Prevalence of Asthma, Allergic Rhinitis and Eczema among Lebanese Adolescents
Bibliographic record
Abstract
OBJECTIVE: Studies on allergic diseases remain scarce in Lebanon. The aim of the present study was to determine prevalence and characteristics of asthma, allergic rhinitis and eczema among Lebanese school children. METHODS: The study was cross-sectional in design performed on a convenient sample of 3,115 students (13-14 yr) selected from 13 schools in 5 Lebanese provinces. Students were asked to complete the Arabic version of the International Study of Asthma and Allergies in Childhood questionnaire. Logistic regression was performed to assess the characteristics of having asthma, allergic rhinitis and eczema in the past year. RESULTS: The prevalence of ever having asthma, rhinitis and eczema was 8.3%, 45.2% and 12.8% respectively, while the prevalence of the symptoms of these diseases in the past year was 24.1%, 38.6% and 20.9%, respectively. Residing in the South and the North provinces of Lebanon and living in a busy area increased the likelihood of developing asthma and rhinitis. Higher rates of asthma and eczema, however, was noted among students going to private schools (Odds Ratio (OR) = 1.6, 95% confidence interval (CI): 1.3-2.1 and OR = 1.3, 95% CI: 1.0-1.7, respectively). Passive smoking was significantly associated with asthma only (OR = 1.3, 95% CI: 1.1-1.7). In addition to the above, the odds of having any of the three outcomes increases to at least 2.4-fold when accompanied by another allergic disease. CONCLUSIONS: Allergic diseases are highly prevalent in Lebanon and are catching up with the rates of developed countries. Moreover, the role of each of the three diseases in the existence of the other two had the greatest impact on their prevalence.
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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 | 0.000 | 0.001 |
| 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 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".