A multinational cohort study of trends in survival following dementia diagnosis
Bibliographic record
Abstract
Information on the survival of people living with dementia over time and across systems can help policymakers understand the real-world impact of dementia on health and social care systems. This multinational cohort study examines the trends in relative mortality risk following a dementia diagnosis. A common protocol was applied to population-based data from the UK, Germany, Finland, Canada (Ontario), New Zealand, South Korea, Taiwan and Hong Kong. Individuals aged 60+ with an incident dementia diagnosis recorded between 2000 and 2018 were followed until death or the end of the study period. Cox proportional hazards regression was used to assess the association of mortality in dementia patients with the year of dementia diagnosis. Data from 1,272,495 individuals, with the mean age at diagnosis ranging from 76.8 years (South Korea) to 82.9 years (Germany), show that the overall median length of survival following recorded diagnosis ranges from 2.4 years (New Zealand) to 7.9 years (South Korea). Hazard ratios (HRs) estimated from Cox proportional hazard models decline consistently over the study period in the UK, Canada, South Korea, Taiwan and Hong Kong, which accounted for 84% of all participants. For example, the HR decreases from 0.97 (95% CI: 0.92–1.02) in 2001 to 0.72 (0.65–0.79) in 2016 in comparison to year 2000 in the UK. This study shows a steady trend of decreasing risk of mortality in five out of eight databases, which signals the potential positive effect of dementia plans and associated policies and provides reference for future policy evaluation. Luo et al. conducted a multinational cohort study examining the trends in mortality risk among 1,272,495 individuals with incident dementia between 2000 and 2018. There is a consistent decline in mortality risk following dementia diagnosis in the UK, Canada, South Korea, Taiwan and Hong Kong, indicating potential advancements in dementia care. How long people live after being diagnosed with dementia can vary between countries and over time. Understanding recent trends in survival following dementia diagnosis across countries can help policymakers better plan support and services for dementia. This multinational study analysed data from over 1.2 million people aged 60 years and older who were diagnosed with dementia between 2000 and 2018 in eight regions: the UK, Germany, Finland, Canada (Ontario), New Zealand, South Korea, Taiwan and Hong Kong. In five of these regions, people diagnosed with dementia in more recent years had a lower risk of dying compared to those diagnosed in earlier years. These improvements in survival may be due to earlier diagnosis and better dementia care.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".