Treatment gaps in severe asthma across nine OECD countries and recommendations for addressing them: an international survey of clinicians
Bibliographic record
Abstract
OBJECTIVES: Despite recent advancements in the pharmacological management of severe asthma (SA), there is a significant lack of robust, system level data on the diagnostic, prescribing and care practices in SA. This hinders performance measurement and improved quality in SA care outcomes. This paper aimed to capture clinician and patient insights on outcomes relevant to the clinical and pharmacological management of SA and understand the extent to which this evidence can facilitate performance and quality measurement efforts in SA care. METHODS: Following an in-depth review of the relevant literature, we developed a robust framework of SA relevant indicators around: a) policy structure and organisation; b) diagnosis; c) treatment; and d) care delivery. We conducted an international web survey of respiratory specialists to capture information on the above indicators and provide comparative insights and benchmarking of performance in SA across 9 countries. RESULTS: Twenty-six responses were received from respiratory specialists in Australia, Brazil, Canada, France, Germany, Italy, Japan, Spain, and UK. The insights revealed that SA-specific quality-of-care frameworks are largely unavailable across all study countries, while discrepancies existed across and within settings on practices and timings for specialist referral and appointment booking: the former ranging from 19 days in Germany and up to 24 months in Brazil. Variation was more prominent in the diagnostic criteria and tests used, with only about a third of surveyed clinicians reporting using SA diagnostic pathway guidelines. A disconnect both between and within countries exists in the prescribing pathways followed to treat and manage SA, especially in relation to the use of oral/systemic corticosteroids. CONCLUSION: Data around the clinical and pharmacological management of SA is not routinely available, and despite the increased use of biologics there are still significant gaps in patient care holding back improved outcomes. In light of the COVID-19 pandemic, action in the form of measures to enhance outcomes in SA care is vital now more than ever. These include measures for: i) the systematic collection of outcomes data in SA, ii) improved referral pathways and capacities in SA and iii) measures to support increased understanding and incentivisation of guidelines in the prescribing of treatment and SA diagnostic criteria.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".