Key population size estimation in Nigeria: applying novel Bayesian methods for analysis of three-source capture-recapture data (Preprint)

BACKGROUND Nigeria has the fourth largest burden of HIV globally. Key populations (KP) including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have poor social visibility and are more vulnerable to HIV than the general population due to stigma, discrimination, and criminalization of KP-defining behaviors. Reliable, empirical population size estimates (PSE) are needed to guide focused and appropriately scaled HIV epidemic response efforts for KP. We used novel approaches to sampling and analysis to calculate PSE in Nigeria. OBJECTIVE We sampled the population using three-source capture-recapture (3S-CRC) and analyzed results using Bayesian nonparametric latent-class models to generate median PSE with 80% highest density intervals. METHODS During October–December 2018, we used three-source capture-recapture (3S-CRC) to estimate the size of KP in seven United States President’s Emergency Plan for AIDS Relief (PEPFAR) priority states in Nigeria. Hotspots were mapped before 3S-CRC started. We sampled FSW, MSM, and PWID during three independent captures approximately one week apart. During encounters in KP hotspots, distributors offered inexpensive and memorable objects to KP, unique to each capture round and KP type. In subsequent rounds, participants were offered an object and asked to produce or identify objects received during previous rounds (if any); affirmative responses were tallied upon producing or identifying the correct object. Distributors recorded responses on tablets and uploaded to a secure server after each encounter. Data were aggregated by KP and state for analysis. Median PSE were derived using Bayesian nonparametric latent-class models with 80% highest density intervals for precision. RESULTS We sampled approximately 310,000 persons at 9,015 hotspots during three independent captures in all seven states. Overall, FSW PSE ranged from 14,500-64,300; MSM PSE, 3,200-41,400; and PWID PSE, 3,400-30,400. CONCLUSIONS This study represents the first implementation of these 3S-CRC sampling and novel analysis methods for large-scale population size estimation in Nigeria. Overall, our estimates were larger than previously documented for each KP in all states. The current Bayesian models account for factors (i.e., social visibility and stigma) that influence heterogeneous capture probabilities resulting in more reliable PSE. The larger estimates suggest a need for programmatic scale-up to reach these populations at highest risk for HIV.

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

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.

Population size estimation of seasonal forest-going populations in southern Lao PDR

Forest-going populations are key to malaria transmission in the Greater Mekong Sub-region (GMS) and are therefore targeted for elimination efforts. Estimating the size of this population is essential for programs to assess, track and achieve their elimination goals. Leveraging data from three cross-sectional household surveys and one survey among forest-goers, the size of this high-risk population in a southern province of Lao PDR between December 2017 and November 2018 was estimated by two methods: population-based household surveys and capture–recapture. During the first month of the dry season, the first month of the rainy season, and the last month of the rainy season, respectively, 16.2% [14.7; 17.7], 9.3% [7.2; 11.3], and 5.3% [4.4; 6.1] of the adult population were estimated to have engaged in forest-going activities. The capture–recapture method estimated a total population size of 18,426 [16,529; 20,669] forest-goers, meaning 61.0% [54.2; 67.9] of the adult population had engaged in forest-going activities over the 12-month study period. This study demonstrates two methods for population size estimation to inform malaria research and programming. The seasonality and turnover within this forest-going population provide unique opportunities and challenges for control programs across the GMS as they work towards malaria elimination.

Population Size Estimation of Seasonal Forest-going Populations in Southern Lao PDR.

Forest-going populations are key to malaria transmission in the Greater Mekong Sub-region (GMS) and are therefore targeted for elimination efforts. Estimating the size of this population is essential for programs to assess, track and achieve their 2030 elimination goals.Leveraging data from three cross-sectional household surveys and one survey among forest-goers, the size of this high-risk population in a southern province of Lao PDR between December 2017 and November 2018 was estimated by two methods: population-based household surveys and capture-recapture.During the first month of the dry season, the first month of the rainy season, and the last month of the rainy season, respectively, 16.2% [14.7; 17.7], 9.3% [7.2; 11.3], and 5.3% [4.4; 6.1] of the adult population were estimated to have engaged in forest-going activities. The capture-recapture method estimated a total population size of 18,426 [16,529; 20,669] forest-goers, meaning 61.0% [54.2; 67.9] of the adult population had engaged in forest-going activities over the 12-month study period.This study demonstrates two methods for population size estimation to inform malaria research and programming. The seasonality and turnover within this forest-going population provide unique opportunities and challenges for control programs across the GMS as they work towards malaria elimination.

Methods for Estimating Population Size for Key Populations: A Global Scoping Review for HIV Research

Background: Estimating the population sizes of key populations is critical for understanding the overall HIV burden. This scoping review aims to synthesize existing methods for population size estimation among key populations (people who inject drugs, men who have sex with men (MSM), transgender persons, sex workers, and incarcerated individuals), and provide recommendations for future application of the existing methods.Main text: A scoping review was conducted and 39 of 688 studies met the inclusion criteria and were assessed. Estimation methods included five digital methods, one in-person method, and four hybrid methods. We summarized and organized the methods for population size estimation into the following five categories: methods based on independent samples (including capture-recapture method and multiplier method), methods based on population counting (including Delphi method and mapping method), methods based on the official report (including workbook method), methods based on social network (including respondent-driven sampling method and network scale-up method) and methods based on data-driven technologies (Bayesian estimation method, Stochastic simulation method, and LMS estimation method). 36 (92%) articles were published after 2010 and 23 (59%) used multiple methods. These include 11 in high-income countries and 28 in low-income countries. A total of 10 estimates the size of sex workers, 14 focused on MSM, and 10 focused on PWID. Conclusion: There was no gold standard for population size estimation. Among 120 studies that were related to population size estimation of key populations, the most commonly used population estimation method is the multiplier method (26/120 studies). Every method has its strengths. For example, some traditional methods are simple and easy to use for researchers. Some novel methods are time- and resources- saving. However, each method has its limitations and bias. For example, for the respondent-driven sampling method, stigma and discrimination may lead to the "hiddenness" of the key population; for the multiplier method, the quality of authentic data may also influence the accuracy of the estimation. In recent years, novel methods based on data-driven technologies such as Bayesian estimation have been developed and applied in many surveys.