Bayesian Estimation of MSM Population Size in Côte d'Ivoire

Area of Science:

• Public Health
• Epidemiology
• Biostatistics

Background:

• Côte d'Ivoire faces a generalized HIV epidemic, with key populations like men who have sex with men (MSM) disproportionately affected.
• Accurate subnational population size estimates for MSM are essential for effective HIV prevention and treatment program planning.
• Existing data on MSM populations in Côte d'Ivoire are limited to urban centers with community infrastructure.

Purpose of the Study:

• To estimate the population size of men who have sex with men (MSM) in areas of Côte d'Ivoire lacking empirical data.
• To inform evidence-based decisions for scaling HIV prevention and treatment programs targeting MSM.
• To guide the prioritization of future data collection efforts on MSM populations.

Main Methods:

• Utilized a Bayesian hierarchical regression model to estimate MSM population sizes.
• Imputed missing covariate data using geo-spatial information.
• Incorporated uncertainty quantification to provide confidence bounds for the estimates.

Main Results:

• Generated population size estimates for MSM in prioritized regions of Côte d'Ivoire where no prior data existed.
• Provided confidence bounds for the estimated MSM population sizes, reflecting model uncertainty.
• Identified areas where further empirical data collection on MSM could be beneficial.

Conclusions:

• The Bayesian modeling approach successfully provided crucial MSM population size estimates in data-scarce regions of Côte d'Ivoire.
• These estimates are vital for optimizing the allocation of resources for HIV prevention and treatment programs.
• The methodology facilitates evidence-based public health decision-making in HIV response efforts for key populations.