scholarly journals Population Size Estimations Among Hidden Populations Using Respondent-Driven Sampling Surveys: Case Studies From Armenia

10.2196/12034 ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e12034 ◽  
Author(s):  
Katherine R McLaughlin ◽  
Lisa G Johnston ◽  
Laura J Gamble ◽  
Trdat Grigoryan ◽  
Arshak Papoyan ◽  
...  
Keyword(s):  
Population Size ◽  
Case Studies ◽  
Respondent Driven Sampling ◽  
Hidden Populations

scholarly journals Population Size Estimations Among Hidden Populations Using Respondent-Driven Sampling Surveys: Case Studies From Armenia (Preprint)

2018 ◽  
Author(s):  
Katherine R McLaughlin ◽  
Lisa G Johnston ◽  
Laura J Gamble ◽  
Trdat Grigoryan ◽  
Arshak Papoyan ◽  
...  
Keyword(s):  
Population Size ◽  
Measurement Errors ◽  
Network Size ◽  
Respondent Driven Sampling ◽  
Size Estimation ◽  
Hidden Populations ◽  
Care Needs ◽  
Personal Network ◽  
Successive Sampling ◽  
Population Size Estimates

BACKGROUND Estimates of the sizes of hidden populations, including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID), are essential for understanding the magnitude of vulnerabilities, health care needs, risk behaviors, and HIV and other infections. OBJECTIVE This article advances the successive sampling-population size estimation (SS-PSE) method by examining the performance of a modification allowing visibility to be jointly modeled with population size in the context of 15 datasets. Datasets are from respondent-driven sampling (RDS) surveys of FSW, MSM, and PWID from three cities in Armenia. We compare and evaluate the accuracy of our imputed visibility population size estimates to those found for the same populations through other unpublished methods. We then suggest questions that are useful for eliciting information needed to compute SS-PSE and provide guidelines and caveats to improve the implementation of SS-PSE for real data. METHODS SS-PSE approximates the RDS sampling mechanism via the successive sampling model and uses the order of selection of the sample to provide information on the distribution of network sizes over the population members. We incorporate visibility imputation, a measure of a person’s propensity to participate in the study, given that inclusion probabilities for RDS are unknown and social network sizes, often used as a proxy for inclusion probability, are subject to measurement errors from self-reported study data. RESULTS FSW in Yerevan (2012, 2016) and Vanadzor (2016) as well as PWID in Yerevan (2014), Gyumri (2016), and Vanadzor (2016) had great fits with prior estimations. The MSM populations in all three cities had inconsistencies with expert prior values. The maximum low prior value was larger than the minimum high prior value, making a great fit impossible. One possible explanation is the inclusion of transgender individuals in the MSM populations during these studies. There could be differences between what experts perceive as the size of the population, based on who is an eligible member of that population, and what members of the population perceive. There could also be inconsistencies among different study participants, as some may include transgender individuals in their accounting of personal network size, while others may not. Because of these difficulties, the transgender population was split apart from the MSM population for the 2018 study. CONCLUSIONS Prior estimations from expert opinions may not always be accurate. RDS surveys should be assessed to ensure that they have met all of the assumptions, that variables have reached convergence, and that the network structure of the population does not have bottlenecks. We recommend that SS-PSE be used in conjunction with other population size estimations commonly used in RDS, as well as results of other years of SS-PSE, to ensure generation of the most accurate size estimation.

scholarly journals Assessing Bias in Population Size Estimates Among Hidden Populations When Using the Service Multiplier Method Combined With Respondent-Driven Sampling Surveys: Survey Study

10.2196/15044 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e15044
Author(s):  
Sungai T Chabata ◽  
Elizabeth Fearon ◽  
Emily L Webb ◽  
Helen A Weiss ◽  
James R Hargreaves ◽  
...  
Keyword(s):  
Population Size ◽  
Sex Workers ◽  
Multiplier Method ◽  
Survey Study ◽  
Respondent Driven Sampling ◽  
Size Estimation ◽  
Hidden Populations ◽  
Increased Risk ◽  
Population Size Estimates ◽  
Size Estimates

Background Population size estimates (PSEs) for hidden populations at increased risk of HIV, including female sex workers (FSWs), are important to inform public health policy and resource allocation. The service multiplier method (SMM) is commonly used to estimate the sizes of hidden populations. We used this method to obtain PSEs for FSWs at 9 sites in Zimbabwe and explored methods for assessing potential biases that could arise in using this approach. Objective This study aimed to guide the assessment of biases that arise when estimating the population sizes of hidden populations using the SMM combined with respondent-driven sampling (RDS) surveys. Methods We conducted RDS surveys at 9 sites in late 2013, where the Sisters with a Voice program (the program), which collects program visit data of FSWs, was also present. Using the SMM, we obtained PSEs for FSWs at each site by dividing the number of FSWs who attended the program, based on program records, by the RDS-II weighted proportion of FSWs who reported attending this program in the previous 6 months in the RDS surveys. Both the RDS weighting and SMM make a number of assumptions, potentially leading to biases if the assumptions are not met. To test these assumptions, we used convergence and bottleneck plots to assess seed dependence of RDS-II proportion estimates, chi-square tests to assess if there was an association between the characteristics of FSWs and their knowledge of program existence, and logistic regression to compare the characteristics of FSWs attending the program with those recruited to RDS surveys. Results The PSEs ranged from 194 (95% CI 62-325) to 805 (95% CI 456-1142) across 9 sites from May to November 2013. The 95% CIs for the majority of sites were wide. In some sites, the RDS-II proportion of women who reported program use in the RDS surveys may have been influenced by the characteristics of selected seeds, and we also observed bottlenecks in some sites. There was no evidence of association between characteristics of FSWs and knowledge of program existence, and in the majority of sites, there was no evidence that the characteristics of the populations differed between RDS and program data. Conclusions We used a series of rigorous methods to explore potential biases in our PSEs. We were able to identify the biases and their potential direction, but we could not determine the ultimate direction of these biases in our PSEs. We have evidence that the PSEs in most sites may be biased and a suggestion that the bias is toward underestimation, and this should be considered if the PSEs are to be used. These tests for bias should be included when undertaking population size estimation using the SMM combined with RDS surveys.

scholarly journals Assessing Bias in Population Size Estimates Among Hidden Populations When Using the Service Multiplier Method Combined With Respondent-Driven Sampling Surveys: Survey Study (Preprint)

2019 ◽  
Author(s):  
Sungai T Chabata ◽  
Elizabeth Fearon ◽  
Emily L Webb ◽  
Helen A Weiss ◽  
James R Hargreaves ◽  
...  
Keyword(s):  
Population Size ◽  
Sex Workers ◽  
Multiplier Method ◽  
Survey Study ◽  
Respondent Driven Sampling ◽  
Size Estimation ◽  
Hidden Populations ◽  
Increased Risk ◽  
Population Size Estimates ◽  
Size Estimates

BACKGROUND Population size estimates (PSEs) for hidden populations at increased risk of HIV, including female sex workers (FSWs), are important to inform public health policy and resource allocation. The service multiplier method (SMM) is commonly used to estimate the sizes of hidden populations. We used this method to obtain PSEs for FSWs at 9 sites in Zimbabwe and explored methods for assessing potential biases that could arise in using this approach. OBJECTIVE This study aimed to guide the assessment of biases that arise when estimating the population sizes of hidden populations using the SMM combined with respondent-driven sampling (RDS) surveys. METHODS We conducted RDS surveys at 9 sites in late 2013, where the Sisters with a Voice program (the program), which collects program visit data of FSWs, was also present. Using the SMM, we obtained PSEs for FSWs at each site by dividing the number of FSWs who attended the program, based on program records, by the RDS-II weighted proportion of FSWs who reported attending this program in the previous 6 months in the RDS surveys. Both the RDS weighting and SMM make a number of assumptions, potentially leading to biases if the assumptions are not met. To test these assumptions, we used convergence and bottleneck plots to assess seed dependence of RDS-II proportion estimates, chi-square tests to assess if there was an association between the characteristics of FSWs and their knowledge of program existence, and logistic regression to compare the characteristics of FSWs attending the program with those recruited to RDS surveys. RESULTS The PSEs ranged from 194 (95% CI 62-325) to 805 (95% CI 456-1142) across 9 sites from May to November 2013. The 95% CIs for the majority of sites were wide. In some sites, the RDS-II proportion of women who reported program use in the RDS surveys may have been influenced by the characteristics of selected seeds, and we also observed bottlenecks in some sites. There was no evidence of association between characteristics of FSWs and knowledge of program existence, and in the majority of sites, there was no evidence that the characteristics of the populations differed between RDS and program data. CONCLUSIONS We used a series of rigorous methods to explore potential biases in our PSEs. We were able to identify the biases and their potential direction, but we could not determine the ultimate direction of these biases in our PSEs. We have evidence that the PSEs in most sites may be biased and a suggestion that the bias is toward underestimation, and this should be considered if the PSEs are to be used. These tests for bias should be included when undertaking population size estimation using the SMM combined with RDS surveys.

scholarly journals Respondent-driven sampling and an unusual epidemic

2016 ◽  
Vol 53 (2) ◽  
pp. 518-530 ◽  
Author(s):  
J. Malmros ◽  
F. Liljeros ◽  
T. Britton
Keyword(s):  
Population Size ◽  
Current Practice ◽  
Relative Size ◽  
Respondent Driven Sampling ◽  
Study Participation ◽  
Hidden Populations ◽  
Infinite Population ◽  
Major Outbreak

Abstract Respondent-driven sampling (RDS) is frequently used when sampling from hidden populations. In RDS, sampled individuals pass on participation coupons to at most c of their acquaintances in the community (c = 3 being a common choice). If these individuals choose to participate, they in turn pass coupons on to their acquaintances, and so on. The process of recruiting is shown to behave like a new Reed–Frost-type network epidemic, in which 'becoming infected' corresponds to study participation. We calculate R0, the probability of a major 'outbreak', and the relative size of a major outbreak for c < ∞ in the limit of infinite population size and compare to the standard Reed–Frost epidemic. Our results indicate that c should often be chosen larger than in current practice.

scholarly journals B-Graph Sampling to Estimate the Size of a Hidden Population

2015 ◽  
Vol 31 (4) ◽  
pp. 723-736 ◽  
Author(s):  
Marinus Spreen ◽  
Stefan Bogaerts
Keyword(s):  
Social Network ◽  
Sampling Design ◽  
Respondent Driven Sampling ◽  
Sampling Frame ◽  
Hidden Populations ◽  
Hidden Population ◽  
Graph Sampling ◽  
Graph Design ◽  
Applied Design ◽  
Incomplete Sampling

Abstract Link-tracing designs are often used to estimate the size of hidden populations by utilizing the relational links between their members. A major problem in studies of hidden populations is the lack of a convenient sampling frame. The most frequently applied design in studies of hidden populations is respondent-driven sampling in which no sampling frame is used. However, in some studies multiple but incomplete sampling frames are available. In this article, we introduce the B-graph design that can be used in such situations. In this design, all available incomplete sampling frames are joined and turned into one sampling frame, from which a random sample is drawn and selected respondents are asked to mention their contacts. By considering the population as a bipartite graph of a two-mode network (those from the sampling frame and those who are not on the frame), the number of respondents who are directly linked to the sampling frame members can be estimated using Chao’s and Zelterman’s estimators for sparse data. The B-graph sampling design is illustrated using the data of a social network study from Utrecht, the Netherlands.

scholarly journals Software Application Profile: The Anchored Multiplier calculator—a Bayesian tool to synthesize population size estimates

2019 ◽  
Vol 48 (6) ◽  
pp. 1744-1749
Author(s):  
Paul D Wesson ◽  
Willi McFarland ◽  
Cong Charlie Qin ◽  
Ali Mirzazadeh
Keyword(s):  
Population Size ◽  
Web Application ◽  
Probability Distributions ◽  
Public Health Research ◽  
Forest Plot ◽  
Hidden Populations ◽  
Software Application ◽  
Posterior Probability Distribution ◽  
Population Size Estimates ◽  
Size Estimates

Abstract Estimating the number of people in hidden populations is needed for public health research, yet available methods produce highly variable and uncertain results. The Anchored Multiplier calculator uses a Bayesian framework to synthesize multiple population size estimates to generate a consensus estimate. Users submit point estimates and lower/upper bounds which are converted to beta probability distributions and combined to form a single posterior probability distribution. The Anchored Multiplier calculator is available as a web browser-based application. The software allows for unlimited empirical population size estimates to be submitted and combined according to Bayes Theorem to form a single estimate. The software returns output as a forest plot (to visually compare data inputs and the final Anchored Multiplier estimate) and a table that displays results as population percentages and counts. The web application ‘Anchored Multiplier Calculator’ is free software and is available at [http://globalhealthsciences.ucsf.edu/resources/tools] or directly at [http://anchoredmultiplier.ucsf.edu/].

scholarly journals Estimating the Population Size of Female Sex Workers in Namibia Using a Respondent-Driven Sampling Adjustment to the Reverse Tracking Method: A Novel Approach (Preprint)

2018 ◽  
Author(s):  
Paul Douglas Wesson ◽  
Rajatashuvra Adhikary ◽  
Anna Jonas ◽  
Krysta Gerndt ◽  
Ali Mirzazadeh ◽  
...  
Keyword(s):  
Population Size ◽  
Sex Workers ◽  
Female Sex Workers ◽  
Respondent Driven Sampling ◽  
Double Counting ◽  
Size Estimation ◽  
Tracking Method ◽  
Female Sex ◽  
Population Size Estimates ◽  
Size Estimates

BACKGROUND Key populations, including female sex workers (FSWs), are at a disproportionately high risk for HIV infection. Estimates of the size of these populations serve as denominator data to inform HIV prevention and treatment programming and are necessary for the equitable allocation of limited public health resources. OBJECTIVE This study aimed to present the respondent-driven sampling (RDS) adjusted reverse tracking method (RTM; RadR), a novel population size estimation approach that combines venue mapping data with RDS data to estimate the population size, adjusted for double counting and nonattendance biases. METHODS We used data from a 2014 RDS survey of FSWs in Windhoek and Katima Mulilo, Namibia, to demonstrate the RadR method. Information from venue mapping and enumeration from the survey formative assessment phase were combined with survey-based venue-inquiry questions to estimate population size, adjusting for double counting, and FSWs who do not attend venues. RadR estimates were compared with the official population size estimates, published by the Namibian Ministry of Health and Social Services (MoHSS), and with the unadjusted RTM. RESULTS Using the RadR method, we estimated 1552 (95% simulation interval, SI, 1101-2387) FSWs in Windhoek and 453 (95% SI: 336-656) FSWs in Katima Mulilo. These estimates were slightly more conservative than the MoHSS estimates—Windhoek: 3000 (1800-3400); Katima Mulilo: 800 (380-2000)—though not statistically different. We also found 75 additional venues in Windhoek and 59 additional venues in Katima Mulilo identified by RDS participants’ responses that were not detected during the initial mapping exercise. CONCLUSIONS The RadR estimates were comparable with official estimates from the MoHSS. The RadR method is easily integrated into RDS studies, producing plausible population size estimates, and can also validate and update key population maps for outreach and venue-based sampling.

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