Kerry Glover, Tabitha Osler, Kayode Adetunji, Tanya Akumu, Gershim Asiki, Diana Awuor, Palwendé Boua, Victoria Bronstein, Joan Byamugisha, Jacques D Du Toit, Barry Dwolatzky, Jaya George, Paul A Harris, Kobus Herbst, Karen Hofman, Celeste Holden, Samuel Iddi, Damazo T Kadengye, Kathleen Kahn, Michelle Kamp, Nhlamulo Khoza, Faith Kimongo, Isaac Kisiangani, Dekuwin E Kogda, Michael Klipin, Stephen P Levitt, Dylan Maghini, Karabo Maila, Eric Maimela, Daniel Maina Nderitu, Ndivhuwo Makondo, Molulaqhooa Linda Maoyi, Reineilwe Given Mashaba, Nkosinathi Gabriel Masilela, Theophilous Mathema, Phelelani Thokozani Mpangase, Daphine T Nyachowe, Daniel Ohene-Kwofie, Helen Robertson, Skyler Speakman, Evelyn Thsehla, Siphiwe A Thwala, Roy Zent, Francesc Xavier Gómez-Olivé, Chodziwadziwa W Kabudula, Patrick Opiyo Owili, Catherine Kyobutungi, Michèle Ramsay, Stephen Tollman, Scott Hazelhurst
{"title":"利用数据科学了解和解决撒哈拉以南非洲地区的多重疾病：MADIVA协议。","authors":"Kerry Glover, Tabitha Osler, Kayode Adetunji, Tanya Akumu, Gershim Asiki, Diana Awuor, Palwendé Boua, Victoria Bronstein, Joan Byamugisha, Jacques D Du Toit, Barry Dwolatzky, Jaya George, Paul A Harris, Kobus Herbst, Karen Hofman, Celeste Holden, Samuel Iddi, Damazo T Kadengye, Kathleen Kahn, Michelle Kamp, Nhlamulo Khoza, Faith Kimongo, Isaac Kisiangani, Dekuwin E Kogda, Michael Klipin, Stephen P Levitt, Dylan Maghini, Karabo Maila, Eric Maimela, Daniel Maina Nderitu, Ndivhuwo Makondo, Molulaqhooa Linda Maoyi, Reineilwe Given Mashaba, Nkosinathi Gabriel Masilela, Theophilous Mathema, Phelelani Thokozani Mpangase, Daphine T Nyachowe, Daniel Ohene-Kwofie, Helen Robertson, Skyler Speakman, Evelyn Thsehla, Siphiwe A Thwala, Roy Zent, Francesc Xavier Gómez-Olivé, Chodziwadziwa W Kabudula, Patrick Opiyo Owili, Catherine Kyobutungi, Michèle Ramsay, Stephen Tollman, Scott Hazelhurst","doi":"10.1136/bmjhci-2024-101294","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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 <i>Multimorbidity in Africa: Digital Innovation, Visualisation and Application</i> Research Hub (MADIVA) aims to address MM by developing data science solutions informed by stakeholder engagement.</p><p><strong>Methods and analysis: </strong>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.</p><p><strong>Ethics and dissemination: </strong>Ethics approval has been granted. Dissemination will occur through scientific meetings and publications. 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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 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.
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