The association between asthma and perinatal mental illness: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Asthma is a risk factor for mental illness, but few studies have explored this association around the time of pregnancy. We studied the association between asthma and perinatal mental illness and explored the modifying effects of social and medical complexities. METHODS: In a population-based cohort of 846 155 women in Ontario, Canada, with a singleton live birth in 2005-2015 and no recent history of mental illness, modified Poisson regression models were constructed to examine the association between asthma diagnosed before pregnancy and perinatal mental illness, controlling for socio-demographics and medical history. We explored the modifying effects of social and medical complexities using relative excess risk due to interaction. Additional analyses examined the association between asthma and perinatal mental illness by timing and type of mental illness. RESULTS: Women with asthma were more likely than those without asthma to have perinatal mental illness [adjusted relative risk (aRR) 1.14; 95% (confidence interval) CI: 1.13, 1.16]. Asthma was associated with increased risk of diagnosis of mental illness prenatally (aRR 1.11; 95% CI: 1.08, 1.13) and post-partum (aRR 1.17; 95% CI: 1.15, 1.19) and specifically diagnoses of mood and anxiety disorders (aRR 1.14; 95% CI: 1.13, 1.16), psychotic disorders (aRR 1.20; 95% CI: 1.10, 1.31) and substance- or alcohol-use disorders (aRR 1.24; 95% CI: 1.14, 1.36). There was no effect modification related to social or medical complexity for these outcomes. CONCLUSIONS: Women with asthma predating pregnancy are at slightly increased risk of mental illness in pregnancy and post-partum. A multidisciplinary management strategy may be required to ensure timely identification and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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, 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".