Global Burden of Asthma, and Its Impact on Specific Subgroups: Nasal Polyps, Allergic Rhinitis, Severe Asthma, Eosinophilic Asthma
Bibliographic record
Abstract
Adrian Paul J Rabe,1,2 Wei Jie Loke,3 Khushboo Gurjar,4 Allison Brackley,5 Don Eliseo Lucero-Prisno III6,7 1BioPharmaceutical Medical, Respiratory & Immunology, AstraZeneca, Cambridge, UK; 2Imperial College, London, UK; 3East and North Hertfordshire NHS Trust, Stevenage, UK; 4RWA Health Economics, Cytel Health Canada, Toronto, Ontario, Canada; 5RWA Health Economics, Cytel, Inc, Waltham, MA, USA; 6Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK; 7University of the Philippines Open University, Los Baños City, PhilippinesCorrespondence: Allison Brackley, Cytel, Inc, 1050 Winter Street, Center Entrance 3rd Floor, Waltham, MA, 02451, USA, Tel +1 617 661 2011, Fax +1 617 661 4405, Email brackleyallison@gmail.comBackground: The complex nature of asthma has resulted in a poor understanding of its epidemiology, particularly in low-and middle-income countries (LMIC). Clinical subgroups, such as patients with severe asthma, eosinophilic asthma, allergic rhinitis, or nasal polyps, experience additional barriers to care.Methods: Prevalence estimates for asthma and key clinical subgroups were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 and from a targeted literature review conducted through PubMed in October of 2021. National estimates were calculated and the roles of potential explanatory factors were explored through qualitative analysis.Results: In total, 162 publications from 69 countries were included. Across continents, asthma prevalence values ranged from 3.44% (Asia), 3.67% (Africa), 4.90% (South America), 5.69% (Europe), 8.29% (North America), to 8.33% (Oceania). Globally, of those with asthma, 26.70% had severe asthma, 30.99% had eosinophilic asthma, 48.95% had allergic rhinitis, and 7.0% to 25.40% had nasal polyps. Countries with higher air quality, income status, and healthcare access and quality reported a higher asthma prevalence.Conclusion: Asthma prevalence values were low in LMICs, potentially indicating health system deficiencies resulting in low diagnosis and reporting. The prevalence of eosinophilic asthma and severe asthma phenotypes was high in many countries, although the prevalence estimates of all asthma subgroups were quite variable.Keywords: epidemiology, diagnosis, prevalence, low- and middle-income countries
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".