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

TY - JOUR

T1 - Leveraging data science to understand and address multimorbidity in sub-Saharan Africa

T2 - The MADIVA protocol

AU - Glover, Kerry

AU - Osler, Tabitha

AU - Adetunji, Kayode

AU - Akumu, Tanya

AU - Asiki, Gershim

AU - Awuor, Diana

AU - Boua, Palwendé

AU - Bronstein, Victoria

AU - Byamugisha, Joan

AU - Du Toit, Jacques D.

AU - Dwolatzky, Barry

AU - George, Jaya

AU - Harris, Paul A.

AU - Herbst, Kobus

AU - Hofman, Karen

AU - Holden, Celeste

AU - Iddi, Samuel

AU - Kadengye, Damazo T.

AU - Kahn, Kathleen

AU - Kamp, Michelle

AU - Khoza, Nhlamulo

AU - Kimongo, Faith

AU - Kisiangani, Isaac

AU - Kogda, Dekuwin E.

AU - Klipin, Michael

AU - Levitt, Stephen P.

AU - Maghini, Dylan

AU - Maila, Karabo

AU - Maimela, Eric

AU - Nderitu, Daniel Maina

AU - Makondo, Ndivhuwo

AU - Maoyi, Molulaqhooa Linda

AU - Mashaba, Reineilwe Given

AU - Masilela, Nkosinathi Gabriel

AU - Mathema, Theophilous

AU - Mpangase, Phelelani Thokozani

AU - Nyachowe, Daphine T.

AU - Ohene-Kwofie, Daniel

AU - Robertson, Helen

AU - Speakman, Skyler

AU - Thsehla, Evelyn

AU - Thwala, Siphiwe A.

AU - Zent, Roy

AU - Gómez-Olivé, Francesc Xavier

AU - Kabudula, Chodziwadziwa W.

AU - Owili, Patrick Opiyo

AU - Kyobutungi, Catherine

AU - Ramsay, Michèle

AU - Tollman, Stephen

AU - Hazelhurst, Scott

N1 - Publisher Copyright: © Author(s) (or their employer(s)) 2025.

PY - 2025/7/10

Y1 - 2025/7/10

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105010606810

U2 - 10.1136/bmjhci-2024-101294

DO - 10.1136/bmjhci-2024-101294

M3 - Article

C2 - 40639840

AN - SCOPUS:105010606810

SN - 2632-1009

VL - 32

JO - BMJ Health and Care Informatics

JF - BMJ Health and Care Informatics

IS - 1

M1 - e101294

ER -