Practice Variation in Prescribing SGLT2-Inhibitors: A Population-Based Analysis from Alberta, Canada
Bibliographic record
Abstract
Background: Sodium glucose-cotransporter 2 inhibitors (SGLT2is) have been shown to reduce the risk of cardiorenal complications in select patient populations, yet their real-world uptake in clinical practice is limited. The objective of this study was to assess the factors influencing prescriber variation in SGLT2i use. Methods: Using administrative data from Alberta, Canada, we conducted a population-based cohort study of adults with a new prescription for any oral antihyperglycemic medication between 2014-2021. We used multilevel logistic regression to examine how patient and prescriber characteristics were associated with SGLT2i prescription and used the median odds ratio to quantify variation at the prescriber level. Results: Of 339,314 patients prescribed a new antihyperglycemic medication, 0.8% (n = 2852) were prescribed an SGLT2i. SGLT2i prescribing was more likely among male patients, younger patients, and those with obesity or heart failure. However, substantial variation was present in prescriber behaviour for SGLT2i, and prescriber factors had a greater influence on SGLT2i prescribing than patient-level factors (median odds ratio 3.55). Although family physicians accounted for greatest numbers of SGLT2i prescriptions overall, subspecialists, such as cardiologists, were more likely to prescribe SGLT2is (odds ratio 13.5, 95% confidence interval 8.9-20.5). Conclusions: The rate of new SGLT2i prescriptions was low during the study period. Both patient- and prescriber-level factors are associated with SGLT2i prescription, although variation in prescribing SGLT2i medications appears to be driven primarily by prescriber factors. Family physicians are responsible for the majority of SGLT2i prescriptions and represent a key provider group in aligning SGLT2i prescribing with guideline recommendations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".