Global differences in the epidemiology and exacerbations among patients with moderate-to-severe asthma
Bibliographic record
Abstract
Background: Asthma is a common disease but global differences in prevalence of moderate-to-severe disease and exacerbations remain understudied. Reducing exacerbation risk is a key goal in asthma therapy. Understanding global differences in epidemiology and exacerbations will illustrate areas of unmet needs. Aims: A review of systematic literature reviews (SLRs) and primary observational studies on the disease history explored the epidemiology and exacerbation rates in patients with moderate-to-severe asthma by countries and clinical characteristics. Methods: Electronic databases were searched for SLRs (2014–24) of observational studies, with a second search for observational studies (2022–24). This analysis reports prevalence of disease and annualised exacerbation rates (AER) at baseline by disease severity. Results: Overall, 44 SLRs and 88 observational studies were found. Prevalence for moderate-to-severe asthma varied widely, ranging from 19% in China to 59% in Spain. Uncontrolled asthma represented as much as 49% among patients with moderate-to-severe asthma in the US. Globally, AER for all severities at baseline were 0.8–4.4 in Europe and 0.3–3.2 in North America. Proportion of severe patients experiencing ≥1 exacerbation at baseline was up to 84.4% in Asia, 53.6–96.4% in Europe and 84.6% in the US. While moderate-to-severe patients in the US experiencing ≥1 exacerbation at baseline was 23.2–22.7%. Conclusions: The prevalence of moderate-to-severe asthma varies greatly globally with a significant burden remaining. Optimising treatment to reduce exacerbation risk may be of relevance for patients with moderate-to-severe asthma. Funding: GSK 222232.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".