Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

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.

The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option.

Introduction: Travestis and transgender women resort to the use of hormones for body modification. Due to restrictions in the access to health services, self-medication is frequent. The aim of this study was to describe the self-reported prevalence of hormones used by travestis and transgender women in the Federal District. Method: This is a cross-sectional study with Respondent Driven Sampling (RDS) and Knowledge, Attitudes and Practices questionnaire (KAP) along with travestis and transgender women over 18 years in the FD. Prevalence was calculated using the RDS-II estimator. Logistic models were used to investigate the associated factors. A total of 201 volunteers participated. Results: There was a young sample (median age of 24 years). The overall prevalence of continuous use of hormones was 64.5%. The most used formulation was the combination of estrogen and progesterone (86.2%) by injectable (75.1%) and oral (66%) administration. Most participants (84%) got the hormones without a prescription. Guidance on the use of these hormones came from their peers in 41% of the cases. We observed that the continuous use of hormones is associated with race, income and age, as well as the search for guidance of healthcare professionals, which is also associated with schooling. Discussion: The reality of the process of hormone use by these people in the quest for femininity is reflected in high rates of self-medication. Conclusion: This study contributes to the visibility of the need to improve the access conditions of these people to health services.