First‐line pharmacotherapy for incident type 2 diabetes: Prescription patterns, adherence and associated costs
Bibliographic record
Abstract
AIMS: To use real-world prescription data from Alberta, Canada to: (a) describe the prescribing patterns for initial pharmacotherapy for those with newly diagnosed uncomplicated type 2 diabetes; (b) describe medication-taking behaviours (adherence and persistence) in the first year after initiating pharmacotherapy; and (c) explore healthcare system costs associated with prescribing patterns. METHODS: We employed a retrospective cohort design using linked administrative datasets from 2012 to 2017 to define a cohort of those with uncomplicated incident diabetes. We summarized the initial prescription patterns, adherence and costs (healthcare and pharmaceutical) over the first year after initiation of pharmacotherapy. Using multivariable regression, we determined the association of these outcomes with various sociodemographic characteristics. RESULTS: The majority of individuals for whom metformin was indicated as first-line therapy received a prescription for metformin monotherapy (89%). Older individuals, those with higher baseline A1C and those with no comorbidities, were most likely to be started on non-metformin agents. Adherence with the initially prescribed regimen was suboptimal overall, with nearly half (48%) being non-adherent over the first year. One-third of those who started metformin discontinued it in the first 3 months. Those started on non-metformin agents had roughly twice the healthcare costs, and five to seven times higher medication costs, compared to those started on metformin, in the first year after starting therapy. CONCLUSIONS: With the addition of new classes of medications, healthcare providers who look after those with type 2 diabetes have more pharmaceutical options than ever. Most individuals continue to be prescribed metformin monotherapy. However, adherence is suboptimal, and drops off considerably within the first 3 months.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".