Asthma incidence and risk factors in a national longitudinal sample of adolescent Canadians: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Estimates of asthma incidence and its possible determinants in adolescent populations have rarely been obtained using prospective designs. We sought to identify socio-demographic and other patterns in the incidence of asthma among Canadian adolescents and to examine possible behavioural and environmental determinants of asthma incidence using longitudinal analyses. METHODS: We used data from the National Population Health Survey (NPHS), a nationally representative longitudinal survey of Canadians. All persons aged 12-18 years without asthma at baseline were followed up to a maximum of 12 years. The outcome was a reported diagnosis of asthma during the follow-up period. Analyses were weighted to the population and bootstrapping procedures were used to estimate variances. RESULTS: Participants (n = 956) represented 2,038,890 adolescents of whom 293,450 (14.4%) developed asthma over the 21,274,890 person-years of follow-up. Overall, the incidence of asthma was 10.2 per 1000 person-years. In adjusted Cox regression analysis, being female (HR = 2.13, 95% CI = 1.26-3.62, p = 0.005) and being exposed to passive smoking (HR = 2.06, 95% CI = 1.27-3.34, p = 0.003) were associated with the development of asthma while no statistically significant associations were identified for rural residence, being overweight, and other health behaviours. There was also an apparent cohort effect among girls where girls who were older at baseline reported being diagnosed with asthma more over the follow-up than their younger counterparts. This was not observed among males. CONCLUSIONS: Asthma prevention initiatives for adolescents should target girls and focus on smoking exposures. The role that differential diagnostic patterns play in these observations should be investigated to more accurately assess the incidence of asthma.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".