A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi):Description of Methods and Characteristics

Background: Thailand's HIV epidemic is heavily concentrated among men who have sex with men (MSM), and surveillanceefforts are mostly based on case surveillance and local biobehavioral surveys.Objective: We piloted Kai Noi, a web-based respondent-driven sampling (RDS) survey among MSM.Methods: We developed an application coded in PHP that facilitated all procedures and events typically used in an RDS officefor use on the web, including e-coupon validation, eligibility screening, consent, interview, peer recruitment, e-coupon issuance,and compensation. All procedures were automated and e-coupon ID numbers were randomly generated. Participants'phonenumbers were the principal means to detect and prevent duplicate enrollment. Sampling took place across Thailand; residents ofBangkok were also invited to attend 1 of 10 clinics for an HIV-related blood draw with additional compensation.Results: Sampling took place from February to June 2022; seeds (21 at the start, 14 added later) were identified through bannerads, micromessaging, and in online chat rooms. Sampling reached all 6 regions and almost all provinces. Fraudulent (duplicate)enrollment using "borrowed" phone numbers was identified and led to the detection and invalidation of 318 survey records. Afurther 106 participants did not pass an attention filter question (asking recruits to select a specific categorical response) and wereexcluded from data analysis, leading to a final data set of 1643 valid participants. Only one record showed signs of straightlining(identical adjacent responses). None of the Bangkok respondents presented for a blood draw.Conclusions: We successfully developed an application to implement web-based RDS among MSM across Thailand. Measuresto minimize, detect, and eliminate fraudulent survey enrollment are imperative in web-based surveys offering compensation.Efforts to improve biomarker uptake are needed to fully tap the potential of web-based sampling and data collection