Bastos Francisco I, Bastos Leonardo Soares, Coutinho Carolina, Toledo Lidiane, Mota Jurema Corrêa, Velasco-de-Castro Carlos Augusto, Sperandei Sandro, Brignol Sandra, Travassos Tamiris Severino, Dos Santos Camila Mattos, Malta Monica Siqueira
Institute of Communication and Information on Science and Technology in Health Scientific Computing Program Department of Clinical Pathology, National Institute of Women, Child and Adolescent Health Fernandes Figueira, Oswaldo Cruz Foundation, Rio de Janeiro Department of Epidemiology and Biostatistics, Institute of Collective Health, Fluminense Federal University, Niterói Social Science Department, National School of Public Health, Fiocruz, Rio de Janeiro, Brazil Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore MD.
Medicine (Baltimore). 2018 May;97(1S Suppl 1):S16-S24. doi: 10.1097/MD.0000000000009447.
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.
为了在基于应答驱动抽样（RDS）的研究背景下，更好地评估不同难以接触人群的特征及其各自的感染率（以及他们的社会人口特征、相关危害和需求），人们提出了不同的抽样策略、分析方法和估计器。尽管在方法上取得了一些进展，并且在全球范围内开展了数百项实证研究，但仍存在一些初步的研究结果和方法学挑战。当目标人群稀少且高度受污名化时，对网络局部结构的深入评估和可用估计器的性能就显得尤为重要。在这类人群中，瓶颈以及其他偏差来源（例如，由于同质性和/或个体群体过于稀疏或分散）可能很常见，从而影响估计结果。
在本研究中，数据来自一项在巴西12个城市对跨性别女性（TGW）开展的横断面、多城市RDS研究。总体而言，艾滋病毒和梅毒的感染率非常高，不同城市之间存在一些差异。尽管如此，考虑到女性TGW不仅很难接触到，而且面临着根深蒂固的偏见，并且大多数治疗和预防项目都无法惠及她们，这些研究结果令人十分担忧。
我们交叉比较了使用两种估计器（通常称为估计器II的经典估计器，最初由沃尔兹和赫卡索恩提出）和一种全新策略调整后得出的结果，该全新策略部分基于贝叶斯统计对RDS生成的数据进行调整，在本文中称为RDS - B估计器。将调整后的患病率与通过非加权分析得出的估计值进行交叉比较，我们使用的是所谓的简单估计器或粗略估计值。
