Treatment pattern trends of medications for type 2 diabetes in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Several new oral drug classes for type 2 diabetes (T2DM) have been introduced in the last 20 years accompanied by developments in clinical evidence and guidelines. The uptake of new therapies and contemporary use of blood glucose-lowering drugs has not been closely examined in Canada. The objective of this project was to describe these treatment patterns and relate them to changes in provincial practice guidelines. RESEARCH DESIGN AND METHODS: We conducted a longitudinal drug utilization study among persons with T2DM aged ≥18 years from 2001 to 2020 in British Columbia (BC), Canada. We used dispensing data from community pharmacies with linkable physician billing and hospital admission records. Laboratory results were available from 2011 onwards. We identified incident users of blood glucose-lowering drugs, then determined sequence patterns of medications dispensed, with stratification by age group, and subgroup analysis for patients with a history of cardiovascular disease. RESULTS: Among a cohort of 362 391 patients (mean age 57.7 years old, 53.5% male) treated for non-insulin-dependent diabetes, the proportion who received metformin monotherapy as first-line treatment reached a maximum of 90% in 2009, decreasing to 73% in 2020. The proportion of patients starting two-drug combinations nearly doubled from 3.3% to 6.4%. Sulfonylureas were the preferred class of second-line agents over the course of the study period. In 2020, sodium-glucose cotransporter type 2 inhibitors and glucagon-like peptide-1 receptor agonists accounted for 21% and 10% of second-line prescribing, respectively. For patients with baseline glycated hemoglobin (A1C) results prior to initiating diabetic treatment, 41% had a value ≤7.0% and 27% had a value over 8.5%. CONCLUSIONS: Oral diabetic medication patterns have changed significantly over the last 20 years in BC, primarily in terms of medications used as second-line therapy. Over 40% of patients with available laboratory results initiated T2DM treatment with an A1C value ≤7.0%, with the average A1C value trending lower over the last decade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".