The joint influence of area income, income inequality, and immigrant density on adverse birth outcomes: a population-based study
Bibliographic record
Abstract
BACKGROUND: The association between area characteristics and birth outcomes is modified by race. Whether such associations vary according to social class indicators beyond race has not been assessed. METHODS: This study evaluated effect modification by maternal birthplace and education of the relationship between neighbourhood characteristics and birth outcomes of newborns from 1999-2003 in the province of Québec, Canada (N = 353,120 births). Areas (N = 143) were defined as administrative local health service delivery districts. Multi-level logistic regression was used to model the association between three area characteristics (median household income, immigrant density and income inequality) and the two outcomes preterm birth (PTB) and small-for-gestational age (SGA) birth. Effect modification by social class indicators was evaluated in analyses stratified according to maternal birthplace and education. RESULTS: Relative to the lowest tertile, high median household income was associated with SGA birth among Canadian-born mothers (odds ratio (OR) 1.13, 95% confidence interval (CI) 1.06, 1.20) and mothers with high school education or less (OR 1.13, 95% CI 1.02, 1.24). Associations between median household income and PTB were weaker. Relative to the highest tertile, low immigrant density was associated with a lower odds of PTB among foreign-born mothers (OR 0.79, 95% CI 0.63, 1.00) but a higher odds of PTB among Canadian-born mothers (OR 1.14, 95% CI 1.07, 1.21). Associations with income inequality were weak or absent. CONCLUSION: The association between area factors and birth outcomes is modified by maternal birthplace and education. Studies have found that race interacts in a similar manner. Public health policies focussed on perinatal health must consider the interaction between individual and area characteristics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".