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We assessed how the high-skilled migrants' statuses in Mainland China and Hong Kong and the discrepancies between the two statuses are related to their well-being outcomes. We used respondent-driven sampling method and obtained an analytical sample of 2,864 participants. The majority of the high-skilled migrants perceived higher status in Mainland China than in Hong Kong which is more consequential for their health and well-being. Compared to those who perceived similar statuses in the two locations, migrants who perceived different statuses tended to report lower levels of psychological well-being, providing evidence to the detrimental effects of status inconsistency. [ABSTRACT FROM AUTHOR]

Background: Gay geosocial networking apps, also known as "gay apps," have gained increasing popularity in the men who have sex with men (MSM) community. Certain sexuality traits and gay app use are both associated with high-risk sexual behaviors among MSM. However, little is known about the underlying mechanism of such relationships. Objective: Based on the uses and gratifications theory, this study aimed to test the mediation effect of gay app use on the relationship between sexuality traits (sexual compulsivity and sexual sensation seeking) and high-risk sexual behaviors (multiple sexual partners and unprotected anal intercourse) among MSM. Methods: A cross-sectional, multicenter study was conducted in Wuhan and Changsha, China, from August to October 2020. A representative sample of 402 MSM was recruited through respondent-driven sampling. A self-administered web-based structured questionnaire was used to collect data on sociodemographic information, high-risk sexual behaviors, gay app use, sexual compulsivity, and sexual sensation seeking. Path analysis was conducted to assess the mediation effect. Results: Our study revealed that 67.42% (n=271) of MSM used gay apps for seeking potential sexual partners, with 37.06% (n=149) of them engaging in unprotected anal intercourse, and 45.42% (n=218) of them having multiple sexual partners. Of the participants, 17.16% (n=69) reported significant sexual compulsivity, while 29.10% (n=117) reported significant sexual sensation seeking. Notably, gay app usage partially mediated the relationship between sexual compulsivity and multiple sexual partners but fully mediated the relationship between sexual compulsivity and unprotected anal intercourse. Furthermore, gay app usage partially mediated the relationship between sexual sensation seeking and multiple sexual partners but fully mediated the relationship between sexual sensation seeking and unprotected anal intercourse. Conclusions: High-risk sexual behaviors are common among MSM. Most MSM rely on gay apps to find sexual partners, which, when combined with higher levels of sexual compulsivity and sexual sensation seeking, can increase the likelihood of engaging in high-risk sexual behaviors. Therefore, interventions aimed at reducing these behaviors among MSM should focus on addressing the use of gay apps, while also considering the influence of their sexuality traits on gay app use. [ABSTRACT FROM AUTHOR]

• Bias from respondent-driven samples derives mainly from differences in respondent degree, and differential recruitment. • Two different ways of analytically decomposing bias into a degree component (DC) and a recruitment component (RC) are shown. • Simulated RDS samples from empirical networks show that estimates of DC and RC can predict which RDS estimators perform best. • A key implication is that data from the sample itself can be used for RDS estimator selection. Respondent-Driven Sampling (RDS) is a method of network sampling that is used to sample hard-to-reach populations. The resultant sample is non-random, but different weighting methods can account for the over-sampling of (1) high-degree individuals and (2) homophilous groups that recruit members more effectively. While accounting for degree-bias is almost universally agreed upon, accounting for recruitment-bias has been debated as it can further increase estimate variance without substantially reducing bias. Simulation-based research has examined which weighting procedures perform best given underlying population network structures, group recruitment differences, and sampling processes. Yet, in the field, analysts do not have a priori knowledge of the network they are sampling. We show that the RDS sample data itself can determine whether a degree-based estimator is sufficient. Formulas derived from the decomposition of a 'dual-component' estimator can approximate the 'recruitment component' (RC) and 'degree component' (DC) of a sample's bias. Simulations show that RC and DC values can predict the performance of different classes of estimators. Samples with extreme 'RC' values, a consequence of network homophily and differential recruitment, are better served by a classical estimator. The use of sample data to improve estimator selection is a promising innovation for RDS, as the population network features that should guide estimator selection are typically unknown. [ABSTRACT FROM AUTHOR]

Respondent-driven sampling (RDS) is a survey method used to create samples of populations that are hidden and hard to reach. Even though the method has been used since the 1990s in studies internationally, it has not yet been used in Czech research. The RDS methodology tends to be presented as a statistical tool that makes it possible to produce unbiased estimates of hidden or hard-to-reach populations, and at the same as a tool with which to effectively recruit respondents from the given populations. The goal of the article is to introduce RDS methodology and its uses and to present and assess its application in a homeless survey conducted in two Czech cities - Prague (N=322) and Pilsen (N=146). We show that as long as certain preconditions are met the method proves to be fact and effective, especially with respect to the speed at which it is possible to sample the homeless population. We compare the outcome of the RDS survey with that of a survey of the homeless population in Prague (2010) and assess whether and how the outcomes of the two samples differ in certain population characteristics. Finally, we offer practical suggestions and observations on using the RDS method for sampling homeless populations. [ABSTRACT FROM AUTHOR]

The purpose was to assess RDS estimators in populations simulated with diverse connectivity characteristics, incorporating the putative influence of misreported degrees and transmission processes. Four populations were simulated using different random graph models. Each population was “infected” using four different transmission processes. From each combination of population x transmission, one thousand samples were obtained using a RDS-like sampling strategy. Three estimators were used to predict the population-level prevalence of the “infection”. Several types of misreported degrees were simulated. Also, samples were generated using the standard random sampling method and the respective prevalence estimates, using the classical frequentist estimator. Estimation biases in relation to population parameters were assessed, as well as the variance. Variability was associated with the connectivity characteristics of each simulated population. Clustered populations yield greater variability and no RDS-based strategy could address the estimation biases. Misreporting degrees had modest effects, especially when RDS estimators were used. The best results for RDS-based samples were observed when the “infection” was randomly attributed, without any relation with the underlying network structure. [ABSTRACT FROM AUTHOR]

Highlights: [•] An estimator is developed for respondent-driven sampling with ego network data. [•] The estimator has improved precision in estimating population characteristics. [•] The estimator is robust to differential recruitment and to variations in network structure. [•] Effect of reporting error is evaluated by simulations on both empirical and synthetic networks. [Copyright &y& Elsevier]

High prevalences of depression have been reported in male-to-female (MTF) transgender communities. We explored factors associated with depressive symptomatology among MTF spectrum trans people in Ontario, using data from the Trans PULSE Project Phase II respondent-driven sampling survey (n = 433 participants, including 191 MTFs with data needed for this analysis). We estimated the prevalence of depression at 61.2%. Factors associated with higher odds of depressive symptomatology included living outside of Toronto, having some college or university (vs. completed), being unemployed, and experiencing higher levels of transphobia. Increasing social support was associated with reduced odds of depressive symptomatology. Multivariable analyses suggested complex relationships between these factors, passing, and childhood abuse, which require additional study. [ABSTRACT FROM AUTHOR]

Although depression is understudied in transgender and transsexual communities, high prevalences have been reported. This paper presents original research from the Trans PULSE Project, an Ontario-wide, community-based initiative that surveyed 433 participants using respondent-driven sampling. The purpose of this analysis was to determine the prevalence of, and risk and protective factors for, depression among female-to-male (FTM) Ontarians (n = 207). We estimate that 66.4% of FTMs have symptomatology consistent with depression. In multivariable analyses, sexual satisfaction was a strong protective factor. Conversely, experiencing transphobia and being at the stage of planning but not having begun a medical transition (hormones and/or surgery) adversely affected mental health in FTMs. [ABSTRACT FROM AUTHOR]

This paper presents respondent-driven sampling (RDS) as a viable method of sampling and analyzing social networks with survey data. RDS is a network based sampling and analysis method that provides a middle ground compliment to ego-centric and saturated methods of social network analysis. The method provides survey data, similar to ego-centric approaches, on individuals who are connected by behaviorally documented ties, allowing for macro-level analysis of network structure, similar to that supported by saturated approaches. Using racial interaction of university undergraduates as an empirical example, the paper examines whether and to what extent racial diversity at the institutional level is reflected as racial integration at the interpersonal level by testing hypotheses regarding the quantity and quality of cross-race friendships. The primary goal of this article, however, is to introduce RDS to the network community and to stimulate further research toward the goal of expanding the analytical capacity of RDS. Advantages, limitations, and areas for future research to network analysis using RDS are discussed. [Copyright &y& Elsevier]

Background: Web-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.Objective: Our 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.Methods: We 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.Results: We 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.Conclusions: Web-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. [ABSTRACT FROM AUTHOR]

The global adolescent population is larger than ever before and is rapidly urbanizing. Global surveillance systems to monitor youth health typically use household- and school-based recruitment methods. These systems risk not reaching the most marginalized youth made vulnerable by conditions of migration, civil conflict, and other forms of individual and structural vulnerability. We describe the methodology of the Well-Being of Adolescents in Vulnerable Environments survey, which used respondent-driven sampling (RDS) to recruit male and female youth aged 15–19 years and living in economically distressed urban settings in Baltimore, MD; Johannesburg, South Africa; Ibadan, Nigeria; New Delhi, India; and Shanghai, China (migrant youth only) for a cross-sectional study. We describe a shared recruitment and survey administration protocol across the five sites, present recruitment parameters, and illustrate challenges and necessary adaptations for use of RDS with youth in disadvantaged urban settings. We describe the reach of RDS into populations of youth who may be missed by traditional household- and school-based sampling. Across all sites, an estimated 9.6% were unstably housed; among those enrolled in school, absenteeism was pervasive with 29% having missed over 6 days of school in the past month. Overall findings confirm the feasibility, efficiency, and utility of RDS in quickly reaching diverse samples of youth, including those both in and out of school and those unstably housed, and provide direction for optimizing RDS methods with this population. In our rapidly urbanizing global landscape with an unprecedented youth population, RDS may serve as a valuable tool in complementing existing household- and school-based methods for health-related surveillance that can guide policy. [ABSTRACT FROM AUTHOR]

Background: Smoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions. Objective: The objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system. Methods: We will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population. Results: This protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation. Conclusions: Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions. (JMIR Res Protoc 2013;2(2):e37) [ABSTRACT FROM AUTHOR]