Targeted literature review: epidemiology of severe and uncontrolled asthma and associated biomarkers in France, Germany, Italy, Spain and the UK
Bibliographic record
Abstract
Introduction: Severe and uncontrolled asthma are associated with high disease burden. Use of biological therapies in severe asthma is guided by the presence of specific biomarker levels. Objective: To summarize data on the prevalence of severe and uncontrolled asthma and associated biomarkers in France, Germany, Italy, Spain and the UK. Methods: We conducted targeted searches of Medline, Embase and websites of governmental bodies and asthma agencies. Included studies reported relevant data from patients with moderate to severe asthma in France, Germany, Italy, Spain or the UK. We primarily extracted data on severe asthma. A further search for biomarker data was conducted, with no restriction by asthma severity. Results: Among 29 relevant articles identified (EU [4], France [4], Germany [1], Italy [3], Spain [4], UK [13]), the reported asthma prevalence ranged from 5% (Germany) to ~ 8% (UK). Across studies, 4 – 20% of patients with asthma had severe asthma and up to 50% had uncontrolled asthma. Literature sources used different definitions of severity, making cross-study comparison difficult. In Italy, 59 – 71% of patients with severe asthma had ≥ 300 blood eosinophils/µL and 48 – 59% had fractional exhaled nitric oxide levels of ≥ 25 ppb. Of 363 558 patients in the UK, 0.8% had severe, uncontrolled eosinophilic asthma (≥ 300 blood eosinophils/µL). No high-quality data on biomarker prevalence in the other three countries were identified. Conclusion: Severe asthma prevalence varied by country. Further research using a standardized definition is needed on severe asthma prevalence and its phenotypes based on biomarker levels and other clinical attributes. Publication History Article published online: 30 April 2021 © 2021. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".