Abstract
Respondent Driven Sampling study (RDS) is a population sampling method developed to study hard-to-reach populations. A sample is obtained by chain-referral recruitment in a network of contacts within the population of interest. Such self-selected samples are not representative of the target population and require weighing observations to reduce estimation bias. Recently, the Network Model-Assisted (NMA) method was described to compute the required weights. The NMA method relies on modeling the underlying contact network in the population where the RDS was conducted, in agreement with directly observable characteristics of the sample such as the number of contacts, but also with more difficult-to-measure characteristics such as homophily or differential characteristics according to the response variable. Here we investigated the use of the NMA method to estimate HIV prevalence from RDS data when information on homophily is limited. We show that an iterative procedure based on the NMA approach allows unbiased estimations even in the case of strong population homophily and differential activity and limits bias in case of preferential recruitment. We applied the methods to determine HIV prevalence in men having sex with men in Brazilian cities and confirmed a high prevalence of HIV in these populations from 3.8% to 22.1%.
Highlights
Respondent-driven sampling (RDS) is a method to sample hard-to-reach populations such as injecting drug users, men who have sex with men (MSM), and sex workers[1]
The Network Model-Assisted (NMA)-Iter method allowed estimating prevalence even when serostatus information was limited to Respondent Driven Sampling study (RDS) participants, with estimates close to the target value (20%; Fig. 1)
Additional preferential recruitment led to an increase in bias for both the NMA and the NMA-Iter methods, even though the true prevalence value remained in the confidence intervals for the simulated RDS sample sizes (Fig. 1)
Summary
Respondent-driven sampling (RDS) is a method to sample hard-to-reach populations such as injecting drug users, men who have sex with men (MSM), and sex workers[1]. It uses chain-referral sampling, building on the underlying contact network for recruitment of participants. RDS starts by selecting seed individuals from the population of interest. They receive a fixed number of coupons to distribute to individuals in their contact network who meet certain eligibility criteria. Individuals receiving a coupon recruit new participants among their contacts, leading to successive recruitment waves until the target number of individuals for the survey is reached[2]. A drawback of the method is that the final sample is not representative of the target population, introducing bias in naïve estimates of, say, prevalence
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