Resident-Level Predictors of Dementia Pharmacotherapy at Long-Term Care Admission: The Impact of Different Drug Reimbursement Policies in Ontario and Saskatchewan
Bibliographic record
Abstract
Objectives:Cholinesterase inhibitors (ChEIs) and memantine are approved for Alzheimer disease in Canada. Regional drug reimbursement policies are associated with cross-provincial variation in ChEI use, but it is unclear how these policies influence predictors of use. Using standardized data from two provinces with differing policies, we compared resident-level characteristics associated with dementia pharmacotherapy at long-term care (LTC) admission.Methods:Using linked clinical and administrative databases, we examined characteristics associated with dementia pharmacotherapy use among residents with dementia and/or significant cognitive impairment admitted to LTC facilities in Saskatchewan (more restrictive reimbursement policies; n = 10,599) and Ontario (less restrictive; n = 93,331) between April 1, 2009, and March 31, 2015. Multivariable logistic regression models were utilized to assess resident demographic, functional, and clinical characteristics associated with dementia pharmacotherapy.Results:On admission, 8.1% of Saskatchewan residents were receiving dementia pharmacotherapy compared to 33.2% in Ontario. In both provinces, residents with severe cognitive impairment, aggressive behaviors, and recent antipsychotic use were more likely to receive dementia pharmacotherapy; while those who were unmarried, admitted in later years, had a greater degree of frailty, and recent hospitalizations were less likely. The direction of the association for older age, rural residency, medication number, and anticholinergic therapy differed between provinces.Conclusions:While more restrictive criteria for dementia pharmacotherapy coverage in Saskatchewan resulted in fewer residents entering LTC on dementia pharmacotherapy, there were relatively few differences in the factors associated with use across provinces. Longitudinal studies are needed to assess how differences in prevalence and characteristics associated with use impact patient outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".