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Africa is aging. While the continent is home to the world's youngest population today, adults over 60 are expected to grow from 5.6% to over 15% of Africa's population by 2050—a demographic transformation that will reshape health systems, social structures, and economic policy across the region. Kenya alone expects its elderly population to quadruple within that same timeframe. Yet population-level data on aging in Kenya and across sub-Saharan Africa remains critically limited, leaving researchers, clinicians, and policymakers without the evidence needed to understand aging trajectories, anticipate care needs, or design effective interventions. Key conditions associated with aging—including Alzheimer's disease and related dementias, mental health disorders, and climate- and pollution-related illness—are poorly characterized in the Kenyan context. Without longitudinal data tracking individuals over time, the risk factors shaping health, disability, and well-being in older Kenyan adults cannot be identified or addressed.

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The Longitudinal Study of Health and Aging in Kenya (LOSHAK) responds to this gap by establishing a establishing a nationally representative cohort study of Kenyan adults aged 45 and older that will eventually enroll thousands of participants and follow them over the course of years. LOSHAK is co-led by Anthony Ngugi of AKU and Joshua Ehrlich of the University of Michigan, whose partnership exemplifies CGHE's model of truly co-designed, equity-centered international collaboration. LOSHAK is also part of the Health and Retirement Study (HRS) global network—a family of aging studies operating in 45 countries—making it only the second such study on the African continent. All data collected will be made publicly available, consistent with HRS network standards.

Current NIH pilot funding supports the groundwork necessary to launch the full-scale study, including refining data collection infrastructure, building community trust in areas new to research partnerships, and developing methods to reach both rural and urban populations across Kenya's diverse geography. Key research focus areas include Alzheimer's disease and related dementias, mental health, the health and economic impacts of extreme weather and air pollution, and factors influencing late-life economic well-being.

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Expected outcomes include a fully operational longitudinal cohort study with national representation; population-level data informing Kenyan health and aging policy; pioneering analysis of the connections between extreme weather, aging, and health in sub-Saharan Africa; peer-reviewed scholarship contributing to global aging research; and publicly available datasets accessible to researchers worldwide. By centering Kenyan researchers and institutions as equal partners in study design and implementation, LOSHAK advances both the science of aging and a model of international collaboration grounded in mutual respect and shared capacity. Learnings from LOSHAK will be widely applicable beyond Kenya, including to aging population in the US, where many of the same exposures and stressors affect older adults.

This project prepares 15 years of longitudinal village- and household-level survey data from rural Tanzania for public access. Collected by a U-M-led team across 52 villages and 3,000+ households in six regions, the dataset captures healthcare access, energy access, food aid, and health insurance enrollment. With CGHE support, the team will clean and integrate the data, conduct pilot analyses exploring relationships between electricity access and health outcomes, and publish the findings as an open-access resource through U-M's Institute for Social Research. The project concludes with a stakeholder workshop in Tanzania to socialize the resource and gather feedback for refinement.

This project develops AQUA (Automated Quantitative Ulcer Analysis), a free, open-source deep-learning algorithm to help clinicians in low-resource settings identify fungal organism types in microbial keratitis — a leading cause of blindness that disproportionately affects agricultural communities in warm-climate LMICs. Built on 1,700+ prospectively collected cases from Aravind Eye Care Systems in India, the algorithm analyzes slit-lamp photography and clinical risk factors to classify fungal subtypes at the point of care, filling a critical gap where current gold-standard lab methods are too slow, expensive, or unavailable. The project delivers both the algorithm and a cloud-based clinical decision tool accessible to under-resourced eye clinics worldwide.

This project lays the groundwork for a federated surgical database in Rwanda by assessing the feasibility of standardizing surgical data extraction from electronic health records. Building on an existing COVID-19 data infrastructure that used the OMOP Common Data Model across 15 facilities, the team will use design ethnography and stakeholder interviews to map how surgical data is currently collected and processed, and to identify barriers and facilitators to scaling that approach for surgical quality improvement. The long-term goal is a multi-institutional database that drives collaborative learning across Rwanda's surgical system, ultimately reducing the country's high perioperative complication and mortality rates.