Longitudinal data resource from the Wellbeing of Older People cohort of people aged >50 years in Uganda and South Africa from 2009 to 2019 - Mugisha, J O; Edwards, A; Naidoo, N; Chatterji, S; Seeley, J; Kowal, P (South African Medical Journal, 2023)
Background. The population of people aged ≥60 years continues to increase globally, and has been projected by the United Nations Population Division to increase to 21% of the total population by 2050. In addition, the number of older people living with HIV has continued to increase owing to the introduction of antiretroviral therapy as a treatment for HIV-infected people. Most of the older people living with HIV are in sub-Saharan Africa, an area that faces the biggest burden of HIV globally. Despite the high burden, there are limited reliable data on how HIV directly and indirectly affects the health and wellbeing of older people within this region. Objective. To showcase the availability of data on how HIV directly and indirectly affects the health and wellbeing of older people in Uganda and South Africa (SA). Methods. The World Health Organization Study on global AGEing and adult health (SAGE), in collaboration with Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI and LSHTM) Uganda Research Unit and the Africa Health Research Institute (AHRI) in SA, started the SAGE Wellbeing of Older People Study (WOPS) in Uganda and SA in 2009. Since initiation, respondents have been surveyed every 2 years, with four waves of surveys conducted in Uganda and three waves in South Africa. Results. The available datasets consist of two cohorts of people, aged >50 years, who were surveyed every 2 years between 2009 and 2018. The prevalence of HIV positivity over this period increased from 39% to 54% in Uganda and 48% to 62% in SA. The datasets provide comparisons of variables at a household level and at an individual level. At the individual level, the following measures can be compared longitudinally for a 10-year period for the following variables: sociodemographic characteristics; work history and benefits; health states and descriptions; anthropometrics performance tests and biomarkers; risk factors and preventive health behaviours; chronic conditions and health services coverage; healthcare utilisation; social cohesion; subjective wellbeing and quality of life; and impact of caregiving. Conclusion. This article describes the WOPS in Uganda and SA, the population coverage of this study, and the survey frequency of WOPS, survey measures, data resources available, the data resource access and the strengths and weaknesses of the study. The article invites interested researchers to further analyse the data and answer research questions of interest to enhance the impact of these data.