Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol - Kobus Herbst; Stephen Tollman; Kathleen Kahn; Francesc Xavier Gómez-Olivé; Jaya George; Catherine Kyobutungi; Karen Hofman; Gershim Asiki; Michèle Ramsay; Daniel Ohene-Kwofie; Chodziwadziwa W Kabudula; Helen Robertson; et al. (BMJ Health & Care Informatics, 2025)
Introduction Multimorbidity (MM), defined as two or more chronic diseases in an individual, is linked to adverse outcomes. MM is increasing in sub-Saharan Africa due to rapidly advancing epidemiological and social transitions. The Multimorbidity in Africa: Digital Innovation, Visualisation and Application Research Hub (MADIVA) aims to address MM by developing data science solutions informed by stakeholder engagement.Methods and analysis MADIVA uses complex, individual-level datasets from research centres in rural Bushbuckridge, South Africa and urban Nairobi, Kenya. These datasets will be harmonised, linked and curated, and then used to develop MM risk prediction models, novel data science methods and interactive dashboards for research and clinical use. Pilot projects and mentorship programmes will support data science capacity development.Ethics and dissemination Ethics approval has been granted. Dissemination will occur through scientific meetings and publications. MADIVA is committed to making data FAIR: findable, accessible, interoperable and reusable.