Trends and predictors of preterm birth among Asian Americans by ethnicity, 1992–2018
Bibliographic record
Abstract
In an increasingly diverse United States (US) population, racial disparities in preterm birth outcomes continue to widen. In this study, we examined temporal trends and risk of preterm birth among Asian American women over a quarter century (1992–2018). This is a retrospective cohort study using the 1992–2018 Natality data files. We conducted joinpoint regression analyses to examine trends in preterm birth among Asian Americans and non-Hispanic (NH) Whites. Bivariate and multivariable analyses were used to identify risk factors associated with preterm birth among Asian Americans and their ethnic sub-groups as compared to NH-Whites. There were a total of 251,278 preterm births among Asian American women, corresponding to a rate of 10.0%, which was relatively stable over time. The incidence of extremely, very and moderate-to-late preterm birth among Asian Americans was 0.4%, 0.9% and 8.7% respectively. Overall, Asian American women exhibited lower adjusted odds (OR = 0.92; 95% CI: 0.88–0.97) of preterm birth than their NH-White counterparts. Comparing Asian American subgroups to NH-Whites, Filipinas and Vietnamese mothers had increased adjusted odds, whereas Chinese, Korean, Japanese and Asian Indian women showed decreased adjusted odds for preterm birth. The risk of preterm birth varied among the ethnic subgroups of Asian Americans in the United States. Future studies should explore the socio-cultural and environmental nuances that might explain these differences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".