Obesity May Increase the Incidence of Asthma in Women but Not in Men: Longitudinal Observations from the Canadian National Population Health Surveys
Bibliographic record
Abstract
To investigate the possibility of gender specificity for the effect of body mass index (BMI) on development of asthma, the authors used the longitudinal data from the first and second cycles of the National Population Health Survey, conducted in Canada in 1994-1995 and 1996-1997, respectively. Data from 9,149 subjects (4,266 men and 4,883 women) aged 20-64 years who reported no asthma at baseline were used in this analysis. The 2-year cumulative incidence of asthma was estimated by using a bootstrap procedure to take sampling weights and design effects into account. During the 2-year study period, 1.6% of the men and 2.9% of the women developed asthma. Average changes in body weight and BMI over the 2-year observation period were relatively small and were not associated with asthma incidence. However, baseline BMI was a significant predictor for asthma incidence in women. The adjusted odds ratio for women whose baseline BMI was at least 30.0 kg/m(2) versus 20.0-24.9 kg/m(2) was 1.9 (95% confidence interval: 1.1, 3.4), whereas the corresponding odds ratio of 1.1 (95% confidence interval: 0.3, 3.6) for men was not significantly different from unity. The authors concluded that obesity was related to development of asthma in women but not in men.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, 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".