Effectiveness of respondent-driven sampling for recruiting drug users in New York City: findings from a pilot study.
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ABSTRACT:
SUBMITTER: Abdul-Quader AS
PROVIDER: S-EPMC2527186 | biostudies-other | 2006 May
REPOSITORIES: biostudies-other
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Project description:BackgroundInternationally, wheelchair users are an emerging demographic phenomenon, due to their increased prevalence and rapidly increasing life-span. While having significant healthcare implications, basic robust epidemiological information about wheelchair users is often lacking due, in part, to this population's 'hidden' nature. Increasingly popular in epidemiological research, Respondent Driven Sampling (RDS) provides a mechanism for generating unbiased population-based estimates for hard-to-reach populations, overcoming biases inherent within other sampling methods. This paper reports the first published study to employ RDS amongst wheelchair users.MethodsBetween October 2015 and January 2016, a short, successfully piloted, internet-based national survey was initiated. Twenty seeds from diverse organisations were invited to complete the survey then circulate it to peers within their networks following a well-defined protocol. A predetermined reminder protocol was triggered when seeds or their peers failed to respond. All participants were entered into a draw for an iPad.ResultsOverall, 19 people participated (nine women); 12 initial seeds, followed by seven second-wave participants arising from four seeds . Completion time for the survey ranged between 7 and 36 minutes. Despite repeated reminders, no further people were recruited.DiscussionWhile New Zealand wheelchair user numbers are unknown, an estimated 14% of people have physical impairments that limited mobility. The 19 respondents generated from adopting the RDS methodology here thus represents a negligible fraction of wheelchair users in New Zealand, and an insufficient number to ensure equilibrium required for unbiased analyses. While successful in other hard-to-reach populations, applying RDS methodology to wheelchair users requires further consideration. Formative research exploring areas of network characteristics, acceptability of RDS, appropriate incentive options, and seed selection amongst wheelchair users is needed.
| S-EPMC5017286 | biostudies-literature
Project description:Respondent-driven sampling (RDS) relies on productive peer recruitment to capture hidden populations. Domestic studies have identified characteristics of productive recruitment among RDS samples of men who have sex with men and persons who use drugs, but not of women who exchange sex, a group vulnerable to HIV infection. We examined sociodemographic-, behavioral-, exchange-sex-, and protocol-related factors associated with recruitment among seeds (n = 25) and peers (n = 297) in the 2016 New York City National HIV Behavioral Surveillance Study cycle focused on women who exchange sex. Recruiter productivity was significantly associated with not having been recently incarcerated, lower rate of HIV testing, and larger exchange sex networks among seeds, and with HIV-prevention services usage among peers. We describe challenges and lessons learned from implementing RDS in this population. Our study identifies seed characteristics and protocol improvements researchers can utilize when implementing future RDS studies among women who exchange sex.
| S-EPMC8201473 | biostudies-literature
Project description:Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied.
| S-EPMC2872407 | biostudies-literature
Project description:Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total population data.Total population data on age, tribe, religion, socioeconomic status, sexual activity, and HIV status were available on a population of 2402 male household heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, using current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample).We recruited 927 household heads. Full and small RDS samples were largely representative of the total population, but both samples underrepresented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven sampling statistical inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven sampling bootstrap 95% confidence intervals included the population proportion.Respondent-driven sampling produced a generally representative sample of this well-connected nonhidden population. However, current respondent-driven sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience sampling method, and caution is required when interpreting findings based on the sampling method.
| S-EPMC3277908 | biostudies-literature
Project description:Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for HIV. Data are collected through peer-referral over social networks. RDS has proven practical for data collection in many difficult settings and is widely used. Inference from RDS data requires many strong assumptions because the sampling design is partially beyond the control of the researcher and partially unobserved. We introduce diagnostic tools for most of these assumptions and apply them in 12 high risk populations. These diagnostics empower researchers to better understand their data and encourage future statistical research on RDS.
| S-EPMC4877136 | biostudies-literature
Project description:Respondent-driven sampling (RDS) is a chain-referral method for sampling members of hidden or hard-to-reach populations, such as sex workers, homeless people, or drug users, via their social networks. Most methodological work on RDS has focused on inference of population means under the assumption that subjects' network degree determines their probability of being sampled. Criticism of existing estimators is usually focused on missing data: the underlying network is only partially observed, so it is difficult to determine correct sampling probabilities. In this article, the author shows that data collected in ordinary RDS studies contain information about the structure of the respondents' social network. The author constructs a continuous-time model of RDS recruitment that incorporates the time series of recruitment events, the pattern of coupon use, and the network degrees of sampled subjects. Together, the observed data and the recruitment model place a well-defined probability distribution on the recruitment-induced subgraph of respondents. The author shows that this distribution can be interpreted as an exponential random graph model and develops a computationally efficient method for estimating the hidden graph. The author validates the method using simulated data and applies the technique to an RDS study of injection drug users in St. Petersburg, Russia.
| S-EPMC6788810 | biostudies-literature
Project description:Respondent-driven sampling is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals' social relationships. As such, a respondent-driven sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily, or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modelling strategies for respondent-driven sampling to address peer effects driven by homophily and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using respondent-driven sampling data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent-driven sampling study in Montreal, Canada.
| S-EPMC8424528 | biostudies-literature
Project description:This article reports on the use of respondent-driven sampling (RDS) in participatory and community-based research. Participant-driven recruitment (PDR) retains all of the analytic capabilities of RDS while enhancing the role of respondents in framing research questions, instrument development, data interpretation, and other aspects of the research process. Merging the capabilities of RDS with participatory research methods, PDR creates new opportunities for engaging community members in research addressing social issues and in utilizing research findings within community contexts. This article outlines PDR's synthesis of RDS and participatory research approaches, describes how PDR is implemented in community contexts, and provides two examples of the use of PDR, illustrating its process, potentials, and challenges.
| S-EPMC1705484 | biostudies-other
Project description:Respondent driven sampling (RDS) is a relatively new method to sample hard-to-reach populations. Until this study, female sex workers (FSWs) in Vietnam were sampled using a variety of methods, including time location sampling (TLS), which may not access the more hidden types of FSWs. This paper presents an analysis from an HIV biological and behavioral surveillance survey to assess the feasibility and effectiveness of RDS to sample FSWs, to determine if RDS can reach otherwise inaccessible FSWs in Vietnam and to compare RDS findings of HIV risk factors with a theoretical TLS. Through face-to-face interviews with FSWs in Ho Chi Minh City (HCMC) and Hai Phong (HP), data were collected about the venues where they most often solicit their clients. These data were used to create three variables to assess whether FSWs solicit their clients in locations that are visible, semi-visible and non-visible. For this analysis, the visible group simulates a sample captured using TLS. Survey results in HIV prevalence and related risk factors and service utilization, adjusted for sampling methodology, were compared across each of the three FSW visibility groups to assess potential bias in TLS relative to RDS. The number of self-reported visible FSWs (HCMC: n=311; HP: n=162) was much larger than those of the semi-visible (HCMC: n=65; HP: n=43) and non-visible (HCMC: n=37; HP: n=10) FSWs in HCMC and HP. Non-visible FSWs in both cities were just as likely as visible and semi-visible FSWs to be HIV positive (HCMC: visible 14.5%, semi-visible 13.8%, non-visible 13.5%, p value = 0.982; HP: visible 35.2%, semi-visible 30.2%, non-visible 30.0%, p value = 0.801), to practice behaviors that put them at risk for contracting and transmitting HIV (injecting drug use-HCMC: visible 13.8%, semi-visible 12.3%, non-visible 5.4%, p value = 0.347; HP: visible 38.9%, semi-visible 23.3%, non-visible 30.0%, p value = 0.378, to have no condom use in the past month -HCMC only: visible 52.7%, semi-visible 63.1%, non-visible 48.6%, p value = 0.249) and to have symptoms of a sexually transmitted infection (STI) in the past year (HCMC: visible 16.1%, semi-visible 12.3%, non-visible 16.2%, p value = 0.742; HP: visible 13.6%, semi-visible 18.6%, non-visible 20.0%, p value = 0.640). There was a difference found among the visible, semi-visible and non-visible groups in HP for no past month condom use (visible 53.1%, semi-visible 79.1%, non-visible 60.0%, p value = 0.009). This study found that RDS was successful at recruiting hidden types of FSWs in Vietnam. Past reports of FSWs in Vietnam have assessed the more visible FSWs as being the most vulnerable and at risk for HIV. Although the number of visible FSWs is much higher than those of the semi and non-visible groups, this study found that the non-visible FSWs are very vulnerable to HIV infection. If prevention programs are targeting and responding to those who are most likely to be assessed (e.g., more visible types of FSWs) then this analysis indicates that a significant proportion of the FSW population at risk for HIV may not be receiving optimal HIV information and services.
| S-EPMC1705478 | biostudies-other
Project description:Respondent-driven sampling is an approach for estimating features of populations that are difficult to access using standard survey tools, e.g., the fraction of injection drug users who are HIV positive. Baraff et al. (2016) introduced an approach to estimating uncertainty in population proportion estimates from respondent-driven sampling using the tree bootstrap method. In this paper we establish the consistency of this tree bootstrap approach in the case of [Formula: see text]-trees.
| S-EPMC7228542 | biostudies-literature