The Health System Costs of Potentially Inappropriate Prescribing: A Population-Based, Retrospective Cohort Study Using Linked Health Administrative Databases in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the health system costs from hospitalizations, emergency department (ED) visits, and medications due to potentially inappropriate prescribing (PIP) in Ontario, Canada, at the population-level. METHODS: A retrospective cohort of individuals ≥ 66 years of age and prescribed at least one medication from April 2002 to March 2015 was identified using linked population-level health administrative databases from Ontario, Canada. Patients were identified as having PIP or no PIP by applying a subset of the Screening Tool of Older Persons' Potentially Inappropriate Prescribing/Screening Tool to Alert Doctors to Right Treatment (STOPP/START) criteria. The number of days spent in hospital, new medications prescribed, and ED visits in the 90 days following PIP or patient index date were captured, as well as the total costs from each of these health services. Count regression models were used to generate incidence rate ratios (IRRs) for each outcome given the presence of PIP versus no PIP and combined with the prevalence of PIP to generate population attributable fractions (PAFs). The PAF was then multiplied by the cost for each health service to obtain the costs attributable to PIP in the whole cohort, and by age and sex. RESULTS: PIP was associated with an increased rate of hospitalization (IRR 2.77, 95% confidence interval [CI] 2.72-2.82), ED visits (IRR 1.87, 95% CI 1.82-1.92), and newly prescribed medications (IRR 1.13, 95% CI 1.13-1.14), resulting in PAFs of 55.7, 37.9, and 5.0% for each outcome, respectively. PIP was associated with 38.8% of the total spent on these healthcare services ($1.22 billion) in the 90 days after PIP. Costs attributable to PIP decreased with age despite increasing prevalence. CONCLUSIONS: PIP in older adults is a significant source of health system costs from healthcare service use beyond medication costs, with a significant portion of hospitalizations and ED visit costs attributable to PIP. Future work should focus on identifying strategies and priorities for intervention.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".