Prevalence of chronic respiratory diseases and medication use among children and adolescents in Brazil - a population based cross-sectional study
Bibliographic record
Abstract
Abstract Objectives: to describe the prevalence of chronic respiratory diseases and their pharmacological management in children and adolescents in Brazil. Methods: data from the Pesquisa Nacional de Acesso, Uso e Promoção do Uso Racional de Medicamentos no Brasil (PNAUM)(National Access Survey, Use and Promotion of Rational Use of Medicines in Brazil),a population-based cross-sectional study, were analyzed. Household surveys were conducted between September 2013 and February 2014. We included the population under 20 years of age with chronic respiratory diseases. Prevalence of disease, indication of pharmacological treatment, and their use were assessed. Results: the prevalence of chronic respiratory diseases in children aged less than 6 years old was 6.1% (CI95%= 5.0-7.4), 4.7% (CI95%= 3.4-6.4) in those 6-12 years, and 3.9% (CI95%= 2.8-5.4) in children 13 years and older. Children under 6 showed a higher prevalence of pharmacological treatment indication (74.6%; CI95%= 66.0-81.7), as well as medication use (72.6%; CI95%= 62.8-80.7). Of those using inhalers, 56.6% reported using it with a spacer. The most frequent pharmacologic classes reported were short-acting β2 agonists (19.0%), followed by antihistamines (17.2%). Conclusion: children and adolescents who report chronic respiratory diseases living in urban areas in Brazil seem to be undertreated for their chronic conditions. Pharmacological treatment, even if indicated, was not used, an important finding for decision-making in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".