Rising life expectancy across sub-Saharan Africa is an outcome of the profound health and social transitions underway. In countries where prevalence of HIV/AIDS is high, such as South Africa and Kenya, many living with HIV are now on lifelong antiretroviral therapy and surviving to older ages. Unavoidably, the HIV epidemic intersects with an accelerating epidemic of cardiometabolic and other non-communicable diseases. With population aging, mental health challenges of cognitive decline and dementias are emerging.
African health systems have been structured to manage acute conditions and infectious disease, with clinicians trained to treat patients at one-off visits. However evolving multimorbidity – the presence of two or more comorbid conditions –manifests at younger ages than high-income countries, exposing a critical mismatch between prevailing models of care and the changing nature of healthcare required. Project 1, focusing on Data Integration and Visualization, seeks a far greater understanding of multimorbidity, its biological and social determinants, and populations at risk – to inform an effective health system response.
We capitalize on exceptional population, health service and survey data in two archetypal African settings: Nairobi urban slums and resource-poor rural South Africa (Agincourt). Both settings are underpinned by high-performing health and socio-demographic surveillance systems, variably linked to patient records of nearby clinics/hospitals, with nested cohorts (some of which support gene-environment analyses) adding to the depth of available data, including genomic data. Respectful relationships with local communities and engagement with health service leadership are common to both sites.
We hypothesize that well integrated data, effectively analysed, visualised and shared with service leaders, practitioners and researchers will enable a step-change in the care of patients with multimorbidities. We aim first to link then integrate these diverse datasets; this is critical to the advanced analyses of Project 2: Public Precision Health. Exploiting this integrated dataset, we will provide a best current description of multimorbidity and the demand for healthcare. This will provide a foundation for a health system dashboard with which to engage service leadership and clinicians; and a highly navigable researcher dashboard.
We expect our data science-driven findings and methods to be widely applicable across African settings, with potential for major impact on multimorbidity in sub-Saharan Africa.