An investigation of psychoactive polypharmacy and related gender-differences in older adults with dementia: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Older adults living with dementia may express challenging responsive behaviours. One management strategy is pharmacologic treatment though these options often have limited benefit, which may lead to multiple treatments being prescribed. METHODS: The aim of the present study was to describe psychoactive medication polypharmacy and explore factors associated with psychoactive polypharmacy in a cohort of older adults living with dementia in Nova Scotia, Canada, including a gender-stratified analysis. This was a retrospective cohort study of those aged 65 years or older with a recorded diagnosis of dementia between 2005 and 2015. Medication dispensation data was collected from April 1, 2010, or dementia diagnosis (cohort entry) to either death or March 31, 2015 (cohort exit). Psychoactive medication claims were captured. Psychoactive medication polypharmacy was defined as presence of three or more psychoactive prescription medications dispensed to one subject and overlapping for more than 30 days. Psychoactive polypharmacy episodes were described in duration, quantity, and implicated medications. Regression analysis examined factors associated with experience and frequency of psychoactive polypharmacy. All analysis were stratified by gender. RESULTS: The cohort included 15,819 adults living with dementia (mean age 80.7 years; 70.0% female), with 99.4% (n = 15,728) receiving at least one psychoactive medication over the period of follow-up. Psychoactive polypharmacy was present in 19.3% of the cohort. The gender specific logistic regressions demonstrated that for both men and women a younger age was associated with an increased risk of psychoactive polypharmacy (women: OR 0.97, 95%CI[0.96, 0.98], men: OR 0.96, 95%CI[0.95, 0.97]). Men were less likely to experience psychoactive polypharmacy if their location of residence was urban (OR 0.86, 95%CI[0.74, 0.99]). There was no significant association between location of residence (urban or rural) and psychoactive polypharmacy for women living with dementia. Antidepressants were the most dispensed medication class, while quetiapine was the most dispensed medication. CONCLUSIONS: This study suggests that of adults living with dementia those of younger ages were more likely to experience psychoactive polypharmacy and that men living with dementia in rural locations may benefit from increased access to non-pharmacological options for dementia management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".