Racial and Ethnic Disparities in the Perinatal Health of Infants Conceived by ART
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although racial and ethnic disparities in adverse birth outcomes have been well documented, it is unknown whether such disparities diminish in women who use medically assisted reproduction (MAR). We examined differences in the association between maternal race and ethnicity and adverse birth outcomes among women who conceived spontaneously and those who used MAR, including assisted reproduction technology (ART), eg, in-vitro fertilization, and also non-ART MAR, eg, fertility drugs. METHODS: We conducted a population-based retrospective cohort study using data on all singleton births (N = 7 545 805) in the United States from 2016 to 2017. The outcomes included neonatal and fetal death, preterm birth, and serious neonatal morbidity, among others. Modified Poisson regression was used to estimate adjusted rate ratios (aRR) and 95% confidence intervals (CI) and to assess the interactions between race and ethnicity and mode of conception. RESULTS: Overall, 93 469 (1.3%) singletons were conceived by MAR. Neonatal mortality was twofold higher among infants of non-Hispanic Black versus non-Hispanic White women in the spontaneous-conception group (aRR = 1.9, 95% CI: 1.8-1.9), whereas in the ART-conception group, neonatal mortality was more than fourfold higher in infants of non-Hispanic Black women (aRR = 4.1, 95% CI: 2.9-5.9). Racial and ethnic disparities between Hispanic versus non-Hispanic White women were also significantly larger among women who conceived using MAR with regard to preterm birth (<34 weeks) and perinatal mortality. CONCLUSIONS: Compared to women who conceived spontaneously, racial and ethnic disparities in adverse perinatal outcomes were larger in women who used MAR. More research is needed to identify preventive measures for reducing risks among vulnerable women who use medically assisted reproduction.
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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 | 0.000 | 0.000 |
| 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".