Magnitude of income-related disparities in adverse perinatal outcomes
Bibliographic record
Abstract
BACKGROUND: To assess and compare multiple measurements of socioeconomic position (SEP) in order to determine the relationship with adverse perinatal outcomes across various contexts. METHODS: A birth registry, the Nova Scotia Atlee Perinatal Database, was confidentially linked to income tax and related information for the year in which delivery occurred. Multiple logistic regression was used to examine odds ratios between multiple indicators of SEP and multiple adverse perinatal outcomes in 117734 singleton births between 1988 and 2003. Models for after tax family income were also adjusted for neighborhood deprivation to gauge the relative magnitude of effects related to SEP at both levels. Effects of SEP were stratified by single- versus multiple-parent family composition, and by urban versus rural location of residence. RESULTS: The risk of small for gestational age and spontaneous preterm birth was higher across all the indicators of lower SEP, while risk for large for gestational age was lower across indicators of lower SEP. Higher risk of postneonatal death was demonstrated for several measures of lower SEP. Higher material deprivation in the neighborhood of residence was associated with increased risk for perinatal death, small for gestational age birth, and iatrogenic and spontaneous preterm birth. Family composition and urbanicity were shown to modify the association between income and some perinatal outcomes. CONCLUSIONS: This study highlights the importance of understanding the definitions of SEP and the mechanisms that lead to the association between income and poor perinatal outcomes, and broadening the types of SEP measures used in some cases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".