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 - 2058-4555
VL - 32
JO - Journal of Innovation in Health Informatics
JF - Journal of Innovation in Health Informatics
IS - 1
M1 - e101294
ER -