Using Respondent-Driven Sampling Methodology for HIV Biological and Behavioral Surveillance in International Settings: A Systematic Review

Malekinejad, Mohsen; Johnston, Lisa G.; Kendall, Carl; Kerr, Lígia Regina Franco Sansigolo; Rifkin, Marina; Rutherford, George W

doi:10.1007/s10461-008-9421-1

Cited by 377 publications

(396 citation statements)

References 31 publications

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“…Yet another possible explanation is suggested by the example of respondent-driven sampling (RDS) [Salganik and Heckathorn 2004;Goel and Salganik 2010]-a type of snowball sampling in which survey respondents enlist the next wave of participants-where recruitment chains routinely propagate for many generations, and usually terminate only as a result of deliberate action by the survey organizers. The success of this sampling methodology, however, depends critically on the provision of substantial financial incentives for recruiting [Malekinejad et al 2008]. Thus, as with email viruses, the feature that drives the viral nature of RDS is unlikely to be present in typical online adoption processes, which lack direct financial incentives.…”

Section: Discussionmentioning

confidence: 99%

The structure of online diffusion networks

Goel

Watts

Goldstein

2012

Proceedings of the 13th ACM Conference on Electronic Commerce

325

260

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Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. Here we describe the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing. We find strikingly similar patterns across all domains. In particular, the vast majority of cascades are small, and are described by a handful of simple tree structures that terminate within one degree of an initial adopting "seed." In addition we find that structures other than these account for only a tiny fraction of total adoptions; that is, adoptions resulting from chains of referrals are extremely rare. Finally, even for the largest cascades that we observe, we find that the bulk of adoptions often takes place within one degree of a few dominant individuals. Together, these observations suggest new directions for modeling of online adoption processes.

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Section: Discussionmentioning

confidence: 99%

The structure of online diffusion networks

Goel

Watts

Goldstein

2012

Proceedings of the 13th ACM Conference on Electronic Commerce

325

260

View full text Add to dashboard Cite

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“…Specifically, (i) initial sample members are chosen independently and proportional to network degree; (ii) relationships within the population are symmetric (i.e., if A is a contact of B, then B is also a contact of A); (iii) participants recruit uniformly at random from their contacts; (iv) those who are recruited always participate in the study; (v) individuals can be recruited into the sample more than once; (vi) the number of recruits per participant does not depend on individual traits; and (vii) respondents accurately report their social network degree. The remaining parameters of our simulations are modeled after common RDS study features (5). Starting from ten initial seeds, each participant recruits between 0 and 3 other individuals.…”

Section: Methodsmentioning

confidence: 99%

“…RDS is now widely used in the public health community and has been recently applied in more than 120 studies in more than 20 countries, involving a total of more than 32,000 participants (5). In particular, in helping to track the HIV epidemic, RDS is used by the Centers for Disease Control and Prevention (CDC) (6,7) and by the United States President's Emergency Plan for AIDS Relief.…”

mentioning

confidence: 99%

Assessing respondent-driven sampling

Goel

Salganik

2010

Proc. Natl. Acad. Sci. U.S.A.

321

349

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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.disease surveillance | snowball sampling | social networks T he development and evaluation of public health policies often require detailed information about so-called hard-to-reach or hidden populations. For example, HIV researchers are especially interested in monitoring risk behavior and disease prevalence among injection drug users, men who have sex with men, and commercial sex workers-the groups at highest risk for HIV in most countries. Unfortunately, however, these high-risk groups are not easily studied with standard sampling methods, including institutional sampling, targeted sampling, and time-location sampling (1).Respondent-driven sampling (RDS) (2-4) facilitates examination of such hidden populations via a chain-referral procedure in which participants recruit one another, akin to snowball sampling. RDS is now widely used in the public health community and has been recently applied in more than 120 studies in more than 20 countries, involving a total of more than 32,000 participants (5). In particular, in helping to track the HIV epidemic, RDS is used by the Centers for Disease Control and Prevention (CDC) (6, 7) and by the United States President's Emergency Plan for AIDS Relief.RDS is a method both for data collection and for statistical inference. To generate an RDS sample, one begins by selecting a small number of initial participants ("seeds") from the target population who are asked-and typically provided financial incentive-to recruit their contacts in the population (2). The sampling proceeds with current sample members recruiting the next wave of sample members, continuing until the desired sample size is reached. Participants are usually all...

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“…RDS was developed by Douglas Heckathorn in conjunction with the AIDS prevention intervention program in the US (Heckathorn 1997), as a means of providing robust, representative information on hardto-reach groups Lansky et al 2007;Malekinejad et al 2008;McCreesh et al 2012).…”

Section: Respondent-driven Samplingmentioning

confidence: 99%