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.