The global macroeconomic burden of Alzheimer's disease and other dementias: estimates and projections for 152 countries or territories
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease and other dementias (ADODs) severely threaten the wellbeing of older people, their families, and communities, especially with projected exponential growth. Understanding the macroeconomic implications of ADODs for policy making is essential but under-researched. METHODS: We used a health-augmented macroeconomic model to calculate the macroeconomic burden of ADODs for 152 countries or territories, accounting for: the effect on labour supply of reduced working hours of informal caregivers; the effect on labour supply of ADODs-related mortality and morbidity; age-sex-specific differences in education, work experience, labour market participations, and informal caregivers; and treatment and formal care costs diverting from savings and investments. FINDINGS: ADODs will cost the world economy 14 513 billion international dollars (INT$, measured in the base year 2020; 95% uncertainty interval [UI] 12 106-17 778) from 2020 to 2050, equivalent to 0·421% (95% UI 0·351-0·515) of annual global GDP. Japan incurs the largest annual GDP loss at 1·463% (1·225-1·790). China (INT$2961 billion [2507-3564]), the USA (INT$2331 billion [1989-2829]), and Japan (INT$1758 billion [1471-2150]) face the largest absolute economic burdens. The economic burden of informal care ranges from 60·97% in high-income countries to 85·45% in lower-middle-income countries, and treatment and formal care costs range from 10·50% in lower-middle-income countries to 30·80% in high-income countries. INTERPRETATION: The macroeconomic burden of ADODs is substantial and unequally distributed across countries and regions. Global efforts to reduce the burden, especially with regard to informal care, are urgently needed. FUNDING: National Institute on Aging, National Institutes of Health; Chinese Academy of Engineering; Chinese Academy of Medical Sciences; Bill & Melinda Gates Foundation; Davos Alzheimer's Collaborative through Data for Decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".