The burden of stroke, ischaemic heart disease, and dementia in Africa, 1990–2021: an ecological analysis of the Global Burden of Disease 2021
Bibliographic record
Abstract
BACKGROUND: Stroke, ischaemic heart disease, and dementia share risk factors and influence one another, substantially affecting brain health. Limited health-care resources in Africa might exacerbate the burden of these diseases, with serious brain health consequences. We analysed trends from 1990 to 2021 to inform optimised prevention strategies. METHODS: Using The Global Burden of Diseases, Injuries, and Risk Factors Study 2021 data, we assessed the burden of these conditions, measured by disability-adjusted life-years (DALYs) lost attributed to 12 risk factors, and their changes from 1990 to 2021. Bayesian modelling generated means and 95% uncertainty intervals (UIs) based on the 2·5th and 97·5th percentiles of 500 posterior distribution draws. FINDINGS: In Africa in 2021, 17·3 (95% UI 15·5-19·2) million DALYs were lost due to strokes, 17·6 (15·5-19·6) million were lost due to ischaemic heart diseases, and 1·8 (0·8-4·0) million were lost due to dementia. New and prevalent cases doubled from 1990 to 2021, with two-thirds of DALYs occurring before age 70 years. Among five continents, Africa had the highest age-standardised DALY rates per 100 000 population for stroke (2628·1 [2367·4-2893·0]) and ischaemic heart disease (2743·5 [2451·8-3033·6]), and the lowest for dementia (423·4 [190·6-934·6]). Regionally, central and southern Africa showed higher stroke DALY rates, northern Africa had the highest rates for ischaemic heart disease, and central and northern Africa had the highest rates for dementia. Among 12 modifiable risk factors, high systolic blood pressure, unhealthy diet, and air pollution contributed most to DALYs. Stroke DALYs rose prominently due to high BMI, high fasting plasma glucose, high LDL cholesterol, low physical activity, and high systolic blood pressure. INTERPRETATION: Africa faces substantial challenges from stroke, heart disease, and dementia, including the highest DALY rates globally, with worsening trends over the past three decades, including younger ages of onset. These patterns, coupled with limited health resources, necessitate urgent and targeted strategies to protect, preserve, and promote brain health in Africa. FUNDING: Weston Family Foundation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".