Prevalence of and Risk Factors for Childhood Asthma, Rhinitis, and Eczema in Hong Kong: Proposal for a Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that particulate matter is a major problem in indoor air quality in Hong Kong schools, but little has been done to assess its relationship with health indicators in the children attending those schools. Our study aims to address this research gap by collecting aerosol data in schools to examine the link between different air pollutants with childhood respiratory health. It is important to explore whether or not the prevalence of asthma, allergic rhinoconjunctivitis, and eczema are increasing in local children. OBJECTIVE: Our aim is to (1) examine the prevalence of asthma, allergic rhinitis, and eczema in school children aged 6-7 years in Hong Kong between 2001 and 2017, and (2) measure air quality at primary schools and explore its relationship with health outcomes measured by the International Study of Asthma and Allergies in Childhood (ISAAC) survey. METHODS: This is a cross-sectional study consisting of an ISAAC questionnaire and aerosol data collection. We have recruited over 2000 parents of primary school students aged 6-7 years old for the questionnaire, and so far 19 schools have completed aerosol data collection. RESULTS: The study is expected to be completed this year. CONCLUSIONS: We predict that our study will show a significant change in the prevalence of asthma, allergic rhinitis, and eczema in school children aged 6-7 years old in recent years. In addition, we expect to show a significant association between air quality at school and health outcomes measured by the ISAAC survey.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".