Associations between psychological therapy outcomes for depression and incidence of dementia
Bibliographic record
Abstract
BACKGROUND: Depression is an important, potentially modifiable dementia risk factor. However, it is not known whether effective treatment of depression through psychological therapies is associated with reduced dementia incidence. The aim of this study was to investigate associations between reduction in depressive symptoms following psychological therapy and the subsequent incidence of dementia. METHODS: National psychological therapy data were linked with hospital records of dementia diagnosis for 119808 people aged 65+. Participants received a course of psychological therapy treatment in Improving Access to Psychological Therapies (IAPT) services between 2012 and 2019. Cox proportional hazards models were run to test associations between improvement in depression following psychological therapy and incidence of dementia diagnosis up to eight years later. RESULTS: Improvements in depression following treatment were associated with reduced rates of dementia diagnosis up to 8 years later (HR = 0.88, 95% CI 0.83-0.94), after adjustment for key covariates. Strongest effects were observed for vascular dementia (HR = 0.86, 95% CI 0.77-0.97) compared with Alzheimer's disease (HR = 0.91, 95% CI 0.83-1.00). CONCLUSIONS: Reliable improvement in depression across psychological therapy was associated with reduced incidence of future dementia. Results are consistent with at least two possibilities. Firstly, psychological interventions to improve symptoms of depression may have the potential to contribute to dementia risk reduction efforts. Secondly, psychological therapies may be less effective in people with underlying dementia pathology or they may be more likely to drop out of therapy (reverse causality). Tackling the under-representation of older people in psychological therapies and optimizing therapy outcomes is an important goal for future research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".