Trends in diabetes medication prescribing from 2018 to 2021: A cross-sectional analysis
Bibliographic record
Abstract
Several new classes of medications for diabetes have recently become available newer medication classes have been increasing in use. It is unclear how their utilization varied across provinces and how the COVID-19 pandemic may have affected these trends. Our objective was to investigate Canada-wide and province-specific trends in diabetes medication dispensed by drug class over time, while also examining the impact of the COVID-19 pandemic and related restrictions on diabetes medication dispensing. We conducted a repeated cross-sectional analysis study. Data were obtained from IQVIA's CompuScript database for Canada-wide prescription dispensing patterns in primary care from January 2018 to December 2021. Drug classes of interest were biguanides dipeptidyl peptidase 4 inhibitors, sulfonylurea's, insulins, sodium-glucose co-transporter 2 inhibitors, and glucagon-like peptide-1 receptor agonists. We examined trends before and after the onset of the pandemic with special attention to changes during periods of high COVID-19 activity. Most drug classes displayed a stable number of prescriptions each month throughout, except for glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter 2 inhibitors, which demonstrated a consistent pattern of increased dispensing. Sodium-glucose co-transporter inhibitors and glucagon-like peptide-1 receptor agonists exhibited the greatest growth over the examined period, of 7.9% and 5.0% increases, respectively. For sodium-glucose co-transporter 2 inhibitors, Prince Edward Island (4.0%) displayed the greatest growth while Ontario showed the least (2.5%). For glucagon-like peptide-1 receptor analogs, Saskatchewan (11.3%) displayed the greatest growth and Newfoundland the least (4.5%). The pandemic did not impact overall dispensing trends. However, spikes in COVID-19 cases corresponded to changes in dispensing for most drug classes. Important variations across Canada in guideline-recommended medication classes seems to be increasing over time. This is likely due to differing formulary listing and access to drug coverages. If so, future research could explore national formulary harmonization across Canada and health outcomes for patients with diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".