Social Network Characteristics and HIV Vulnerability Among Transgender Persons in San Salvador: Identifying Opportunities for HIV Prevention Strategies

The purpose of this study is to improve understanding of HIV vulnerability and opportunities for HIV prevention within the social networks of male-to-female transgender persons in San Salvador, El Salvador. We compare HIV prevalence and behavioral data from a sample of gay-identified men who have sex with men (MSM) (n = 279), heterosexual or bisexual identified MSM (n = 229) and transgender persons (n = 67) recruited using Respondent Driven Sampling. Transgender persons consistently reported higher rates of HIV risk behavior than the rest of the study population and were significantly more likely to be involved in sex work. While transgender persons reported the highest rates of exposure to HIV educational activities they had the lowest levels of HIV-related knowledge. Transgender respondents’ social networks were homophilous and efficient at recruiting other transgender persons. Findings suggest that transgender social networks could provide an effective and culturally relevant opportunity for HIV prevention efforts in this vulnerable population

Estimating Design Effect and Calculating Sample Size for Respondent-Driven Sampling Studies of Injection Drug Users in the United States

Respondent-driven sampling (RDS) has become increasingly popular for sampling hidden populations, including injecting drug users (IDU). However, RDS data are unique and require specialized analysis techniques, many of which remain underdeveloped. RDS sample size estimation requires knowing design effect (DE), which can only be calculated post hoc. Few studies have analyzed RDS DE using real world empirical data. We analyze estimated DE from 43 samples of IDU collected using a standardized protocol. We find the previous recommendation that sample size be at least doubled, consistent with DE = 2, underestimates true DE and recommend researchers use DE = 4 as an alternate estimate when calculating sample size. A formula for calculating sample size for RDS studies among IDU is presented. Researchers faced with limited resources may wish to accept slightly higher standard errors to keep sample size requirements low. Our results highlight dangers of ignoring sampling design in analysis

Extensions Of Respondent-Driven Sampling: Web-Based Rds, Empirical Validation, And The Dual Homophily Model

This dissertation makes contributions to Respondent-Driven Sampling (RDS) and the study of social networks. RDS is a new network-based method of collecting and analyzing data from hidden populations in a statistically viable way. The first chapter provides an introduction to RDS procedures and estimation. After describing the operating procedures, the chapter introduces the statistical theory behind RDS, including the models assumptions and how it accounts for sources of bias commonly associated with network samples. It then compares two distinct families of RDS estimator, RDS I and RDS II, by describing the evolution of all seven RDS estimators. Chapter Two introduces WebRDS, an online version of RDS that has been shown to produce samples in record speeds, and describes the two WebRDS samples on which the remaining analyses are based. Chapter Three provides an in depth empirical test of RDS estimators and confidence intervals. While RDS estimation has been validated analytically and computationally, it has not been empirically tested on a population with known parameters. Chapter Three utilizes RDS data on university undergraduates to compare the accuracy of RDS point and variance estimates across two estimation techniques (RDS I and RDS II), self-report measures of degree, and multiple cutpoints for excluding early wave data. The chapter RDS I and RDS II estimates to be accurate and convergent, but estimates of variance to be problematic in opposite ways. The RDS I bootstrap method tends to under estimate variance, while RDS II analytical variance estimation provides an over estimate. For both methods, the problem is exacerbated in small groups. Differences in degree measure and cutting early wave data resulted in only minor differences in the estimation. Chapter Four presents the Dual Homophily Model, which breaks a common measure of homophily into two components, one due to relational preferences and one due to differential degree. Applications of the model, including examples where standard homophily measures miss important differences between groups, are discussed

Early Linkage to HIV Care and Antiretroviral Treatment among Men Who Have Sex with Men--20 Cities, United States, 2008 and 2011.

Early linkage to care and antiretroviral (ARV) treatment are associated with reduced HIV transmission. Male-to-male sexual contact represents the largest HIV transmission category in the United States; men who have sex with men (MSM) are an important focus of care and treatment efforts. With the release of the National HIV/AIDS Strategy and expanded HIV treatment guidelines, increases in early linkage to care and ARV treatment are expected. We examined differences in prevalence of early linkage to care and ARV treatment among HIV-positive MSM between 2008 and 2011. Data are from the National HIV Behavioral Surveillance System, which monitors behaviors among populations at high risk of HIV infection in 20 U.S. cities with high AIDS burden. MSM were recruited through venue-based, time-space sampling. Prevalence ratios comparing 2011 to 2008 were estimated using linear mixed models. Early linkage was defined as an HIV clinic visit within 3 months of diagnosis. ARV treatment was defined as use at interview. Prevalence of early linkage to care was 79% (187/236) in 2008 and 83% (241/291) in 2011. In multivariable analysis, prevalence of early linkage did not differ significantly between years overall (P = 0.44). Prevalence of ARV treatment was 69% (790/1,142) in 2008 and 79% (1,049/1,336) in 2001. In multivariable analysis, ARV treatment increased overall (P = 0.0003) and among most sub-groups. Black MSM were less likely than white MSM to report ARV treatment (P = 0.01). While early linkage to care did not increase significantly between 2008 and 2011, ARV treatment increased among most sub-groups. Progress is being made in getting MSM on HIV treatment, but more efforts are needed to decrease disparities in ARV coverage

A Key Comprehensive System for Biobehavioral Surveillance of Populations Disproportionately Affected by HIV (National HIV Behavioral Surveillance): Cross-sectional Survey Study

BackgroundThe National HIV Behavioral Surveillance (NHBS) is a comprehensive system for biobehavioral surveillance conducted since 2003 in 3 populations disproportionately affected by HIV: gay, bisexual, and other men who have sex with men (MSM); people who inject drugs; and heterosexually active persons at increased risk for HIV infection (HET). This ongoing and systematic collection and analysis of data is needed to identify baseline prevalence of behavioral risk factors and prevention service use, as well as to measure progress toward meeting HIV prevention goals among key populations disproportionately affected by HIV. ObjectiveThis manuscript provides an overview of NHBS from 2003 to 2019. MethodsNHBS is conducted in rotating, annual cycles; these 3 annual cycles are considered a round. Venue-based, time-space sampling is used for the MSM population. Respondent-driven sampling is used for people who inject drugs and HET populations. A standardized, anonymous questionnaire collects information on HIV-related behavioral risk factors, HIV testing, and use of prevention services. In each cycle, approximately 500 eligible persons from each participating area are interviewed and offered anonymous HIV testing. ResultsFrom 2003 to 2019, 168,600 persons were interviewed and 143,570 agreed to HIV testing across 17 to 25 cities in the United States. In the fifth round (2017 to 2019), over 10,000 (10,760-12,284) persons were interviewed each of the 3 population cycles in 23 cities. Of those, most (92%-99%) agreed to HIV testing. Several cities also conducted sexually transmitted infection or hepatitis C testing. ConclusionsNHBS is critical for monitoring the impact of the Ending the HIV Epidemic in the United States initiative. Data collected from NHBS are key to describe trends in key populations and tailor new prevention activities to ensure high prevention impact. NHBS data provide valuable information for monitoring and evaluating national HIV prevention goals and guiding national and local HIV prevention efforts. Furthermore, NHBS data can be used by public health officials and researchers to identify HIV prevention needs, allocate prevention resources, and develop and improve prevention programs directed to the populations of interest and their communities

Evaluating Variance Estimators for Respondent-Driven Sampling.

Respondent-Driven Sampling (RDS) is a network-based method for sampling hard-to-reach populations that is widely used by public health agencies and researchers worldwide. Estimation of population characteristics from RDS data is challenging due to the unobserved population network, and multiple point and variance estimators have been proposed. Research evaluating these estimators has been limited and largely focused on point estimation; this analysis is the first evaluation of multiple variance estimators currently in use. We evaluated the performance of RDS variance estimators via simulations of RDS on synthetic networked populations constructed from 40 RDS surveys of injection drug users in the United States. In these simulations, average design effects (DEs) were lower and average 95% confidence interval (CI) coverage percentages were higher than suggested in previous work: typical DE range=1-3; average 95% CI coverage=93%. However, DE and CI coverage vary across the 40 sets of simulations, suggesting that the characteristics of a given study should be evaluated to assess estimator performance. We also found that simulation results are sensitive to whether sampling is conducted with replacement and the approach used to create CIs. We conclude that CI coverage rates and DEs are often acceptable but not perfect and that RDS estimates are usually reliable in scenarios where RDS assumptions are met. While RDS estimation performed reasonably well, we found strong evidence that the simple random sample variance estimator and corresponding CIs significantly underestimate variance and should not be used to analyze RDS data

Evaluating Variance Estimators for Respondent-Driven Sampling.

Respondent-Driven Sampling (RDS) is a network-based method for sampling hard-to-reach populations that is widely used by public health agencies and researchers worldwide. Estimation of population characteristics from RDS data is challenging due to the unobserved population network, and multiple point and variance estimators have been proposed. Research evaluating these estimators has been limited and largely focused on point estimation; this analysis is the first evaluation of multiple variance estimators currently in use. We evaluated the performance of RDS variance estimators via simulations of RDS on synthetic networked populations constructed from 40 RDS surveys of injection drug users in the United States. In these simulations, average design effects (DEs) were lower and average 95% confidence interval (CI) coverage percentages were higher than suggested in previous work: typical DE range=1-3; average 95% CI coverage=93%. However, DE and CI coverage vary across the 40 sets of simulations, suggesting that the characteristics of a given study should be evaluated to assess estimator performance. We also found that simulation results are sensitive to whether sampling is conducted with replacement and the approach used to create CIs. We conclude that CI coverage rates and DEs are often acceptable but not perfect and that RDS estimates are usually reliable in scenarios where RDS assumptions are met. While RDS estimation performed reasonably well, we found strong evidence that the simple random sample variance estimator and corresponding CIs significantly underestimate variance and should not be used to analyze RDS data

Evaluation of Rapid Testing Algorithms for Venue-based Anonymous HIV Testing among Non-HIV-Positive Men Who Have Sex with Men, National HIV Behavioral Surveillance (NHBS), 2017

HIV rapid testing algorithms (RTAs) using any two orthogonal rapid tests (RTs) allow for on-site confirmation of infection. RTs vary in performance characteristics therefore the selection of RTs in an algorithm may affect identification of infection, particularly if acute. National HIV Behavioral Surveillance (NHBS) assessed RTAs among men who have sex with men recruited using anonymous venue-based sampling. Different algorithms were evaluated among participants who self-reported never having received a positive HIV test result prior to the interview. NHBS project areas performed sequential or parallel RTs using whole blood. Participants with at least one reactive RT were offered anonymous linkage to care and provided a dried blood spot (DBS) for testing at CDC. Discordant results (RT-1 reactive/RT-2 non-reactive) were tested at CDC with lab protocols modified for DBS. DBS were also tested for HIV-1 RNA (VL) and antiretroviral (ARV) drug levels. Of 6500 RTAs, 238 were RT-1 reactive; of those, 97.1% (231/238) had concordant results (RT-1/RT-2 reactive) and 2.9% (7/238) had discordant results. Five DBS associated with discordant results were available for confirmation at CDC. Four had non-reactive confirmatory test results that implied RT-1 false reactivity; one had ambiguous confirmatory test results which was non-reactive in further testing. Regardless of order and type of RT used, RTAs demonstrated high concordant results in the population surveyed. Additional laboratory testing on DBS following discordant results confirmed no infection. Implementing RTAs in the context of anonymous venue-based HIV testing could be an option when laboratory follow-up is not practicable

Adjusted prevalence^a of current ARV treatment by race/ethnicity among MSM—NHBS, 2008 and 2011.

<p>Adjusted prevalences from a model adjusted for year, current age, annual household income, current insurance, venue type where recruitment occurred, and city (random effect) show that the percent of blacks currently on antiretroviral therapy is significantly less than the percent of whites currently on antiretroviral therapy in both years. ^<b>a</b>Adjusted prevalence estimated from the following model: current ARV = α + β1*race + β 2*age + β 3*current insurance + β 4*income + β 5*venue type + β 6*year + β 7*race*year + β 8*age*year + β 9*current insurance*year + β 10*income*year; city is included as a random effect; adjusted prevalence ratio based on combined 2008, 2011 data comparing whites to blacks was 1.09 (CI: 1.02–1.16); ^<b>b</b>Hispanics can be of any race; ^<b>c</b>Includes MSM reporting American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, other race, or multiple races.</p