Trends in diabetes medications in Canada, England, Scotland and Australia: a repeated cross-sectional analysis (2012-2017)
Bibliographic record
Abstract
BackgroundWe studied the uptake of new classes of glucose lowering medications, such as Dipeptidyl peptidase-4 inhibitors (DPP4s) and Sodium-glucose cotransporter 2 inhibitors (SGLT2s) amongst patients living with type 2 diabetes. We compared this in Australia, Canada, England and Scotland, and explored whether these new drugs are supplementing or replacing older classes of medications. Research Design and MethodsWe used primary care Electronic Medical Data on prescriptions (Canada, UK) and dispensing data (Australia) from 2012 to 2017. We included persons aged 40 years or over on at least one glucose lowering medication in each year of interest; we excluded those on insulin only. We determined proportions of patients in each nation on each class of medication, as well as on combinations of classes. ResultsIn 2017, data from 28,063 patients in Canada, 106,000 in Australia, 88,953 in England and 15,603 in Scotland were included. The proportion of patients on metformin increased by 3.4% in Australia (95% CI: 3.24% to 3.55%) and decreased in the other nations. Canada had the greatest decrease, at 4.7% (95% CI: -5.05% to -4.34%). Sulfonylurea use decreased in most nations, while DPP4s increased in all. By 2017, between 10.1% and 15.3% of patients were on a SGLT2 and the use of either a DPP4 or SGLT2 combined with metformin approached or exceeded the use of sulfonylureas with metformin. ConclusionsNewer, more expensive medications are replacing sulfonylureas and, to a lesser degree, metformin. The effects of these trends on health outcomes and overall costs should be examined.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| 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".