Country-specific cost-effectiveness of early intervention with budesonide in mild asthma
Bibliographic record
Abstract
Early intervention with budesonide is an effective strategy for mild persistent asthma, which has been shown to provide additional clinical benefits at a low incremental cost using USA cost data. The present authors analysed whether this strategy would be cost-effective using cost data for other countries. Based on the 3-yr prospective, randomised, double-blind inhaled Steroid Treatment As Regular Therapy (START) in early asthma study (comparing budesonide and placebo combined with usual asthma therapy), the cost-effectiveness was estimated separately for eight different countries, from both healthcare payer and societal perspectives, of adding budesonide to usual asthma therapy. Local unit costs were applied to data for the total trial population. Incremental cost-effectiveness ratios (ICER) were estimated as cost per symptom-free day (SFD) gained. Budesonide increased SFDs by an average of 14.1 days annually. From a healthcare payer perspective, budesonide would reduce the total cost of asthma care in Australia. In Sweden, Canada, France, Spain, UK, China and the USA, the ICER ranged from US$2.4-11.3 per SFD. From a societal perspective, budesonide would be cost-saving in Australia, Canada and Sweden. In conclusion, for countries where costs with budesonide are higher, the policy implication has to be determined by that health system's willingness to pay for an additional symptom-free day. However, where budesonide therapy increases symptom-free days and reduces total costs, the policy conclusion clearly favours early intervention.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".