HIV, HCV, HBV, and syphilis among transgender women from Brazil: Assessing different methods to adjust infection rates of a hard-to-reach, sparse population

Abstract

Different sampling strategies, analytic alternatives, and estimators have been proposed to better assess the characteristics of different hard-to-reach populations and their respective infection rates (as well as their sociodemographic characteristics, associated harms, and needs) in the context of studies based on respondent-driven sampling (RDS). Despite several methodological advances and hundreds of empirical studies implemented worldwide, some inchoate findings and methodological challenges remain. The in-depth assessment of the local structure of networks and the performance of the available estimators are particularly relevant when the target populations are sparse and highly stigmatized. In such populations, bottlenecks as well as other sources of biases (for instance, due to homophily and/or too sparse or fragmented groups of individuals) may be frequent, affecting the estimates.In the present study, data were derived from a cross-sectional, multicity RDS study, carried out in 12 Brazilian cities with transgender women (TGW). Overall, infection rates for HIV and syphilis were very high, with some variation between different cities. Notwithstanding, findings are of great concern, considering the fact that female TGW are not only very hard-to-reach but also face deeply-entrenched prejudice and have been out of the reach of most therapeutic and preventive programs and projects.We cross-compared findings adjusted using 2 estimators (the classic estimator usually known as estimator II, originally proposed by Volz and Heckathorn) and a brand new strategy to adjust data generated by RDS, partially based on Bayesian statistics, called for the sake of this paper, the RDS-B estimator. Adjusted prevalence was cross-compared with estimates generated by non-weighted analyses, using what has been called by us a naïve estimator or rough estimates.

Figure 1

Distribution of recruitment chains according to the number of recruitment waves.

Figure 2

Recruitment networks according to research site.

Figure 3

Network size per site, according to different questions. Top: using the question “How many transwomen do you know by name/nickname, who also know you by name/nickname and live/work/study at this city?”. Botton: using the question “Of these, how many have you seen or spoken over phone, Facebook, or WhatsApp in the last 30 days?”. This was the network size used in estimates.

Figure 4

Point estimates and 95% confidence intervals for the three estimators according to research site (city) and outcome. BEL = Belm, BH = Belo Horizonte, BSB = Brasília, CGR = Campo Grande, CWB = Curitiba, FOR = Fortaleza, MAO = Manaus, POA = Porto Alegre, REC = Recife, RIO = Rio de Janeiro, SAO = São Paulo, SSA = Salvador.

Figure 5

Bland-Altman plots comparing the assessment of differences between estimators. The differences of estimates for each pair of estimators is ploted against the mean of the same estimates. Solid lines are the mean differences, while traced lines represent the 95% confidence interval for the differences (limits of agreement). It is clear the concordance between RDS-II and RDS-B estimators, regardless of the outcome assessed.