Syndemic mapping of HIV and other STIs in KwaZulu-Natal: a Bayesian spatio-temporal modeling approach using latent constructs.

Syndemics involving Human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs) remain a major public health challenge in sub-Saharan Africa, and understanding their spatial and temporal dynamics is critical for effective interventions. Using data from two consecutive, population-based cross-sectional surveys conducted in 2014 and 2015 under the HIV Incidence Provincial Surveillance System (HIPSS) in KwaZulu-Natal, South Africa, we applied a Bayesian spatio-temporal framework grounded in latent variable modeling to quantify and map the syndemic burden of HIV and other STIs. A confirmatory factor analysis constructed a continuous latent syndemic score from four binary indicators (HIV diagnosis, HIV testing, STI diagnosis, and STI symptoms), which was modeled using Bayesian hierarchical spatial methods via Integrated Nested Laplace Approximation (INLA), incorporating spatial random effects through the Stochastic Partial Differential Equation (SPDE) approach and temporal effects via a first-order random walk. Local spatial autocorrelation, assessed using Local Moran's