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Article

Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis

by
Yuxia Zhao
1,
Yicen Jiang
1,
Wei Zhang
1 and
Yanchun Zhu
2,*
1
School of Information, Central University of Finance and Economics, Beijing 100081, China
2
Business School, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Systems 2023, 11(4), 181; https://doi.org/10.3390/systems11040181
Submission received: 1 February 2023 / Revised: 10 March 2023 / Accepted: 29 March 2023 / Published: 31 March 2023
(This article belongs to the Section Systems Engineering)
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Abstract

:
Complex mechanisms exist between public risk perception, emotions, and coping behaviors during health emergencies. To unravel the relationship between these three phenomena, a meta-analytic approach was employed in this study. Using Comprehensive Meta-Analysis 3.0, 81 papers were analyzed after selection. The results of the meta-analysis showed that (1) risk perception (perceived severity, perceived susceptibility) and negative emotions (especially fear) are both correlated with coping behaviors; (2) risk perception is strongly correlated with fear and moderately correlated with anxiety; and (3) anxiety predicts the adoption of coping behaviors. The existing research provided an empirical basis for implementing effective coping behavior interventions and implied that management decisionmakers need to consider reasonable interventions through multiple channels to maintain the public’s risk perception and emotions within appropriate levels. Finally, future research directions are suggested.

1. Introduction

In December 2019, multiple cases of an unknown form of pneumonia were reported, which were later confirmed to be caused by novel coronavirus (COVID-19) infections. COVID-19 has spread to many countries around the world and has been recognized by the World Health Organization as a global pandemic. In addition to the novel coronavirus outbreak, many other public health events have occurred or are occurring in various countries, such as SARS, Ebola, H1N1, and H7N9 outbreaks. Globally, for the period from January 2020 to December 2021, the World Health Organization estimates that there were 14.83 million excess deaths associated with the COVID-19 pandemic [1]. During the 2009 influenza A (H1N1) pandemic, there were an estimated 201,200 deaths worldwide due to respiratory diseases [2]. Illness and death caused by infectious diseases have exerted a huge burden and negative impact on the world.
Epidemics and pandemics pose a threat to public health, and the uncertainty and hazards associated with such events often cause negative emotions such as public panic, anxiety, and nervousness. Excessive public panic and anxiety can lead to irrational snap behavior [3]. Individuals can even develop mental health problems such as stress disorders, depression, and suicidal behavior [4,5]. Risk perception is considered to be the key factor affecting mood. The higher the risk perception of an individual is, the stronger the impact it has on one’s mental health, leading to the development of negative emotions such as fear and anxiety [6].
In the face of a public health event, before vaccines and preventive medicines are developed and produced, the public adopts a range of nonpharmaceutical interventions, such as washing hands, using masks, and avoiding public transportation. These protective behaviors are simple, inexpensive, and effective in minimizing the spread and impact of diseases. High levels of public support, participation, and cooperation relating to health-protective behaviors are key to overcoming an epidemic or pandemic [7]. Research on health behaviors suggests that risk perception is central to determining individual preventive behaviors. Risk communication forms the basis of risk perception, which facilitates the formation of accurate knowledge and enables the practice of preventive behaviors [8,9]. In addition, studies have shown that emotions are an important factor in decision-making processes among individuals [10,11]. In the context of a public health event, the public may develop negative emotions such as fear and anxiety, and these negative emotions increase the implementation of preventive behaviors [12,13].
As noted above, individuals’ risk perception, emotional states, and coping behaviors are related to their own physical and mental health and then to the normal functioning of society. Coping behavior is directly related to controlling the spread of the outbreak. Individual factors contribute to their correct coping decisions, notably risk perception and emotion [14,15]. Understanding the public’s cognitive and perceptual patterns is important for guiding prevention programs and mental health communications. Media exposure, government intervention, etc., affect the psychology, perceptions, and behavior of individuals [16,17]. It is necessary to examine the current state of public perception and identify the gap between it and the actual situation regarding the development of pandemics. For this purpose, many scholars have applied Protection Motivation Theory (PMT); Knowledge, Attitude, and Practice (KAP); Health Belief Model (HBM); and other health behavior change theories to conduct several studies on public risk perception, emotions, and coping behaviors, which provide a basis for national decisionmakers to adopt precise prevention and control strategies, including health education, for the public [18,19,20,21,22,23]. The research is based on public perceptions of risk, emotions, and coping behaviors. Cross-sectional surveys have been used to understand the performance of the public in terms of risk perception, emotions, and coping behaviors and the relationship between them.
Each survey is independent and decentralized, ranging from targeting a small group of people engaged in a particular career in a city to a nationwide sample of people. Although such studies can provide good guidance in the short term, there are limitations due to their sample size and regional limitations. Inconsistent findings exist in numerous studies. For example, Wang et al. concluded that risk perception inhibits coping behavior [24], whereas Kim et al. found no significant association between the perceived risk of infection and coping behavior in the context of the H1N1 outbreak in 2009 [23]. However, in previous studies, the path of influence between two variables has often been considered or studied for only one variable. Few researchers have explored this issue from the perspective of the internal mechanisms of the three phenomena. However, the existence of linkage effects between these three phenomena may affect the effectiveness of interventions. Therefore, it is necessary to systematically study how they relate to each other. For example, Levkovich et al. examined only the effect of perceived vulnerability on behavior [25]. Shen et al. focused on the role of risk perception in preventive behavior [26]. Yildirim et al. discussed the relationship between risk perception and fear in preventive behavior but lacked consideration of the pathway of risk perception and emotion [12]. Therefore, a systematic study is needed. The strength of the relationship between risk perception, emotion, and behavior also varies considerably across studies. Few studies have been conducted to integrate the results in this area; address the controversies in this area; avoid bias in the results of previous individual studies influenced by sample size, age, and region; and draw more general and accurate conclusions from a macroscopic perspective. The current study aims to integrate and analyze existing research on public health events through meta-analysis and explore future research directions. By estimating the strength of the relationship between public risk perception, negative emotions, and coping behaviors, a critical path is identified. Thus, the current findings will provide a basis for decisionmakers to intervene in risk perception and regulate public emotions and preventive behaviors. By analyzing existing research, we will seek to identify potential research gaps in risk perception, emotions, and behaviors related to public health events and pave the way for future research.

2. Methods

The present study used a meta-analysis technique that describes the outcomes of various empirical studies by using the correlation coefficient as an effect size estimate [27]. Effect size estimates were combined to provide useful insights [19]. A meta-analysis typically involves the identification of studies, the determination of inclusion and exclusion criteria, the coding of studies, statistical analysis, and data analysis. These steps are described below in detail.

2.1. Identification of Studies

Step 1: CNKI (China National Knowledge Internet) is one of the most complete and powerful databases of dissertations and articles in China. It has obvious advantages in terms of the types of journals, the number of citations, and the update cycle. Web of Science is an internationally renowned database with relatively high-quality articles. The CNKI and Web of Science electronic databases were searched using keywords related to public health events, risk perception, emotion, and coping behavior. We identified the keywords to be searched by pre-reading the literature. The search terms used were as follows.
Public health events: “COVID-19”; “SARS-CoV-2”; “novel coronavirus”; “coronavirus disease 2019”; “Ebola”; “H1N1”; “polio”; “poliomyelitis”; “SARS”; “atypical pneumonia”; “Zika”; “H7N9”; “flu”; “influenza”; “grippe”.
Risk perception: “Risk Perception”.
Emotion: “emotion”; “sadness”; “anxious”; “anxiety”; “fear”.
Coping behavior: “practice”; “behavior”; “coping behavior”; “prevention”.
Boolean operators such as “AND” and “OR” were used to combine these search terms. The search was performed from database inception to May 2022. For Chinese studies, the restricted sources were the Peking University Core, Chinese Social Sciences Citation Index (CSSCI), and Chinese Science Citation Database (CSCD). After aggregating the studies and removing duplicates, a total of 8187 publications were found.
Step 2: The inclusion and exclusion criteria (see Section 2.2) were discussed and agreed upon by all authors. These criteria and the relevance of the topic were used as a guide for the initial screening. Two authors individually read the titles, keywords, and abstracts of all 8187 publications to weed out the irrelevant ones. The two authors resolved their differences through discussion. A total of 871 were found to be potentially eligible in this step.
Step 3: For the publications shortlisted in step 2, the two authors further consulted the full text of each study. The studies that could be included in the final meta-analysis were screened according to the criteria in step 2 above. Again, disagreements were resolved by discussions between the two authors. A total of 790 studies were excluded for various reasons, such as unavailability of the original text, irrelevant topics, inconsistent research directions, and nondisclosure of the required data. Ultimately, 81 documents were included in the meta-analysis.

2.2. Inclusion and Exclusion Criteria

The inclusion criteria were as follows: (1) cross-sectional study—there were no restrictions regarding the method of data collection (paper questionnaire, telephone survey, Google Forms, etc.); (2) the survey used Likert scales for quantitative measures as much as possible and reported Pearson correlation coefficients or Spearman correlation coefficients between at least two variables involved in the study model; (3) the sample size was determined; (4) the publication was a journal paper, conference paper, etc., that had been peer-reviewed; (5) the study population included individuals aged 7 years or older; and (6) the behaviors examined in the study involved nonpharmacological intervention behaviors. Coping behaviors included hygienic behaviors (such as washing one’s hands, coughing or sneezing into one’s hands or a tissue, cleaning surfaces); mask wearing; social distancing (such as avoiding crowds, postponing or canceling large public gatherings, working online); and seeking the advice of a professional or a relative.
The exclusion criteria were as follows (in Figure 1): (1) a systematic review or purely theoretical study; (2) the article only examined the basic performance of the public during the health event; (3) duplicate reports by the same author or based on the same survey; (4) the study examined people under 7 years of age; (5) Pearson correlation coefficients or Spearman correlation coefficients were not reported; (6) the study was too detailed, i.e., classified nonpharmacological interventions for protective behaviors and studied them individually; and (7) the behaviors in the study were presented as pharmacological interventions.

2.3. Literature Coding Procedure

Basic information about the included studies was recorded: title, authors, source, year of publication, sample size, correlation coefficient, events involved, and study population.
To perform Spearman’s correlation analysis, we transformed the statistical derivation of the two sides of the following equation, in accordance with Rupinski and Dunlap [28]:
r = 2 ∗ sin ( π 6 ∗   r s )

2.4. Statistical Analysis

The fixed effects model and random effects model are two popular statistical models that have been used for meta-analysis. The fixed effects model assumes that there should be a single effect in the whole population and that the difference between effect sizes is due to variance. The random effects model, on the other hand, assumes that the different effects occur in the aggregate of the extracted sample. This study applied the Q statistic to establish the presence of heterogeneity in the study and used the I2 statistic to estimate the magnitude of heterogeneity [29]. If heterogeneity was significant, the analysis was performed using a random effects model. If there was no significant heterogeneity, a fixed effects model was used.

2.5. Data Analysis Procedures

In this study, Pearson’s correlation coefficient (r) was used as the effect value indicator, where r ≤ 0.10 indicated a low correlation, 0.10 < r < 0.40 indicated a medium correlation, and r ≥ 0.40 indicated a high correlation [30]. The effect size provides an estimate of the degree of existence of the phenomenon of the relationship. The higher the value of the effect size, the greater the existence of the phenomenon. Meta-analysis main effects tests and moderating effects tests were performed using the Comprehensive Meta-Analysis Version 3.3 (CMA 3.3) software. In the process of effect value estimation, to ensure the independence of effect values, if more than 2 effect values appeared for a study factor pair, the effect size of the total sample was included if the overall performance of the sample was disclosed, i.e., the overall effect. If the overall performance of the sample was not disclosed, we decided whether all effect values were included separately depending on the situation.

3. Results

3.1. Basic Characteristics of the Included Literature

The literature was searched using the CNKI and Web of Science databases and screened using strict inclusion and exclusion criteria, and 81 papers were included. In total, 5 papers included studies conducted on multiple occasions at different times and in different groups, and, thus, a total of 86 studies were included. The timespan of the studies ranged from 2005 to 2022, and the public health events included ZIKA, SARS, H1N1, H7N9, MERS, influenza, and the current COVID-19 pandemic. The included studies were all journal papers. A total of 63,358 subjects were identified as the general public, university students, medical-related students, medical workers, service industry workers, firefighters, etc. The basic information of the included studies is shown in Table 1.
These studies were conducted in a piecemeal fashion rather than in a systematic way. Of the 86 studies included, 5 studies (5.8%) examined the relationship between emotion and behavior, 10 studies (11.6%) examined the relationship between risk perception and emotion, 59 studies (68.6%) examined the relationship between risk and coping behavior, and 8 studies (9.3%) examined the relationship between risk and behavior as well as emotion and behavior. Only four studies (4.7%) examined the relationship between all three phenomena simultaneously. There is a need to conduct systematic and comprehensive research on this subject.
We reviewed the full text of the 81 included studies. They were relatively uniform in the way they measured variables such as fear, anxiety, risk perception, and coping behavior. All had multiple items set, scored on a scale, and responses were summed to obtain a total score, with the level of the score representing the level of the variable. Fear was measured using the FCV-19S or other self-administered scales. Anxiety was measured using the SAS Anxiety Self-Rating scale, the GAD-7 scale, the POMS scale, the Brunel Mood scale, etc. Risk perception was measured by items such as the PRQ scale, with questions such as “I have a high risk of infection...”, etc. Perceived susceptibility was measured by items such as “I have a high risk of infection with novel coronavirus”. For perceived severity, items such as “I think this novel coronavirus is very serious” were used. Coping behavior was measured using preventive protective behaviors recommended by health agencies. The factors of the included studies are shown in Table 2.

3.2. Main Effects Test

The corresponding pooled and transformed effect sizes (Pearson’s correlation coefficient r and sample size n) under each of the relationships were imported into CMA 3.3 for heterogeneity test. The results show that the Q statistic was significant for each relationship, indicating the existence of different effects in the population from which the sample was extracted, which does not support the hypothesis of homogeneity. The I2 was greater than 50% for all relationships, indicating a high degree of heterogeneity; therefore, the random effects model was chosen to ensure the stability of effect values.
The results of this study are shown in Table 3. The corresponding forest diagrams are shown in Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8. The results show that the combined correlation coefficient between risk perception and coping behavior was 0.189 [95% CI (0.121, 0.256)]; the combined correlation coefficient between perceived susceptibility and coping behavior was 0.207 [95% CI (0.133, 0.279)]; the combined correlation coefficient between perceived severity and coping behavior was 0.296 [95% CI (0.237, 0.354)]; the combined correlation coefficient between risk perception and fear level was 0.481 [95% CI (0.327, 0.610)]; the combined correlation coefficient between risk perception and anxiety level was 0.338 [95% CI (0.208, 0.457)]; the combined correlation coefficient between fear and coping behavior was 0.239 [95% CI (0.089, 0.377)]; and the combined correlation coefficient between anxiety and coping behaviors was 0.122 [95% CI (−0.088, 0.321)].
The results indicate that risk perception, perceived susceptibility, perceived severity, and fear were significant predictors of coping behavior. The effect sizes of anxiety and coping behavior were not statistically significant. Perceived severity had the strongest relationship with coping behavior, followed by perceived susceptibility and risk perception. For emotion, risk perception was significantly and positively associated with fear and anxiety. Risk perception had the strongest relationship with fear, followed by anxiety.

3.3. Publication Bias Test

Review studies based on empirical results are often prone to publication bias because the effect values of meta-analyses are determined and influenced by the data included in the study. For publication, authors and journal editors may prefer studies that are statistically significant, resulting in the omission of studies that have smaller effect sizes, which is why publication bias exists. In this study, quantitative tests for publication bias were conducted using the fail-safe number, Begg’s test, and Egger’s test.
For a meta-analysis process under a certain relationship, if the meta-analysis process includes the study data of X, the number of fail-safes must be much greater than 5X to indicate that there is no publication bias [107]. Egger’s test is the most commonly used test for publication bias. When the intercept term of Egger’s regression is not significant, it indicates that there is no publication bias [108]. Begg’s test determines the existence of publication bias by testing the correlation between the effect and sample size [109].
The number of insecurities under the meta-analysis process for each relationship in this study is shown in the table below, all of which satisfy this condition, indicating that there is no publication bias. The results of Begg’s test were not significant, with p-values greater than 0.05, indicating that the results show no publication bias; the intercept terms of the Egger regressions under all relationships except for risk perception–anxiety and negative emotion–behavior were not significant, and the intercepts and 95% confidence intervals are shown in Table 4, further supporting the absence of publication bias in these studies. Due to the small number of included studies, the results of the meta-analysis studies of the risk perception–anxiety relationship under Egger regression were significant with a p-value of less than 0.05; publication bias was present, but the results did not change significantly in the follow-up sensitivity test, indicating that publication bias did not have a significant effect on the results.

3.4. Sensitivity Analyses

The samples used in the meta-analysis were individual studies, and outliers may occur. Sensitivity analysis was performed using the leave-one-out method [110] to observe changes in effect sizes and 95% confidence intervals. One study was removed at a time, and a meta-analysis of the remaining studies yielded multiple combined effect sizes with the intervals shown in Table 5. None of the total effect estimates after removing a study exceeded the upper and lower 95% confidence interval limits of all study estimates, indicating that the results were robust.

4. Discussion

To examine the relationship between risk perception, emotion, and behavior in the context of public health events, this meta-analysis quantitatively synthesized the results of 86 relevant studies. The results of the random effects model showed positive correlations between risk perception, perceived severity, perceived susceptibility, fear, and coping behavior. These results were highly statistically significant. Only the combined correlation coefficient between anxiety and coping behavior was statistically insignificant. The following findings can be confirmed. The higher the individual’s risk perception of a public health event, the more actively the individual takes precautions with non-pharmacological interventions. Risk perception, as the primary evaluation of a public health emergency, is a key factor in the emotional response and determines the individual’s emotional feelings. Fear can increase alertness and trigger coping behaviors. We performed a quantitative test for publication bias and found no significant publication bias. Sensitivity analysis was used to test the robustness of the results. This study also provides various theoretical and practical implications that can provide suggestions for future research and government interventions.

4.1. Theoretical and Practical Implications

This study offers multiple theoretical implications. First, it synthesizes the results of 86 studies to reveal the link between risk perception, emotions, and behaviors in the context of public health events. These included cross-sectional studies that looked at people’s performance across dimensions and the relationships that existed between them. The relationship between the two or the three phenomena is explored in depth. However, research into combining them to gain in-depth knowledge is limited. These studies were conducted in a piecemeal fashion rather than in a systematic way. This meta-analysis provides strong support for the relationship between risk perception, emotion, and behavior. This meta-analysis provides strong support for the relationship between risk perception, emotion, and behavior, and provides a theoretical tool for studying individuals’ coping behaviors in the face of a pandemic. Second, by combing and analyzing previous studies, we have identified some research gaps, which will help provide scholars with ideas for conducting subsequent studies. In the studies we have included, the regions covered are biased toward Asia, followed by European countries, with relatively few studies in Africa, the Americas, etc. This may be due to the different research hotspots preferred by scholars in different countries because of their different cultural backgrounds. Different cultural backgrounds do have an impact on the associations between risk perception, emotions, and coping behaviors [111,112]. The moderating role of cultural elements such as individualism and collectivism could be studied in the future. Third, religious beliefs deserve to be taken into account in future research. Different countries have different dominant religious beliefs. Religious beliefs have been associated with emotional well-being and healthy behaviors [113]. Religious beliefs can also be added as a moderating factor. Fourth, we are in the post-pandemic era as the pandemic progresses and policies change. Psychological mechanisms may change across different time periods. Anderson et al. found that fear of COVID-19 did not appear to predict COVID-19 prevention behavior 6 months after initial measurement [114]. Future studies should rigorously test these associations longitudinally and should consider alternative approaches to long-term public health prevention campaigns.
This study also provides key insights from a practical perspective that will help in the development of practical applications. Increased risk perception will motivate people to adopt coping behaviors. The government needs to adjust the public’s risk perception in comparison to the actual disease risk. However, it is worth noting that risk perception is related to populations, individuals, geography, and time [115]. In China, people tend to trust the government’s advice more and obey the government’s guidance. In contrast, in countries where government trust is low, the public tends to be less motivated to adopt coping behaviors because of other factors. In our study, perceived severity plays a greater role in promoting the adoption of health behaviors. However, this can lead to negative emotions such as fear and anxiety. Negative emotions such as fear will motivate people to take precautionary measures, but excessive negative emotions will lead to irrational behavior [3]. Thus, the intervention has to be adapted to the local context and also consider the gap between perceptions and the actual situation. Different governments should assess the gap between individual perception and actual disease severity, as well as the current emotional state. By considering different focuses for risk communication, governments can reasonably intervene in the public’s perception of disease severity to promote the adoption of self-protective behaviors.
For intervention, the direction of entry is the monitoring and adjustment of information sources. The public’s risk perception and emotions are greatly influenced by the sources of information to which individuals are exposed [116,117]. The media is the main channel of information dissemination and should report the development of events objectively and positively so that the public can form reasonable risk judgments and thus establish a correct risk perception. The use of social media influences users’ risk perception through negative sentiment mediation [118]. At high risk perception levels, the public will seek more emotional support from social support systems and vent their emotions in multiple ways [119]. Currently, technologies such as artificial intelligence and big data have become important forces in the field of public management. In response to the public’s emotional expression through social media such as Twitter and Facebook, natural language processing tools for artificial intelligence and text mining technology can be used to build a monitoring system to understand the public’s current situation and response to intervention strategies. Management can develop better public policy strategies based on the public’s identified level of risk perception and changes in sentiment. Inefficient prevention behaviors should be avoided to prevent too low negative emotions and risk perception as well as too high negative sentiment, which can influence larger public opinion. In addition, rumors on social media influence public sentiment through emotional infection, leading to irrational behavior. Managers can use big data technology and specific artificial intelligence algorithms to establish a rumor assessment and graded warning system [120]. By performing rapid response actions to address rumors, their spread can be curbed. Effective risk information communication is essential for establishing correct risk perception and reducing the generation and spread of panic and anxiety. Timely and accurate information disclosure is particularly important for effective risk communication [121]. Managers can use blockchain technology and big data technology for information disclosure platform development to accurately, openly, and transparently release epidemic/pandemic information and respond to public concerns.

4.2. Strengths

To the best of our knowledge, this is the first study to conduct a quantitative meta-analysis of the relationship between risk perception, negative emotions, and coping behaviors based on the context of a public health event. Wang et al. and Teasdale et al. conducted a qualitative systematic evaluation [15,122], but they lacked comprehensive data results to support their findings. Brewer et al. conducted a meta-analysis of the relationship between risk perception and health behavior in the context of vaccination, a pharmacological intervention [123]. This was not conducted for non-pharmacological preventive behaviors. Our study is novel and complements quantitative meta-analyses of the relationship between risk, emotion, and coping behavior in the context of public health events.
In addition, this study proposed future research directions from the perspectives of culture, religion, and time. Further research could focus on a meta-analysis of these possible behavioral determinants or moderators for a more comprehensive understanding of public health protective behaviors during a pandemic. We also suggest practical applications based on our findings, including risk communication and emotion monitoring through big data algorithms or artificial intelligence.

4.3. Limitations

In this study, when searching for studies in major popular electronic databases, the keywords used may not reach all of the relevant literature, and some relevant studies may be missed as a result. In addition, studies in other electronic databases may have been overlooked. Since journals tend to publish studies with significant findings, some studies with insignificant results may be omitted. Thus, we tried to discover whether there was any publication bias in our study. No significant publication bias was found to exist among the included studies.
Pre-published papers were not taken into account considering the quality of the study. Additionally, some relevant studies may have been missed due to limited key search terms. These factors resulted in the relatively low number of studies we included. As such relevant studies accumulate, future reviews should include more research to further validate the results. The advent of the post-pandemic era and changes in policy may lead to changes in the strength of these linkage effects. More comprehensive studies are needed.

5. Conclusions

This study used meta-analysis to evaluate the correlation between risk perception, negative emotions, and coping behaviors in the context of the current COVID-19 pandemic and other public health events. Individual risk perception, especially perceived severity, can prompt people to perform coping actions. High risk perception will lead to negative emotions such as fear and anxiety. Fear predicts the adoption of coping behaviors. This provides a theoretical reference for practical health behavior interventions. The magnitude and direction of effects between relationships may vary across cultures, religions, and time periods. For future research, the moderating role of these factors should be considered.

Author Contributions

Study conception and design: W.Z. and Y.Z. (Yanchun Zhu ); data acquisition: Y.Z. (Yuxia Zhao) and Y.J.; data analysis and/or interpretation: Y.Z. (Yuxia Zhao), manuscript drafting: Y.Z. (Yuxia Zhao), W.Z. and Y.Z. (Yanchun Zhu); critical manuscript revision for important intellectual content: W.Z., Y.Z. (Yuxia Zhao), Y.Z. (Yanchun Zhu) and Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (Grant number: 72061147005, 71874215, 72004244, 71571191), the National Social Science Foundation of China (Grant number: 21BZZ108), the Beijing Natural Science Foundation (Grant number: 9182016, 9194031), the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant number: 17YJAZH120, 19YJCZH253), the Fundamental Research Funds for the Central Universities (SKZZY2015021), and Political Education Special Fund in Central University of Finance and Economics (“Diffusion mechanism and synergistic analysis of epidemic prevention policies during COVID-19 epidemics”, SZJ2208).

Informed Consent Statement

Not applicable.

Data Availability Statement

The extracted data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flowchart of selected articles included in this systematic review and meta-analysis.
Figure 1. Flowchart of selected articles included in this systematic review and meta-analysis.
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Figure 2. The correlation between risk perception and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 2. The correlation between risk perception and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Figure 3. The correlation between perceived severity and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 3. The correlation between perceived severity and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Figure 4. The correlation between perceived susceptibility and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 4. The correlation between perceived susceptibility and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Figure 5. The correlation between risk perception and fear. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 5. The correlation between risk perception and fear. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Figure 6. The correlation between risk perception and anxiety. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 6. The correlation between risk perception and anxiety. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Figure 7. The correlation between fear and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 7. The correlation between fear and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Figure 8. The correlation between anxiety and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
Figure 8. The correlation between anxiety and coping behavior. The columns of the figure as labeled above in the figure represent the following: study, correlation, upper limit, lower limit, Z-value, p-value, correlation, and 95% CI. Favors A indicates the degree of negative correlation between the two factors. Favors B indicates the degree of positive correlation.
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Table
Table 1. Basic information of the included studies.
Table 1. Basic information of the included studies.
No.Study Code No.LocationSample SizeEventSubjects
1Yildirim 2022 [31]Turkey3190COVID-19The general public
2Song 2021 [32]Republic of Korea211COVID-19The general public
3Alijanzadeh 2021 [33]Iran3652COVID-19The general public
4Wong 2020 [34]China352COVID-19Hong Kong South Asians
5Mirakzadeh2021 [35]Iran80COVID-19Rural tourism operators
6Wang 2021 [24]China429COVID-19The general public
7Levkovich 2021 [25]Israel482COVID-19The general public
8Wang 2021 [36]China200COVID-19The general public
9Mihelic 2021 [37]Slovenia394COVID-19The general public
10Shabu 2021 [38]Iraq976COVID-19University teachers and students
11Rayani 2021 [39]Iran319COVID-19general student population
12Alagili 2021 [40]Saudi Arabia1027COVID-19The general public
13Yazdanpanah 2020 [41]Iran305COVID-19Rural youth
14Fathian-Dastgerdi 2021 [42]Iran797COVID-19Teenagers
15Iorfa 2020 [43]Nigeria890COVID-19The general public
16Iorfa2 2020 [43]Nigeria664COVID-19The general public
17Rad 2021 [44]Iran2032COVID-19The general public
18Shen 2021 [26]China3000COVID-19The general public
19Moghadam 2022 [45]Iran305COVID-19Rural adults
20Xie 2020 [46]China317COVID-19The general public
21Yildirim2 2021 [12]Turkey4536COVID-19The general public
22Pilch 2021 [47]Poland397COVID-19The general public
23Jadil 2021 [48]Morocco215COVID-19The general public
TJadil2 2021 [48]India229COVID-19The general public
25Rabin 2022 [49]America186COVID-19The general public
26Batra 2021 [50]India381COVID-19Medical students
27Shi 2021 [51]China2830COVID-19The general public
28Karimy 2021 [52]Iran1090COVID-19The general public
29Cui 2010 [53]Republic of Korea484H1N1Medical students
30Choi 2018 [54]Republic of Korea249ZIKAMedical students
31Hu 2020 [55]China1063COVID-19The general public
32Tang 2021 [56]China627Public health eventsThe general public
33Alhaimer 2022 [57]Kuwait746COVID-19The general public
34Mehanna 2021 [58]Sudan680COVID-19The general public
35Arceo 2021 [59]The Philippines304COVID-19The general public
36Liu 2021 [13]America590COVID-19The general public
37Bagherzadeh 2021 [60]Iran660COVID-19Parents of primary school students
38Zhang 2021 [61]Republic of Korea299COVID-19The general public
39Grano 2022 [62]Italy309COVID-19The general public
40Grano2 2022 [62]Italy237COVID-19The general public
41Kurnia 2021 [63]Indonesia112COVID-19Nursing students
42Kim 2021 [64]Republic of Korea186COVID-19The general public
43DeDonno 2022 [65]America719COVID-19The general public
44Suk 2021 [66]Republic of Korea300COVID-19Firemen and marine police
45Magano 2021 [67]Portugal1122COVID-19The general public
46Idrees 2022 [68]Pakistan440COVID-19The general public
47Elsayed 2022 [69]Egypt582COVID-19The general public
48Feng 2022 [70]China632COVID-19The general public
49Zhang2 2021 [71]China192COVID-19The general public
50Gungor 2021 [72]Turkey1473COVID-19The general public
51Das 2021 [73]India550COVID-19The general public
52Jouybari 2018 [74]Iran300influenzaHigh school students
53Sadeghi 2018 [75]Iran400H1N1Pregnant women
54Zhang3 2020 [76]China710H7N9The general public
55Gutierrez-Dona 2012 [77]Costa Rica428H1N1The general public
56Gutierrez-Dona2 2012 [77]Costa Rica97H1N1The general public
57Wong2 2005 [78]Hong Kong, China1537SARSThe general public
58Karademas 2013 [79]Greece273H1N1The general public
59Karademas2 2013 [79]Greece273H1N1The general public
60Li 2022 [80]China306COVID-19The general public
61Bults 2014 [81]Netherlands1249Q FeverThe general public
62Kim2 2016 [82]Republic of Korea249MERSNursing students
63Park 2022 [83]Republic of Korea193respiratory tract infectionNursing students
64Borges 2022 [84]Ireland364COVID-19College students
65Kwak 2021 [85]Republic of Korea159COVID-19Nursing students
66Haejin 2021 [86]Republic of Korea291COVID-19Nursing students
67Lee 2021 [87]Republic of Korea222COVID-19Nursing students
68Donizzetti 2022 [88]Italy1301COVID-19Old people
69Haeran 2020 [89]Republic of Korea400COVID-19Medically inclined college students
70Kyung 2021 [90]Republic of Korea184COVID-19Nursing students
71Jeon 2021 [91]Republic of Korea200COVID-19Nurses
72Zancu 2022 [92]Romania634COVID-19College students
73Fu 2022 [93]China522COVID-19Youths
74Jeong 2022 [94]Republic of Korea187COVID-19Nursing students
75Kim3 2021 [95]Republic of Korea500COVID-19College students
76Jeon2 2022 [96]Republic of Korea237COVID-19Service workers
77Minjung 2020 [97]Republic of Korea412COVID-19Adults
78Park2 2021 [98]Republic of Korea241COVID-19Nursing students
79Lee2 2022 [99]Republic of Korea371COVID-19Aircraft crews
80Lee 2021 [100]Republic of Korea261COVID-19College students
81Li 2021 [101]China802COVID-19The general public
82Zhang 2015 [102]China2709H7N9The general public
83Ayandele 2021 [103]Nigeria172COVID-19The general public
84Janis 2020 [104]Norway4338COVID-19The general public
85Li 2020 [105]China454COVID-19The general public
86Zeidi 2021 [106]Iran340COVID-19Dentists
Table
Table 2. The factors included in the studies.
Table 2. The factors included in the studies.
VariableExample of Measurement Items
Fear“It makes me uncomfortable to think about coronavirus-19”, “I am afraid that someone in my family may get sick from the coronavirus”, “I am frightened because of COVID-19”, “I feel fearful about COVID-19”, etc.
Anxiety“I feel calm and can sit still easily”, “I feel that everything is all right and nothing bad will happen”, etc.
Risk PerceptionPerceived Severity“I think this new coronavirus is very serious”, “I think the new coronavirus poses a serious threat to public health”,” I think this new coronavirus is very powerful”, etc.
Perceived Susceptibility“I am at risk for novel coronavirus”, “My family/friends are likely to have novel coronavirus”, “People around me are likely to have novel coronavirus”, etc.
Coping Behavior“I avoided going to places outside the home where there were other people”, “Regularly and thoroughly clean your hands with an alcohol-based hand rub or wash them with soap and water”, “How often do you perform the following preventive measures?”, etc.
Table
Table 3. Main effects test results.
Table 3. Main effects test results.
RelationshipsNumber of StudiesSample
Size		Overall Effectp-ValueHeterogeneity TestI2 (%)95% CI
QBdfPLower LimitUpper Limit
Risk Perception–Behavior2820,2730.1890.000636.986270.000 95.80.1210.256
Perceived Susceptibility–Behavior4125,9510.2070.0001475.235400.00097.30.1330.279
Perceived Severity–Behavior3419,3950.2960.000643.657330.00094.90.2370.354
Risk Perception–Fear811,3840.4810.000643.96170.00098.90.3270.610
Risk Perception–
Anxiety		1088970.3380.000388.23390.00097.70.2080.457
Fear–
Behavior		1119,7110.2390.0021083.559100.00099.10.0890.377
Anxiety–
Behavior		960860.1220.254456.46180.00098.3−0.0880.321
Table
Table 4. Publication bias test.
Table 4. Publication bias test.
RelationshipskFail-
Safe
Number		Begg’s TestEgger’s Test
TauZPInterceptSE95% CIP
Risk Perception–Behavior2836260.0000.0001.0001.9871.935[−1.991, 5.965]0.314
Perceived Susceptibility–Behavior4188050.1411.3030.1930.2202.110[−4.048, 4.487]0.918
Perceived Severity–Behavior3420490.0940.7860.4323.1981.810[−0.489, 6.886]0.087
Risk Perception–
Fear		844680.1070.3710.71115.3777.803[−3.717, 34.471]0.096
Risk Perception–
Anxiety		1020290.0440.1790.85811.1544.817[0.047, 22.262]0.049
Fear–
Behavior		1125920.0000.0001.000−2.0676.830[−17.517, 13.382]0.769
Anxiety–
Behavior		9289−0.1390.5210.602−1.3895.616[−14.667, 11.890]0.812
Table
Table 5. Sensitivity analysis.
Table 5. Sensitivity analysis.
RelationshipsMain Effect95% CILeave-One-Out
Lower LimitUpper LimitMinMax
Risk Perception–Behavior0.1920.1210.2560.1800.208
Perceived Susceptibility–Behavior0.2070.1330.2790.1910.218
Perceived Severity–Behavior0.2960.2370.3540.2840.305
Risk Perception–Fear0.4810.3270.6100.4520.528
Risk Perception–Anxiety0.3380.2080.4570.2990.372
Fear–Behavior0.2390.0890.3770.1860.278
Anxiety–Behavior0.122−0.0880.3210.0390.163
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Zhao, Y.; Jiang, Y.; Zhang, W.; Zhu, Y. Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis. Systems 2023, 11, 181. https://doi.org/10.3390/systems11040181

AMA Style

Zhao Y, Jiang Y, Zhang W, Zhu Y. Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis. Systems. 2023; 11(4):181. https://doi.org/10.3390/systems11040181

Chicago/Turabian Style

Zhao, Yuxia, Yicen Jiang, Wei Zhang, and Yanchun Zhu. 2023. "Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis" Systems 11, no. 4: 181. https://doi.org/10.3390/systems11040181

APA Style

Zhao, Y., Jiang, Y., Zhang, W., & Zhu, Y. (2023). Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis. Systems, 11(4), 181. https://doi.org/10.3390/systems11040181

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