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...
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2025-07-01
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| author | 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 |
| author_facet | 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 |
| author_sort | Kobus Herbst |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-82b17f04004640b8b0dd722788a1d87a |
| institution | Kabale University |
| issn | 2632-1009 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-82b17f04004640b8b0dd722788a1d87a2025-08-20T03:50:11ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092025-07-0132110.1136/bmjhci-2024-101294Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocolKobus Herbst0Stephen Tollman1Kathleen Kahn2Francesc Xavier Gómez-Olivé3Jaya George4Catherine Kyobutungi5Karen Hofman6Gershim Asiki7Michèle Ramsay8Daniel Ohene-Kwofie9Chodziwadziwa W Kabudula10Helen Robertson11Isaac Kisiangani12Palwende Boua13Eric Maimela14Damazo T Kadengye15Michelle Kamp16Daniel Maina Nderitu17Phelelani Thokozani Mpangase18Kayode Adetunji19Samuel Iddi20Skyler Speakman21Scott Hazelhurst22Kerry Glover23Tabitha Osler24Tanya Akumu25Diana Awuor26Victoria Bronstein27Joan Byamugisha28Jacques D Du Toit29Barry Dwolatzky30Paul A Harris31Celeste Holden32Nhlamulo Khoza33Faith Kimongo34Dekuwin E Kogda35Michael Klipin36Stephen P Levitt37Dylan Maghini38Karabo Maila39Ndivhuwo Makondo40Molulaqhooa Linda Maoyi41Reineilwe Given Mashaba42Nkosinathi Gabriel Masilela43Theophilous Mathema44Daphine T Nyachowe45Evelyn Thsehla46Siphiwe A Thwala47Roy Zent48Patrick Opiyo Owili49Africa Health Research Institute, Somkhele, South Africa1 SAMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Faculty Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, South Africa2University of the Witwatersrand, Johannesburg, South AfricaAfrican Population and Health Research Center, Nairobi, KenyaSAMRC/Centre for Health Economics and Decision Science—PRICELESS SA, School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa1 Chronic Disease Management Unit, African Population and Health Research Center, Nairobi, KenyaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Faculty of Health Sciences, Johannesburg, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaSchool of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South AfricaEmerging and Re-emerging Infectious Diseases Unit, African Population and Health Research Center, Nairobi, KenyaClinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Ouagadougou, Burkina FasoDIMAMO PHRC, University of Limpopo, Polokwane, South AfricaAfrican Population and Health Research Center, Nairobi, KenyaDivision of Human Genetics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaAfrican Population and Health Research Center (APHRC), APHRC Campus, Nairobi, KenyaSydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaAfrican Population and Health Research Center (APHRC), APHRC Campus, Nairobi, KenyaIBM Research Africa, Nairobi, KenyaSydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaIBM Research – Africa, Nairobi, KenyaResearch and Related Capacity Strengthening Unit, African Population and Health Research Center, Nairobi, KenyaSchool of Law, University of the Witwatersrand, Johannesburg, South AfricaIBM Research – Africa, Johannesburg, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), University of the Witwatersrand Johannesburg, Johannesburg, South AfricaSchool of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South AfricaDepartments of Biomedical Informatics, Biostatistics, Biomedical Informatics & Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USASAMRC/Wits Centre for Health Economics and Decision Science – PRICELESS SA, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), University of the Witwatersrand Johannesburg, Johannesburg, South AfricaClinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Sante, Nanoro, Burkina FasoDepartment of Surgery, University of the Witwatersrand, Johannesburg, South AfricaSchool of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaIBM Research – Africa, Johannesburg, South AfricaDSI-SAMRC South African Population Research Infrastructure Network (SAPRIN), South African Medical Research Council, Durban, South AfricaDIMAMO Population Health Research Centre, University of Limpopo, Polokwane, South AfricaMRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), University of the Witwatersrand Johannesburg, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaSydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South AfricaSAMRC/Wits Centre for Health Economics and Decision Science – PRICELESS SA, University of the Witwatersrand, Johannesburg, South AfricaIBM Research – Africa, Johannesburg, South AfricaDivision of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USAResearch and Related Capacity Strengthening Unit, African Population and Health Research Center, Nairobi, KenyaIntroduction 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.https://informatics.bmj.com/content/32/1/e101294.full |
| spellingShingle | 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 Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol BMJ Health & Care Informatics |
| title | Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol |
| title_full | Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol |
| title_fullStr | Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol |
| title_full_unstemmed | Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol |
| title_short | Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol |
| title_sort | leveraging data science to understand and address multimorbidity in sub saharan africa the madiva protocol |
| url | https://informatics.bmj.com/content/32/1/e101294.full |
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