Disutility of injectable therapies in obesity and type 2 diabetes mellitus: general population preferences in the UK, Canada, and China
Bibliographic record
Abstract
INTRODUCTION: Once-daily and once-weekly injectable glucagon-like peptide-1 receptor agonist therapies (GLP-1 RAs) are established in obesity and type 2 diabetes mellitus (T2DM). In T2DM, both once-daily and once-weekly insulin are expected to be available. This study elicited utilities associated with these treatment regimens from members of the general public in the UK, Canada, and China, to quantify administration-related disutility of more-frequent injectable treatment, and allow economic modelling. METHODS: Two anchor states (no pharmacological treatment), and seven treatment states (daily oral tablet and generic injectable regimens of variable frequency), with identical outcomes were tested A broadly representative sample of the general public in each country participated (excluding individuals with diabetes or pharmacologically treated obesity). An adapted Measurement and Valuation of Health protocol was administered 1:1 in web-enabled interviews by trained moderators: visual analogue scale (VAS) as a "warm-up", and time trade-off (TTO) using a 20-year time horizon for utility elicitation. RESULTS: A total of 310 individuals participated. The average disutility of once-daily versus once-weekly GLP-1 RA was - 0.048 in obesity and - 0.033 in T2DM; the corresponding average disutility for insulin was - 0.064. Disutilities were substantially greater in China, relative to UK and Canada. DISCUSSION: Within obesity and T2DM, more-frequent treatment health states had lower utility. Scores by VAS also followed a logical order. The generated utility values are suitable for use in modelling injectable therapy regimens in obesity and T2DM, due to the use of generic descriptions and assumption of equal efficacy. Future research could examine the reasons for greater administration-related disutility in China.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".