Rapid reduction in hospitalisations after an intervention to manage severe asthma
Bibliographic record
Abstract
Asthma is the third cause of hospitalisations due to clinical illnesses in Brazil. The Programme for Control of Asthma in Bahia (ProAR) leads an initiative in Salvador City (Brazil) to manage severe asthma for free. The aim of this study was to identify trends in asthma hospitalisation in the entire city and to evaluate the impact of ProAR. Information on asthma hospitalisations from 1998 to 2006 was collected. We analysed trends in Salvador (2.8 million inhabitants) before and after ProAR, taking pneumonia and myocardial infarction into account for local comparison. As an external control we obtained information on asthma from Recife, which is the most comparable Brazilian city. In Salvador, asthma hospital admissions declined by 82.3% (1998-2006). A greater proportion of this reduction (74%) occurred after 2003, in parallel with the implementation of ProAR. The reduction in asthma admissions in Recife was smaller. The rates of hospitalisation in 2006 were 2.25 per 10,000 inhabitants in Salvador and 17.06 in Recife. In Salvador, we found an inverse correlation between the provision of medication for asthma and hospitalisation (-0.801; p<0.0001). A rapid reduction in asthma admissions in the entire city of Salvador was associated with ProAR, a public health intervention targeting severe asthma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".