A performance assessment of web-based respondent driven sampling among workers with precarious employment in Sweden.
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ABSTRACT:
SUBMITTER: Jonsson J
PROVIDER: S-EPMC6328181 | biostudies-other | 2019
REPOSITORIES: biostudies-other
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Project description: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.
| S-EPMC7846441 | biostudies-literature
Project description:BackgroundRespondent-driven sampling (RDS) is a peer chain-recruitment method for populations without a sampling frame or that are hard-to-reach. Although RDS is usually done face-to-face, the online version (WebRDS) has drawn a lot of attention as it has many potential benefits, despite this, to date there is no clear framework for its implementation. This article aims to provide guidance for researchers who want to recruit through a WebRDS.MethodsDescription of the development phase: guidance is provided addressing aspects related to the formative research, the design of the questionnaire, the implementation of the coupon system using a free software and the diffusion plan, using as an example a web-based cross-sectional study conducted in Spain between April and June 2022 describing the working conditions and health status of homecare workers for dependent people.ResultsThe application of the survey: we discuss about the monitoring strategies throughout the recruitment process and potential problems along with proposed solutions.ConclusionsUnder certain conditions, it is possible to obtain a sample with recruitment performance similar to that of other RDS without the need for monetary incentives and using a free access software, considerably reducing costs and allowing its use to be extended to other research groups.
| S-EPMC10546631 | 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:OBJECTIVE: Lack of representative data about hidden groups, like men who have sex with men (MSM), hinders an evidence-based response to the HIV epidemics. Respondent-driven sampling (RDS) was developed to overcome sampling challenges in studies of populations like MSM for which sampling frames are absent. Internet-based RDS (webRDS) can potentially circumvent limitations of the original RDS method. We aimed to implement and evaluate webRDS among a hidden population. METHODS AND DESIGN: This cross-sectional study took place 18 February to 12 April, 2011 among MSM in Vietnam. Inclusion criteria were men, aged 18 and above, who had ever had sex with another man and were living in Vietnam. Participants were invited by an MSM friend, logged in, and answered a survey. Participants could recruit up to four MSM friends. We evaluated the system by its success in generating sustained recruitment and the degree to which the sample compositions stabilized with increasing sample size. RESULTS: Twenty starting participants generated 676 participants over 24 recruitment waves. Analyses did not show evidence of bias due to ineligible participation. Estimated mean age was 22 years and 82% came from the two large metropolitan areas. 32 out of 63 provinces were represented. The median number of sexual partners during the last six months was two. The sample composition stabilized well for 16 out of 17 variables. CONCLUSION: Results indicate that webRDS could be implemented at a low cost among Internet-using MSM in Vietnam. WebRDS may be a promising method for sampling of Internet-using MSM and other hidden groups.
| S-EPMC3495883 | 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 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 (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:BackgroundThe COVID-19 pandemic has highlighted the role of personal support workers (PSWs) in health care, as well as their work conditions. Our study aimed to understand the characteristics of the PSW workforce, their work conditions and their job security, as well as to explore the health of PSWs and the impact of precarious employment on their health.MethodsOur community-based participatory action research focused on PSWs in the Greater Toronto Area. We administered an online, cross-sectional survey between June and December 2020 using respondent-driven sampling. Data on sociodemographics, employment precarity, worker empowerment and health status were collected. We assessed the association between precarious employment and health using multivariable logistic regression models.ResultsWe contacted 739 PSWs, and 664 consented to participate. Overall, 658 (99.1%) completed at least part of the survey. Using data adjusted for our sampling approach, the participants were predominantly Black (76.5%, 95% confidence interval [CI] 68.2%-84.9%), women (90.1%, 95% CI 85.1%-95.1%) and born outside of Canada (97.4%, 95% CI 94.9%-99.9%). Most worked in home care (43.9%, 95% CI 35.2%-52.5%) or long-term care (34.5%, 95% CI 27.4%-42.0%). Although most participants had at least some postsecondary education (unadjusted proportion = 83.4%, n = 529), more than half were considered low income (55.1%, 95% CI 46.3%-63.9%). Most participants were precariously employed (86.5%, 95% CI 80.7%-92.4%) and lacked paid sick days (89.5%, 95% CI 85.8%-93.3%) or extended health benefits (74.1%, 95% CI 66.8%-81.4%). Nearly half of the participants described their health as less than very good (46.7%, 95% CI 37.9%-55.5%). Employment precarity was significantly associated with higher risk of depression (odds ratio 1.02, 95% CI 1.01-1.03).InterpretationDespite being key members of health care teams, most PSWs were precariously employed with low wages that keep them in poverty; the poor work conditions they faced could be detrimental to their physical and mental health. Equitable strategies are needed to provide decent work conditions for PSWs and to improve their health.
| S-EPMC9343122 | biostudies-literature
Project description:Respondent Driven Sampling study (RDS) is a population sampling method developed to study hard-to-reach populations. A sample is obtained by chain-referral recruitment in a network of contacts within the population of interest. Such self-selected samples are not representative of the target population and require weighing observations to reduce estimation bias. Recently, the Network Model-Assisted (NMA) method was described to compute the required weights. The NMA method relies on modeling the underlying contact network in the population where the RDS was conducted, in agreement with directly observable characteristics of the sample such as the number of contacts, but also with more difficult-to-measure characteristics such as homophily or differential characteristics according to the response variable. Here we investigated the use of the NMA method to estimate HIV prevalence from RDS data when information on homophily is limited. We show that an iterative procedure based on the NMA approach allows unbiased estimations even in the case of strong population homophily and differential activity and limits bias in case of preferential recruitment. We applied the methods to determine HIV prevalence in men having sex with men in Brazilian cities and confirmed a high prevalence of HIV in these populations from 3.8% to 22.1%.
| S-EPMC7021777 | biostudies-literature