Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Carrillo SA

PROVIDER: S-EPMC8201473 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Carrillo Sidney A SA Rivera Alexis V AV Braunstein Sarah L SL

AIDS and behavior 20200201 2

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 ...[more]

PMID: 30929151

Similar Datasets

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.

Project description:Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988-1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely.

Project description:BackgroundType 2 diabetes mellitus is preventable through lifestyle intervention. Diabetes prevention programmes (DPPs) aim to deliver prevention-based behaviour change interventions to reduce incidence. Such programmes vary from usual primary care in terms of where, how, and by whom they are delivered. Implementation is therefore likely to face new commissioning, incentive and delivery challenges. We report on the implementation of a national DPP in NHS England, and identify lessons learned in addressing the implementation challenges.MethodsIn 2017/18, we conducted 20 semi-structured telephone interviews covering 16 sampled case sites with the designated lead(s) responsible for local implementation of the programme. Interviews explored the process of implementation, including organisation of the programme, expectations and attitudes to the programme, funding, target populations and referral and clinical pathways. We drew on constant comparative methods to analyse the data and generate over-arching themes. We complemented our qualitative data with a survey focused on variation in the financial incentives used across sites to ensure usual primary care services recruited patients to new providers.ResultsWe identified five over-arching areas of learning for implementing this large-scale programme: 1) managing new providers; 2) promoting awareness of services; 3) recruiting patients; 4) incentive payments; and 5) mechanisms for sharing learning. In general, tensions appeared to be caused by a lack of clear roles/responsibilities between hierarchical actors, and lack of communication. Both local sites and the national NHS coordination team gained experience through learning by doing. Initial tensions with roles and expectations have been worked out during implementation.ConclusionsImplementing a national disease prevention programme is a major task, and one that will be increasingly faced by health systems globally as they aim to adjust to demand pressures. We provide practical learning opportunities for the wider uptake and sustainability of prevention programmes. Future implementers might wish to define clear responsibilities for each actor prior to implementation, ensure early engagement with new providers, offer mechanisms/forums for sharing learning, generate evidence and provide advice on incentive payments, and prioritise public and professional awareness of the programme.

Project description:IntroductionRespondent-driven sampling (RDS) is a variant of a link-tracing design intended for generating unbiased estimates of the composition of hidden populations that typically involves giving participants several coupons to recruit their peers into the study. RDS may generate biased estimates if coupons are distributed non-randomly or if potential recruits present for interview non-randomly. We explore if biases detected in an RDS study were due to either of these mechanisms, and propose and apply weights to reduce bias due to non-random presentation for interview.MethodsUsing data from the total population, and the population to whom recruiters offered their coupons, we explored how age and socioeconomic status were associated with being offered a coupon, and, if offered a coupon, with presenting for interview. Population proportions were estimated by weighting by the assumed inverse probabilities of being offered a coupon (as in existing RDS methods), and also of presentation for interview if offered a coupon by age and socioeconomic status group.ResultsYounger men were under-recruited primarily because they were less likely to be offered coupons. The under-recruitment of higher socioeconomic status men was due in part to them being less likely to present for interview. Consistent with these findings, weighting for non-random presentation for interview by age and socioeconomic status group greatly improved the estimate of the proportion of men in the lowest socioeconomic group, reducing the root-mean-squared error of RDS estimates of socioeconomic status by 38%, but had little effect on estimates for age. The weighting also improved estimates for tribe and religion (reducing root-mean-squared-errors by 19-29%), but had little effect for sexual activity or HIV status.ConclusionsData collected from recruiters on the characteristics of men to whom they offered coupons may be used to reduce bias in RDS studies. Further evaluation of this new method is required.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets