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Respondent-driven sampling (RDS) has been widely used for recruiting hard-to-sample populations, particularly men who have sex with men and people who inject drugs from large urban centers. The aim of this article was to examine t...
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Respondent-driven sampling (RDS) has been widely used for recruiting hard-to-sample populations, particularly men who have sex with men and people who inject drugs from large urban centers. The aim of this article was to examine the feasibility of using RDS among nonmetropolitan youth who use drugs. Between May 2017 and June 2018, RDS was used to recruit youth who use drugs, ages 16–30, in three nonmetropolitan Canadian cities. All participants completed a 1-hr interviewer-administered survey. Youth received $25 for the interview, up to five coupons to recruit peers and $5 per coupon returned. Crude and RDS-weighted descriptive statistics were produced using RDS-II weights as were homophily (the tendency for people to be similar) and network size estimates. Statistically significant differences between seeds and recruits were identified using logistic regression. A subsample of recruits participated in qualitative interviews ( n = 38). Data from these interviews were inductively analyzed to identify barriers that could be used to explain the challenges with chain-referral recruitment among this population. In total, 449 youth were recruited. Due to unproductive chains, 57.2% ( n = 257) of the sample was comprised of seeds and 322 (72%) did not have a single coupon returned. Barriers to recruiting other youth included logistical challenges, fear of police, concerns about confidentiality, stigma of substance use, and poor financial incentive. Our study shows that RDS can be used to reach younger participants but also highlights the need for formative research and flexibility in recruitment to help mitigate unsuccessful RDS among nonmetropolitan youth who use drugs.
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Purpose: Respondent-driven sampling (RDS) is a form of peer-based study recruitment and analysis that incorporates features designed to limit and adjust for biases in traditional snowball sampling. It is being widely used in studi...
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Purpose: Respondent-driven sampling (RDS) is a form of peer-based study recruitment and analysis that incorporates features designed to limit and adjust for biases in traditional snowball sampling. It is being widely used in studies of hidden populations. We report an empirical evaluation of RDS's consistency and variability, comparing groups recruited contemporaneously, by identical methods and using identical survey instruments.
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Respondent-driven sampling is a network sampling technique typically employed for hard-to-reach populations (for example, drug users, men who have sex with men, people with HIV). Similarly to snowball sampling, initial seed respon...
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Respondent-driven sampling is a network sampling technique typically employed for hard-to-reach populations (for example, drug users, men who have sex with men, people with HIV). Similarly to snowball sampling, initial seed respondents recruit additional respondents from their network of friends. The recruiting process repeats iteratively, thereby forming long referral chains. Unlike in snowball sampling, it is crucial to obtain estimates of respondents' personal network sizes (that is, number of acquaintances in the target population) and information about who recruited whom. Markov chain theory makes it possible to derive population estimates and sampling weights. We introduce a new Stata command for respondent-driven sampling and illustrate its use.
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Respondent-driven sampling (RDS) is often viewed as a superior method for recruiting hard-to-reach populations disproportionately burdened with poor health outcomes. As an analytic approach, it has been praised for its ability to ...
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Respondent-driven sampling (RDS) is often viewed as a superior method for recruiting hard-to-reach populations disproportionately burdened with poor health outcomes. As an analytic approach, it has been praised for its ability to generate unbiased population estimates via post-stratified weights which account for non-random recruitment. However, population estimates generated with RDSAT (RDS Analysis Tool) are sensitive to variations in degree weights. Several assumptions are implicit in the degree weight and are not routinely assessed. Failure to meet these assumptions could result in inaccurate degree measures and consequently result in biased population estimates. We highlight potential biases associated with violating the assumptions implicit in degree weights for the RDSAT estimator and propose strategies to measure and possibly correct for biases in the analysis.
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Respondent-driven sampling (RDS) is a study design used to investigate populations for which a probabilistic sampling frame cannot be efficiently generated. Biases in parameter estimates may result from systematic non-random recru...
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Respondent-driven sampling (RDS) is a study design used to investigate populations for which a probabilistic sampling frame cannot be efficiently generated. Biases in parameter estimates may result from systematic non-random recruitment within social networks by geography. We investigate the spatial distribution of RDS recruits relative to an inferred social network among heterosexual adults in New York City in 2010. Mean distances between recruitment dyads are compared to those of network dyads to quantify bias. Spatial regression models are then used to assess the impact of spatial structure on risk and prevalence outcomes. In our primary distance metric, network dyads were an average of 1.34 (95 % CI 0.82-1.86) miles farther dispersed than recruitment dyads, suggesting spatial bias. However, there was no evidence that demographic associations with HIV risk or prevalence were spatially confounded. Therefore, while the spatial structure of recruitment may be biased in heterogeneous urban settings, the impact of this bias on estimates of outcome measures appears minimal.
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Purpose: We compared data from two respondent-driven sampling (RDS) surveys of Seattle-area injection drug users (IDU) to evaluate consistency in repeat RDS surveys. Methods: The RDS-adjusted estimates for 16 key sociodemographic,...
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Purpose: We compared data from two respondent-driven sampling (RDS) surveys of Seattle-area injection drug users (IDU) to evaluate consistency in repeat RDS surveys. Methods: The RDS-adjusted estimates for 16 key sociodemographic, drug-related, sexual behavior, and HIV- and hepatitis C virus-related variables were compared in the 2005 and the 2009 National HIV Behavioral Surveillance system surveys (NHBS-IDU1 and NHBS-IDU2). Time trends that might influence the comparisons were assessed by the use of data from reported HIV cases in IDU, surveys of needle exchange users, and two previous IDU studies. Results: NHBS-IDU2 participants were more likely than NHBS-IDU1 participants to report older age, heroin as their primary injection drug, male-to-male sex, unprotected sex with a partner of nonconcordant HIV status, and to self-report HIV-positive status. NHBS-IDU2 participants were less likely to report residence in downtown Seattle, amphetamine injection, and a recent HIV test. Time trends among Seattle-area IDU in age, male-to-male sex, and HIV testing could have influenced these differences. Conclusions: The number and magnitude of the estimated differences between the two RDS surveys appeared to describe materially different populations. This could be a result of changes in the characteristics of Seattle-area IDU over time, of accessing differing subpopulations of Seattle IDU, or of high variability in RDS measurements.
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? 2023 Elsevier B.V.Background: Multiple HIV outbreaks have been recorded among people who inject drugs (PWID) since 2010. During an intervention for PWID in 2019–2021 in Thessaloniki, Greece, an increasing number of HIV cases wa...
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? 2023 Elsevier B.V.Background: Multiple HIV outbreaks have been recorded among people who inject drugs (PWID) since 2010. During an intervention for PWID in 2019–2021 in Thessaloniki, Greece, an increasing number of HIV cases was documented. Here, we provide an analysis of this new outbreak. Methods: ALEXANDROS was a community-based program and participation included interviewing, rapid HIV/HCV tests, counselling and linkage to care. PWID were recruited through Respondent-Driven Sampling (RDS) in five sampling rounds. Crude and RDS-weighted HIV prevalence estimates were obtained. HIV incidence was estimated from data on 380 initially seronegative PWID with at least two tests. Multivariable Cox proportional hazards model was used to assess risk factors for HIV seroconversion. Results: In total, 1,101 PWID were recruited. At first participation, 53.7% were current PWID, 20.1% homeless, 20.3% on opioid substitution treatment and 4.8% had received syringes in the past 12 months. HIV prevalence (95% CI) was 7.0% (5.6–8.7%) and an increasing trend was observed over 2019–2021 (p = 0.002). Two-thirds of the cases (67.5%) were new diagnoses. HIV incidence was 7.0 new infections/100 person-years (95% CI:4.8–10.2). Homelessness in the past 12 months (HR:2.68; 95% CI:1.24–5.81) and receptive syringe sharing (HR:3.86; 95% CI:1.75–8.51) were independently associated with increased risk of seroconversion. By the end of the program, 67.3% of the newly diagnosed cases initiated antiretroviral treatment. Conclusions: A new HIV outbreak among PWID was documented in Greece during the COVID-19 pandemic with homelessness and syringe sharing being associated with increased risk of HIV acquisition. Peer-driven programs targeting the population of high-risk underserved PWID can be used to early identify emerging outbreaks and to improve linkage to HIV care.
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Hidden populations are defined as subsets of a larger population that are hard to target with traditional (e.g., random) sampling methods. For qualitative research, difficulties of achieving a good sample could include the time of...
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Hidden populations are defined as subsets of a larger population that are hard to target with traditional (e.g., random) sampling methods. For qualitative research, difficulties of achieving a good sample could include the time of day surveys are conducted, the safety of interviewers in areas with high crime rates, or the unwillingness of members in a hidden population to interact with researchers. Various chain-driven methods, such as snowball sampling (SS) and respondent-driven sampling (RDS), have been developed as techniques to reach hidden populations. Such methodologies have been implemented in previous research for investigations into the networks of people associated with illicit drug use and other risky behavior. To date, some of these studies have considered the contribution to variance inflation attributed to the effects of social network (SN) autocorrelation but not to spatial autocorrelation. This article implements a probabilistic simulation based on two RDS network data sets: one from Rio de Janeiro and another from the Colorado Springs metropolitan region. The network configurations are studied with respect to their associated geographic landscapes and a set of selected census variables. The results of the simulations demonstrate a lack of bias on the mean of the demographic variables and impacts on sample-to-sample variability attributed to both SN autocorrelation and spatial autocorrelation in the presence of other sources of excess variance. Findings reported in this article offer insights into designing future studies using network-based sampling strategies.
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Alternatives to convenience sampling (CS) are needed for HIV/STI surveillance of most-at-risk populations in Latin America. We compared CS, time space sampling (TSS), and respondent driven sampling (RDS) for recruitment of men who...
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Alternatives to convenience sampling (CS) are needed for HIV/STI surveillance of most-at-risk populations in Latin America. We compared CS, time space sampling (TSS), and respondent driven sampling (RDS) for recruitment of men who have sex with men (MSM) and transgender women (TW) in Lima, Peru. During concurrent 60-day periods from June-August, 2011, we recruited MSM/TW for epidemiologic surveillance using CS, TSS, and RDS. A total of 748 participants were recruited through CS, 233 through TSS, and 127 through RDS. The TSS sample included the largest proportion of TW (30.7 %) and the lowest percentage of subjects who had previously participated in HIV/STI research (14.9 %). The prevalence of newly diagnosed HIV infection, according to participants' self-reported previous HIV diagnosis, was highest among TSS recruits (17.9 %) compared with RDS (12.6 %) and CS (10.2 %). TSS identified diverse populations of MSM/TW with higher prevalences of HIV/STIs not accessed by other methods.
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