Respondent-driven Sampling Studies Research Articles

The social determinants of migrant domestic worker (MDW) health and well-being in the Western Pacific Region: A Scoping Review.

The health and well-being of transnational migrant domestic workers (MDWs) is a pressing but largely neglected public health concern. The Asia Pacific region is home to over 20% of the global MDW population. Living and working conditions, social contexts, political environments, and migration regimes are recognized as consequential to the health of this population, but currently no synthesis of available literature to prioritize research or policy agenda setting for MDW has yet been conducted. This scoping review screened 6,006 peer-reviewed articles and 1,217 gray literature sources, identifying 173 articles and 276 gray literature sources that reported key MDW health outcomes, social determinants of health, and related interventions. The majority of identified studies were observational and focused on the prevalence of common mental disorders and chronic physical conditions, with most studies lacking population representativeness. Identified social determinants of health were primarily concerned with personal social and financial resources, and health knowledge and behaviors, poor living and working conditions, community resources, experienced stigma and discrimination, poor healthcare access, exploitation within the MDW employment industry, and weak governance. Six interventional studies were identified that targeted individual-level health determinants such as financial and health knowledge with mixed effectiveness. Future population representative epidemiological and respondent driven sampling studies are needed to estimate population health burdens. In addition, randomized control trials and public health intervention studies are needed to improve women's health outcomes and address proximal health determinants to reduce health inequalities. Leveraging social networks and community facing non-governmental organizations (NGOs) are promising directions to overcome access to care for this population.

HIV prevalence among transgender women in Northeast Brazil – Findings from two Respondent Driven Sampling studies

BackgroundThe HIV epidemic still high among key-populations in Brazil, especially among transgender women (TGW). The aim of this study was to investigate the prevalence of HIV infection among TGW and to analyze factors associated with HIV seropositivity across two cross-sectional surveys conducted in Salvador, Bahia, one of the largest urban centers of Brazil.MethodsThe studies were conducted between 2014 and 2016 and 2016-2017 and employed Respondent-Driven Sampling (RDS) sampling, comprising 127 and 161 TGW residents of Salvador, Bahia. The outcome was the positive rapid antigen testing for HIV infection. Odds ratios (OR) and 95% confidence intervals (95%CI) were obtained using binomial logistic regression.ResultsThe HIV prevalence was 9.0% (95%CI: 4.2-18.2) and 24.3% (95%CI: 16.2-34.9). In the first study, factors associated with HIV prevalence were experiencing discrimination by the family (OR 8.22; 95%CI: 1.49-45.48) and by neighbors (OR 6.55; 95%CI: 1.12-38.14) as well as having syphilis (OR 6.56; 95%CI:1.11-38.65); in the subsequent study gender-based discrimination (OR 8.65; 95%CI:1.45-51.59) and having syphilis (OR 3.13; 95%CI: 1.45-51.59) were associated with testing positive for HIV.ConclusionWe found disproportionately high HIV prevalence among TGW, which underscores the context of vulnerability for this population. The data point to the urgency for intensification and expansion of access to HIV prevention and strategies to stop discrimination in health care and services for this population.

Recruiting Sexual and Gender Minority United States Military Personnel for Research Via Peer Networks: Addressing the Challenges

Novel sampling methods have been utilized to reach “hidden” and “hard-to-reach” populations. These communities are referred to in this way because they are also stigmatized and may not wish to disclose their identities to researchers. Men who have sex with men, people who inject drugs, and female sex workers, for example, have been sampled using respondent driven sampling (RDS) worldwide. RDS leverages the social ties between members of a group to build rapport and acceptance of a research endeavor, which results in high levels of participation through peer-to-peer recruitment. Despite the success of “state of the art” RDS in reaching “hidden” populations, researchers have encountered challenges in implementing “state of the art” RDS studies in multiple contexts and have addressed these challenges through novel adaptations to the methodology including situations where populations have weak ties to each other, difficulty reaching seed participants, and small personal networks which limit length of recruitment chains and increases number of seeds. In the context of a study of LGBT military personnel using RDS, we adapted seed recruitment methods, explored analytic approaches, and implemented a comprehensive fraud checking protocol to determine if these adaptations would successfully produce a diverse sample of LGBT active duty military personnel. The present paper describes the steps taken to overcome some of the common challenges encountered by other researchers in reaching hidden populations using RDS and offers methodological insights to other researchers who seek to work with this population of interest.

Assessing logistic regression applied to respondent-driven sampling studies: a simulation study with an application to empirical data

ABSTRACT The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option.

Estimating Young Women Working in Kathmandu’s Adult Entertainment Sector: A Hybrid Application of Respondent Driven Sampling and Venue Site Sampling

ABSTRACT This study estimates the prevalence of young women, defined as women aged 17 and younger, working in Kathmandu Valley’s adult entertainment sector. A hybrid strategy based on respondent-driven sampling and venue-based studies, giving rise to a mark-recapture estimation strategy, was applied. A sample of 600 female workers were reached through respondent driven sampling, and a sub-sampled set of female workers surveyed within the sampled venues. This unique method was intended to reach the “hidden” population and generate a representative sample, and enable an inference procedure which could overcome the limitations present with either strategy. The resulting estimation procedure made use of elaborate sample weighting schemes, multiple imputation-based procedures, and mark-recapture estimation procedures in order to obtain efficient estimates of the population size and characteristics. The resulting estimation was that the population of young women working in adult entertainment venues in the Kathmandu Valley is 1,650, or approximately 17% of all women in this service sector. This study constitutes the most statistically rigorous estimation of young women working in the adult entertainment sector in the Kathmandu Valley to date, and can serve as a baseline for future anti-human trafficking interventions and may guide the methodology of future studies.

Applications and Recruitment Performance of Web-Based Respondent-Driven Sampling: Scoping Review.

BackgroundWeb-based respondent-driven sampling is a novel sampling method for the recruitment of participants for generating population estimates, studying social network characteristics, and delivering health interventions. However, the application, barriers and facilitators, and recruitment performance of web-based respondent-driven sampling have not yet been systematically investigated.ObjectiveOur objectives were to provide an overview of published research using web-based respondent-driven sampling and to investigate factors related to the recruitment performance of web-based respondent-driven sampling.MethodsWe conducted a scoping review on web-based respondent-driven sampling studies published between 2000 and 2019. We used the process evaluation of complex interventions framework to gain insights into how web-based respondent-driven sampling was implemented, what mechanisms of impact drove recruitment, what the role of context was in the study, and how these components together influenced the recruitment performance of web-based respondent-driven sampling.ResultsWe included 18 studies from 8 countries (high- and low-middle income countries), in which web-based respondent-driven sampling was used for making population estimates (n=12), studying social network characteristics (n=3), and delivering health-related interventions (n=3). Studies used web-based respondent-driven sampling to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the number of seeds recruited, the proportion of successfully recruiting participants, the number of recruitment waves, the type of incentives offered to participants, and the duration of data collection. Studies that recruited relatively more seeds, through online platforms, and with less rigorous selection procedures reported relatively low percentages of successfully recruiting seeds. Studies that did not offer at least one guaranteed material incentive reported relatively fewer waves and lower percentages of successfully recruiting participants. The time of data collection was shortest in studies with university students.ConclusionsWeb-based respondent-driven sampling can be successfully applied to recruit individuals for making population estimates, studying social network characteristics, and delivering health interventions. In general, seed and peer recruitment may be enhanced by rigorously selecting and motivating seeds, offering at least one guaranteed material incentive, and facilitating adequate recruitment options regarding the target population’s online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing web-based respondent-driven sampling, such as having less opportunities to implement rigorous seed selection procedures when recruiting many seeds, as well as issues around online rather than physical participation, such as the risk of cheaters participating repeatedly.

Implementing Respondent-Driven Sampling to Recruit Women Who Exchange Sex in New York City: Factors Associated with Recruitment and Lessons Learned.

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.

Empirical evidence of recruitment bias in a network study of people who inject drugs

ABSTRACTBackground: Epidemiologic surveys of people who inject drugs (PWID) can be difficult to conduct because potential participants may fear exposure or legal repercussions. Respondent-driven sampling (RDS) is a procedure in which subjects recruit their eligible social contacts. The statistical validity of RDS surveys of PWID and other risk groups depends on subjects recruiting at random from among their network contacts.Objectives: We sought to develop and apply a rigorous definition and statistical tests for uniform network recruitment in an RDS survey.Methods: We undertook a detailed study of recruitment bias in a unique RDS study of PWID in Hartford, CT, the USA in which the network, individual-level covariates, and social link attributes were recorded. A total of participants (402 male, 123 female, and two individuals who did not specify their gender) within a network of 2626 PWID were recruited.Results: We found strong evidence of recruitment bias with respect to age, homelessness, and social relationship characteristics. In the discrete model, the estimated hazard ratios regarding the significant features of recruitment time and choice of recruitee were: alter’s age 1.03 [1.02, 1.05], alter’s crack-using status 0.70 [0.50, 1.00], homelessness difference 0.61 [0.43, 0.87], and sharing activities in drug preparation 2.82 [1.39, 5.72]. Under both the discrete and continuous-time recruitment regression models, we reject the null hypothesis of uniform recruitment.Conclusions: The results provide the evidence that for this study population of PWID, recruitment bias may significantly alter the sample composition, making results of RDS surveys less reliable. More broadly, RDS studies that fail to collect comprehensive network data may not be able to detect biased recruitment when it occurs.

Estimating the Population Size of Female Sex Workers in Namibia Using a Respondent-Driven Sampling Adjustment to the Reverse Tracking Method: A Novel Approach.

BackgroundKey populations, including female sex workers (FSWs), are at a disproportionately high risk for HIV infection. Estimates of the size of these populations serve as denominator data to inform HIV prevention and treatment programming and are necessary for the equitable allocation of limited public health resources.ObjectiveThis study aimed to present the respondent-driven sampling (RDS) adjusted reverse tracking method (RTM; RadR), a novel population size estimation approach that combines venue mapping data with RDS data to estimate the population size, adjusted for double counting and nonattendance biases.MethodsWe used data from a 2014 RDS survey of FSWs in Windhoek and Katima Mulilo, Namibia, to demonstrate the RadR method. Information from venue mapping and enumeration from the survey formative assessment phase were combined with survey-based venue-inquiry questions to estimate population size, adjusting for double counting, and FSWs who do not attend venues. RadR estimates were compared with the official population size estimates, published by the Namibian Ministry of Health and Social Services (MoHSS), and with the unadjusted RTM.ResultsUsing the RadR method, we estimated 1552 (95% simulation interval, SI, 1101-2387) FSWs in Windhoek and 453 (95% SI: 336-656) FSWs in Katima Mulilo. These estimates were slightly more conservative than the MoHSS estimates—Windhoek: 3000 (1800-3400); Katima Mulilo: 800 (380-2000)—though not statistically different. We also found 75 additional venues in Windhoek and 59 additional venues in Katima Mulilo identified by RDS participants’ responses that were not detected during the initial mapping exercise.ConclusionsThe RadR estimates were comparable with official estimates from the MoHSS. The RadR method is easily integrated into RDS studies, producing plausible population size estimates, and can also validate and update key population maps for outreach and venue-based sampling.

Application of the Respondent-Driven Sampling methodology in a biological and behavioral surveillance survey among female sex workers, Brazil, 2016.

Respondent-Driven Sampling (RDS) has been used in surveys with key populations at risk of HIV infection, such as female sex workers (FSW). This article describes the application of the RDS method among FSW in 12 Brazilian cities, during a survey carried out in 2016. A biological and behavioral surveillance study carried out in 12 Brazilian cities, with a minimum sample of 350 FSW in each city. Tests were performed for HIV, syphilis, and hepatitis B and C infections. A social-behavioral questionnaire was also applied. The sample was comprised of 4,328 FSW. For data analysis, the sample was weighted according to each participant's network size (due consideration to the implications of RDS complex design and to the effects of homophilia are recommended). Although RDS methods for obtaining a statistical sample are based on strong statistical assumptions, allowing for an estimation of statistical parameters, with each new application the method has been rethought. In the analysis of whole-sample data, estimators were robust and compatible with those found in 2009. However, there were significant variations according to each city. The achieved sample size was of great relevance for assessing progress and identifying problems regarding the prevention and treatment of Sexually Transmitted Infections. New RDS studies with more time and operational resources should be envisaged. This could further network development.

Using data from respondent-driven sampling studies to estimate the number of people who inject drugs: Application to the Kohtla-Järve region of Estonia.

Estimating the size of key risk populations is essential for determining the resources needed to implement effective public health intervention programs. Several standard methods for population size estimation exist, but the statistical and practical assumptions required for their use may not be met when applied to HIV risk groups. We apply three approaches to estimate the number of people who inject drugs (PWID) in the Kohtla-Järve region of Estonia using data from a respondent-driven sampling (RDS) study: the standard “multiplier” estimate gives 654 people (95% CI 509–804), the “successive sampling” method gives estimates between 600 and 2500 people, and a network-based estimate that uses the RDS recruitment chain gives between 700 and 2800 people. We critically assess the strengths and weaknesses of these statistical approaches for estimating the size of hidden or hard-to-reach HIV risk groups.

The use of respondent-driven sampling to assess malaria knowledge, treatment-seeking behaviours and preventive practices among mobile and migrant populations in a setting of artemisinin resistance in Western Cambodia

BackgroundMulti-drug-resistant Plasmodium falciparum threatens malaria elimination efforts in Cambodia and the Greater Mekong Subregion (GMS). Malaria burden in the GMS is higher among certain high-risk demographic groups in Cambodia, especially among migrant and mobile populations (MMPs). This respondent driven sampling (RDS) study was conducted in order to determine malaria knowledge, treatment-seeking behaviours and preventive practices among two MMP groups in Western Cambodia.MethodsAn RDS survey of MMPs was implemented in four purposively-selected communes along the Thai–Cambodia border; two in Veal Veang District and two in Pailin Province, chosen due to their sizeable MMP groups, their convenience of access, and their proximity to Thailand, which allowed for comparison with RDS studies in Thailand.ResultsThere were 764 participants in Pailin Province and 737 in Veal Veang District. Health messages received in Veal Veang were most likely to come from billboards (76.5%) and family and friends (57.7%), while in Pailin they were most likely to come from sources like radio (57.1%) and television (31.3%). Knowledge of malaria transmission by mosquito and prevention by bed net was above 94% in both locations, but some misinformation regarding means of transmission and prevention methods existed, predominantly in Veal Veang. Ownership of treated bed nets was lower in Pailin than in Veal Veang (25.3% vs 53.2%), while reported use the night before the survey was higher in Pailin than in Veal Veang (57.1% vs 31.6%). Use of private sector health and pharmaceutical services was common, but 81.1% of patients treated for malaria in Pailin and 86.6% in Veal Veang had received a diagnostic test. Only 29.6% of patients treated in Pailin and 19.6% of those treated in Veal Veng reported receiving the indicated first-line treatment.DiscussionBarriers in access to malaria prevention and case management were common among MMPs, with marked variation by site. Resolving both nation-wide and MMP-specific challenges will require targeted interventions that take into account this heterogeneity.

Identification of Homophily and Preferential Recruitment in Respondent-Driven Sampling.

Respondent-driven sampling (RDS) is a link-tracing procedure used in epidemiologic research on hidden or hard-to-reach populations in which subjects recruit others via their social networks. Estimates from RDS studies may have poor statistical properties due to statistical dependence in sampled subjects' traits. Two distinct mechanisms account for dependence in an RDS study: homophily, the tendency for individuals to share social ties with others exhibiting similar characteristics, and preferential recruitment, in which recruiters do not recruit uniformly at random from their network alters. The different effects of network homophily and preferential recruitment in RDS studies have been a source of confusion and controversy in methodological and empirical research in epidemiology. In this work, we gave formal definitions of homophily and preferential recruitment and showed that neither is identified in typical RDS studies. We derived nonparametric identification regions for homophily and preferential recruitment and showed that these parameters were not identified unless the network took a degenerate form. The results indicated that claims of homophily or recruitment bias measured from empirical RDS studies may not be credible. We applied our identification results to a study involving both a network census and RDS on a population of injection drug users in Hartford, Connecticut (2012-2013).

THE GRAPHICAL STRUCTURE OF RESPONDENT-DRIVEN SAMPLING.

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.

Lessons learned from respondent-driven sampling recruitment in Nairobi: experiences from the field.

BackgroundRespondent-driven sampling (RDS) is used in a variety of settings to study hard-to-reach populations at risk for HIV and sexually transmitted infections. However, practices leading to successful recruitment among diverse populations in low-resource settings are seldom reported. We implemented the first, integrated, bio-behavioural surveillance survey among men who have sex with men, female sex workers and people who injected drugs in Nairobi, Kenya.MethodsThe survey period was June 2010 to March 2011, with a target sample size of 600 participants per key populations. Formative research was initially conducted to assess feasibility of the survey. Weekly monitoring reports of respondent characteristics and recruitment chain graphs from NetDraw illustrated patterns and helped to fill recruitment gaps.ResultsRDS worked well with men who have sex with men and female sex workers with recruitment initiating at a desirable pace that was maintained throughout the survey. Networks of people who injected drugs were well-integrated, but recruitment was slower than the men who have sex with men and female sex workers surveys.ConclusionBy closely monitoring RDS implementation and conducting formative research, RDS studies can effectively develop and adapt strategies to improve recruitment and improve adherence to the underlying RDS theory and assumptions.

Update on respondent-driven sampling: Theory and practical considerations for studies of persons who inject drugs

In the last 5 years, more than 600 articles using respondent-driven sampling has been published. This article aims to provide an overview of this sampling technique with an update on the key questions that remain when using respondent-driven sampling, with regard to its application and estimators. Respondent-driven sampling was developed by Heckathorn in 1997 and was based on the principle of individuals recruiting other individuals, who themselves were recruited in previous waves. When there is no sampling frame, respondent-driven sampling has demonstrated its ability to capture individuals belonging to “hidden” or “hard-to-reach” populations in numerous epidemiological surveys. People who use drugs, sex workers, or men who have sex with men are notable examples of specific populations studied using this technique, particularly by public agencies such as the Centers for Disease Control and Prevention in the United States. Respondent-driven sampling, like many others, is based on a set of assumptions that, when respected, can ensure an unbiased estimator. Based on a literature review, we will discuss, among other topics, the effect of violating these assumptions. A special focus is made on surveys of persons who inject drugs. Publications show two major thrusts—methodological and applied researches—for providing practical recommendations in conducting respondent-driven sampling studies. The reasons why respondent-driven sampling did not work for a given population of interest will usually provide important insights for designing health-promoting interventions for that population.

Estimating the Size of Hidden Populations Using Respondent-driven Sampling Data: Case Examples from Morocco.

Respondent-driven sampling is used worldwide to estimate the population prevalence of characteristics, such as HIV/AIDS and associated risk factors in hard-to-reach populations. Estimating the total size of these populations is of great interest to national and international organizations; however, reliable measures of population size often do not exist. Successive sampling-population size estimation (SS-PSE) along with network size imputation allows population size estimates to be made without relying on separate studies or additional data (as in network scale-up, multiplier, and capture-recapture methods), which may be biased. Ten population size estimates were calculated for people who inject drugs, female sex workers, men who have sex with other men, and migrants from sub-Saharan Africa in six different cities in Morocco. SS-PSE estimates fell within or very close to the likely values provided by experts and the estimates from previous studies using other methods. SS-PSE is an effective method for estimating the size of hard-to-reach populations that leverages important information within respondent-driven sampling studies. The addition of a network size imputation method helps to smooth network sizes allowing for more accurate results. However, caution should be used particularly when there is reason to believe that clustered subgroups may exist within the population of interest or when the sample size is small in relation to the population.

A Qualitative Examination of Respondent-Driven Sampling (RDS) Peer Referral Challenges Among Young Transwomen in the San Francisco Bay Area.

BackgroundEfforts have focused on developing innovative recruitment strategies to engage the most marginalized of populations in public health research. Respondent-driven sampling (RDS) has been found to be an effective sampling strategy for hard-to-reach, hidden populations. Though studies have documented RDS peer referral as challenging, literature contextualizing these challenges is scant and rarely do they discuss the role of Internet technologies.ObjectiveThe objective of the study was to explore reasons for peer referral challenges in a human immunodeficiency virus (HIV) risk and resilience study among a hidden population of youth, specifically, young transwomen. These findings amplify the unique opportunities Internet technologies bring to public health research and methodology.MethodsWe conducted focused, semistructured, qualitative interviews with 16 young transwomen to investigate the reasons why youth did or did not refer peers to an RDS study for transwomen ages 16-24 in the San Francisco Bay Area. Qualitative interview data were coded and analyzed using grounded theory.ResultsParticipants discussed specific barriers and facilitators related to four factors that include study design, study implementation, community characteristics, and individual characteristics, which contributed to RDS peer referral challenges.ConclusionsOur grounded theory analysis identifies important considerations for future RDS studies with hidden youth populations. Exploring research participants’ experiences is integral in strengthening future epidemiologic research efforts that plan to use RDS to sample and estimate the hidden epidemics among at-risk youth and transgender women. Additionally, Internet technologies and Web-based adaptations offer solutions to traditional RDS peer referral challenges, having the potential to increase accessibility and use among hidden youth populations.

Estimating the size of populations at high risk for HIV using respondent-driven sampling data.

The study of hard-to-reach populations presents significant challenges. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. This is especially true of populations at high risk for HIV/AIDS. Respondent-driven sampling (RDS) is often used in such settings with the primary goal of estimating the prevalence of infection. In such populations, the number of people at risk for infection and the number of people infected are of fundamental importance. This article presents a case-study of the estimation of the size of the hard-to-reach population based on data collected through RDS. We study two populations of female sex workers and men-who-have-sex-with-men in El Salvador. The approach is Bayesian and we consider different forms of prior information, including using the UNAIDS population size guidelines for this region. We show that the method is able to quantify the amount of information on population size available in RDS samples. As separate validation, we compare our results to those estimated by extrapolating from a capture-recapture study of El Salvadorian cities. The results of our case-study are largely comparable to those of the capture-recapture study when they differ from the UNAIDS guidelines. Our method is widely applicable to data from RDS studies and we provide a software package to facilitate this.

Differences between recent quitters and current smokers during pregnancy

Differences between recent quitters and current smokers during pregnancy