Implications of decreasing incidence of dementia in some countries during 1990‐2019: A global burden of disease study
Bibliographic record
Abstract
Abstract Background Stroke and dementia share the same risk and protective factors and pose risks for each other, consequently they are considered together. Their prevalence is rising, making them the most burdensome neurological diseases worldwide. However, their incidence is falling in some countries. It becomes imperative to find out what is happening or being done right in these countries and help apply the lessons widely. Method We analyzed systematically trends of changes in age‐standardized dementia and stroke incidence rate per 100,000 population in 204 countries from 1990 to 2019 using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. We also calculated the changes in the age‐standardized population attributable fraction (PAF) of dementia and stroke burden, measured by disability‐adjusted life years (DALYs), and deaths attributed to 12 risk factors with 95% uncertainty intervals (UIs). Result From 1990 to 2019, dementia incidence declined in 71 countries, of which 18 showed statistically significant declines, ranging from ‐12.1% (95% UI ‐16.9 to ‐6.8) to ‐2.4% (‐4.6 to ‐0.5). However, from 2010 to 2019, only 16 countries showed declines, although non‐statistically significant. The PAF of global dementia DALYs attributable to metabolic risk factors increased by 25.3% (16.9 to 38.9) from 1990 to 2019, while it decreased for behavioral risk factors by ‐17.3% (‐22.8 to ‐13.0). In particular, 163 countries showed declines in the PAF of dementia DALYs attributable to tobacco use, and 160 countries in dementia mortality attributable to high fasting plasma glucose. Conclusion The declining incidence of dementia in some countries, despite the growing and aging population is encouraging and urges further investigation. This approach would complement and enhance the search for a drug against Alzheimer’s disease pathology. Finding out what accounts for the decline of dementia in some countries and scaling up the lessons could begin having an impact in the near future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".