Preterm birth and social inequality: assessing the effects of material and psychosocial disadvantage in a UK birth cohort
Bibliographic record
Abstract
OBJECTIVE: To examine the association between social inequalities and preterm birth, testing both psychosocial and material determinants. DESIGN: Retrospective cohort study with linked hospital data. SETTING AND POPULATION: 17 285 women in England, Scotland, Northern Ireland and Wales giving birth to singletons included in wave 1 of the UK Millennium Cohort Study. METHODS: Social inequalities were measured with material (household income, housing tenure) and psychosocial (education, occupational class, employment, social support) indicators. Analysis using multivariable logistic regression assessed odds of preterm birth, adjusting for demographics, health and health-related behaviors, pregnancy and delivery conditions, and pregnancy complications. MAIN OUTCOME MEASURE: Preterm birth between 24 and 36 weeks, 6 days' gestation. RESULTS: Initial bivariable analysis suggested associations between preterm birth and household income, housing tenure, and education. These effects were largely explained by adjustment for other social determinants in multivariable models. Following full adjustment, effects of unemployment [OR = 1.52, 95% confidence interval (CI) 1.21-1.90, p < 0.001] and one indicator of poor social support (OR = 1.17, 95% CI 1.01-1.35, p = 0.04) remained significant. CONCLUSION: Unemployment and lack of social support are associated with higher risk of preterm birth, supporting the hypothesis that poor psychosocial circumstances elevate a woman's risk of this adverse perinatal outcome. Further research is needed to examine the causal pathways through which social inequalities affect preterm birth.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".