Socioeconomic Inequalities in Distributions of Birth Outcomes: A Cross-National Comparative Study of Canada and the United States
Bibliographic record
Abstract
Background: Despite well-established associations between entire distributions of birth outcomes (i.e., birth weight and gestational age) and health outcomes across the life course, the extant literature on socioeconomic inequalities has focused almost exclusively on examining inequalities in their high-risk range (i.e., low birth weight and preterm birth). Objective: To examine if, and why, socioeconomic inequalities in low birth weight and preterm birth extend to inequalities over the entire distributions of birth weight and gestational age, in Canada and its peer nations. Methods and aims: A scoping review was conducted to understand how researchers have conceptualized and analyzed social inequalities in distributions of birth outcomes (Aim 1). Data from the U.S. 2006 Pregnancy Risk Assessment Monitoring System (PRAMS) (N=892,635) were used to measure and explain socioeconomic inequalities across distributions of birth outcomes (Aim 2). Harmonized data from the 2006 U.S. PRAMS and the 2006 Canadian Maternity Experiences Survey (MES) (N=61,230) were used to examine whether socioeconomic inequalities in distributions of birth weight and gestational age vary between two comparable countries, Canada and the United States. Results: This dissertation uncovered the presence of socioeconomic inequalities not only in low birth weight and preterm birth, but also over the entire distributions of birth weight and gestational age. Compared to the United States, Canada was characterized by more favourable birth-outcome distributions (i.e., a greater proportion of infants weighing between 2500 and 4000 grams and born between 37 and 41 weeks of gestation), both for the full populations and between similar socioeconomic groups. Observed individual level characteristics could not fully explain cross-national disparities, pointing to the explanatory power of unobserved (and perhaps structural) factors. Conclusions: Applying a distributional lens to investigate health inequalities within and between societies provides further insights regarding the population-level scope of the problem of health inequalities. Socioeconomic inequalities are present over the entire birth outcome distributions, which have implications both for clinical guidelines and population health prevention strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".