Prevalence and predictors of potentially inappropriate prescribing using codified STOPP-START and Beers criteria: a retrospective cohort study in Ontario's older population
Bibliographic record
Abstract
OBJECTIVES: To quantify potentially inappropriate prescribing (PIP) and assess the association between patient characteristics and PIP using previously coded STOPP-START and Beers criteria in Ontario's older population. STUDY DESIGN AND SETTING: An established subset of the 2014 STOPP-START and 2015 Beers criteria applicable to health administrative data were used to identify instances of PIP in health administrative data. Associations between the patient characteristics and PIP were examined using multivariable logistic regression. Using Ontario's large health administrative databases, which comprise individual-level, linked information on medication dispensation, physician services use, emergency room visits, hospitalizations, mortality, and sociodemographic data, a cohort including all patients ≥65 years who were issued at least 1 prescription between April 2003 and March 2017 (N = 2,937,927) was formed. RESULTS: From a total of 2,937,927 patients, 2,220,641 (75.6%) patients were identified with at least 1 PIP using the STOPP-START criteria. Using the Beers criteria, 1,505,243 (51.2%) patients were identified. The most common PIP identified by the STOPP-START criteria was the lack of pneumococcal vaccine to be given at least once after age 65 years according to national guidelines (75.9% of patients). Patient characteristics that were found to be strongly associated with PIP identified by both STOPP-START and Beers criteria were age, female sex, long-term care resident, lack of MedsCheck prior to index date, and frailty, among others. CONCLUSION: Applying coding for identifying PIP in health administrative data is a promising approach to screen for PIP at the population level in an impactful and cost-effective manner. This approach will allow investigators to identify areas for intervention in terms of PIP in a population. PLAIN LANGUAGE SUMMARY: We studied medication use in Ontario adults aged 65+ years to identify both potentially harmful or unnecessary prescriptions and important medications that were missing, using established prescribing guidelines. Among nearly 3 million Ontarians, 75% were prescribed at least 1 potentially inappropriate medication with the pneumococcal vaccine (76% of cases) being the most commonly missing medication. Those most at risk included adults over 85 years of age, women, long-term care residents, and people with frailty or no prior medication review. These findings show how health data can efficiently identify prescribing gaps, helping target interventions like improved vaccine programs or medication reviews to enhance safety for older adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".