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

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

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

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

Whole Earth telescope observations of the ZZ Ceti star HL Tau 76

This paper analyses the Whole Earth Telescope observations of HL Tau 76, the first discovered pulsating DA white dwarf. The star was observed during two Whole Earth Telescope campaigns. It was a second priority target during the XCOV13 campaign in 1996 and the first priority one during the XCOV18 campaign in 1999. The 1999 campaign reached 66.5% duty cycle. With a total duration of 18 days, the frequency resolution achieved is 0.68 Hz. With such a frequency resolution, we were able to find as many as 78 significant frequencies in the power spectrum, of which 34 are independent frequencies after removal of all linear combinations. In taking into account other frequencies present during the 1996 WET campaign and those present in earlier data, which do not show up in the 1999 data set, we find a total of 43 independent frequencies. This makes HL Tau 76 the richest ZZ Ceti star in terms of number of observed pulsation modes. We use those pulsation frequencies to determine as much as possible of the internal structure of HL Tau 76. The pulsations in HL Tau 76 cover a wide range of periods between 380 s and 1390 s. We propose an identification for 39 of those 43 frequencies in terms of l = 1 and l = 2 non-radial g-modes split by rotation. We derive an average rotation period of 2.2 days. The period distribution of HL Tau 76 is best reproduced if the star has a moderately "thick" hydrogen mass fraction log qH = -7.0. The results presented in this paper constitute a starting point for a detailed comparison of the observed periods with the periods calculated for models as representative as possible of HL Tau 76. ESO 2006