Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol

Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol

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 Appli...

Full description

Saved in:
Bibliographic Details
Main Authors: 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, Isaac Kisiangani, Palwende Boua, Eric Maimela, Damazo T Kadengye, Michelle Kamp, Daniel Maina Nderitu, Phelelani Thokozani Mpangase, Kayode Adetunji, Samuel Iddi, Skyler Speakman, Scott Hazelhurst, Kerry Glover, Tabitha Osler, Tanya Akumu, Diana Awuor, Victoria Bronstein, Joan Byamugisha, Jacques D Du Toit, Barry Dwolatzky, Paul A Harris, Celeste Holden, Nhlamulo Khoza, Faith Kimongo, Dekuwin E Kogda, Michael Klipin, Stephen P Levitt, Dylan Maghini, Karabo Maila, Ndivhuwo Makondo, Molulaqhooa Linda Maoyi, Reineilwe Given Mashaba, Nkosinathi Gabriel Masilela, Theophilous Mathema, Daphine T Nyachowe, Evelyn Thsehla, Siphiwe A Thwala, Roy Zent, Patrick Opiyo Owili
Format: Article
Language:English
Published: BMJ Publishing Group 2025-07-01
Series:BMJ Health & Care Informatics
Online Access:https://informatics.bmj.com/content/32/1/e101294.full
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2632-1009

Similar Items