Patterns of social inequalities across pregnancy and birth outcomes: a comparison of individual and neighborhood socioeconomic measures
Bibliographic record
Abstract
BACKGROUND: This paper identifies patterns of health inequalities (consistency and magnitude) of socioeconomic disparities for multiple maternal and child health (MCH) outcomes that represent different health care needs of mothers and infants. METHODS: Using cross-sectional national data (unweighted sample = 6,421, weighted =76,508) from the Canadian Maternity Experiences Survey linked with 2006 Canadian census data, we categorized 25 health indicators of mothers of singletons into five groups of MCH outcomes (A. maternal and infant health status indicators; B. prenatal care; C. maternal experience of labor and delivery; D. neonatal medical care; and E. postpartum infant care and maternal perceptions of health care services). We then examined the association of these health indicators with individual socioeconomic position (SEP) (education and income), neighborhood SEP and combined SEP (a four-level measure of low and high individual and neighborhood SEP), and compared the magnitude (odds ratios and 95% confidence intervals) and direction of the associations within and between MCH outcome groups. RESULTS: We observed consistent positive gradients of socioeconomic inequalities within most groups and for 23/25 MCH outcomes. However, more significant associations and stronger gradients were observed for the MCH outcomes in the maternal and infant health status group as opposed to other groups. The neonatal medical care outcomes were weakly associated with SEP. The direction of associations was negative between some SEP measures and HIV testing, timing of the first ultrasound, caesarean section, epidural for vaginal births, infant needing non-routine neonatal care after discharge and any breastfeeding at 3 or 6 months. Gradients were steep for individual SEP but moderate for neighborhood SEP. Combined SEP had no consistent gradients but the subcategory of low individual-high neighborhood SEP often showed the poorest health outcomes compared to the categories within this SEP grouping. CONCLUSION: By examining SEP gradients in multiple MCH outcomes categorized into groups of health care needs, we identified large and consistent inequalities both within and between these groups. Our results suggest differences in pathways and mechanisms contributing to SEP inequalities across groups of MCH outcomes that can be examined in future research and inform prioritization of policies for reducing these inequalities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".