Maternal History of Childhood Abuse and Risk of Asthma and Allergy in 2-Year-Old Children
Bibliographic record
Abstract
OBJECTIVES: Exposure to child abuse (CA) is associated with an increased risk of developing asthma and allergies; it is unknown if that risk is present across generations. This study investigated if 2-year-old children born to mothers with a history of CA were at an increased risk of receiving a diagnosis of asthma or allergies. METHODS: Data from 1,551 participants were collected as part of the All Our Babies (AOB) study, a prospective pregnancy cohort. During pregnancy, each woman provided information about her own history of CA, and at 24 months postpartum, she provided information about her child's medical diagnoses. Symptoms of maternal depression and anxiety were assessed during pregnancy and at 24 months postpartum. RESULTS: Unadjusted models showed that compared to children born to mothers without a history of CA, 2-year-old children born to mothers with a history of CA were more likely to have had a diagnosis of asthma (7.4% vs 4.2%, p = .016) or allergy (15.6% vs 9.2%, p < .001). Maternal symptoms of depression assessed in late pregnancy and symptoms of depression and anxiety at 24 months postpartum were significant mediators of the relationship between maternal CA and 2-year-old asthma diagnosis. Maternal symptoms of depression and anxiety assessed in late pregnancy were also significant mediators of the relationship between maternal CA and 2-year-old allergy diagnosis. CONCLUSIONS: The results indicate that maternal exposure to CA is associated with increased risk of asthma and allergy in their 2-year-old children; symptoms of maternal depression and anxiety were identified as pathways linking the variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".