Global and regional projections of the economic burden of Alzheimer's disease and related dementias from 2019 to 2050: A value of statistical life approach
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Bibliographic record
Abstract
Background: The burden of Alzheimer's disease and related dementias (ADRDs) is expected to grow rapidly with population aging, especially in low- and middle-income countries, in the next few decades. We used a willingness-to-pay approach to project the global, regional, and national economic burden of ADRDs from 2019 to 2050 under status quo. Methods: We projected age group and country-specific disability-adjusted life years (DALYs) lost to ADRDs in future years based on historical growth in disease burden and available population projections. We used country-specific extrapolations of the value of a statistical life (VSL) year and its future projections based on historical income growth to estimate the economic burden - measured in terms of the value of lost DALYs - of ADRDs. A probabilistic uncertainty analysis was used to calculate point estimates and 95% uncertainty bounds of the economic burden. Findings: In 2019, the global VSL-based economic burden of ADRDs was an estimated $2.8 trillion. The burden was projected to increase to $4.7 trillion (95% uncertainty bound: $4 trillion-$5.5 trillion) in 2030, $8.5 trillion ($6.8 trillion-$10.8 trillion) in 2040, and $16.9 trillion ($11.3 trillion-$27.3 trillion) in 2050. Low- and middle-income countries (LMICs) would account for 65% of the global VSL-based economic burden in 2050, as compared with only 18% in 2019. Within LMICs, upper-middle income countries would carry the largest VSL-based economic burden by 2050 (92% of LMICs burden and 60% of global burden). Interpretation: ADRDs have a large and inequitable projected future VSL-based economic burden. Funding: The Davos Alzheimer's Collaborative.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it