Concordance between high antibiotic prescribing and high opioid prescribing among primary care physicians: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance and opioid misuse both present major public health challenges, and identifying high prescribers of both of these agents can help provide a common target for intervention. We sought to determine the association between being a high prescriber of antibiotics and being a high prescriber of opioids in the primary care setting. METHODS: We performed a cross-sectional study of the antibiotic- and opioid-prescribing habits of primary care physicians in Ontario, Canada between Mar. 1, 2017, and Feb. 28, 2018, using administrative databases. We defined high prescribers as the top quartile of antibiotic or opioid prescribers using 3 antibiotic-prescribing metrics (prescriptions per patient visit, proportion of prescriptions that were broad spectrum and proportion of prescriptions > 8 d) and 3 opioid-prescribing metrics (prescriptions per patients seen, proportion of prescriptions > 90 mg of morphine equivalents and proportion of prescriptions > 28 d). We tabulated agreement between prescribing metrics using the κ statistic. RESULTS: We included 9994 physicians. We observed minimal overlap between high antibiotic initiation and high opioid initiation (618 physicians [6.2%]) (κ = 0.00, 95% confidence interval -0.02 to 0.02). There was slight agreement between the antibiotic-prescribing indices and between the opioid-prescribing indices (within-class, range of κ 0.05 to 0.18). There was slight disagreement to slight agreement across antibiotic- and opioid-prescribing metrics (between-class, range of κ -0.09 to 0.16). INTERPRETATION: Among primary care physicians, there was a lack of association between high antibiotic prescribing and high opioid prescribing. Our findings suggest that separate tailored approaches to antibiotic and opioid stewardship strategies are needed.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".