Estimating dementia prevalence using remote diagnoses and algorithmic modelling: a population-based study of a rural region in South Africa.

Background: Dementia is a leading cause of global death and disability. High-quality data describing dementia prevalence and burden remain scarce in sub-Saharan Africa. Health and Aging in Africa: A Longitudinal Study in South Africa (HAALSI) fills evidence gaps with longitudinal data on cognition, biomarkers, and everyday function in a population-based cohort of Black South Africans, aged 40 years and older, in a rural subdistrict. This study uses consensus diagnoses and prediction algorithms to estimate dementia prevalence.
Methods: Data were from eligible HAALSI Wave 2 respondents aged 50 years or older (n=3662) and were collected between September, 2019, and January, 2020. An enriched sub-cohort (ie, including a high proportion of individuals with cognitive impairment; n=632) completed a battery of rigorous neuropsychological and clinical assessments and received expert classification of cognitively unimpaired, mild cognitive impairment, or dementia. Logistic regression was used to predict dementia status within the sub-cohort using predictor variables from the parent HAALSI wave. Coefficients were applied to the parent cohort to obtain dementia probability scores and calculate dementia prevalence. Optimal probability cut points to classify individual cases were selected for each model.
Findings: When the sub-cohort was reweighted to reflect the full HAALSI population, the estimated prevalence of dementia was 18% (95% CI 15-22), with steep age gradients. Four models of increasing complexity showed good discrimination between dementia and non-dementia (area under receiver operating characteristic curves 0·78-0·84; classification accuracy 74-81%). Model-based dementia prevalence estimates aligned closely with weighted prevalence; model performance was consistent in cross-validated datasets.
Interpretation: HAALSI is among the first studies to use algorithmic methods to describe dementia prevalence in a population-based sample in South Africa. These efforts could provide a foundation to expand understanding of dementia epidemiology in a region of the world experiencing rapid population ageing.
Funding: National Institute on Aging.
Competing Interests: Declaration of interests MTF reports employment at IQVIA. AMB reports consulting fees from Cogstate, Cognito Therapeutics, Cognition Therapeutics, and IQVIA; payment or honoraria for presentation or writing from Cedara; travel support from the International Neuropsychological Society; an editorial role at Alzheimer's & Dementia; patents or pending patents (#9867566 and #20230298170); and participation in advisory boards at the University of Illinois, Urbana-Champaign (Chicago, IL). KML reports consulting fees from National Institutes of Health-funded projects at Harvard University (Boston, MA), University of Pennsylvania (Philadelphia, PA), University of Minnesota (Minneapolis, MN), University of Colorado (Boulder, CO), Dartmouth College (Hanover, NH), University of Southern California (Los Angeles, CA); payment for expert testimony for a legal case related to decisional capacity in a person with dementia; and participation in a data safety monitoring board for a clinical trial at Indiana University (Bloomington, IN). JJM reports consulting fees from University of Mississippi (University, MS) and support for meeting attendance from the Alzheimer's Associations International Conference. RGW reports grants from the Bill & Melinda Gates Foundation. All other authors declare no competing interests.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)