Frontiers in epidemiology · 2026
Spatial modeling of HIV prevalence in Malawi using generalized additive models.
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Tsala Dimbuene Zacharie, Mabika Mabika Crispin, Bawawana Bavwidinsi Blandine, Juakaly Wayisovia Emmanuel, Sampasa-Kanyinga Hugues
Аннотация
INTRODUCTION: Malawi has made substantial progress in HIV prevention and treatment, yet HIV prevalence remains unevenly distributed across the country. Sub-national estimates are needed to guide targeted interventions.
METHODS: We analyzed individual-level HIV biomarker data from the 2016 Malawi Demographic and Health Survey. A spatial modeling approach was applied to capture broad geographic patterns alongside sociodemographic determinants. High-resolution maps of predicted HIV prevalence were generated to visualize fine-scale differences across districts.
RESULTS: The analysis revealed persistent geographic disparities, with the highest prevalence concentrated in southern Malawi and more varied patterns in central and northern regions. Although geography contributed to explaining HIV variation, sociodemographic factors-including age, education, sex, and household characteristics-were the primary drivers in most districts. Geography emerged as the leading contributor in only 18% of areas.
DISCUSSION AND CONCLUSION: These findings provide policy-relevant, sub-national evidence to support more precise targeting of HIV prevention, testing, and treatment efforts. They underscore the importance of tailoring interventions to both geographic and sociodemographic contexts to accelerate progress toward epidemic control.
METHODS: We analyzed individual-level HIV biomarker data from the 2016 Malawi Demographic and Health Survey. A spatial modeling approach was applied to capture broad geographic patterns alongside sociodemographic determinants. High-resolution maps of predicted HIV prevalence were generated to visualize fine-scale differences across districts.
RESULTS: The analysis revealed persistent geographic disparities, with the highest prevalence concentrated in southern Malawi and more varied patterns in central and northern regions. Although geography contributed to explaining HIV variation, sociodemographic factors-including age, education, sex, and household characteristics-were the primary drivers in most districts. Geography emerged as the leading contributor in only 18% of areas.
DISCUSSION AND CONCLUSION: These findings provide policy-relevant, sub-national evidence to support more precise targeting of HIV prevention, testing, and treatment efforts. They underscore the importance of tailoring interventions to both geographic and sociodemographic contexts to accelerate progress toward epidemic control.