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
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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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".