Neighbourhood deprivation and small‐for‐gestational‐age term births in the United States
Bibliographic record
Abstract
Residential context has received increased attention as a possible contributing factor to race/ethnic and socio-economic disparities in birth outcomes in the United States. Utilising vital statistics birth record data, this study examined the association between neighbourhood deprivation and the risk of a term small-for-gestational-age (SGA) birth among non-Hispanic whites and non-Hispanic blacks in eight geographical areas. An SGA birth was defined as a newborn weighing <10th percentile of the sex- and parity-specific birthweight distribution for a given gestational week. Multi-level random intercept logistic regression models were employed and statistical tests were performed to examine whether the association between neighbourhood deprivation and SGA varied by race/ethnicity and study site. The risk of term SGA was higher among non-Hispanic blacks (range 10.8-17.5%) than non-Hispanic whites (range 5.1-9.2%) in all areas and it was higher in cities than in suburban locations. In all areas, non-Hispanic blacks lived in more deprived neighbourhoods than non-Hispanic whites. However, the adjusted associations between neighbourhood deprivation and term SGA did not vary significantly by race/ethnicity or study site. The summary fully adjusted pooled odds ratios, indicating the effect of one standard deviation increase in the deprivation score, were 1.15 [95% CI 1.08, 1.22] for non-Hispanic whites and 1.09 [95% CI 1.05, 1.14] for non-Hispanic blacks. Thus, neighbourhood deprivation was weakly associated with term SGA among both non-Hispanic whites and non-Hispanic blacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".