Public reporting of antipsychotic prescribing in nursing homes: population-based interrupted time series analyses
Bibliographic record
Abstract
BACKGROUND: Although sometimes appropriate, antipsychotic medications are associated with increased risk of significant adverse events. In 2014, a series of newspaper articles describing high prescribing rates in nursing homes in Ontario, Canada, garnered substantial interest. Subsequently, an online public reporting initiative with home-level data was launched. We examined the impact of these public reporting interventions on antipsychotic prescribing in nursing homes. METHODS: Time series analysis of all nursing home residents in Ontario, Canada, between 1 October 2013 and 31 March 2016. The primary outcome was the proportion of residents prescribed antipsychotics each month. Balance measures were prescriptions for common alternative sedating agents (benzodiazepines and/or trazodone). We used segmented regression to assess the effects on prescription trends of the newspaper articles and the online home-level public reporting initiative. RESULTS: We included 120 009 nursing home resident admissions across 636 nursing homes. Following the newspaper articles, the proportion of residents prescribed an antipsychotic decreased by 1.28% (95% CI 1.08% to 1.48%) and continued to decrease at a rate of 0.2% per month (95% CI 0.16% to 0.24%). The online public reporting initiative did not alter this trend. Over 3 years, there was a net absolute reduction in antipsychotic prescribing of 6.0% (95% CI 5.1% to 6.9%). Trends for benzodiazepine prescribing did not change as substantially during the period of observation. Trazodone use has been gradually increasing, but its use did not change abruptly at the time of the mass media report or the public reporting initiative. INTERPRETATION: The rapid impact of mass media on prescribing suggests both an opportunity to use this approach to invoke change and a warning to ensure that such reporting occurs responsibly.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".