Initiation of four basal insulins and subsequent treatment modification in people treated for type 2 diabetes in the United Kingdom: Changes over the period 2003–2018
Bibliographic record
Abstract
AIMS: Aim of this study is to describe changes in the utilization of basal insulins (glargine, detemir, degludec, neutral protamine Hagedorn [NPH]) among individuals with type 2 diabetes between 2003 and 2018 in the United Kingdom (UK). MATERIALS AND METHODS: Using the UK Clinical Practice Research Datalink (CPRD) Aurum, we created three study cohorts of individuals with type 2 diabetes: (1) all users of antidiabetic drugs (n = 686,170); (2) initiators of antidiabetic drugs (n = 382,247); and (3) initiators of basal insulins (n = 85,369). Trends in prescription rates were determined using Poisson regression overall and stratified by sex, cardiovascular disease history, and obesity. Crude and adjusted Cox proportional hazards models were used to obtain hazard ratios (HRs) and confidence intervals (CI) comparing rates of treatment change between classes of basal insulins, with an intention-to-treat exposure definition. RESULTS: During the study period, prescription rates of insulin analogues increased in the all-user cohort from 118.3 (95% CI: 116.4, 120.2) prescriptions per 1000 person-years in 2003 to 579.4 (95% CI: 576.9, 582.0) in 2018. Prescription rates of NPH decreased from 770.5 (95% CI: 765.0, 775.3) in 2003 to 457.7 (95% CI: 455.5, 460.0) in 2018. Compared to initiators of NPH, initiators of detemir were more likely to change treatment (adjusted HR: 1.31, 95% CI: 1.25, 1.37) while glargine initiators were less likely to change treatment (adjusted HR: 0.85, 95% CI: 0.82, 0.88). CONCLUSIONS: Basal insulin prescription evolved between 2003 and 2018. Our study provides insight into the evolving use of basal insulin among individuals with type 2 diabetes in the UK.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".