Effect of neighbourhood income and maternal education on birth outcomes: a population-based study
Bibliographic record
Abstract
BACKGROUND: Maternal socioeconomic status (SES) is an important determinant of inequity in maternal and fetal health. We sought to determine the extent to which associations between adverse birth outcomes and SES can be identified using individual-level measures (maternal level of education) and community-level measures (neighbourhood income). METHODS: In Quebec, the birth registration form includes a field for the mother's years of education. Using data from birth registration certificates, we identified all births from 1991 to 2000. Using maternal postal codes that can be linked to census enumeration areas, we determined neighbourhood income levels that reflect SES. RESULTS: Lower levels of both maternal education and neighbourhood income were associated with elevated crude risks of preterm birth, small-for-gestational-age (SGA) birth, stillbirth and neonatal and postneonatal death. The effects of maternal education were stronger than, and independent of, those of neighbourhood income. Compared with women in the highest neighbourhood income quintile, women in the lowest quintile were significantly more likely to have a preterm birth (adjusted odds ratio [OR] 1.14, 95% confidence interval [CI] 1.10-1.17), SGA birth (OR 1.18, 95% CI 1.15-1.21) or stillbirth (OR 1.30, 95% CI 1.13-1.48); compared with mothers who had completed community college or at least some university, mothers who had not completed high school were significantly more likely to have a preterm birth (adjusted OR 1.48, 95% CI 1.44-1.52), SGA birth (OR 1.86, 95% CI 1.82-1.91) or stillbirth (OR 1.54, 95% CI 1.36-1.74). INTERPRETATION: Individual and, to a lesser extent, neighbourhood-level SES measures are independent indicators for subpopulations at risk of adverse birth outcomes. Women with lower education levels and those living in poorer neighbourhoods are more vulnerable to adverse birth outcomes and may benefit from heightened clinical vigilance and counselling.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".