Polypharmacy as a risk factor for dementia: Scottish population‐based longitudinal record linkage study of 1 225 894 people
Bibliographic record
Abstract
Abstract Background Polypharmacy, the concurrent use of multiple medicines, is increasing. Population‐wide studies of its association with dementia are lacking. We examined this relationship longitudinally, at a national level. Method We used National Health Service community prescribing data from all adults in Scotland aged ≥50 years who received at least one drug in the first quarter of 2009. These data were linked to death records, including cause of death. We used Cox proportional hazards models to assess associations between the number of unique medicines dispensed in one quarter and mortality with any subtype of dementia over 8.5 years, in the whole sample and stratified by age. Result The sample contained 1,225,894 people aged ≥50 years (mean age 67.4 (SD = 10.8) years, 56.1% female, 3.8% care home residents). The mean number of drugs dispensed at baseline was 5.0 (SD = 3.7). Over 8.5 years, there were 336,244 deaths, of which 58,358 had any subtype of dementia on the death certificate. Among the whole sample, the hazard ratio (HR) for dementia mortality with each additional medicine was 1.027 (95% CI 1.024‐1.028). In people aged 50‐64 years, the HR was 1.075 (1.061‐1.089); for 65‐79 year‐olds, HR = 1.043 (1.040‐1.047) and for those aged ≥80 years, HR = 1.009 (1.006‐1.012). All models were adjusted for baseline age, gender, care home residence status and deprivation index based on postcode. Conclusion There was higher mortality with dementia as the number of dispensed medicines increased. Age‐stratified analyses showed that the association was stronger in younger age groups, perhaps reflecting that younger people taking medication for more comorbidities had an increased risk of dying with dementia. These analyses did not allow adjustment for multimorbidity or the consideration of individual drug classes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".