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

Triangulating Truth and Reaching Consensus on Population Size, Prevalence, and More: Modeling Study

BACKGROUND

Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate.

OBJECTIVE

This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data.

METHODS

In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation.

RESULTS

The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation.

CONCLUSIONS

The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model's effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.

Population size estimation of transgender women and men in Bhutan

INTRODUCTION

Transgender persons experience health disparities and are marginalized in many societies worldwide. Even their numbers are unknown in many countries. We conducted the first effort to estimate the population size of transgender women (TGW) and transgender men (TGM) in Bhutan from November 2019 to January 2020.

METHODS

Community-based surveys of TGW and TGM integrated several methods to estimate the size of hidden populations, including key informant mapping, wisdom-of-the-crowd, the service multiplier, and the unique object multiplier. Results of the several methods were synthesized using a Bayesian approach.

RESULTS

Surveys included 34 TGW and 124 TGM. TGW was persons assigned to the male sex at birth and currently self-identified as "trans women" (91%), "women" (6%), or another gender (3%). TGM were persons assigned female sex at birth and self-identified as "trans men" (100%). Bayesian synthesis of the multiple methods estimated 84 TGW (credible interval 61-110) and 166 TGM (credible interval 124-211) in Bhutan.

CONCLUSIONS

Our study documented that TGW and TGM are part of Bhutanese society, with TGW constituting 0.03% of adult women and TGM 0.06% of adult men. Estimates can help advocate for resources and programs to address the health and well-being of these communities.

Characteristics and Population Size Estimation of Female Sex Workers in Bhutan

BACKGROUND

Knowing the number of persons at risk for sexually transmitted diseases (STDs) and their risk behaviors are needed to allocate limited resources, set targets for prevention and care activities, gauge the reach of programs, and assess their impact. Female sex workers (FSWs) are a population at high risk for STD worldwide; little is known about FSW in Bhutan.

METHODS

We conducted a community-based survey and population size exercise of FSW in Bhutan. The survey used a hybrid venue-based/peer-referral sampling method. Population size estimation methods were key informant mapping, census and enumeration, unique object multiplier, and capture-recapture.

RESULTS

Of 517 women surveyed, 67.5% provided sex for money in the last year. Compared with FSWs at venues, FSWs referred by peers were more likely to have sex with alcohol (80.1% vs. 51.5%, P < 0.001) and more paying partners (mean, 3.5 vs. 2.0; P = 0.001), and less likely to have tested for an STD (28.3% vs. 51.0%, P < 0.001) or have outreach worker contact (27.6% vs. 41.5%, P = 0.007). The estimated number of FSWs in the 9 districts was 353 (95% confidence interval, 345-362). Extrapolation to the whole country projected 597 (417-777) FSWs nationally.

CONCLUSIONS

Our estimate of the number of FSW in Bhutan corresponds to 0.71% of adult urban women, a figure in line with other countries in South and Southeast Asia. Our data highlight the need for outreach beyond venues where women are employed to reach FSW at higher risk for STD but who have less access to interventions.

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

Background

Population size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis.

Objective

This study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE.

Methods

shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). 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 the 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 demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa.

Data Smoothing, Extrapolation, and Triangulation

While population size data at the national and subnational levels are critical for planning and resource allocation, most PSE studies are conducted in a few selected cities and towns due to resource and time constraints. Extrapolation methods to estimate the population size for locations not included in the PSE exercise are critical to providing data for all subnational jurisdictions and the entire country as a whole. The decision on how to select cities or towns that reflect best the national picture is a critical first step. You may need to include cities or towns from areas with low, medium, or high prevalence of your target population(s). Having some data from all these (three) areas increase the power of your study and helps to better extrapolate the results to unobserved areas, and to the national level. Through this process, data smoothing of subnational data is often needed due to relatively small samples size problem. In this chapter, we describe methods that are required for smoothing subnational data, model subnational data to extrapolate estimates to regional and national levels, and triangulate findings from different size estimation methods by a Bayesian framework to produce credible estimates.