Ethnic Density and Preterm Birth in African-, Caribbean-, and US-Born Non-Hispanic Black Populations in New York City
Bibliographic record
Abstract
Segregation studies suggest that the health of blacks in the United States is poorer in majority-black compared with mixed-race neighborhoods. However, segregation studies have not examined black immigrants, who may benefit from social support and country-of-origin foods in black immigrant areas. The authors used 1995-2003 New York City birth records and a spatial measure of ethnic density to conduct a cross-sectional investigation of the risks of preterm birth for African-, Caribbean-, and US-born non-Hispanic black women associated with neighborhood-level African-, Caribbean-, and US-born non-Hispanic black density, respectively. Preterm birth risk differences were computed from logistic model coefficients, comparing neighborhoods in the 90th percentile of ethnic density with those in the 10th percentile. African black preterm birth risks increased with African density, especially in more deprived neighborhoods, where the risk difference was 6.1 per 1,000 (95% confidence interval: 1.9, 10.2). There was little evidence of an ethnic density effect among non-Hispanic black Caribbeans. Among US-born non-Hispanic blacks, an increase in preterm birth risk associated with US-born black density was observed in more deprived neighborhoods only (risk difference = 12.5, 95% confidence interval: 6.6, 18.4). Ethnic density seems to be more strongly associated with preterm birth for US-born non-Hispanic blacks than for non-Hispanic black immigrants.
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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 | 0.001 | 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".