Preterm birth among the hmong, other Asian subgroups and non-hispanic whites in California
Bibliographic record
Abstract
BACKGROUND: We investigated very preterm (VPTB) and preterm birth (PTB) risk among Hmong women relative to non-Hispanic whites and other Asian subgroups. We also examined the maternal education health gradient across subgroups. METHODS: California birth record data (2002-2004) were used to analyze 568,652 singleton births to white and Asian women. Pearson Chi-square and logistic regression were used to assess variation in maternal characteristics and VPTB/PTB risk by subgroup. RESULTS: White, Chinese, Japanese, Korean, Asian Indian, and Vietnamese women had 36-59% lower odds of VPTB and 30-56% lower odds of PTB than Hmong women. Controls for covariates did not substantially diminish these disparities. Cambodian, Filipino and Lao/Thai women's odds of VPTB were similar to that of Hmong women. But they had higher adjusted odds of PTB compared to the Hmong. There was heterogeneity in the educational gradient of PTB, with significant differences between the least and most educated women among whites, Chinese, Japanese, Asian Indians, Cambodians, and Laoians/Thais. Maternal education was not associated with PTB for Hmong, Vietnamese and Korean women, however. CONCLUSIONS: Studies of Hmong infant health from the 1980s, the decade immediately following the group's mass migration to the US, found no significant differences in adverse birth outcomes between Hmong and white women. By the early 2000s, however, the disparities in VPTB and PTB between Hmong and white women, as well as between Hmong and other Asian women had become substantial. Moreover, despite gains in post-secondary education among childbearing-age Hmong women, the returns to education for the Hmong are negligible. Higher educational attainment does not confer the same health benefits for Hmong women as it does for whites and other Asian subgroups.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".