Clinical and economic outcomes among injection-naïve patients with type 2 diabetes initiating dulaglutide compared with basal insulin in a US real-world setting: the DISPEL Study
Bibliographic record
Abstract
Aims: To report 1-year clinical and economic outcomes from the retrospective DISPEL (Dulaglutide vs Basal InSulin in Injection Naïve Patients with Type 2 Diabetes: Effectiveness in ReaL World) Study. Materials and methods: This observational claims study included patients with type 2 diabetes (T2D) and ≥1 claim for dulaglutide or basal insulin between November 2014 and April 2017 (index date=earliest fill date). Propensity score matching was used to address treatment selection bias. Change from baseline in hemoglobin A1c (HbA1c) was compared between the matched cohorts using analysis of covariance; diabetes-related costs were analyzed using generalized linear models. Results: Matched cohorts (903 pairs total; 523 pairs with complete cost data) were balanced in baseline characteristics with mean HbA1c 8.6%, mean age 54 years. At 1 year postindex, dulaglutide patients had significantly greater reduction in HbA1c than basal insulin (-1.12% vs -0.51%, p<0.01), lower medical costs ($3753 vs $7604, p<0.01), higher pharmacy costs ($9809 vs $6175, p<0.01), and similar total costs ($13 562 vs $13 779, p=0.76). Medical and total costs per 1% HbA1c reduction were lower for dulaglutide than basal insulin (medical: $3128 vs $12 673, p<0.01; total: $11 302 vs $22 965, p<0.01), while pharmacy costs per 1% HbA1c reduction were lower without reaching statistical significance ($8174 vs $10 292, p=0.15). Conclusions: In this real-world study, patients with T2D initiating dulaglutide demonstrated greater HbA1c reduction compared with those initiating basal insulin. Although total diabetes-related costs were similar, the total diabetes-related costs per HbA1c reduction were lower for dulaglutide, highlighting the importance of evaluating effectiveness along with the economic impact of medications.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".