The cost of systemic corticosteroid-induced morbidity in severe asthma: a health economic analysis
Bibliographic record
Abstract
BACKGROUND: Treatment of severe asthma may include high dose systemic-steroid therapy which is associated with substantial additional morbidity. This study estimates the additional healthcare costs associated with steroid-induced morbidity by comparing three patients groups: those with severe asthma, moderate asthma and no asthma. METHODS: Patients with severe asthma (n = 808, GINA step 5 treatment) were matched by age and gender with patients with mild/moderate asthma (n = 3,975, GINA step 2 and 3 treatment) and a non-asthma control cohort (with a diagnosis of rhinitis; n = 2,412) from the Optimum Patient Care Research Database (OPCRD), a nationally representative primary care database. Prescribed drugs and publicly funded healthcare activity were monetised and annual costs per patient estimated. Regression analyses were used to estimate the additional healthcare cost associated with steroid-induced morbidity. RESULTS: Average healthcare costs per person per year range from £2603 - £4533 for the severe asthma cohort, to £978 - £2072 for the mild/moderate asthma cohort, to £560 - £1324 for the non-asthma control cohort, depending on the costing scenario. Differences in induced morbidity costs were evident between patients with asthma differentiated by steroid exposure. In relation to prescription drugs used to treat steroid-induced co-morbidities, females with severe asthma and high steroid exposure cost approximately £789 more per year than a corresponding female with no asthma, while males cost approximately £744 more than their counterparts with no asthma. Estimates were extrapolated to all healthcare costs. CONCLUSIONS: This study provides the first robust estimates of the additional cost of healthcare related to steroid-induced morbidity relative to patients with no steroid exposure. The study will help inform use of steroid-sparing strategies in this patient group.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".