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 <i>I</i> and Getis-Ord Gi^* statistics, revealed consistent hotspots and coldspots. Syndemic burden of HIV and other STIs was higher among younger adults (20-49 years), women, individuals with incomplete secondary education, those engaging in sexual risk behaviors or reporting forced sexual debut, and those facing socioeconomic vulnerabilities such as food insecurity. Access to healthcare and treatment for depression were also positively associated, likely reflecting increased detection. Local Moran's <i>I</i> identified 11 significant clusters (three hotspots, eight coldspots), and Getis-Ord Gi^* identified 32 (17 hotspots, 15 coldspots), with hotspot patterns persisting across both years, indicating temporal stability. These findings highlight the utility of Bayesian latent variable and spatio-temporal modeling in integrating multiple co-occurring health conditions into a single spatial framework, providing actionable evidence to support geographically targeted, multi-sectoral interventions that address structural and behavioral drivers of co-epidemics in resource-limited settings.