Study design and setting. A cross-sectional survey was conducted in HIV voluntary counselling and testing clinics, methadone maintenance treatment clinics and non-governmental organization offices in Guilin, China.
Participants and data collection. The target population for this study was people at risk for HIV, including female sex workers (FSW), persons who inject drugs (PWID) and men who have sex with men (MSM). An eligible participant was one who was aged 18 years or above, self-reported HIV negative or status unknown, able to give informed consent, living in Guilin for at least one year, and within the last 12 months, belonging to one of the following three groups representing the population at risk for HIV:
FSW, defined as having commercial sex;
PWID, defined as having injected drugs;
MSM, defined as having oral or anal sex with men.
From November 2020 to April 2021, a respondent-driven sampling (RDS) method 27 was employed to recruit those hard-to-reach populations. Firstly, we identified 3–4 well connected and trusted participants from each of the population groups as “seeds” to recruit eligible participants from their social networks. However, after about 1 month of recruitment commencement, the initial seeds could not refer any respondents, we therefore increased the number of seeds of each group. Altogether, 49 FSWs, 99 PWID and 55 MSM were identified as seeds. Each participant received three coupons to recruit not more than three new participants. Each new participant was given three coupons after completing the questionnaire and so on until the expected number of samples was reached. Each participant received 50 yuan ($8 USD) for their participation in the study and 20 yuan ($3 USD) for successfully recruiting each peer or partner into the study.
Measures. A structured questionnaire was used to gather information about demographics, acceptability of PrEP and the criteria linked to PrEP acceptability.
Acceptability of PrEP
Given that PrEP is currently unfamiliar to the target population in Guilin, a brief introduction of PrEP was provided. We adopted the term “likely to use” to indicate the likelihood of PrEP acceptability, based on prior studies which assessed acceptability13,28−30. Participants were asked: “Overall, how likely would you use PrEP?” The response, which was on a 7-point Likert scale, ranged from 1 (very strongly unlikely) to 7 (very strongly likely), reflecting a willingness to use or willingness to try a product 31. The outcome was classified into three degrees of acceptability: 1–3 (low level), 4–5 (moderate level), and 6–7 (high level)32.
Identification of influencing factors
An initial set of criteria for PrEP acceptability was first prepared based on research by Caine and colleagues19, in which they provided a framework with the ten most important factors related to product acceptance. To identify factors suitable for our model, we conducted an in-depth interview with two participants in each group (six in total) prior to data collection. Finally, six major criteria of PrEP acceptability were included in this study, including 1) Perceived utility (PU), 2) Perceived ease of use (PEOU), 3) Perceived risk (PR), 4) Perceived privacy (PP), 5) Perceived comparative advantage (PCA), and 6) Perceived image (PI). The questionnaire for this section was composed of 12 statements concerning factors influencing acceptability of PrEP, each with responses on a Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The score of each statement was then classified into three levels (low, moderate and high) by the same cut point used for acceptability of PrEP.
Statistical analysis. To adjust for potential bias in respondent-driven sampling, we used RDS Analyst33 to estimate the prevalence and 95% CI of each variable. We combined data from three population groups and normalized the weights of each population group by multiplying each participant’s sampling weight by a further weight, which was equal to the mean of individual weights in each population divided by the mean weights of all three population groups34. Normalized sampling weight was used in ordinal logistic regression to select variables potentially associated with level of acceptability. Under univariate analysis, variables with a p-value from the likelihood ratio test of less than 0.05 were selected into initial multivariate model. Variables with a p-value < 0.05 from the multivariate analysis were then included in the Bayesian network model. Each factor's result was reported as an odds ratio and accompanying 95% confidence interval (CI). R version 4.1.0 was used to conduct the analyses.
The Bayesian network analysis was constructed in GeNIe 3.0 35. Each factor obtained from the final multivariate logistic regression model represents a node. Greedy Thick Thinning Algorithm was used to learn the structure. Considering the logic and accuracy, we adjusted the structure according to two principal rules: 1) demographic characteristics as the attributes of people; the other nodes cannot be the parent nodes of them, and 2) acceptability of PrEP as the consequence, which cannot be the parent nodes of the other nodes. In the parameter learning step, an expectation-maximization algorithm36 with uniform parameter initialization was set, the marginal probabilities estimated by the corresponding state frequencies were used to denote the conditional probability of the selected root variables, and the child nodes were illustrated by posterior probability. We performed a sensitively analysis based on the method of Kjaerulff and van der Gaag37, which distinguishes the node that can affect the posterior probability of the target variable.
Ethics statement. This study was approved by the Ethics Committee of the Faculty of Medicine, Prince of Songkla University, Thailand (REC.63-321-18-1) and Guilin Center for Disease Control and Prevention (2020#033). To protect participants’ anonymity and eliminate the risk that signatures could reveal their personal information, verbal informed consent was obtained after an introduction of the study was provided. The Ethics Committee of the Faculty of Medicine, Prince of Songkla University, Thailand (REC.63-321-18-1) and the Guilin Center for Disease Control and Prevention (2020#033) both authorized verbal informed consent.