Socioeconomic Status, Preeclampsia Risk and Gestational Length in Black and White Women
Bibliographic record
Abstract
BACKGROUND: Higher socioeconomic status (SES) has less impact on cardio-metabolic disease and preterm birth risk among Black women compared to White women, an effect called "diminishing returns." No studies have tested whether this also occurs for pregnancy cardio-metabolic disease, specifically preeclampsia, or whether preeclampsia risk could account for race-by-SES disparities in birth timing. METHODS: A sample of 718,604 Black and White women was drawn from a population-based California cohort of singleton births. Education, public health insurance status, gestational length, and preeclampsia diagnosis were extracted from a State-maintained birth cohort database. Age, prenatal care, diabetes diagnosis, smoking during pregnancy, and pre-pregnancy body mass index were covariates. RESULTS: In logistic regression models predicting preeclampsia risk, the race-by-SES interaction (for both education and insurance status) was significant. White women were at lower risk for preeclampsia, and higher SES further reduced risk. Black women were at higher risk for preeclampsia, and SES did not attenuate risk. In pathway analyses predicting gestational length, an indirect effect of the race-by-SES interaction was observed. Among White women, higher SES predicted lower preeclampsia risk, which in turn predicted longer gestation. The same was not observed for Black women. CONCLUSIONS: Compared to White women, Black women had increased preeclampsia risk. Higher SES attenuated risk for preeclampsia among White women, but not for Black women. Similarly, higher SES indirectly predicted longer gestational length via reduced preeclampsia risk among White women, but not for Black women. These findings are consistent with diminishing returns of higher SES for Black women with respect to preeclampsia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".