Abstract Body

Nigeria has the fourth largest HIV burden globally. Key populations (KP), including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID), are more vulnerable to HIV than the general population owing to stigma and discrimination, and often have poor social visibility. Previous population size estimates (PSE) in Nigeria were based on programmatic mapping of hotspots with enumeration of KP at venues. The results failed to account for KP who were not present at venues, resulting in underestimates of population sizes that also lacked precision. Reliable PSE are needed to guide focused and appropriately scaled HIV epidemic response efforts for KP. We used novel approaches for sampling and analysis to calculate PSE in Nigeria.

We used three-source capture-recapture (3S-CRC) to estimate the size of KPs in seven states in Nigeria (October–December 2018). Hotspots were mapped just before 3S-CRC sampling. We independently sampled FSW, MSM, and PWID 3 times approximately 1 week apart. During encounters at KP hotspots, distributors offered inexpensive and memorable objects to FSW, MSM, and PWID that were unique to each capture round and KP. In subsequent rounds, participants were offered an object and asked to describe those received during previous rounds; we tallied correct identifications of the object. Distributors recorded responses on tablets using REDCap™ software and uploaded data to a secure central server. Data were aggregated by KP and state for analysis. Median PSEs were derived using Bayesian nonparametric latent-class models with 80% highest density intervals (HDI) for precision.

During three rounds of independent captures in each state, there were approximately 310,000 encounters in 13,899 hotspots. Table 1 summarizes median PSE by KP and state.

We are the first to implement 3S-CRC to calculate median PSE with 80% HDI in Nigeria. Overall, our PSEs were larger than previously documented for each KP in each state. Empirical methods and analysis using Bayesian models that account for factors (i.e., social visibility and stigma) that influence heterogeneous capture probabilities may produce more accurate PSE. The large estimates suggest a need for programmatic scale-up to reach these populations with high HIV risk. 3S-CRC methods, in similar epidemic settings, could help estimate critical population denominator data needed to inform HIV prevention and treatment programs.