A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists

Avery, Lisa; Macpherson, Alison; Flicker, Sarah; Rotondi, Michael

doi:10.1371/journal.pone.0249074

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…Table 1 details the achieved population-level homophily and relative activity for each of the simulated populations. A survey of RDS studies indicated that degree distributions in many real-world populations are approximately log-normally distributed ( Avery et al 2021 ). To allow comparison with previous literature and estimate real-world performance, Figure 2 shows the effect of personal network degree distribution on estimator variability for populations with equal relative activity and no homophily.…”

Section: Resultsmentioning

confidence: 99%

“…Previous RDS validation research has relied on degree distributions generated using ERGM or Poisson distributions, which have less variation than degrees reported by participants in real-world studies. A comprehensive survey of RDS samples showed that self-reported degree is log-normally distributed across a variety of population types (sex workers, men who have sex with men, people who inject drugs, migrants) and geographies (North America, India, Europe) ( Avery et al 2021 ). The degree distributions of the Project 90 and Large Facebook Pages networks ( Figure S5 in the online supplement ) indicate similar distributions.…”

Section: Discussionmentioning

confidence: 99%

“… Fellows (2019) sampled from a Poisson distribution for network degree in his simulations evaluating RDS estimators. A recent survey of 53 RDS samples encompassing more than 36,000 participants indicated that reported degree from every RDS sample was considerably more skewed than a Poisson distribution and was approximately log-normal across all sample types (migrants, men who have sex with men, drug users, sex workers) and geographies (North America, India, Papua New Guinea) ( Avery et al 2021 ). The effect of degree distribution on estimator accuracy has not yet been examined.…”

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confidence: 99%

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Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree

Avery

Rotondi

2023

Sociological Methodology

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Respondent-driven sampling (RDS) is used to measure trait or disease prevalence in populations that are difficult to reach and often marginalized. The authors evaluated the performance of RDS estimators under varying conditions of trait prevalence, homophily, and relative activity. They used large simulated networks ( N = 20,000) derived from real-world RDS degree reports and an empirical Facebook network ( N = 22,470) to evaluate estimators of binary and categorical trait prevalence. Variability in prevalence estimates is higher when network degree is drawn from real-world samples than from the commonly assumed Poisson distribution, resulting in lower coverage rates. Newer estimators perform well when the sample is a substantive proportion of the population, but bias is present when the population size is unknown. The choice of preferred RDS estimator needs to be study specific, considering both statistical properties and knowledge of the population under study.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree

Avery

Rotondi

2023

Sociological Methodology

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“…Thus, our interpretations in Table 1 hold under the assumptions of sufficient confounder control for the mediator–outcome relationship (VanderWeele and Robinson, 2014) and no exposure-induced mediator–outcome confounding. Primary findings are presented without including RDS weights, as no consensus exists on their incorporation in natural effect models (Avery et al , 2021; Gile et al , 2018), although there exists ongoing discussion (Yauck et al , 2021).…”

Section: Methodsmentioning

confidence: 99%

Using an intersectionality-based approach to evaluate mental health services use among gay, bisexual and other men who have sex with men in Montreal, Toronto and Vancouver

Marbaniang,

Moodie,

Latimer

et al. 2024

Epidemiol Psychiatr Sci

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Aims To cope with homonegativity-generated stress, gay, bisexual and other men who have sex with men (GBM) use more mental health services (MHS) compared with heterosexual men. Most previous research on MHS among GBM uses data from largely white HIV-negative samples. Using an intersectionality-based approach, we evaluated the concomitant impact of racialization and HIV stigma on MHS use among GBM, through the mediating role of perceived discrimination (PD). Methods We used baseline data from 2371 GBM enrolled in the Engage cohort study, collected between 2017 and 2019, in Montreal, Toronto and Vancouver, using respondent-driven sampling. The exposure was GBM groups: Group 1 (n = 1376): white HIV-negative; Group 2 (n = 327): white living with HIV; Group 3 (n = 577): racialized as non-white HIV-negative; Group 4 (n = 91): racialized as non-white living with HIV. The mediator was interpersonal PD scores measured using the Everyday Discrimination Scale (5-item version). The outcome was MHS use (yes/no) in the prior 6 months. We fit a three-way decomposition of causal mediation effects utilizing the imputation method for natural effect models. We obtained odds ratios (ORs) for pure direct effect (PDE, unmediated effect), pure indirect effect (PIE, mediated effect), mediated interaction effect (MIE, effect due to interaction between the exposure and mediator) and total effect (TE, overall effect). Analyses controlled for age, chronic mental health condition, Canadian citizenship, being cisgender and city of enrolment. Results Mean PD scores were highest for racialized HIV-negative GBM (10.3, SD: 5.0) and lowest for white HIV-negative GBM (8.4, SD: 3.9). MHS use was highest in white GBM living with HIV (GBMHIV) (40.4%) and lowest in racialized HIV-negative GBM (26.9%). Compared with white HIV-negative GBM, white GBMHIV had higher TE (OR: 1.71; 95% CI: 1.27, 2.29) and PDE (OR: 1.68; 95% CI: 1.27, 2.24), and racialized HIV-negative GBM had higher PIE (OR: 1.09; 95% CI: 1.02, 1.17). Effects for racialized GBMHIV did not significantly differ from those of white HIV-negative GBM. MIEs across all groups were comparable. Conclusions Higher MHS use was observed among white GBMHIV compared with white HIV-negative GBM. PD positively mediated MHS use only among racialized HIV-negative GBM. MHS may need to take into account the intersecting impact of homonegativity, racism and HIV stigma on the mental health of GBM.

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“…Under several assumptions, including that the sample has sufficiently many waves, that the population comprises a connected underlying social network, and that recruitment is random, the RDS sampling process approximates a successive sampling process over the network (Gile, 2011) and reported degree informs an individual's inclusion probability. Numerous surveys have been conducted worldwide using RDS to sample hidden populations, especially for the purposes of estimating trends in HIV prevalence and risk factors (Johnston et al, 2013(Johnston et al, , 2016(Johnston et al, , 2019Malekinejad et al, 2008;Avery & Rotondi, 2020;Avery et al, 2021). Inferential methods for RDS data, as well as their limitations, have been well-studied (Gile et al, 2018).…”

Section: Respondent-driven Samplingmentioning

confidence: 99%

Hidden population size estimation and diagnostics using two respondent‐driven samples with applications in Armenia

Kim

Johnston²,

Grigoryan

et al. 2023

Biometrical J

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Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture-recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture-recapture to illustrate volatility across estimation methods.

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A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists

Cited by 5 publications

References 26 publications

Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree

Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree

Using an intersectionality-based approach to evaluate mental health services use among gay, bisexual and other men who have sex with men in Montreal, Toronto and Vancouver

Hidden population size estimation and diagnostics using two respondent‐driven samples with applications in Armenia

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