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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.
PMID
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Authors

Mayor MeshTerms
Keywords
Journal Title medicine
Publication Year Start




PMID- 29794601
OWN - NLM
STAT- MEDLINE
DCOM- 20180531
LR  - 20180531
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Linking)
VI  - 97
IP  - 1S Suppl 1
DP  - 2018 May
TI  - HIV, HCV, HBV, and syphilis among transgender women from Brazil: Assessing
      different methods to adjust infection rates of a hard-to-reach, sparse
      population.
PG  - S16-S24
LID - 10.1097/MD.0000000000009447 [doi]
AB  - 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 naive estimator or
      rough estimates.
FAU - Bastos, Francisco I
AU  - Bastos FI
AD  - Institute of Communication and Information on Science and Technology in Health.
FAU - Bastos, Leonardo Soares
AU  - Bastos LS
AD  - Scientific Computing Program.
FAU - Coutinho, Carolina
AU  - Coutinho C
AD  - Institute of Communication and Information on Science and Technology in Health.
FAU - Toledo, Lidiane
AU  - Toledo L
AD  - Institute of Communication and Information on Science and Technology in Health.
FAU - Mota, Jurema Correa
AU  - Mota JC
AD  - Institute of Communication and Information on Science and Technology in Health.
FAU - Velasco-de-Castro, Carlos Augusto
AU  - Velasco-de-Castro CA
AD  - Department of Clinical Pathology, National Institute of Women, Child and
      Adolescent Health Fernandes Figueira, Oswaldo Cruz Foundation, Rio de Janeiro.
FAU - Sperandei, Sandro
AU  - Sperandei S
AD  - Institute of Communication and Information on Science and Technology in Health.
AD  - Scientific Computing Program.
FAU - Brignol, Sandra
AU  - Brignol S
AD  - Department of Epidemiology and Biostatistics, Institute of Collective Health,
      Fluminense Federal University, Niteroi.
FAU - Travassos, Tamiris Severino
AU  - Travassos TS
AD  - Institute of Communication and Information on Science and Technology in Health.
FAU - Dos Santos, Camila Mattos
AU  - Dos Santos CM
AD  - Institute of Communication and Information on Science and Technology in Health.
FAU - Malta, Monica Siqueira
AU  - Malta MS
AD  - Social Science Department, National School of Public Health, Fiocruz, Rio de
      Janeiro, Brazil.
AD  - Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of
      Public Health, Baltimore MD.
CN  - "Divas Research Group"
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
SB  - AIM
SB  - IM
MH  - Adult
MH  - Bayes Theorem
MH  - Bias
MH  - Brazil/epidemiology
MH  - Cross-Sectional Studies
MH  - Female
MH  - HIV Infections/*epidemiology
MH  - Hepatitis B/*epidemiology
MH  - Hepatitis C/*epidemiology
MH  - Humans
MH  - Male
MH  - Mass Screening
MH  - Prevalence
MH  - Syphilis/*epidemiology
MH  - Transgender Persons
EDAT- 2018/05/26 06:00
MHDA- 2018/06/01 06:00
CRDT- 2018/05/26 06:00
PHST- 2018/05/26 06:00 [entrez]
PHST- 2018/05/26 06:00 [pubmed]
PHST- 2018/06/01 06:00 [medline]
AID - 10.1097/MD.0000000000009447 [doi]
AID - 00005792-201805251-00007 [pii]
PST - ppublish
SO  - Medicine (Baltimore). 2018 May;97(1S Suppl 1):S16-S24. doi:
      10.1097/MD.0000000000009447.