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.