Severe asthma care and treatment: indicators and data for performance management across ten countries
Bibliographic record
Abstract
OBJECTIVES: This paper outlines the rationale and need for a conceptual framework comprising a standard set of necessary indicators to assess performance in severe asthma (SA) care and practice, together with an analysis of the current availability of statistical data on this topic across ten countries in order to understand the quality of evidence on performance in SA care and practice. METHODS: An expert panel contributed to the creation of the framework and performance indicators, based on what is relevant and clinically meaningful for SA as an indication; the framework consists of four components: diagnosis of SA, treatment of SA, care provision for SA, and the socio-economic impact of SA. Study countries included were Australia, Brazil, Canada, France, Germany, Italy, Japan, Spain, Sweden, and the United Kingdom, all representing different approaches to health care financing, organisation and delivery, and geographic regions. Publicly available data from national and international sources was reviewed against the framework along with research to identify statistical sources and assess the availability of data on SA in each study country. RESULTS: SA is a complex diagnosis and condition, and performance indicators need to be designed to reflect SA care and practice holistically and accurately. 44 indicators were identified across six themes: prevalence, policy structures and organisation, diagnosis, treatment, care delivery, and socio-economic impact. Clear gaps in the statistical evidence for performance in SA care exist across the study countries, as little public national or international data was identified for these indicators. Where available, data is limited to general data on healthcare resource use and drug reimbursement and often exists only for asthma diagnoses, not SA in particular. CONCLUSION: SA remains an area of significant unmet need. There is clear imperative to improve data collection and reporting across all dimensions of SA care to ensure appropriate interventions are designed and implemented to reduce avoidable morbidity and mortality and improve quality of care. Both clinician and patient perspectives should be recognised when considering country-level performance in SA care and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.035 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".