1 Introduction
Global public health is at risk from hepatitis C virus (HCV) infection. Hepatitis brought on by HCV infection can be acute or chronic, mild or life-long, can cause liver cancer and cirrhosis, among other serious complications [1]. According to estimates from the World Health Organization (WHO), there are 58 million chronic HCV infections globally [2]. With an estimated 9.8 million chronic HCV infections, China is among the nations with the highest number of HCV infections [3]. In order to eradicate HCV as a danger to global public health by 2030, the WHO suggested in 2016 that HCV incidence be reduced by 90% and HCV-associated death by 65% [4]. HCV is a mostly occult infection. Although full-course standardized hepatitis C antiviral drugs can cure more than 95% of hepatitis C patients, the accessibility of diagnosis and treatment is very low [5,6]. In 2016, only 18% of HCV patients in China were diagnosed [7]. Early screening and diagnosis remain the primary barriers to eradicating HCV [6].
Men who have sex with men (MSM) are a high - incidence population of HCV [8–10], particularly those people living with HIV [11,12]. According to a worldwide systematic analysis, the combined prevalence of HCV in MSM was estimated to be 3.4% (95%CI: 2.8–4.0%), with 6.3% (95%CI: 5.3–7.5%) of HCV prevalence in HIV-positive populations [13]. HCV prevalence among MSM in China was estimated to be 0.67% (95%CI: 0.51–0.86%) [14]. However, the active test of MSM is poor, and the HCV testing rate is generally low. According to a 2017 nationwide survey, just 41% of Chinese MSM had ever undergone HCV testing [15].
HCV testing can diagnose and treat person with HCV earlier, so as to prevent or delay the development of hepatitis-related liver diseases and prevent the further transmit of HCV. Few studies have explored the variables that influence HCV testing in MSM, and past research has relied on univariate analysis without considering interrelationships. Therefore, we aimed to explore the willingness of MSM to accept HCV testing and construct a theoretical model based on the Health Belief Model (HBM) to explore the influencing factors of HCV testing in MSM population. To clarify the degree and mode of influence of each core element (such as perceived susceptibility, perceived severity, perceived benefits and perceived barriers) in the model on the willingness to test for HCV in MSM in China, and provide evidence for the promotion of HCV testing in MSM population.
2 Methods
2.1 Participants and recruitment
From December 1–13, 2023, MSM volunteers in China were sought via the non-probability sampling method, making use of online platforms such as WeChat official accounts frequently used by MSM, peer introductions, non-governmental organization (NGO) collaboration, and the “snowballing” of core members. The following were the requirements for MSM's inclusion: (1) have had sex with men within the last 12 months; (2) be able to fill out the questionnaire independently; (3) between the ages of 18 and 65. Exclusion criteria include: (1) taking fewer than three minutes to complete the questionnaire (after testing, we assessed that participants should have completed the questionnaire in at least 3 min.); (2) having logical problems in the questionnaires (the logical test questions were answered incorrectly, or the answers to some questions in the questionnaire were contradictory). The questionnaire was set up with screening questions to identify the key population. An anonymous cross-sectional online poll was used to perform this investigation. Participants that completed the questionnaire and passed the review earned a reward of 5 RMB (about $0.69), which was informed to them prior to obtaining informed consent.
2.2 Measurements
2.2.1 Personal characteristics questionnaire.
The collected information consisted of three parts: (1) sociodemographic characteristics (such as age, ethnicity, household registration, education level, employment status, marital status, etc.); (2) sexual behavioral characteristics (e.g., number of male sex partners, condom use, presence of female sex partners, commercial sex, stimulant use, etc.) in the past 6 months; (3) other questions (we collected self-reported data on whether participants were diagnosed with any sexually transmitted infection in the last six months, and ever tested for HCV in the past, etc.)
2.2.2 HBM theoretical model and scale.
HBM is a valuable theoretical model for explaining health-related behaviors using social psychology approaches, highlighting the role of perception in decision-making. The factors that determine behavioral intention are perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and self-efficacy [16,17]. In addition, studies have shown that perceived benefits or barriers can indirectly affect outcome variable through self-efficacy [18–21]. Based on the above theories, the initial SEM of HBM is shown in Fig 1.
[Figure omitted. See PDF.]
The 23-item scale has five categories (Cronbach's α = 0.816) and was based on the HBM and previous research [22–24]. MSM were asked about their views on HCV testing, which helped to assess factors influencing HCV testing among MSM. The Likert five-point method was used to assess the scale (Table 1).
[Figure omitted. See PDF.]
Perceived susceptibility is a person's evaluation of their likelihood of HCV. The perceived susceptibility scale was composed of three questions (Cronbach's α = 0.714), with a greater score suggesting a higher probability of HCV. Perceived severity is the perception of the severity and probable consequences of HCV infection. It included three questions (Cronbach's α = 0.812), and the higher the score, the more severe hepatitis C was considered. Perceived benefits relate to a person's view of the physical and psychological benefits of HCV testing. There were six items in this section (Cronbach's α = 0.896), and higher scores showed more perceived benefits from HCV testing. Perceived barriers refer to an assessment of the difficulty and cost of HCV testing. This section was measured using four questions (Cronbach's α = 0.785), with a higher score suggesting greater perceived barriers. Self-efficacy relates to confidence in one's capacity to finish HCV testing, this part included five items (Cronbach's α = 0.847), with higher scores indicating greater confidence in the capacity to successfully test for HCV. Behavioral intention to test for HCV was measured by willingness and likelihood in the next six months, with higher scores indicating higher intention to test for HCV.
2.3 Statistical analysis
In this study, descriptive analysis was performed using SPSS 26.0. Categorical data were described using frequencies and percentages. Mplus8.3 was used for structural equation modeling, and weighted least squares with mean and variance adjusted (WLSMV) was used to estimate the parameters. Composite reliability (CR) values above 0.70 indicate strong internal consistency. Convergent validity was measured using average variance extraction (AVE), with scores above 0.50 indicating good convergence. To assess discriminant validity, the square root of the AVE value and correlation coefficient were used. Good discriminant validity was shown when the square root of each common factor's AVE value exceeded the standardized correlation coefficient with other components. R2 was employed to represent the degree to which the independent variable was interpreted in terms of the dependent variable.
Model fit was evaluated using the chi-squared and degree of freedom ratio (χ2/df), comparative fit index (CFI), Tucker Lewis index (TLI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) [25]. The model fitted well when χ2/df < 5, CFI > 0.90, TLI > 0.90, SRMR < 0.08 and RMSEA < 0.08. A p-value of < 0.05 was judged statistically significant. The Bootstrap method was used to test the 95% confidence interval [26].
2.4 Ethical approval
This study was approved by the Ethics Committee of Chongqing Medical University (2019001, 28 May 2019). The participants were informed consent. Before starting the survey, the study objectives were informed to the participants. Respondents were adequately informed that their information would be confidential and no identifiable information would be disclosed. Moreover, it is clearly stated that the participants can withdraw from the survey at any point. We obtained implied consent. We have provided a detailed description of the above content on the first page of the questionnaire. If participants agree to take part in the study, they can proceed to the next page on their own to fill out the questionnaire.
3 Results
3.1 Sociodemographic characteristics
The study recruited 1215 participants in total, of whom 358 (29.47%) were eliminated for the reasons listed below: 8 were age < 18 or > 65, 124 were completed questionnaires in less than 3 minutes, 226 had problems with logical check, there were finally 857 qualified respondents. 67.9% of whom were 18–26 years old, 98.9% were Han nationalities, 74.2% were urban, 64.3% College and above, 77.6% were employed, 83.9% were unmarried, and 50.1% had a monthly disposable income of 5000–10000 RMB. In terms of sexual behavior characteristics in the past 6 months, 65.3% of MSM had only one sex partner, 65.9% of MSM used condoms each time they had anal intercourse with a male partner, 31.2% used it sometimes or occasionally, 20.5% said they had a female sex partner, 12.6% had had commercial sex. In addition, 11.7% had been diagnosed with an STD in the last six months. 55.7% had been ever tested for HCV, 90.9% of MSM said they were willing or very willing to be tested for HCV in the future, and 71.7% of MSM said they were high or very likely to be tested for HCV in the next six months. (Table 2)
[Figure omitted. See PDF.]
3.2 Measurement Model
Table 3 shows the factor loading, CR, and AVE for each construct, and discriminant validity. All factor loads were over 0.6, indicating acceptable levels and statistical significance (p < 0.001). The data have strong internal consistency and convergence validity (CR > 0.7, AVE > 0.5). Additionally, the square root of AVE was higher than other correlation coefficients, showing adequate discriminant validity. The SEM was utilized for confirmatory factor analysis. The model fit indices were as follows: χ2/df = 4.621, CFI = 0.954, TLI = 0.946, SRMR = 0.048, RMSEA = 0.065. The fit indices reached the specified levels, suggesting the model was acceptable.
[Figure omitted. See PDF.]
3.3 Structural Model
SEM showed (Fig 2) that self-efficacy (β = 0.482, p < 0.001), perceived susceptibility (β = 0.312, p < 0.001), and perceived barriers (β = -0.114, p = 0.024) had a direct effect on behavioral intention, and perceived benefits (β = 0.642, p < 0.001) and perceived barriers (β = -0.289, p < 0.001) indirectly affected self-efficacy. Contrary to the hypothetical model, perceived benefits (β = 0.241, p = 0.061) and perceived severity (β = -0.113, p = 0.293) had no direct effect on behavioral intention. The final model explained 58.0% of the variance of HCV testing behavioral intention in MSM, indicating that the model had sufficient predictive utility.
[Figure omitted. See PDF.]
The mediating effect was tested using the Bootstrap approach, with findings presented in Table 4. The study found that perceived benefits had an indirect influence on self-efficacy (β = 0.309, p < 0.001, 95%CI = 0.221 ~ 0.438). Perceived barriers had an indirect impact on self-efficacy (β = -0.139, p < 0.001, 95%CI = -0.214 ~ -0.090).
[Figure omitted. See PDF.]
4 Discussion
MSM are a population with greater likelihood of HCV, but the active test of MSM is poor. HCV infection is mostly occult, and early identification of infected individuals is important for the treatment and prevention of further transmit of the disease [27]. Investigating the influencing elements of HCV testing willingness is essential to raising the MSM HCV testing rate.
The results showed that 55.7% of respondents had been ever tested for HCV, higher than the 2017 survey result (41.0%) [15]. 90.9% of respondents were willing or very willing to take an HCV test in the future, and 71.7% said they were highly or very likely to take an HCV test in the next six months. Consistent with previous research results, MSM have a high willingness to test for HCV [28]. MSM's intention to test for HCV was positively influenced by perceived susceptibility, benefits, and self-efficacy, according to SEM analysis. Perceived barriers had a negative effect, while perceived severity had no significant effect, which was consistent with the research conclusions of female cervical cancer screening [29]. These factors explained 58.0% of the intention to test for HCV among MSM. Self-efficacy and perceived susceptibility are the most important factors in determining behavioral intention.
The study confirms that self-efficacy positively affects behavioral intention, which accords with prior research findings [30,31]. People with high self-efficacy mean they have higher confidence in completing the test and therefore have stronger behavioral intentions to test for HCV. In addition, consistent with previous studies, self-efficacy acted as a mediator between perceived benefits or obstacles and behavioral intentions [20,21]. Enhancing self-efficacy can significantly raise behavioral intention for HCV testing among MSM, and it is facilitated by raising awareness of the advantages of test and removing obstacles.
We found that behavioral intention was directly and positively influenced by perceived susceptibility. If MSM believe that they are more susceptible to HCV infection, they are more likely to test to determine whether they are infected, and their willingness to test for HCV will be higher. Previous studies have shown that the reasons for unwillingness to test for HCV often include “no risk factors” and “low risk of conscious infection” [32,33]. The lack of understanding of HCV will lead to a lower risk of self-perceived infection [32]. Therefore, when providing HCV-related knowledge to MSM, relevant organizations should emphasize the route of HCV transmission and high-risk behaviors, so that MSM can correctly understand their risk of HCV infection.
The study found no substantial influence of perceived severity on behavioral intention, which is similar to the results of other similar studies [29,34,35]. In addition, a review also found that the predictive effect of perceived severity on behavior in HBM is weak [36]. It may be that the public is more aware of the severity of HCV and believes that HCV infection can cause adverse health effects, resulting in a slight effect of perceived severity on desire to test. Therefore, the perceived severity cannot be used to identify the difference in the intention of MSM to detect HCV.
Our study showed that perceived benefits positively influenced self-efficacy and behavioral intention. Although there was no significant direct association between perceived advantages and behavioral intention, there was a strong link between perceived benefits and self-efficacy. These results have also been observed in earlier research [19]. The mediating effect test showed that self-efficacy played a mediating role. Therefore, the self-efficacy and behavioral intention of HCV testing among MSM can be raised by publicizing the benefits of HCV testing. In addition, the initial phases of HCV infection is not easy to detect, and often the disease has progressed to a severe stage by the time it is found [37]. Therefore, relevant organizations should emphasize the benefits of early detection of HCV infection in disease treatment and prognosis to improve the perceived benefits of MSM.
Perceived barriers had a negative effect on behavioral intention in this study. Perceived barriers included “lack of information about HCV testing”, “perceived discomfort of testing”, “lack of time to test”, and “high cost of testing”, which were associated with decreased willingness to test for HCV among MSM. Similar results were found in previous studies [32, 33]. Self-efficacy mediated the effect of perceived barriers on behavioral intentions. When MSM perceived more barriers to HCV testing, this also reduced their confidence in their ability to complete HCV testing. Therefore, it is very important to take measures to eliminate the barriers in the process of HCV testing to improve the willingness of MSM to test.
Based on the above findings, health education on hepatitis C and HCV testing is recommended to increase risk awareness among MSM. In addition, efforts should be made to emphasize the benefits of HCV testing while reducing perceived barriers to HCV testing through improvements in external conditions, thereby enhancing self-efficacy and promoting active HCV screening in this population.
The present study has some limitations. This study employs non-probability sampling to reach the marginalized MSM community, which may result in biased results. Whether the proposed model can be generalized to other samples needs further verification. Second, the cross-sectional approach reduces our capacity to infer causal conclusions. Longitudinal follow-up investigations are needed to confirm the causal mechanism.
5 Conclusions
In China, MSM have a high willingness to test for HCV. Applying the SEM, we found that perceived susceptibility, benefits, barriers and self-efficacy are the predictors of behavioral intention. In addition, self-efficacy is an intermediary factor between perceived benefits or barriers to behavioral intention. Therefore, we should carry out targeted health education activities, publicize the positive significance of HCV testing, improve the accessibility of testing services, reduce existing barriers, and enhance the self - efficacy, so as to promote the practice of HCV testing.
Supporting information
S1 File. Raw Research Data with Identifying Information Removed.
https://doi.org/10.1371/journal.pone.0321469.s001
(XLSX)
Acknowledgments
Thanks to Guiqian Shi and Bin Lin for comments and suggestions throughout the investigation. We also thank all participants and investigators for their help.
References
1. 1. Thrift AP, El-Serag HB, Kanwal F. Global epidemiology and burden of HCV infection and HCV-related disease. Nat Rev Gastroenterol Hepatol. 2017;14(2):122–32. pmid:27924080
* View Article
* PubMed/NCBI
* Google Scholar
2. 2. Organization WH. Global progress report on HIV, viral hepatitis and sexually transmitted infections, 2021. Accountability for the global health sector strategies 2016–2021: actions for impact 2021 [cited 2024 April 10]. Available from: https://www.who.int/publications/i/item/9789240027077.
* View Article
* Google Scholar
3. 3. Blach S, Zeuzem S, Manns M, Altraif I, Duberg A-S, Muljono DH, et al. Global prevalence and genotype distribution of hepatitis C virus infection in 2015: a modelling study. Lancet Gastroenterol Hepatol. 2017;2(3):161–76. pmid:28404132
* View Article
* PubMed/NCBI
* Google Scholar
4. 4. Organization WH. Global health sector strategy on viral hepatitis 2016-2021. Towards ending viral hepatitis 2016-06 [cited 2024 April 10]. Available from: https://www.who.int/publications/i/item/WHO-HIV-2016.06
* View Article
* Google Scholar
5. 5. Parums DV. Editorial: effective direct-acting antiviral treatments support global and national programs to eliminate hepatitis C. Med Sci Monit. 2023;29:e940519. pmid:37002682
* View Article
* PubMed/NCBI
* Google Scholar
6. 6. Kåberg M, Weiland O. Hepatitis C elimination - Macro-elimination. Liver Int. 2020;40 Suppl 1:61–6. pmid:32077600
* View Article
* PubMed/NCBI
* Google Scholar
7. 7. Zhao Z, Chu M, Guo Y, Yang S, Abudurusuli G, Frutos R, et al. Feasibility of hepatitis C elimination in China: from epidemiology, natural history, and intervention perspectives. Front Microbiol. 2022;13:884598. pmid:35722351
* View Article
* PubMed/NCBI
* Google Scholar
8. 8. Midgard H, Weir A, Palmateer N, Lo Re V 3rd, Pineda JA, Macías J, et al. HCV epidemiology in high-risk groups and the risk of reinfection. J Hepatol. 2016;65(1 Suppl):S33–45. pmid:27641987
* View Article
* PubMed/NCBI
* Google Scholar
9. 9. Davlidova S, Haley-Johnson Z, Nyhan K, Farooq A, Vermund SH, Ali S. Prevalence of HIV, HCV and HBV in central Asia and the caucasus: a systematic review. Int J Infect Dis. 2021;104:510–25. pmid:33385583
* View Article
* PubMed/NCBI
* Google Scholar
10. 10. Remis RS, Liu J, Loutfy MR, Tharao W, Rebbapragada A, Huibner S, et al. Prevalence of sexually transmitted viral and bacterial infections in HIV-positive and HIV-Negative men who have sex with men in toronto. PLoS One. 2016;11(7):e0158090. pmid:27391265
* View Article
* PubMed/NCBI
* Google Scholar
11. 11. Martinello M, Hajarizadeh B, Grebely J, Dore GJ, Matthews GV. Management of acute HCV infection in the era of direct-acting antiviral therapy. Nat Rev Gastroenterol Hepatol. 2018;15(7):412–24. pmid:29773899
* View Article
* PubMed/NCBI
* Google Scholar
12. 12. Platt L, Easterbrook P, Gower E, McDonald B, Sabin K, McGowan C, et al. Prevalence and burden of HCV co-infection in people living with HIV: a global systematic review and meta-analysis. Lancet Infect Dis. 2016;16(7):797–808. pmid:26922272
* View Article
* PubMed/NCBI
* Google Scholar
13. 13. Jin F, Dore GJ, Matthews G, Luhmann N, Macdonald V, Bajis S, et al. Prevalence and incidence of hepatitis C virus infection in men who have sex with men: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021;6(1):39–56. pmid:33217341
* View Article
* PubMed/NCBI
* Google Scholar
14. 14. Liu C-R, Li X, Chan P-L, Zhuang H, Jia J-D, Wang X, et al. Prevalence of hepatitis C virus infection among key populations in China: a systematic review. Int J Infect Dis. 2019;80:16–27. pmid:30529371
* View Article
* PubMed/NCBI
* Google Scholar
15. 15. Fitzpatrick T, Pan SW, Tang W, Guo W, Tucker JD. HBV and HCV test uptake and correlates among men who have sex with men in China: a nationwide cross-sectional online survey. Sex Transm Infect. 2018;94(7):502–7. pmid:29779005
* View Article
* PubMed/NCBI
* Google Scholar
16. 16. Rosenstock IM. The health belief model and preventive health behavior. Health Educ Behav. 1974;2(4):354–86.
* View Article
* Google Scholar
17. 17. Janz NK, Becker MH. The health belief model: a decade later. Health Educ Q. 1984;11(1):1–47. pmid:6392204
* View Article
* PubMed/NCBI
* Google Scholar
18. 18. Yang S, He C, Zhang X, Sun K, Wu S, Sun X, et al. Determinants of antihypertensive adherence among patients in Beijing: application of the health belief model. Patient Educ Couns. 2016;99(11):1894–900. pmid:27378081
* View Article
* PubMed/NCBI
* Google Scholar
19. 19. Tshuma N, Muloongo K, Nkwei ES, Alaba OA, Meera MS, Mokgobi MG, et al. The mediating role of self-efficacy in the relationship between premotivational cognitions and engagement in multiple health behaviors: a theory-based cross-sectional study among township residents in South Africa. J Multidiscip Healthc. 2017;10:29–39. pmid:28176923
* View Article
* PubMed/NCBI
* Google Scholar
20. 20. Bishop AC, Baker GR, Boyle TA, MacKinnon NJ. Using the Health Belief Model to explain patient involvement in patient safety. Health Expect. 2015;18(6):3019–33. pmid:25303173
* View Article
* PubMed/NCBI
* Google Scholar
21. 21. Yu B, Zhou J, Gong Y, Han J, Dong P, Yang S, et al. Self-efficacy mediates perceived benefits and barriers of adherence of heroin-dependent patients to methadone for addiction treatment: a health belief model study. J Addict Med. 2020;14(4):e110–7. pmid:32142052
* View Article
* PubMed/NCBI
* Google Scholar
22. 22. Berni I, Menouni A, Filali Zegzouti Y, Kestemont M-P, Godderis L, El Jaafari S. Factors associated with COVID-19 vaccine acceptance in morocco: applying the health belief model. Vaccines (Basel). 2022;10(5):784. pmid:35632540
* View Article
* PubMed/NCBI
* Google Scholar
23. 23. Lau J, Lim T-Z, Jianlin Wong G, Tan K-K. The health belief model and colorectal cancer screening in the general population: a systematic review. Prev Med Rep. 2020;20:101223. pmid:33088680
* View Article
* PubMed/NCBI
* Google Scholar
24. 24. Adam PCG, de Wit JBF, Bourne CP, Knox D, Purchas J. Promoting regular testing: an examination of HIV and STI testing routines and associated socio-demographic, behavioral and social-cognitive factors among men who have sex with men in New South Wales, Australia. AIDS Behav. 2014;18(5):921–32. pmid:24569887
* View Article
* PubMed/NCBI
* Google Scholar
25. 25. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55.
* View Article
* Google Scholar
26. 26. MacKinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods. 2002;7(1):83–104. pmid:11928892
* View Article
* PubMed/NCBI
* Google Scholar
27. 27. Rockstroh JK, Boesecke C. Hepatitis C virus treatment as prevention: challenges and opportunities in men who have sex with men. J Infect Dis. 2020;222(Suppl 9):S782–8. pmid:33245348
* View Article
* PubMed/NCBI
* Google Scholar
28. 28. Tang Y, Yu F, Pan L, Su XW, Mi GD, Yuan Q, et al. Study on cognition, willingness to test and influencing factors of hepatitis C based on social software for men who have sex with men. Zhonghua Gan Zang Bing Za Zhi. 2020;28(10):850–4. pmid:33105930
* View Article
* PubMed/NCBI
* Google Scholar
29. 29. Al-Ani A, Hammouri M, Sultan H, Al-Huneidy L, Mansour A, Al-Hussaini M. Factors affecting cervical screening using the health belief model during the last decade: a systematic review and meta-analysis. Psychooncology. 2024;33(1):e6275. pmid:38282232
* View Article
* PubMed/NCBI
* Google Scholar
30. 30. Qian P, Duan L, Lin R, Du X, Wang D, Zeng T, et al. Decision-making process of breastfeeding behavior in mothers with gestational diabetes mellitus based on health belief model. BMC Pregnancy Childbirth. 2023;23(1):242. pmid:37046224
* View Article
* PubMed/NCBI
* Google Scholar
31. 31. Sheeran P, Maki A, Montanaro E, Avishai-Yitshak A, Bryan A, Klein WMP, et al. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: a meta-analysis. Health Psychol. 2016;35(11):1178–88. pmid:27280365
* View Article
* PubMed/NCBI
* Google Scholar
32. 32. Shehata N, Austin T, Ha S, Timmerman K. Barriers to and facilitators of hepatitis C virus screening and testing: a scoping review. Can Commun Dis Rep. 2018;44(7–8):166–72. pmid:31011297
* View Article
* PubMed/NCBI
* Google Scholar
33. 33. Grannan S. Understanding patient perceptions and risk for hepatitis C screening. J Viral Hepat. 2017;24(8):631–5. pmid:28199776
* View Article
* PubMed/NCBI
* Google Scholar
34. 34. Liu H, Lai G, Shi G, Zhong X. The influencing factors of HIV-preventive behavior based on health belief model among HIV-Negative MSMs in Western China: a structural equation modeling analysis. Int J Environ Res Public Health. 2022;19(16):10185. pmid:36011822
* View Article
* PubMed/NCBI
* Google Scholar
35. 35. Hu D, Liu Z, Gong L, Kong Y, Liu H, Wei C, et al. Exploring the willingness of the COVID-19 vaccine booster shots in china using the health belief model: web-based online cross-sectional study. Vaccines (Basel). 2022;10(8):1336. pmid:36016224
* View Article
* PubMed/NCBI
* Google Scholar
36. 36. Carpenter CJ. A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun. 2010;25(8):661–9. pmid:21153982
* View Article
* PubMed/NCBI
* Google Scholar
37. 37. Organization WH. Hepatitis C 2023 [cited 2024 April 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/hepatitis-c
* View Article
* Google Scholar
Citation: Li J, He J, Yang G, Cao Z, Zhong X (2025) Influencing factors of HCV testing willingness among men who have sex with men in China: A structural equation modeling analysis. PLoS ONE 20(4): e0321469. https://doi.org/10.1371/journal.pone.0321469
About the Authors:
Jiayan Li
Roles: Data curation, Investigation, Methodology, Writing – original draft
Affiliation: School of Public Health, Chongqing Medical University, Chongqing, China
Jing He
Roles: Data curation, Investigation
Affiliation: School of Public Health, Chongqing Medical University, Chongqing, China
Guohui Yang
Roles: Data curation, Investigation
Affiliation: School of Public Health, Chongqing Medical University, Chongqing, China
Zhen Cao
Roles: Methodology
Affiliations: School of Public Health, Chongqing Medical University, Chongqing, China, Chongqing Yubei District Center for Disease Control and Prevention, Chongqing, China
Xiaoni Zhong
Roles: Supervision, Writing – review & editing
E-mail: [email protected]
Affiliation: School of Public Health, Chongqing Medical University, Chongqing, China
ORICD: https://orcid.org/0000-0002-8035-1841
1. Thrift AP, El-Serag HB, Kanwal F. Global epidemiology and burden of HCV infection and HCV-related disease. Nat Rev Gastroenterol Hepatol. 2017;14(2):122–32. pmid:27924080
2. Organization WH. Global progress report on HIV, viral hepatitis and sexually transmitted infections, 2021. Accountability for the global health sector strategies 2016–2021: actions for impact 2021 [cited 2024 April 10]. Available from: https://www.who.int/publications/i/item/9789240027077.
3. Blach S, Zeuzem S, Manns M, Altraif I, Duberg A-S, Muljono DH, et al. Global prevalence and genotype distribution of hepatitis C virus infection in 2015: a modelling study. Lancet Gastroenterol Hepatol. 2017;2(3):161–76. pmid:28404132
4. Organization WH. Global health sector strategy on viral hepatitis 2016-2021. Towards ending viral hepatitis 2016-06 [cited 2024 April 10]. Available from: https://www.who.int/publications/i/item/WHO-HIV-2016.06
5. Parums DV. Editorial: effective direct-acting antiviral treatments support global and national programs to eliminate hepatitis C. Med Sci Monit. 2023;29:e940519. pmid:37002682
6. Kåberg M, Weiland O. Hepatitis C elimination - Macro-elimination. Liver Int. 2020;40 Suppl 1:61–6. pmid:32077600
7. Zhao Z, Chu M, Guo Y, Yang S, Abudurusuli G, Frutos R, et al. Feasibility of hepatitis C elimination in China: from epidemiology, natural history, and intervention perspectives. Front Microbiol. 2022;13:884598. pmid:35722351
8. Midgard H, Weir A, Palmateer N, Lo Re V 3rd, Pineda JA, Macías J, et al. HCV epidemiology in high-risk groups and the risk of reinfection. J Hepatol. 2016;65(1 Suppl):S33–45. pmid:27641987
9. Davlidova S, Haley-Johnson Z, Nyhan K, Farooq A, Vermund SH, Ali S. Prevalence of HIV, HCV and HBV in central Asia and the caucasus: a systematic review. Int J Infect Dis. 2021;104:510–25. pmid:33385583
10. Remis RS, Liu J, Loutfy MR, Tharao W, Rebbapragada A, Huibner S, et al. Prevalence of sexually transmitted viral and bacterial infections in HIV-positive and HIV-Negative men who have sex with men in toronto. PLoS One. 2016;11(7):e0158090. pmid:27391265
11. Martinello M, Hajarizadeh B, Grebely J, Dore GJ, Matthews GV. Management of acute HCV infection in the era of direct-acting antiviral therapy. Nat Rev Gastroenterol Hepatol. 2018;15(7):412–24. pmid:29773899
12. Platt L, Easterbrook P, Gower E, McDonald B, Sabin K, McGowan C, et al. Prevalence and burden of HCV co-infection in people living with HIV: a global systematic review and meta-analysis. Lancet Infect Dis. 2016;16(7):797–808. pmid:26922272
13. Jin F, Dore GJ, Matthews G, Luhmann N, Macdonald V, Bajis S, et al. Prevalence and incidence of hepatitis C virus infection in men who have sex with men: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021;6(1):39–56. pmid:33217341
14. Liu C-R, Li X, Chan P-L, Zhuang H, Jia J-D, Wang X, et al. Prevalence of hepatitis C virus infection among key populations in China: a systematic review. Int J Infect Dis. 2019;80:16–27. pmid:30529371
15. Fitzpatrick T, Pan SW, Tang W, Guo W, Tucker JD. HBV and HCV test uptake and correlates among men who have sex with men in China: a nationwide cross-sectional online survey. Sex Transm Infect. 2018;94(7):502–7. pmid:29779005
16. Rosenstock IM. The health belief model and preventive health behavior. Health Educ Behav. 1974;2(4):354–86.
17. Janz NK, Becker MH. The health belief model: a decade later. Health Educ Q. 1984;11(1):1–47. pmid:6392204
18. Yang S, He C, Zhang X, Sun K, Wu S, Sun X, et al. Determinants of antihypertensive adherence among patients in Beijing: application of the health belief model. Patient Educ Couns. 2016;99(11):1894–900. pmid:27378081
19. Tshuma N, Muloongo K, Nkwei ES, Alaba OA, Meera MS, Mokgobi MG, et al. The mediating role of self-efficacy in the relationship between premotivational cognitions and engagement in multiple health behaviors: a theory-based cross-sectional study among township residents in South Africa. J Multidiscip Healthc. 2017;10:29–39. pmid:28176923
20. Bishop AC, Baker GR, Boyle TA, MacKinnon NJ. Using the Health Belief Model to explain patient involvement in patient safety. Health Expect. 2015;18(6):3019–33. pmid:25303173
21. Yu B, Zhou J, Gong Y, Han J, Dong P, Yang S, et al. Self-efficacy mediates perceived benefits and barriers of adherence of heroin-dependent patients to methadone for addiction treatment: a health belief model study. J Addict Med. 2020;14(4):e110–7. pmid:32142052
22. Berni I, Menouni A, Filali Zegzouti Y, Kestemont M-P, Godderis L, El Jaafari S. Factors associated with COVID-19 vaccine acceptance in morocco: applying the health belief model. Vaccines (Basel). 2022;10(5):784. pmid:35632540
23. Lau J, Lim T-Z, Jianlin Wong G, Tan K-K. The health belief model and colorectal cancer screening in the general population: a systematic review. Prev Med Rep. 2020;20:101223. pmid:33088680
24. Adam PCG, de Wit JBF, Bourne CP, Knox D, Purchas J. Promoting regular testing: an examination of HIV and STI testing routines and associated socio-demographic, behavioral and social-cognitive factors among men who have sex with men in New South Wales, Australia. AIDS Behav. 2014;18(5):921–32. pmid:24569887
25. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55.
26. MacKinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods. 2002;7(1):83–104. pmid:11928892
27. Rockstroh JK, Boesecke C. Hepatitis C virus treatment as prevention: challenges and opportunities in men who have sex with men. J Infect Dis. 2020;222(Suppl 9):S782–8. pmid:33245348
28. Tang Y, Yu F, Pan L, Su XW, Mi GD, Yuan Q, et al. Study on cognition, willingness to test and influencing factors of hepatitis C based on social software for men who have sex with men. Zhonghua Gan Zang Bing Za Zhi. 2020;28(10):850–4. pmid:33105930
29. Al-Ani A, Hammouri M, Sultan H, Al-Huneidy L, Mansour A, Al-Hussaini M. Factors affecting cervical screening using the health belief model during the last decade: a systematic review and meta-analysis. Psychooncology. 2024;33(1):e6275. pmid:38282232
30. Qian P, Duan L, Lin R, Du X, Wang D, Zeng T, et al. Decision-making process of breastfeeding behavior in mothers with gestational diabetes mellitus based on health belief model. BMC Pregnancy Childbirth. 2023;23(1):242. pmid:37046224
31. Sheeran P, Maki A, Montanaro E, Avishai-Yitshak A, Bryan A, Klein WMP, et al. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: a meta-analysis. Health Psychol. 2016;35(11):1178–88. pmid:27280365
32. Shehata N, Austin T, Ha S, Timmerman K. Barriers to and facilitators of hepatitis C virus screening and testing: a scoping review. Can Commun Dis Rep. 2018;44(7–8):166–72. pmid:31011297
33. Grannan S. Understanding patient perceptions and risk for hepatitis C screening. J Viral Hepat. 2017;24(8):631–5. pmid:28199776
34. Liu H, Lai G, Shi G, Zhong X. The influencing factors of HIV-preventive behavior based on health belief model among HIV-Negative MSMs in Western China: a structural equation modeling analysis. Int J Environ Res Public Health. 2022;19(16):10185. pmid:36011822
35. Hu D, Liu Z, Gong L, Kong Y, Liu H, Wei C, et al. Exploring the willingness of the COVID-19 vaccine booster shots in china using the health belief model: web-based online cross-sectional study. Vaccines (Basel). 2022;10(8):1336. pmid:36016224
36. Carpenter CJ. A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun. 2010;25(8):661–9. pmid:21153982
37. Organization WH. Hepatitis C 2023 [cited 2024 April 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/hepatitis-c
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Abstract
Background
Men who have sex with men (MSM) are a population with greater likelihood of the hepatitis C virus (HCV). Early detection of HCV status is beneficial to therapy and prevention of the further transmit of HCV. The testing rate of HCV in MSM is low, so it is important to investigate the factors that influence testing willingness in order to increase the testing rate.
Objectives
Based on the health belief model (HBM), this study investigated the influencing factors of HCV testing willingness among MSM, and provided a basis for promoting HCV testing among MSM.
Methods
Non-probability sampling was employed to collect samples, and electronic questionnaires were used to perform cross-sectional surveys on the samples, including socio-demographic characteristics, sexual behavior characteristics, HCV testing willingness and HBM scale. The data was evaluated with a structural equation model (SEM).
Results
Result of the 857 MSM, 55.7% had ever undergone HCV testing, 90.9% were willing to undergo HCV testing in the future, and 71.7% were anticipated to undergo HCV testing within the next six months. The SEM's findings demonstrated that behavioral intention was positively impacted by self-efficacy (β = 0.482, p < 0.001) and perceived susceptibility (β = 0.312, p < 0.001), while behavioral intention was negatively impacted by perceived barriers (β = -0.254, p < 0.001). Furthermore, behavioral intention was indirectly impacted by perceived benefits (β = 0.309, p < 0.001) and perceived barriers (β = -0.139, p < 0.001), with self-efficacy acting as a mediating factor.
Conclusion
Self-efficacy, perceived susceptibility, benefits, and barriers predict behavioral intention. These findings can inform the development of methods to increase MSM willingness to identify HCV.
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer