et al., 2020; Qian & Yahara, 2020), and knowledge and information sources (Batra et al., 2021; Uchibori et al., 2022), there have been no attempts to characterise such segments based on health literacy and beliefs regarding COVID-19 or to study the relationship between segments and infection prevention behaviour.
We thus conducted two web-based longitudinal surveys with two objectives. First, we aimed to characterise segments based on health literacy and beliefs regarding COVID-19 in the first phase (PHASE 1), using an exploratory cluster analysis. We then investigated the associations between these segments obtained in PHASE 1 and infection prevention/risk-taking behaviours and the fear of COVID-19, which were assessed in the second phase (PHASE 2).
Materials & Methods
Ethics
This study was approved by Osaka University Graduate School of Human Sciences Research Ethics Committee (20095).
Study design
We conducted longitudinal questionnaire surveys on the web during two periods: 1–30 November 2020 (PHASE 1), and 1–31 December 2020 (PHASE 2).
Participants
Participants were individuals living in Japan who had registered with Cross Marketing Inc. Cross Marketing is one of the largest research firms in Japan, with a research panel of approximately 5.41 million people and annual performance of more than 10,000 cases, as well as 20 offices in 10 countries outside Japan (as of 2022). In addition, Cross Marketing is a Privacy Mark certified business under the Privacy Mark System operated by the Japan Information Processing Development Center (JIPDEC) under the guidance of Japan’s Ministry of Economy, Trade and Industry (METI), and is fully committed to handling personal information. In addition, the company is also committed to quality control (HP: https://www.cross-m.co.jp/; Japanese). We therefore considered Cross Marketing to be an appropriate company and requested them to conduct the survey. Participation in the survey was voluntary, and participants received ‘points’ that could be redeemed for products. This provided an incentive for participation in the survey regardless of the individuals’ interest in the survey topic. Only participants who chose to consent to the online survey were included in this study.
In PHASE 1, 6,000 survey participants were recruited from monitors, aged 18–79 years. The participants were selected in terms of age (18–29, 30–39, 40–49, 50–59, 60–69, or 70–79 years), sex (male or female), and residential area (urban or non-urban) to match their actual compositions in Japan. Target numbers were set for each of the above variables, and the survey was conducted until the target number was reached. Next, in PHASE 2, we conducted a continuous survey in which all participants from PHASE 1 were invited to participate. The target number of participants in PHASE 2 was set at 3,800 because of our budget limitation. The total population of Japan is 126.55 million (as of October 1, 2019). For this population, the required sample size is approximately 400 (5% margin of error, 95% confidence interval (Serdar et al., 2021)); therefore, the sample size in this study for both PHASE 1 and 2 was sufficient to measure a population representative of Japan. Inappropriate respondents in both surveys were excluded through an instructional manipulation check (Miura & Kobayashi, 2019). Specifically, from the sample, we excluded those who answered anything other than ‘Not applicable’ to the dummy question that asked them to select ‘Not applicable’. The sex and age of the participants in PHASES 1 and 2 were as follows:
PHASE 1: Male = 3,000, female = 3,000; mean age = 49.4, standard deviation (SD) = 16.6
PHASE 2: Male = 1,969, female = 1,831; mean age = 51.7, SD = 16.0.
In addition, we conducted the χ2 test to examine selection bias, that is, the difference in percentage of participants between both phases (two conditions: participants in PHASE 2 and non-participants in PHASE 2 (those who only participated in PHASE 1 but were not selected in PHASE 2) ×5 clusters). A significant difference was found, but the effect size (Cramér’s V) was small (χ2(4) = 57.95, p <0.001, V = 0.098) (Cohen, 1988).
Survey items
The two web surveys included the following concepts and factors. The details of the questionnaires are described in Appendix S1-a and S1-b.
PHASE 1: Health literacy and beliefs regarding covid-19—susceptibility to infection, infection control, hoax, conspiracy theories, and optimism
We created 82 items (six-point Likert scale, ranging from ‘strongly disagree’ to ‘strongly agree’; the higher the score, the more the ‘agree’ option was selected) regarding the beliefs about COVID-19 based on previous studies on health literacy (Swami & Barron, 2020; Taylor, 2019) and mass media reports (i.e., newspapers, internet news). These included 35 items on susceptibility to infection; 21 items on infection control for COVID-19; and 26 items on hoax, conspiracy theories, and optimism about COVID-19. To ensure content validity, item development was conducted by KH, a health psychologist, AY, an educational psychologist, and MK, a graduate student in the Department of Educational and Health Psychology. They collected a wide range of descriptions of COVID-19 that seemed to fit the aforementioned constructs of the theory, and also removed duplicates. Furthermore, AM, a social psychologist, and MY, a clinical psychologist, reviewed the conceptual and item content for completeness and determined that it was conceptually saturated. For construct-related conceptual validity, factor analysis was conducted after data collection to confirm the factor structure and assess its validity. Furthermore, we used two items regarding belief in just deserts (i.e., a belief that the infected individual deserves to be infected (Murakami et al., 2022); six-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’; the higher the score, the more the ‘agree’ option was selected). For all items in PHASE 1, there were no reversal items.
PHASE 2: Infection prevention/risk-taking behaviour regarding COVID-19 and the fear of infection
The field of risk research has played an important role in disasters, infectious diseases, and other calamities that require individual-level to national-level measures. Individuals’ risk perception can be categorised along two axes: dread and unknown factors. In particular, the more intuitively individuals feel dread, the stronger their demand for measures (Uchibori et al., 2022). Furthermore, there are biases in this risk perception, such as present bias (O’Donoghue & Rabin, 1999) and normalcy bias (Omer & Alon, 1994). Regarding infection prevention behaviours for COVID-19, a bias is considered to exist wherein people put off these behaviours in favour of other behaviours even though they think infection prevention is important (i.e., present bias), alongside a bias that they will not be infected (i.e., normality bias).
Therefore, ideas about infection prevention/risk-taking behaviours regarding COVID-19 were assessed among the participants in this study based on the concepts of present bias and normality bias. Furthermore, fear of COVID-19 was included in the survey items. We originally created 18 items related to infection prevention/risk-taking behaviours regarding COVID-19 (seven-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’; the higher the score, the more the ‘agree’ option was selected). To ensure content validity, item development was conducted by KH and the other co-authors, as described for PHASE 1. To assess the fear of infection, instead of dread or unknown factors (Slovic, 1986), we used the Perceived Vulnerability to Disease scale (PVD; seven-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’; the higher the score, the more the ‘agree’ option was selected) (Duncan, Schaller & Park, 2009; Fukukawa et al., 2014) that consisted of two subscales (i.e., perceived infectability and germ aversion) (see Appendix S1-b). The authors have permission to use this instrument from the copyright holders. For all items in PHASE 2, there were no reversal items.
Statistical analysis
We examined the distribution and homoscedasticity of variables and adopted a parametric test. To examine the demographic profile of participants, we used χ2 test and Cramér’s V. To characterise the target segment, we first conducted three-factor analyses with the maximum likelihood method for questionnaires regarding health literacy and beliefs: susceptibility to infection for COVID-19; infection control for COVID-19; and hoax, conspiracy theories, and optimism about COVID-19. Promax rotation was used in this study because we assumed correlations among the extracted factors (Grieder & Steiner, 2022). The number of factors was comprehensively determined based on parallel analysis (Hori, 2001), the scree test, and their interpretability. Factors with high loadings (≥0.3 or ≤−0.3) were considered for factor interpretation (Hair et al., 2013; Tavakol & Wetzel, 2020). Factor scores were obtained from the factor loadings, which were used as feature values in the subsequent cluster analysis. The adequacy of the factors obtained was confirmed using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy. We then used cluster analysis by the k-means method with 100 iterations to characterise the segments among participants. We examined the number of clusters from three to eight with interpretability and effect size, using one-way analysis of variance (ANOVA) with Welch’s test. Effect sizes of 0.06 and 0.14 were considered medium and large, respectively (Cohen, 1988). The factor scores of the factors extracted in the above factor analyses and belief in just deserts with z-standardisation were used as variables (total 14 variables; belief in just deserts was used in the previous study (Murakami et al., 2022); Cronbach’s α in this study was 0.79). Hereafter, the clusters identified by cluster analysis are referred to as segments. Differences in health literacy and beliefs among clusters were confirmed by a one-way ANOVA with Welch’s test, and the Games-Howell test as a post-hoc test to ensure that the effect sizes were sufficiently large. The criterion for effect size was as follows: η2 = 0.01, small; 0.06, medium; and 0.14, large (Cohen, 1988). The p value was corrected by Bonferroni correction, that is, the p value was multiplied by 16, the number of factors.
Next, we conducted factor analyses using the maximum likelihood method for infection prevention/risk-taking behaviours regarding COVID-19. Factor analyses were performed separately on the questionnaire items based on the concepts of present bias and normality bias. Promax rotation was applied because we assumed that there were correlations among the factors extracted (Grieder & Steiner, 2022). As in the above factor analyses, the number of factors was determined based on parallel analysis (Hori, 2001), the scree test, and their interpretability. Cronbach’s α was also calculated to confirm reliability. We calculated the mean values of the items related to the factors obtained as well as the mean values related to the fear of infection (two variables: perceived infectability and germ aversion, in accordance with the previous study (Duncan, Schaller & Park, 2009; Fukukawa et al., 2014); Cronbach’s α in this study was 0.77 for perceived infectability and 0.76 for germ aversion) (a total of five factors). Finally, we conducted a one-way ANOVA with Welch’s test, and Games–Howell test as a post-hoc test, to examine the differences in these factors among clusters. The p value was corrected by Bonferroni correction, that is, the p value was multiplied by five, the number of factors.
We used SPSS (IBM SPSS, Chicago, IL, USA) version 28.0 for all analyses except the parallel analysis. We performed parallel analysis using Microsoft Excel (Microsoft, Redmond, WA, USA), including codes provided by Hori (2001). This analytical Excel is publicly available on Hori’s website (Hori, 2001). All p-values less than 0.05 were considered significant.
Results
Participants’ demographic profile
There were 6,000 participants in Phase 1 (Male = 3,000, female = 3,000; mean age = 49.4 years, standard deviation (SD) = 16.6). Of 6,000 participants, 2,122 (35.4%) were unmarried and 3,878 (64.6%) were married. In addition, 1,800 individuals (30.0%) had children living together, 1,319 (22.0%) had children but were not living together, and 2,881 (48.0%) did not have any children.
Factor analyses for health literacy and beliefs and characterisation of segments using cluster analysis (PHASE 1)
Factor analyses for health literacy and beliefs
Regarding health literacy and beliefs about one’s susceptibility to infection with COVID-19 (35 items), five factors were obtained through factor analysis (Table 1). An adequate value in the KMO measure of sampling adequacy (=0.95) was shown. The first factor was characterised by items such as ‘People with pre-existing (underlying) diseases are more likely to be severely ill’ and ‘Elderly people are more prone to severe illness’, which we named ‘General ease of infection’. The second factor was named ‘Extreme likelihood of infection’ because of the high factor loadings of items such as ‘Infectious by airborne transmission’, ‘Infectious by train’, and ‘transmitted from animals to humans’. Similarly, the third, fourth, and fifth factors were characterised by items such as ‘The current probability of death from infection with the new coronavirus in Japan is very low, about 1/10 million’, ‘Infections occur during nightlife (bars, clubs, host clubs, etc.)’, and ‘Young people in their 20s and 30s are spreading the novel coronavirus’, respectively; therefore, we named them ‘Low perception of infection threat’, ‘Ease of infection at dinners and parties’, and ‘Ease of infection among young people’, respectively.
| Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | |
|---|---|---|---|---|---|
| ‘General ease of infection’, | |||||
| People with pre-existing (underlying) diseases are more likely to develop severe disease | 0.77 | −0.07 | 0.06 | −0.04 | 0.03 |
| Elderly people are more prone to severe illness | 0.73 | −0.06 | 0.03 | −0.01 | 0.04 |
| Infectious by droplets | 0.72 | −0.02 | −0.04 | −0.01 | −0.01 |
| Infectious when living with others in a confined space | 0.61 | 0.09 | 0.00 | 0.12 | −0.04 |
| Infectious where the three ‘densities’ overlap | 0.57 | −0.05 | −0.01 | 0.20 | −0.01 |
| Infectious in large groups and during long meals | 0.56 | −0.06 | −0.03 | 0.35 | −0.05 |
| Infectious by aerosols (infectious by airborne particles) | 0.51 | 0.23 | 0.01 | −0.14 | −0.01 |
| Infectious at home | 0.42 | 0.29 | 0.00 | −0.13 | 0.01 |
| Currently, the spread of infection is mainly at home | 0.40 | 0.16 | 0.14 | −0.22 | 0.16 |
| Smokers are more prone to severe illness | 0.39 | 0.14 | 0.08 | −0.01 | 0.05 |
| Infectious at schools and workplaces | 0.35 | 0.33 | −0.08 | 0.18 | −0.05 |
| ‘Extreme likelihood of infection’, | |||||
| Infectious by airborne transmission | 0.20 | 0.53 | −0.03 | −0.09 | −0.07 |
| Infectious by train | 0.12 | 0.53 | −0.12 | 0.13 | −0.09 |
| Transmitted from animals to humans | 0.13 | 0.37 | 0.03 | −0.08 | 0.03 |
| Elderly people are the ones spreading novel coronaviruses | −0.20 | 0.33 | 0.27 | 0.15 | −0.15 |
| Infectious by contact | 0.32 | 0.32 | 0.01 | 0.00 | −0.04 |
| Many infected people are passing it on to others | 0.17 | 0.30 | −0.05 | 0.15 | 0.10 |
| The novel coronavirus has a higher mortality rate than influenza | 0.02 | 0.28 | −0.14 | 0.04 | 0.11 |
| I have heard rumours of clusters in stores I know, etc. | −0.16 | 0.26 | 0.10 | 0.08 | 0.02 |
| Elderly people are more likely to be infected with novel coronaviruses | 0.15 | 0.20 | 0.15 | 0.11 | 0.05 |
| ‘Low perception of infection threat’, | |||||
| The current probability of death from infection with the new coronavirus in Japan is very low, about 1/10 million. | 0.14 | −0.08 | 0.64 | 0.04 | −0.08 |
| The probability of ordinary Japanese being infected is less than 0.1 | 0.13 | −0.13 | 0.64 | 0.03 | −0.05 |
| When we see celebrities who have been infected with the new coronavirus appearing on TV again, it is not so serious even if we are infected | −0.11 | 0.11 | 0.55 | −0.01 | −0.03 |
| Once infected, new coronaviruses do not cause re-infection | −0.30 | 0.14 | 0.40 | 0.05 | 0.02 |
| Young people are at low risk of serious illness | 0.35 | −0.18 | 0.37 | 0.02 | 0.12 |
| Human-to-human transmission is possible within a week of onset of illness | 0.04 | 0.12 | 0.30 | 0.03 | 0.11 |
| ‘Ease of infection at dinners and parties’, | |||||
| Infections occur during nightlife (bars, clubs, host clubs, etc.) | 0.26 | −0.06 | 0.02 | 0.65 | −0.01 |
| People who work in the nightlife business are more susceptible to the new coronavirus | 0.02 | 0.08 | 0.10 | 0.56 | 0.07 |
| Infections can occur at karaoke parlors and bars where karaoke is available | 0.36 | −0.02 | −0.03 | 0.53 | −0.01 |
| Become infected in restaurants | 0.25 | 0.13 | −0.03 | 0.49 | −0.06 |
| Become infected at social gatherings where alcohol is consumed | 0.42 | −0.03 | −0.03 | 0.47 | −0.04 |
| People in the show business are more likely to be infected with the new coronavirus | −0.23 | 0.22 | 0.13 | 0.34 | 0.16 |
| ‘Ease of infection among young people’, | |||||
| Young people in their 20s and 30s are spreading the novel coronavirus | 0.01 | −0.04 | −0.08 | 0.11 | 0.76 |
| The number of infected people in their 20s is the highest | 0.06 | −0.01 | −0.02 | −0.02 | 0.64 |
| University students are most likely to cause clusters | −0.02 | 0.17 | 0.00 | 0.32 | 0.32 |
| Inner-factor correlation F1 | — | 0.35 | −0.15 | 0.60 | 0.39 |
| F2 | — | 0.05 | 0.55 | 0.40 | |
| F3 | — | −0.02 | 0.29 | ||
| F4 | — | 0.47 | |||
| F5 | — |
Notes:
Regarding health literacy and beliefs related to infection control (21 items), four factors were extracted (Table 2). There was an adequate value of the KMO measure of sampling adequacy (0.83). The first factor was characterized by items such as ‘If the polymerase chain reaction (PCR) test is negative, there is no need to worry about new coronavirus disease at all’, ‘If you take an antibody test, you do not need to take a PCR test’, and ‘Infection can be completely prevented with measures such as masks and face shields’, which we named ‘Excessive efficacy of infection control measures’. Since the second factor showed high factor loadings of items such as ‘Routine ventilation is necessary’ and ‘vaccine can prevent severe illness after infection’, we named it ‘Efficacy of vaccines and infection control’. Similarly, since the third and fourth factors were characterised as ‘PCR testing is intentionally suppressed’ and ‘Avigan is ineffective’, respectively, we named them ‘Dissatisfaction of PCR testing system and vaccines’ and ‘Inefficacy of therapeutic drugs’, respectively.
| Factor 1 | Factor 2 | Factor 3 | Factor 4 | |
|---|---|---|---|---|
| ‘Excessive efficacy of infection control measures’, | ||||
| If the PCR test is negative, there is no need to worry about the new coronavirus disease at all | 0.68 | −0.22 | 0.00 | 0.09 |
| If you take an antibody test, you do not need to take a PCR test | 0.65 | −0.19 | −0.07 | 0.04 |
| Infection can be completely prevented with measures such as masks and face shields | 0.55 | −0.02 | 0.02 | 0.02 |
| Influenza vaccine can be expected to prevent severe cardiovascular disease in new coronaviruses | 0.47 | 0.10 | 0.07 | −0.02 |
| PCR testing is the only correct test for novel coronavirus disease | 0.46 | 0.06 | 0.12 | 0.10 |
| Supine therapy (treatment with time spent lying on the stomach) is effective in the treatment of severely ill patients | 0.44 | 0.03 | 0.07 | 0.00 |
| Gargles (e.g., Isodine) are effective | 0.42 | 0.08 | 0.04 | −0.07 |
| Sodium hypochlorite is effective against novel coronaviruses | 0.35 | 0.16 | 0.06 | −0.08 |
| Treatment methods have been established to some extent in the medical field, preventing severe cases | 0.32 | 0.26 | −0.06 | −0.07 |
| Summer involves less susceptibility to novel coronavirus, while winter involves more susceptibility to novel coronavirus | 0.25 | 0.18 | 0.10 | 0.00 |
| ‘Efficacy of vaccines and infection control’, | ||||
| Routine ventilation is necessary | −0.31 | 0.59 | 0.19 | 0.09 |
| Vaccine can prevent severe illness after infection | 0.11 | 0.51 | 0.01 | 0.07 |
| Vaccines for novel coronavirus are being researched and developed at a faster pace than usual | −0.08 | 0.50 | 0.04 | −0.08 |
| Japan’s PCR testing practices are correct | 0.19 | 0.50 | −0.43 | 0.14 |
| Vaccine is effective in preventing the onset of disease after infection | 0.28 | 0.33 | 0.04 | 0.09 |
| ‘Dissatisfaction of PCR testing system and vaccines’, | ||||
| PCR testing is intentionally suppressed | 0.14 | −0.12 | 0.64 | −0.01 |
| PCR testing is not readily available | −0.02 | 0.13 | 0.61 | 0.10 |
| PCR testing should be performed on all | 0.06 | 0.08 | 0.50 | 0.07 |
| Vaccine for the novel coronavirus has serious side effects | 0.10 | 0.09 | 0.33 | 0.14 |
| ‘Inefficacy of therapeutic drugs’, | ||||
| Avigan is ineffective | 0.11 | 0.07 | 0.18 | 0.50 |
| Drugs such as Avigan, Lemdecivir, and steroids have been used in severely ill patients and are effective | 0.20 | 0.37 | 0.04 | −0.46 |
| Inner-factor correlation F1; | — | 0.29 | 0.03 | −0.08 |
| F2 | — | 0.25 | −0.34 | |
| F3 | — | −0.12 | ||
| F4 | — | |||
Notes:
Regarding health literacy and beliefs related to hoaxes, conspiracy theories, and optimism for COVID-19 (26 items), four factors were extracted (Table 3). The KMO measure of sampling adequacy showed an adequate value (0.90). The first, second, third, and fourth factors showed high factor loadings for items such as ‘5G radio waves worsen coronavirus symptoms’, ‘The number of patients is increasing, nearly causing a medical collapse’, ‘It is a Chinese conspiracy’, and ‘Since July 2020, novel coronaviruses have attenuated’, respectively. We therefore named these factors ‘Hoax/conspiracy beliefs’, ‘Large social impact beliefs’, ‘China-originated beliefs’, and ‘Optimism’, respectively.
| Factor 1 | Factor 2 | Factor 3 | Factor 4 | |||
|---|---|---|---|---|---|---|
| ‘Hoax/conspiracy beliefs’, | ||||||
| 5G radio waves worsen coronavirus symptoms | 0.77 | −0.02 | 0.01 | −0.09 | ||
| The main ingredient of Seirogan is effective against the new type of coronavirus | 0.69 | 0.01 | −0.03 | 0.06 | ||
| Novel coronavirus is sensitive to heat, so we should drink hot water often | 0.66 | 0.03 | −0.03 | 0.05 | ||
| It is a conspiracy of the Bill & Melinda Gates Foundation | 0.65 | −0.11 | 0.14 | −0.05 | ||
| Novel coronavirus does not really exist | 0.60 | −0.15 | −0.01 | 0.06 | ||
| If we drink tea, we are less susceptible to the novel coronavirus | 0.59 | 0.09 | 0.03 | −0.05 | ||
| Toilet paper is often made in China and is in short supply due to the novel coronavirus | 0.52 | 0.07 | −0.03 | 0.16 | ||
| The outbreak occurred outside China | 0.45 | 0.12 | −0.26 | 0.01 | ||
| ‘Large social impact beliefs’, | ||||||
| The number of patients is increasing, nearly causing a medical collapse | 0.07 | 0.68 | −0.02 | −0.24 | ||
| There is a shortage of medical supplies to fight the novel coronavirus disease | 0.11 | 0.61 | −0.02 | −0.14 | ||
| More companies are going bankrupt as a result of the spread of novel coronavirus disease | −0.21 | 0.59 | 0.05 | 0.16 | ||
| An infection explosion will occur in the near future | −0.03 | 0.55 | −0.03 | 0.15 | ||
| Hospitals accepting patients infected with novel coronavirus are running at a loss | −0.07 | 0.51 | 0.00 | 0.26 | ||
| The number of suicides has increased as a result of the spread of infection by the new coronavirus disease | 0.16 | 0.48 | 0.11 | −0.28 | ||
| Divorce rates increase with the spread of the new coronavirus disease | 0.22 | 0.38 | 0.03 | 0.15 | ||
| ‘China-originated beliefs’, | ||||||
| It is a Chinese conspiracy | 0.06 | −0.07 | 0.86 | −0.06 | ||
| Some country created the virus experimentally | 0.03 | −0.01 | 0.80 | −0.02 | ||
| It spread from a laboratory in Wuhan | −0.15 | 0.12 | 0.69 | 0.08 | ||
| It spread from a bat sold at a seafood market in Wuhan (China) | −0.02 | 0.24 | 0.26 | 0.11 | ||
| ‘Optimism’, | ||||||
| Since July 2020, novel coronaviruses have attenuated | −0.07 | 0.14 | 0.00 | 0.67 | ||
| Mass immunity has already been acquired in Japan | 0.10 | −0.01 | −0.02 | 0.56 | ||
| Japanese are less likely to be severely ill | 0.31 | −0.03 | 0.02 | 0.42 | ||
| There are many open beds for novel coronavirus patients | 0.21 | −0.19 | −0.01 | 0.38 | ||
| The lockdown (city blockade) was a conspiracy by politicians and was not really necessary | 0.27 | −0.08 | 0.06 | 0.35 | ||
| The Ministry of Health, Labor, and Welfare and the Expert Committee are calling for more preventive measures against infection than necessary to control the population | 0.23 | 0.19 | 0.01 | 0.35 | ||
| BCG vaccine is effective in preventing novel coronavirus disease | 0.19 | 0.05 | 0.08 | 0.34 | ||
| Inner-factor correlation F1 | — | −0.14 | 0.27 | 0.57 | ||
| F2 | — | 0.23 | −0.27 | |||
| F3 | — | 0.19 | ||||
| F4 | — | |||||
Notes:
Characterisation of segments using cluster analysis on health literacy and beliefs
We conducted cluster analysis with the k-means method to characterise the participants as per health literacy and beliefs. We examined the number of clusters from three to eight with its interpretability. We discussed, with the co-authors, the number of clusters through cluster analysis. As a result, we discussed and adopted five clusters in this study because of their interpretability. The four clusters were not well separated due to mixed concepts, and six clusters were even more ambiguous in their interpretation, as they picked up a middle layer that was not seen in the five clusters (Tables S1-a, S1-b, S1-c). Table 4 shows the differences in health literacy and beliefs regarding COVID-19 among the five clusters. There were significant differences among the five clusters for all factors (p < 0.001). The effect sizes of η2 were judged as large (≥0.14) for all items. Furthermore, there were significant differences in the factors among clusters according to the results of the post-hoc test.
| Cluster 1 Calm/ hoax denial |
Cluster 2 Hoax affinity/ threat denial |
Cluster 3 Minority/ indifference |
Cluster 4 Over vigilance |
Cluster 5 Optimism |
F (df) | P | η2(95% CI) | ||
|---|---|---|---|---|---|---|---|---|---|
|
n = 1773 (29.6%) |
n = 1425 (23.8%) |
n = 228 (3.8%) |
n = 1293 (21.6%) |
n = 1281 (21.4%) |
|||||
| Susceptibility to infection | General ease of infection | 0.19 (0.61)b | 0.09 (0.55)c | −2.01 (1.34)e | 0.91 (0.48)a | −0.93 (0.58)d | 2069.07 (4, 1345.18) | <0.001 | 0.59 (0.57–0.60) |
| Extreme likelihood of infection | −0.36 (0.67)d | 0.36 (0.63)b | −1.75 (0.91)e | 0.69 (0.74)a | −0.29 (0.55)c | 816.74 (4, 1364.20) | <0.001 | 0.41 (0.39–0.43) | |
| Low perception of infection threat | −0.41 (0.74)c | 0.62 (0.62)a | −0.93 (1.05)d | −0.39 (0.75)c | 0.44 (0.54)b | 767.85 (4, 1359.50) | <0.001 | 0.34 (0.32–0.36) | |
| Ease of infection at dinners and parties | −0.22 (0.68)c | 0.33 (0.56)b | −1.99 (1.09)e | 0.91 (0.60)a | −0.62 (0.53)d | 1527.66 (4, 1355.09) | <0.001 | 0.54 (0.52–0.55) | |
| Ease of infection among young people | −0.35 (0.73)c | 0.48 (0.58)b | −1.80 (0.73)d | 0.58 (0.79)a | −0.31 (0.50)c | 987.19 (4, 1381.23) | <0.001 | 0.41 (0.39–0.43) | |
| Infection control | Excessive efficacy of infection control | −0.45 (0.68)d | 0.75 (0.73)a | −1.39 (0.85)e | −0.36 (0.72)c | 0.40 (0.55)b | 955.50 (4, 1372.19) | <0.001 | 0.41 (0.39–0.43) |
| Efficacy in vaccines and infection control | 0.03 (0.69)b | 0.39 (0.56)a | −2.11 (1.11)d | 0.37 (0.75)a | −0.47 (0.58)c | 671.73 (4, 1353.01) | <0.001 | 0.38 (0.36–0.40) | |
| Dissatisfaction with PCR testing system and vaccines | −0.10 (0.76)c | 0.17 (0.61)b | −1.43 (0.85)e | 0.63 (0.80)a | −0.43 (0.54)d | 593.02 (4, 1373.18) | <0.001 | 0.31 (0.29–0.32) | |
| Inefficacy of therapeutic drugs | 0.01 (0.76)c | −0.10 (0.57)d | 0.48 (0.75)a | −0.17 (0.87)d | 0.19 (0.57)b | 83.22 (4, 1382.23) | <0.001 | 0.05 (0.04–0.06) | |
| Hoax, conspiracy theories, and optimism | Hoax/conspiracy beliefs | −0.64 (0.46)d | 0.74 (0.92)a | −0.67 (0.76)d | −0.46 (0.59)c | 0.64 (0.73)b | 1260.31 (4, 1348.05) | <0.001 | 0.46 (0.44–0.47) |
| Large social impact beliefs | 0.07 (0.66)b | 0.06 (0.55)b | −1.85 (1.10)d | 0.88 (0.61)a | −0.72 (0.57)c | 1365.21 (4, 1353.46) | <0.001 | 0.51 (0.49–0.53) | |
| China-originated beliefs | −0.54 (0.79)c | 0.43 (0.74)a | −1.05 (0.95)d | 0.48 (0.94)a | −0.02 (0.66)b | 483.45 (4, 1373.26) | <0.001 | 0.26 (0.24–0.28) | |
| Optimism | −0.46 (0.65)b | 0.65 (0.65)a | −0.63 (1.03)b,c | −0.62 (0.64)c | 0.66 (0.58)a | 1306.59 (4, 1357.75) | <0.001 | 0.45 (0.44–0.47) | |
| Belief in just deserts | Belief in just deserts | −0.58 (0.81)c | 0.55 (0.85)a | −1.00 (0.74)d | 0.20 (1.15)b | 0.17 (0.70)b | 500.87 (4, 1410.67) | <0.001 | 0.22 (0.21–0.24) |
Notes:
Cluster 1 showed intermediate values for almost all factors among the five clusters, but it showed the lowest value only for ‘Hoax/conspiracy beliefs’. We therefore named this cluster ‘Calm/hoax denial’ (n = 1,773). Cluster 2 was the cluster with the highest group values for ‘Low perception of infection threat’, ‘Excessive efficacy of infection control’, and ‘Efficacy of vaccines and infection control’; lowest group values for ‘Inefficacy of therapeutic drugs’; and highest or second highest values for almost all factors of hoax, conspiracy beliefs, and optimism among the five clusters. Furthermore, this cluster showed the highest value for ‘Belief in just deserts’ among the five clusters. We therefore named this cluster ‘Hoax affinity/threat denial’ (n = 1,425). Cluster 3 showed a unique profile, that is, it showed the lowest group values for almost all factors. It only showed the highest value for ‘Inefficacy of therapeutic drugs’ among the five clusters. We therefore named this cluster ‘Minority/indifference’ (n = 228). Cluster 4 showed the highest group values for ‘General ease of infection’, ‘Extreme likelihood of infection’, ‘Ease of infection at dinners and parties’, ‘Ease of infection among young people’, ‘Efficacy of vaccines and infection control’, ‘Dissatisfaction of PCR testing system and vaccines’, ‘Large social impact beliefs’, and ‘China-originated beliefs’; and the lowest values for ‘Inefficacy of therapeutic drugs’ and ‘Optimism’. Moreover, cluster 4 showed a secondary higher value for ‘Belief in just deserts’. Therefore, we named this cluster ‘Over vigilance’ (n = 1,293). Cluster 5 had the highest value for ‘Optimism’. In addition, it showed secondary higher group values for ‘Low perception of infection threat’, ‘Inefficacy of therapeutic drugs’, ‘Hoax/conspiracy beliefs’, ‘China-originated beliefs’, and ‘Belief in just deserts’. The cluster showed secondary lower group values for ‘General ease of infection’, ‘Ease of infection at dinners and parties’, and ‘Dissatisfaction of PCR testing system and vaccines’. We therefore named this cluster ‘Optimism’ (n = 1,281).
Differences in the demographic characteristics of segments
We examined differences in the demographic characteristics among clusters, that is, the differences in sex, marital status, and the presence or absence of children by each cluster. Significant differences were found, but the effect size, Cramér’s V, was small: sex (χ2 (4) = 33.36, p <0.001, V = 0.075), marital status (χ2 (4) = 126.09, p = 0.001, V = 0.15), and the presence or absence of children (χ2 (8) = 121.25, p < 0.001, V = 0.10).
Factor analyses for infection prevention/risk-taking behaviours regarding COVID-19 and their differences among clusters (PHASE 2)
Selection bias between the participants in PHASE 1 and the ones selected in PHASE 2
The χ2 test conducted to examine selection bias, that is, the difference in percentage of participants between both phases (two conditions: participants in PHASE 2 and non-participants in PHASE 2 (those who only participated in PHASE 1 but were not selected in PHASE 2) ×5 clusters) found a significant difference, but the effect size (Cramér’s V) was small (χ2(4) = 57.95, p < 0.001, V = 0.098).
Factor analyses for infection prevention/risk-taking behaviours regarding COVID-19
Through factor analysis using questionnaire items based on present bias, one factor was extracted. This factor was named ‘lack of infection prevention behaviour’ (α = 0.82), with a consideration of the following items: ‘I am aware of the risk of infection, but I may go to a drinking party if invited’, ‘On occasions when eating or drinking with friends, if I take off my mask, I often don’t put it back on until I leave’, ‘I sometimes go to work or school even though I don’t feel as well as usual’, ‘Sometimes, I have to take off my mask at karaoke because it’s hard to sing’, ‘I sometimes eat without washing my hands and gargling’, ‘I am aware of the risk of infection, but the tourist attractions are less crowded than usual, so I tend to go on trips’, ‘Sometimes, I accidentally talk with my mask off while eating’, and ‘When I get together with friends, I tend to stay in restaurants for a long time’.
Regarding the questionnaire items based on normalcy bias, two factors were extracted (Table 5). The first factor was characterised by items such as ‘I think, “It’s probably safe to go out for a drink today”’. We therefore named it ‘Acceptance of infection risk behaviour’ (α = 0.83). The second factor consisted of four items, such as ‘Compared to others around me, I think I am more likely to be safe because I take better precautions against infection’ and ‘No one close to me has tested positive, so I am naturally not infected with coronavirus’. We named it ‘Sense of uninfected efficacy’ (α = 0.72). We found sufficient consistency for all three factors.
| Factor 1 | Factor 2 | |
|---|---|---|
| “Acceptance of infection risk behaviour,”α = 0.83 | ||
| I think ‘It’s probably safe to go out for a drink today’ | 0.85 | −0.15 |
| I feel that infection is becoming more familiar, but less fearful than around April | 0.39 | 0.23 |
| Even if the number of infected people increases a little, I think it is more important not to stop the economy | 0.39 | 0.22 |
| I think it’s okay to share a drink or chopsticks at least once | 0.81 | −0.12 |
| I am aware of the risk of infection, but I think I will be fine when I travel | 0.61 | 0.20 |
| When I see a lot of people out and about in the city on TV, etc., I don’t think I will be infected even if I go out a little | 0.65 | 0.18 |
| “Sense of uninfected efficacy,”α = 0.72 | ||
| No one close to me has tested positive, so I am naturally not infected with coronavirus | −0.04 | 0.66 |
| I consider myself to be at low risk of infection due to my age and occupation | 0.20 | 0.42 |
| Compared to others around me, I think I am more likely to be safe because I take better precautions against infection | −0.13 | 0.79 |
| As long as I keep disinfecting, I don’t think I will get infected | 0.21 | 0.49 |
| Inner-factor correlation | – | 0.68 |
Notes:
Differences in infection prevention/risk-taking behaviours regarding COVID-19 and the fear of infection among clusters
We found significant differences in all factors for infection prevention/risk-taking behaviours regarding COVID-19 and the fear of infection, among the five clusters: ‘Lack of infection prevention behaviour’, ‘Acceptance of infection risk behaviour’, ‘Sense of uninfected efficacy’, ‘Perceived infectability’, and ‘Germ aversion’ (p <0.001 for all factors; Table 6). In particular, ‘Acceptance of infection risk behaviour’, ‘Sense of uninfected efficacy’, and ‘Germ aversion’ showed medium levels of effect sizes (η2 = 0.11, 0.07, and 0.10, respectively; Table 6).
| Cluster 1 Calm/hoax denial | Cluster 2 Hoax affinity/ threat denial | Cluster 3 Minority/ indifference | Cluster 4 Over vigilance | Cluster 5 Optimism | F (df) | P | η2 (95% CI) | |
|---|---|---|---|---|---|---|---|---|
|
n = 1197 (31.5%) |
n = 907 (23.9%) |
n = 118 (3.1%) |
n = 856 (22.5%) |
n = 722 (19.0%) |
||||
| Lack of infection prevention behaviour | 2.18 (1.01)c | 2.32 (0.98)b | 2.34 (1.42)a,b,c | 1.94 (0.92)d | 2.56 (1.10)a | 40.09 (4, 732.81) | <0.001 | 0.04 (0.03–0.05) |
| Acceptance of infection risk behaviour | 2.28 (0.97)c | 2.53 (0.96)b | 2.63 (1.37)a,b,c | 1.87 (0.86)d | 2.90 (1.09)a | 122.81 (4, 732.44) | <0.001 | 0.11 (0.09–0.13) |
| Sense of uninfected efficacy | 2.64 (1.07)b | 3.06 (1.04)a | 2.51 (1.23)b,c | 2.41 (1.12)c | 3.14 (1.00)a | 69.99 (4, 742.45) | <0.001 | 0.07 (0.05–0.08) |
| Perceived infectability | 4.23 (0.93)b | 4.05 (0.84)c | 3.91 (1.03)c,d | 4.40 (1.05)a | 3.91 (0.81)d | 34.00 (4, 743.81) | <0.001 | 0.04 (0.02–0.05) |
| Germ aversion | 5.14 (0.97)b | 5.23 (0.87)b | 4.52 (1.19)c | 5.71 (0.91)a | 4.81 (0.89)c | 112.83 (4, 738.61) | <0.001 | 0.11 (0.09–0.13) |
Notes:
From the results of the post-hoc test, we found significant differences in the factors among clusters. Calm/hoax denial (n = 1, 197) showed a moderate profile among the five clusters; that is, the values took second or third place among group values for all factors regarding infection prevention/risk-taking behaviours and perceived vulnerability among the clusters. Hoax affinity/threat denial (n = 907) showed the highest group values for ‘Sense of uninfected efficacy’. Minority/indifference (n = 118) showed the highest group value for ‘Lack of infection prevention behaviour’ and ‘Acceptance of infection risk behaviour’; and lowest group values for ‘Sense of uninfected efficacy’, ‘Perceived infectability’, and ‘Germ aversion’ among clusters. Over vigilance (n = 856) showed the highest group values for ‘Perceived infectability’ and ‘Germ aversion’; and the lowest group values for ‘Lack of infection prevention behaviour’, ‘Acceptance of infection risk behaviour’, and ‘Sense of uninfected efficacy’ among clusters. Optimism (n = 722) showed the highest group values for ‘Lack of infection prevention behaviour’, ‘Acceptance of infection risk behaviour’, and ‘Sense of uninfected efficacy’; and the lowest group values for ‘Perceived infectability’ and ‘Germ aversion’ among clusters.
Discussion
To develop a foundation for effective risk communication, this study characterised segments based on COVID-19 health literacy and beliefs among the Japanese in the early stage of the COVID-19 pandemic, and investigated whether infection prevention/risk-taking behaviours and fear of infection differed among the segments. We characterised the Japanese participants into five clusters based on their health literacy and beliefs regarding COVID-19, and found that the five clusters were associated with differences in infection prevention/risk-taking behaviours and fear of infection. In particular, the effect sizes of ‘Acceptance of infection risk behaviour’ and ‘Germ aversion’ were larger than those of the other clusters; these behaviours and feelings were noteworthy for their distinctive differences among clusters.
Calm/hoax denial had intermediate group values for the items on health literacy and belief in PHASE 1, but it had the lowest group value only for ‘Hoax/conspiracy beliefs’. In PHASE 2, this cluster also had intermediate group values for infection prevention/risk-taking behaviours and perceived fear of infection. This cluster was the most numerous of all the clusters, which may indicate that it reflected the thinking of most Japanese participants at the time this study was conducted. Hoax affinity/threat denial had the highest group values for ‘Low perception for infection threat’, ‘Excessive efficacy of infection control’, and ‘Efficacy of vaccines and infection control’; and the lowest group value for ‘Inefficacy of therapeutic drugs’. It also had the highest or second highest group values for almost all factors of hoax, conspiracy beliefs, and optimism among the five clusters. Furthermore, this cluster had the highest value for ‘Belief in just deserts’ among the five clusters in PHASE 1. In PHASE 2, this cluster had the highest group value for ‘Sense of uninfected efficacy’. In other words, this cluster tended to believe in the hoax and conspiracy and had a high sense of efficacy for infection control in Japan during the study period; individuals might have believed that infection was not a threat if society was taking holistic infection control measures. It might also be suggested that if these individuals were infected as a result, they believed that they would not get what they deserve. Minority/indifference showed a unique profile; it showed the lowest group values for almost all factors in PHASE 1. It only showed the highest group value for ‘Inefficacy of therapeutic drugs’ among the five clusters. In PHASE 2, this cluster had the highest group value for ‘Lack of infection prevention behaviour’ and ‘Acceptance of infection risk behaviour’; and lowest group values for ‘Sense of uninfected efficacy’, ‘Perceived infectability’, and ‘Germ aversion’ among clusters. When we interpreted the results obtained for PHASE 1 for this cluster with the questionnaire, we suspected that this cluster was not sincere in responding to the questions. In other words, there is a possibility that the cluster analysis may have selected a group that gave low scores for all items. In PHASE 2, this cluster was considered to have a high sense of efficacy in not becoming infected and a low aversion to infection, thus having a belief in infection prevention and acceptance of risk behaviours. Over vigilance had the highest group values for ‘General ease of infection’, ‘Extreme likelihood of infection’, ‘Ease of infection at dinners and parties’, ‘Ease of infection among young people’, ‘Efficacy of vaccines and infection control’, ‘Dissatisfaction of PCR testing system and vaccines’, ‘Large social impact beliefs’, and ‘China-originated beliefs’; and the lowest values for ‘Inefficacy of therapeutic drugs’ and ‘Optimism’. The second highest group value was for ‘Belief in just deserts’ in PHASE 1. In PHASE 2, this cluster had the highest group values for ‘Perceived infectability’ and ‘Germ aversion’; and the lowest group values for ‘Lack of infection prevention behaviour’, ‘Acceptance of infection risk behaviour’, and ‘Sense of uninfected efficacy’ among clusters. In other words, members of this cluster were overly concerned about infection, with a high aversion to it, they highly estimated the ease and risk of infection, and believed that holistic infection control measures should have been taken. Optimism had the highest group value for ‘Optimism’ in PHASE 1. In addition, it showed secondary higher group values for ‘Low perception of infection threat’, ‘Inefficacy of therapeutic drugs’, ‘Hoax/conspiracy beliefs’, ‘China-originated beliefs’, and ‘Belief in Just Deserts’; including secondary lower values for ‘General ease of infection’, ‘Ease of infection at dinners and parties’, and ‘Dissatisfaction of PCR testing system and vaccines’. In PHASE 2, this cluster had the highest group values for ‘Lack of infection prevention behaviour’, ‘Acceptance of infection risk behaviour’, and ‘Sense of uninfected efficacy’; and the lowest group values for ‘Perceived infectability’ and ‘Germ aversion’ among clusters. In other words, this cluster had a negative attitude toward infection control, was optimistic about infection, downplayed infection prevention behaviours, accepted risk behaviours, and had a low aversion to infection.
Overall, infection prevention/risk-taking behaviours and fear were associated with clusters classified based on health literacy and beliefs regarding COVID-19. Interestingly, the attitude toward strong infection prevention behaviour was found in Over vigilance, which was characterised by high susceptibility to infection and infection control for COVID-19. Conversely, the Minority/indifference cluster, which was characterised by low susceptibility to infection and infection control, did not promote infection prevention behaviours. The cluster Optimism also had a somewhat moderate susceptibility to infection and infection control beliefs, and had the highest levels of optimism, lack of infection prevention/risk-taking behaviours, and acceptance of infection risk behaviours. The individuals placed some emphasis on susceptibility to infection and infection control, but were characterised by optimistic beliefs. Interestingly, although ‘hoax/conspiracy beliefs’ and ‘China-originated beliefs’ contrasted between ‘Calm/hoax denial’ and ‘Hoax affinity/threat denial’, the differences in infection prevention behaviour between these two clusters were smaller than those among the other clusters. This suggests that infection prevention/risk-taking behaviours or the fear of infection were more in harmony with beliefs about susceptibility to infection or infection control for COVID-19 than with affinity for hoaxes and conspiracy theories. The findings in this study were consistent with those of previous studies (Dryhurst et al., 2020; Harper et al., 2021; Nomura et al., 2021) reporting a strong association between infection risk perception and infection prevention behaviours in various countries.
Health insecurity, risk perception, and the resulting infection prevention behaviours in the midst of an infectious disease pandemic are greatly influenced by health literacy, which is created by information obtained from various sources, including the media and Internet (Taylor, 2019). Furthermore, it has also been reported that risk perception and infection prevention behaviours regarding COVID-19 are associated with the availability of information sources (Adachi et al., 2022; Lin et al., 2020; Uchibori et al., 2022). This suggests that several information sources are likely to shape beliefs regarding susceptibility to infection or infection control, rather than through hoaxes and conspiracy theories.
This study provides foundational findings on segment characteristics regarding health literacy and beliefs toward promoting effective infection prevention behaviours. We observed a consistent association between beliefs about susceptibility to infection (or infection control) and infection prevention behaviours as well as fear across clusters, suggesting that providing public information about susceptibility or control measures against infection would be a promising strategy in the case of unilateral information dissemination from government to citizens. This presentation of risk information is known to be fundamental in the development process of risk communication (Fischhoff, 1995). Furthermore, this study yielded implications for tailor-made risk communication based on segment characteristics. For example, although individuals have an affinity for hoaxes and conspiracy theories, they may have a low sense of uninfected efficacy, as seen in the clusters of Minority/indifference and Over vigilance. This illustrates the importance of choosing the content of dialogue about COVID-19 risks according to the characteristics of the segments rather than simply interacting in terms of hoaxes and conspiracy theories. Thus, effective tailor-made risk communication should be developed, with full consideration of associations of characteristics regarding health literacy and beliefs of the target segment with their infection prevention/risk-taking behaviours or fear.
This study had some limitations. First, there were issues regarding selection bias as a potential bias in this study. This study was conducted with online monitors, which is likely to cause a bias in terms of generalizability to the Japanese population as a whole. However, our study mitigated this bias by recruiting participants such that their age, gender, and region of residence matched the national distribution. In addition, by awarding points to respondents, we provided an incentive to encourage participation even among those who were not interested in the survey topic. Furthermore, we examined the difference in the proportion of clusters between participants and non-participants (those who participated in PHASE 1 but were not selected for PHASE 2) in PHASE 2, to evaluate whether there was a selection bias between the clusters in both phases. The results showed that there was a significant difference between the percentage of participants and non-participants in PHASE 2, but the effect size was small. These results implied that the selection bias was small in this study. Moreover, the national population census in 2020 reported that 28.4% of people aged between 20 and 79 years were unmarried in Japan (Statistics Bureau of Japan, 2020), while the percentage in this study was slightly higher at 35.4%. Second, there is an issue regarding sensitivity behaviour as a potential bias in this study. This study assessed health beliefs about COVID-19 and infection prevention behaviours during the COVID-19 outbreak. These were sensitivity behaviour questions, and the responses may have reflected social desirability in the social context of that period. For socially undesirable survey items, direct questioning cannot be expected to yield honest responses from all respondents. Such cases could be solved, for example, by using the Item Count technique (or List Experiment) (Cao et al., 2018; Greenberg et al., 1971; Lensvelt-Mulders et al., 2005), which comprises an indirect questioning method; however, as the purpose of this study was to examine individual differences, such a method was not adopted. Third, this study targeted the Japanese population in the early stage of the COVID-19 pandemic; therefore, caution should be exercised in applying the findings to other regions and populations at different times. It should be noted that the two-wave surveys in this study were conducted in 2020. Japan has been implementing voluntary infection prevention measures rather than mandatory behavioural regulations since the early stages of the pandemic. Therefore, this study is significant in providing foundational knowledge on risk communication to promote infection prevention behaviours in other countries that have shifted to voluntary infection prevention measures. While caution must be exercised in its application to outside populations, the study provided valuable insights into voluntary infection prevention behaviours for future research on the development of effective risk communication. Fourth, based on a two-wave survey, this study examined the associations between clusters based on health literacy and beliefs as well as infection prevention/risk-taking behaviours. Although the advantage of a longitudinal study design is that variables can be analysed before and after their time point, causality was not identified in this study.
Further research is needed to clarify the profile of segments with more demographic details. In this study, we examined gender, age, marital status, and the presence or absence of children as demographic variables. Although there were differences among clusters, the effect sizes were small and insufficient for interpretation. Another future developmental research involves the validation of effectiveness, such as randomised controlled trials, regarding segment-based risk communication for promoting infection prevention behaviours. Specifically, risk communication (i.e., posters, videos) should be conducted for each of the clusters and be validated. This validation would enable the social implementation of risk communication tailored to individual differences.
Conclusions
In this study, we characterised five segments based on health literacy and beliefs regarding COVID-19 in Japan in the early stage of the COVID-19 pandemic, and found that these segments were associated with infection prevention/risk-taking behaviours and fear. In particular, beliefs about susceptibility to infection were found to be coherently associated with infection prevention behaviours and fear of infection across segments, implying that providing public messages about susceptibility to infection would be a promising strategy in case of unilateral information dissemination. Furthermore, the study provided foundational findings that contribute to the development of tailor-made risk communication, taking into account differences in health literacy and beliefs regarding infectious diseases of target segment characteristics.