Neighborhood Deprivation and Preterm Birth among Non-Hispanic Black and White Women in Eight Geographic Areas in the United States
Bibliographic record
Abstract
Disparities in preterm birth by race and ethnic group have been demonstrated in the United States. Recent research has focused on the impact of neighborhood context on racial disparities in pregnancy outcomes. The authors utilized vital-record birth certificate data and US Census data from eight geographic areas in four states (Maryland, Michigan, North Carolina, and Pennsylvania) to examine the relation between neighborhood deprivation and preterm birth among non-Hispanic White and Black women. The years covered by the data varied by site and ranged from 1995 to 2001. Results were adjusted for maternal age and education, and specific attention was paid to racial and geographic differences in the relation between neighborhood deprivation and preterm birth. Preterm birth rates were higher for non-Hispanic Blacks (10.42-15.97%) than for non-Hispanic Whites (5.77-9.13%), and neighborhood deprivation index values varied substantially across the eight areas. A significant association was found between neighborhood deprivation and risk of preterm birth; for the first quintile of the deprivation index versus the fifth, the adjusted summary odds ratio was 1.57 (95% confidence interval: 1.41, 1.74) for non-Hispanic Whites and 1.15 (95% confidence interval: 1.08, 1.23) for non-Hispanic Blacks. In this study, deprivation at the neighborhood level was significantly associated with increased risk of preterm birth among both non-Hispanic White women and non-Hispanic Black women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".