Neighbourhood maternal socioeconomic status indicators and risk of congenital heart disease
Bibliographic record
Abstract
BACKGROUND: This study aimed to examine the relationships between various maternal socioeconomic status (SES) indicators and the risk of congenital heart disease (CHD). METHODS: This was a population-based retrospective cohort study, including all singleton stillbirths and live births in Ontario hospitals from April 1, 2012 to March 31, 2018. Multivariable logistic regression models were performed to examine the relationships between maternal neighbourhood household income, poverty, education level, employment and unemployment status, immigration and minority status, and population density and the risk of CHD. All SES variables were estimated at a dissemination area level and categorized into quintiles. Adjustments were made for maternal age at birth, assisted reproductive technology, obesity, pre-existing maternal health conditions, substance use during pregnancy, rural or urban residence, and infant's sex. RESULTS: Of 804,292 singletons, 9731 (1.21%) infants with CHD were identified. Compared to infants whose mothers lived in the highest income neighbourhoods, infants whose mothers lived in the lowest income neighbourhoods had higher likelihood of developing CHD (adjusted OR: 1.29, 95% CI: 1.20-1.38). Compared to infants whose mothers lived in the neighbourhoods with the highest percentage of people with a university or higher degree, infants whose mothers lived in the neighbourhoods with the lowest percentage of people with university or higher degree had higher chance of CHD (adjusted OR: 1.34, 95% CI: 1.24-1.44). Compared to infants whose mothers lived in the neighbourhoods with the highest employment rate, the odds of infants whose mothers resided in areas with the lowest employment having CHD was 18% higher (adjusted OR: 1.18, 95% CI: 1.10-1.26). Compared to infants whose mothers lived in the neighbourhoods with the lowest proportion of immigrants or minorities, infants whose mothers resided in areas with the highest proportions of immigrants or minorities had 18% lower odds (adjusted OR: 0.82, 95% CI: 0.77-0.88) and 16% lower odds (adjusted OR: 0.84, 95% CI: 0.78-0.91) of CHD, respectively. CONCLUSION: Lower maternal neighbourhood household income, poverty, lower educational level and unemployment status had positive associations with CHD, highlighting a significant social inequity in Ontario. The findings of lower CHD risk in immigrant and minority neighbourhoods require further investigation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".