Estimation and Probabilistic Linkage in Sample Surveys of Anonymous Organisations

12 Steps ◽

Probabilistic Linkage ◽

The Individual ◽

Two Populations

<p>Drug use takes on many forms, normally this will be just the occasional alcoholic drink, certain individuals drug use develops into habitual use, or more extreme drugs, and then into full addiction. Some of these addicted individuals realise the harmful nature of their addition and join the anonymous support group, Narcotics Anonymous. This study focus' on the creation of population size estimates, and an estimate of the size of the persistent population between two survey years. These estimates are created from the 2004 and 2008 surveys run by the Narcotics Anonymous Fellowship, as this is an anonymous organisation with no register of the membership database maintained. Population size estimation for an anonymous organisation is established using simulation methods. The bootstrap estimation was used to estimate characteristics about the two populations. Probabilistic matching was used to identify individuals who were in both the 2004, and 2008 surveys. Once identi ed, a logistic regression model was used to establish what impacts an individual to remain in the programme. Factors that impacted an individual being persistent in the population included the individual education, employment status, and if they had worked through all the 12 steps of Narcotics Anonymous.</p>

Estimation and Probabilistic Linkage in Sample Surveys of Anonymous Organisations

10.26686/wgtn.17064704.v1 ◽

2021 ◽

Author(s):

◽

Nicholas Jury

Keyword(s):

Drug Use ◽

Population Size ◽

Narcotics Anonymous ◽

Size Estimation ◽

Bootstrap Estimation ◽

12 Steps ◽

Probabilistic Linkage ◽

The Individual ◽

Two Populations

Estimating the population size of people who inject drugs in Ho Chi Minh City, Vietnam: A two-survey capture-recapture exercise (Preprint)

10.2196/preprints.16459 ◽

2019 ◽

Author(s):

Abu Abdul-Quader

Keyword(s):

Population Size ◽

Illicit Drugs ◽

People Who Inject Drugs ◽

Care Service ◽

Ho Chi Minh City ◽

Size Estimation ◽

Capture Recapture ◽

The City ◽

BACKGROUND Population size estimation of people who inject drugs (PWID) in Ho Chi Minh City (HCMC), Vietnam relied on the UNAIDS Estimation and Projection Package and reports from the city police department. The two estimates vary widely. OBJECTIVE To estimate the population size of people who inject drugs in Ho Chi Minh City, Vietnam METHODS Using Respondent-driven sampling (RDS), we implemented two-source capture-recapture method to estimate the population size of PWID in HCMC in 2017 in 7 out of 24 districts. The study included men or women aged at least 18 years who reported injecting illicit drugs in the last 90 days and who had lived in the city the past six months. We calculated two sets of size estimates, the first assumed that all participants in each survey round resided in the district where the survey was conducted, the second, used the district of residence as reported by the participant. District estimates were summed to obtain an aggregate estimate for the seven districts. To calculate the city total, we weighted the population size estimates for each district by the inverse of the stratum specific sampling probabilities. RESULTS The first estimate resulted in a population size of 19,155 (95% CI: 17,006–25,039). The second one generated a smaller population size estimate of 12,867 (95% CI: 11,312–17,393). CONCLUSIONS The two-survey capture-recapture exercise provided two disparate estimates of PWID in HCMC. For planning HIV prevention and care service needs among PWID in HCMC, both estimates may need to be taken into consideration together with size estimates from other sources.

Use of Population-Based Surveys for Estimating the Population Size of Persons Who Inject Drugs in the United States

The Journal of Infectious Diseases ◽

10.1093/infdis/jiaa318 ◽

2020 ◽

Vol 222 (Supplement_5) ◽

pp. S218-S229

Author(s):

Heather Bradley ◽

Elizabeth M Rosenthal ◽

Meredith A Barranco ◽

Tomoko Udo ◽

Patrick S Sullivan ◽

...

Keyword(s):

United States ◽

Drug Use ◽

Population Size ◽

Prescription Drugs ◽

The United States ◽

Population Based ◽

Self Report ◽

Persons Who Inject Drugs ◽

Prevalence Estimates

Abstract Background In the United States, injection is an increasingly common route of administration for opioids and other substances. Estimates of the number of persons who inject drugs (PWID) are needed for monitoring risk-specific infectious disease rates and health services coverage. Methods We reviewed design and instruments for 4 national household surveys, 2012–2016, for their ability to produce unbiased injection drug use (IDU) prevalence estimates. We explored potential analytic adjustments for reducing biases through use of external data on (1) arrest, (2) narcotic overdose mortality, and (3) biomarker-based sensitivity of self-reported illicit drug use. Results Estimated national past 12 months IDU prevalence ranged from 0.24% to 0.59% across surveys. All surveys excluded unstably housed and incarcerated persons, and estimates were based on <60 respondents reporting IDU behavior in 3 surveys. No surveys asked participants about nonmedical injection of prescription drugs. Analytic adjustments did not appreciably change IDU prevalence estimates due to suboptimal specificity of data points. Conclusions PWID population size estimates in the United States are based on small numbers and are likely biased by undercoverage of key populations and self-report. Novel methods as discussed in this article may improve our understanding of PWID population size and their health needs.

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)

10.2196/preprints.11737 ◽

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 ◽

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.

Population Size Estimation of Gay and Bisexual Men and Other Men Who Have Sex With Men Using Social Media-Based Platforms (Preprint)

10.2196/preprints.9321 ◽

2017 ◽

Author(s):

Stefan Baral ◽

Rachael M Turner ◽

Carrie E Lyons ◽

Sean Howell ◽

Brian Honermann ◽

...

Keyword(s):

Social Media ◽

Hiv Prevention ◽

Population Size ◽

The United States ◽

Size Estimation ◽

Same Sex ◽

Care Needs ◽

Sex With Men ◽

BACKGROUND Gay, bisexual, and other cisgender men who have sex with men (GBMSM) are disproportionately affected by the HIV pandemic. Traditionally, GBMSM have been deemed less relevant in HIV epidemics in low- and middle-income settings where HIV epidemics are more generalized. This is due (in part) to how important population size estimates regarding the number of individuals who identify as GBMSM are to informing the development and monitoring of HIV prevention, treatment, and care programs and coverage. However, pervasive stigma and criminalization of same-sex practices and relationships provide a challenging environment for population enumeration, and these factors have been associated with implausibly low or absent size estimates of GBMSM, thereby limiting knowledge about the dynamics of HIV transmission and the implementation of programs addressing GBMSM. OBJECTIVE This study leverages estimates of the number of members of a social app geared towards gay men (Hornet) and members of Facebook using self-reported relationship interests in men, men and women, and those with at least one reported same-sex interest. Results were categorized by country of residence to validate official size estimates of GBMSM in 13 countries across five continents. METHODS Data were collected through the Hornet Gay Social Network and by using an a priori determined framework to estimate the numbers of Facebook members with interests associated with GBMSM in South Africa, Ghana, Nigeria, Senegal, Côte d'Ivoire, Mauritania, The Gambia, Lebanon, Thailand, Malaysia, Brazil, Ukraine, and the United States. These estimates were compared with the most recent Joint United Nations Programme on HIV/AIDS (UNAIDS) and national estimates across 143 countries. RESULTS The estimates that leveraged social media apps for the number of GBMSM across countries are consistently far higher than official UNAIDS estimates. Using Facebook, it is also feasible to assess the numbers of GBMSM aged 13-17 years, which demonstrate similar proportions to those of older men. There is greater consistency in Facebook estimates of GBMSM compared to UNAIDS-reported estimates across countries. CONCLUSIONS The ability to use social media for epidemiologic and HIV prevention, treatment, and care needs continues to improve. Here, a method leveraging different categories of same-sex interests on Facebook, combined with a specific gay-oriented app (Hornet), demonstrated significantly higher estimates than those officially reported. While there are biases in this approach, these data reinforce the need for multiple methods to be used to count the number of GBMSM (especially in more stigmatizing settings) to better inform mathematical models and the scale of HIV program coverage. Moreover, these estimates can inform programs for those aged 13-17 years; a group for which HIV incidence is the highest and HIV prevention program coverage, including the availability of pre-exposure prophylaxis (PrEP), is lowest. Taken together, these results highlight the potential for social media to provide comparable estimates of the number of GBMSM across a large range of countries, including some with no reported estimates.

shinyrecap: A Shiny Application for Population Size Estimation from Capture-Recapture Data (Preprint)

10.2196/preprints.32645 ◽

2021 ◽

Author(s):

Anne F. McIntyre ◽

Ian E. Fellows ◽

Steve Gutreuter ◽

Wolfgang Hladik

Keyword(s):

Population Size ◽

Sex Workers ◽

Female Sex Workers ◽

Population Size Estimation ◽

Sub Saharan Africa ◽

Size Estimation ◽

Female Sex ◽

Populations At Risk ◽

Capture Recapture

BACKGROUND Capture-recapture is often used to estimate the size of populations at risk for HIV, including female sex workers, men who have sex with men, and people who inject drugs. These population size estimates are critical in determining resource allocation for HIV services geared toward these communities. OBJECTIVE Compared to the commonly used two-source capture-recapture, capture-recapture relying on three (or more) samples can provide more robust PSE but involve far more complex statistical analysis. shinyrecap is designed to provide a user-friendly interface for the field epidemiologist. METHODS shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages. Additionally, the application may be accessed online or run locally on the user’s machine. RESULTS The application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided. CONCLUSIONS Through a use case, we demonstrate the broad utility of this powerful tool with three-source capture-recapture data to produce population size estimation for female sex workers in a subnational unit of a country in sub-Saharan Africa.

Inferring pandemic growth rates from sequence data

Journal of The Royal Society Interface ◽

10.1098/rsif.2011.0850 ◽

2012 ◽

Vol 9 (73) ◽

pp. 1797-1808 ◽

Cited By ~ 27

Author(s):

Eric de Silva ◽

Neil M. Ferguson ◽

Christophe Fraser

Keyword(s):

Population Size ◽

Effective Population Size ◽

Sequence Data ◽

Simulated Data ◽

Parametric Methods ◽

Sampling Strategies ◽

Size Estimation ◽

Effective Population ◽

Non Parametric

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

Estimating the Size of Key Populations in Kampala, Uganda: 3-Source Capture-Recapture Study (Preprint)

10.2196/preprints.12118 ◽

2018 ◽

Author(s):

Reena H Doshi ◽

Kevin Apodaca ◽

Moses Ogwal ◽

Rommel Bain ◽

Ermias Amene ◽

...

Keyword(s):

Population Size ◽

Latent Class ◽

Latent Class Model ◽

Key Populations ◽

Size Estimation ◽

Public Health Response ◽

Hiv Epidemic ◽

Capture Recapture ◽

BACKGROUND Key populations, including people who inject drugs (PWID), men who have sex with men (MSM), and female sex workers (FSW), are disproportionately affected by the HIV epidemic. Understanding the magnitude of, and informing the public health response to, the HIV epidemic among these populations requires accurate size estimates. However, low social visibility poses challenges to these efforts. OBJECTIVE The objective of this study was to derive population size estimates of PWID, MSM, and FSW in Kampala using capture-recapture. METHODS Between June and October 2017, unique objects were distributed to the PWID, MSM, and FSW populations in Kampala. PWID, MSM, and FSW were each sampled during 3 independent captures; unique objects were offered in captures 1 and 2. PWID, MSM, and FSW sampled during captures 2 and 3 were asked if they had received either or both of the distributed objects. All captures were completed 1 week apart. The numbers of PWID, MSM, and FSW receiving one or both objects were determined. Population size estimates were derived using the Lincoln-Petersen method for 2-source capture-recapture (PWID) and Bayesian nonparametric latent-class model for 3-source capture-recapture (MSM and FSW). RESULTS We sampled 467 PWID in capture 1 and 450 in capture 2; a total of 54 PWID were captured in both. We sampled 542, 574, and 598 MSM in captures 1, 2, and 3, respectively. There were 70 recaptures between captures 1 and 2, 103 recaptures between captures 2 and 3, and 155 recaptures between captures 1 and 3. There were 57 MSM captured in all 3 captures. We sampled 962, 965, and 1417 FSW in captures 1, 2, and 3, respectively. There were 316 recaptures between captures 1 and 2, 214 recaptures between captures 2 and 3, and 235 recaptures between captures 1 and 3. There were 109 FSW captured in all 3 rounds. The estimated number of PWID was 3892 (3090-5126), the estimated number of MSM was 14,019 (95% credible interval (CI) 4995-40,949), and the estimated number of FSW was 8848 (95% CI 6337-17,470). CONCLUSIONS Our population size estimates for PWID, MSM, and FSW in Kampala provide critical population denominator data to inform HIV prevention and treatment programs. The 3-source capture-recapture is a feasible method to advance key population size estimation.

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

10.2196/preprints.12034 ◽

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.

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

JMIR Public Health and Surveillance ◽

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 ◽

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.