Practice variation in opioid prescribing for non-cancer pain in Dutch primary care: A retrospective database study
Bibliographic record
Abstract
BACKGROUND: Prescription opioid use has increased steadily in many Western countries over the past two decades, most notably in the US, Canada, and most European countries, including the Netherlands. Especially the increasing use of prescription opioids for chronic non-cancer pain has raised concerns. Most opioids in the Netherlands are prescribed in general practices. However, little is known about variation in opioid prescribing between general practices. To better understand this, we investigated practice variation in opioid prescribing for non-cancer pain between Dutch general practices. METHODS: Data from 2017-2019 of approximately 10% of all Dutch general practices was used. Each year included approximately 1000000 patients distributed over approximately 380 practices. The primary outcome was the proportion of patients with chronic (>90 days) high-dose (≥90 oral morphine equivalents) opioid prescriptions. The secondary outcome was the proportion of patients with chronic (<90 oral morphine equivalents) opioid prescriptions. Practice variation was expressed as the ratio of the 95th/5th percentiles and the ratio of mean top 10/bottom 10. Funnel plots were used to identify outliers. Potential factors associated with unwarranted variation were investigated by comparing outliers on practice size, patient neighbourhood socioeconomic status, and urbanicity. RESULTS: Results were similar across all years. The magnitude of variation for chronic high-dose opioid prescriptions in 2019 was 7.51-fold (95%/5% ratio), and 15.1-fold (top 10/bottom 10 ratio). The percentage of outliers in the funnel plots varied between 13.8% and 21.7%. Practices with high chronic high-dose opioid prescription proportions were larger, and had more patients from lower income and densely populated areas. CONCLUSIONS: There might be unwarranted practice variation in chronic high-dose opioid prescriptions in primary care, pointing at possible inappropriate use of opioids. This appears to be related to socioeconomic status, urbanicity, and practice size. Further investigation of the factors driving practice variation can provide target points for quality improvement and reduce inappropriate care and unwarranted variation.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".