Risk and protective factors of asthma and mental health condition multimorbidity in a national sample of Canadian children
Bibliographic record
Abstract
BACKGROUND: The coexistence of childhood asthma and mental health (MH) conditions can impact management and health outcomes but we need to better understand the etiology of multimorbidity. We investigated the association between childhood asthma and MH conditions as well as the determinants of their coexistence. METHODS: We used data from the Canadian Health Survey of Children and Youth 2019 (3-17 years; n = 47,871), a cross-sectional, nationally representative Statistics Canada dataset. Our primary outcome was condition status (no asthma or MH condition; asthma only; MH condition only; both asthma, and a MH condition (AMHM)). Predictors of condition status were assessed using multiple multinomial logistic regression. Sensitivity analyses considered individual MH conditions. RESULTS: MH condition prevalence was almost two-fold higher among those with asthma than those without asthma (21.1% vs. 11.6%, respectively). There were increased risks of each condition category associated with having allergies, other chronic conditions, and family members smoking in the home while there were protective associations with each condition status category for being female and born outside of Canada. Four additional variables were associated with AMHM and MH condition presence with one additional variable associated with both AMHM and asthma. In sensitivity analyses, the associations tended to be similar for most characteristics, although there was some variability. CONCLUSION: There are common risk factors of asthma and MH conditions along with their multimorbidity with a tendency for MH risk factors to be associated with multimorbidity. MH condition presence is common and important to assess among children with asthma.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".