Prescriber Variation in Relation to Prescribing Trends within the Preferred Drugs Initiative in Ireland (2012–2015): An Interrupted Time-Series Study Using Latent Curve Models
Bibliographic record
Abstract
OBJECTIVES: To examine the impact of the Preferred Drugs Initiative (PDI), an Irish health policy aimed at reducing prescribing variation. DESIGN: Interrupted time series spanning 2012 to 2015. SETTING: Health Service Executive pharmacy claims data for General Medical Services (GMS) patients, approximately 40% of the Irish population. PARTICIPANTS: Prescribers issuing preferred drug group items to GMS adults before and after PDI guidelines. PRIMARY OUTCOME: The percentage coverage of PDI medications within each drug class per calendar quarter per prescriber. METHODS: Latent curve models with structured residuals (LCM-SRs) were used to model coverage of the preferred drugs over time. The number of GMS adults receiving medication and the percentage who were 65 years and older at the start of the study were included as covariates. RESULTS: In the quarter following PDI guidelines, coverage of the preferred drugs increased most in absolute terms for proton pump inhibitors (PPIs) (1.50% [SE 0.15], P < 0.001) and selective and norepinephrine reuptake inhibitors (SNRIs) (1.17% [SE 0.26], P < 0.001). Variation between prescribers remained relatively unchanged and increased for urology medications. Prescribers who increased coverage of the preferred PPI also increased coverage of the preferred statin immediately following guidelines (correlation 0.47 [SE 0.13], P < 0.001). Where guidelines were disseminated simultaneously, coverage of one preferred drug did not significantly predict coverage of the other preferred drug in the next calendar quarter. Prescribing of preferred drugs was not moderated by prescriber-level factors. CONCLUSIONS: Modest changes in prescribing of the preferred drugs have been observed over the course of the PDI. However, the guidelines have had little impact in reducing variation between prescribers. Further strategies may be necessary to reduce variation in clinical practice and enhance patient care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".