Changes in Polypharmacy and Psychotropic Medication Use After Diagnosis of Major Neurocognitive Disorders
Bibliographic record
Abstract
BACKGROUND: Older adults with major neurocognitive disorder (MNCD) are often exposed to polypharmacy. We aimed to assess the prescribing and discontinuation patterns of medications following diagnosis of MNCD among community-dwelling older adults. METHODS: Using the Quebec Integrated Chronic Disease Surveillance System, we conducted a population-based cohort study comparing 1-year prediagnosis and postdiagnosis use of medications between a group of individuals older than 65 years newly diagnosed with MNCD in 2016-2017 and a control group without MNCD. The difference-in-difference method was used to estimate the prediagnosis and postdiagnosis variation in the number of medications prescribed and in the proportion of psychotropic and anticholinergic medication users. RESULTS: In the MNCD group, the mean number of medications used (excluding Alzheimer disease treatments) increased by 1.25 in the year after the diagnosis. The respective increase was 0.45 in the control group, yielding an adjusted difference-in-differences of 0.81 (95% confidence interval: 0.74; 0.87) between groups. The adjusted difference-in-differences in the proportions of antipsychotic, antidepressant, and anticholinergic medication users was 13.2% (12.5; 13.9), 7.1% (6.5; 7.7), and 3.8% (3.1; 4.6), respectively. CONCLUSIONS: The medication burden among older adults tends to increase in the year following a diagnosis of MNCD. The use of antipsychotics and antidepressants may explain a part of the observed increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 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".