Health system costs of potentially inappropriate prescribing in Ontario, Canada: a protocol for a population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: Adverse drug events (ADEs) are common in older persons and contribute significantly to emergency department visits, hospitalisations and mortality. ADEs are often due to potentially inappropriate prescriptions (PIP) or potentially inappropriate omissions (PIO), and are avoidable if inappropriate prescriptions or omissions are identified and prevented. Identifying PIP/PIO at the population level through the application of PIP/PIO assessment tools to health administrative data can provide a unique opportunity to assess the economic burden of PIP/PIO on the healthcare system beyond medication costs which is yet to be done. The objective of this study is to assess the economic burden associated with PIP/PIO and to estimate the incremental costs associated with distinct PIP/PIO in the province of Ontario. METHODS AND ANALYSIS: We will conduct a retrospective cohort study using Ontario's health administrative databases. Eligible patients aged 66 years and older who were prescribed at least one medication between 1 April 2003 and 31 March 2014 (approximately 2.4 million patients) will be included. Population attributable fraction methodology will be used to assess the overall burden of PIP in Ontario, while regression analyses will be used to estimate the incremental costs of having specific PIP criteria and aid in prioritising targets for intervention. ETHICS AND DISSEMINATION: This study was approved by the Institutional Review Board at Sunnybrook Health Sciences Centre, Toronto, Canada. Dissemination will occur via publication, presentation at national and international conferences, and knowledge exchange with various stakeholders.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".