Longitudinal Studies of Aging in Sub-Saharan Africa: Review, Limitations, and Recommendations in Preparation of Projected Aging Population

Background and Objectives The United Nations has projected a 218% increase in older people in Sub-Saharan Africa (SSA) between 2019 and 2050, underscoring the need to explore changes that would occur over this time. Longitudinal studies are ideal for studying and proffering solutions to these changes. This review aims to understand the breadth and use of longitudinal studies on aging in the SSA regions, proffering recommendations in preparation for the projected aging population.Research Design and Methods This paper is the third of a four-part series paper of a previous systematic mapping review of aging studies in SSA. We updated the search (between 2021 and 2023) and screened the titles/abstracts and full-text articles by a pair of independent reviewers. Data were extracted using a standardized data-charting form, identifying longitudinal studies in SSA.Results We identified 193 studies leveraging 24 longitudinal study data sets conducted at 28 unique sites. The World Health Organization's Study on Global AGEing and Adult Health (WHO-SAGE) (n = 59, 30.5%) and Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) (n = 51, 26.4%) were the most used longitudinal data sets. Four studies used more than one longitudinal study data set. Eighteen of the longitudinal study data sets were used only in 1-4 studies. Most (n = 150, 77.7%) of the studies used a cross-sectional analytical approach.Discussion and Implications Longitudinal studies on aging are sparingly being utilized in SSA. Most analyses conducted across the longitudinal data set were cross-sectional, which hindered the understanding of aging changes that occurred over time that could better inform aging policy and interventions. We call for funding bodies, such as WHO-SAGE, to develop funding competitions that focus on conducting longitudinal analyses, such as structural equation modeling, highlighting changes occurring among the aging population in SSA.