Association of maternal socioeconomic status and race with risk of congenital heart disease: a population-based retrospective cohort study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To investigate the interrelationships between maternal socioeconomic status (SES), race and congenital heart diseases (CHD) among infants. DESIGN: Retrospective cohort study. STUDY SETTING: Ontario, Canada. STUDY POPULATION: All singleton stillbirths and live births born in hospitals between 1 April 2012 and 31 March 2018 in Ontario, Canada (n=804 292). OUTCOME: CHD. ANALYSIS: Multivariable logistic regression models were performed to assess associations between maternal neighbourhood household income, education level, race and CHD while adjusting for maternal age at birth, assisted reproductive technology, obesity, pre-existing health conditions, substance use during pregnancy, maternal rural residence and infant's sex. RESULTS: Compared with infants whose mothers lived in the highest median household income neighbourhoods, infants whose mothers lived in the lowest median income neighbourhoods had a higher likelihood of having CHD (adjusted OR 1.15, 95% CI 1.06 to 1.24). Compared with infants whose mothers lived in neighbourhoods with more people with a university or higher degree, those infants whose mothers lived in neighbourhoods with less people with a university or higher degree had a higher chance of developing CHD (adjusted OR 1.26, 95% CI 1.16 to 1.36). Compared with white mothers, black mothers had a higher odds of giving birth to a child with CHD (adjusted OR 1.40, 95% CI 1.27 to 1.54). No association was detected between White and Asian mothers and CHD among infants. CONCLUSIONS: Our study indicates that there are inequities in CHD burden by maternal SES and race in Ontario, Canada. Further investigation is needed to examine racial variation in CHD using more detailed ethnic data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".