How does high socioeconomic status affect maternal and neonatal pregnancy outcomes? A population-based study among American women
Bibliographic record
Abstract
Objectives: The purpose of this study was to evaluate the effect of high SES on multiple pregnancy outcomes, while controlling for confounding factors. Methods: Using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (HCUP-NIS), the largest American medical database including 20 % of annual hospital admissions, we studied the years 2004-2014 inclusively. We conducted a population-based retrospective cohort study consisting of women from different median household income quartiles throughout the United States. Women in the highest household income quartile were compared to those in all other lower income quartiles combined. Chi-square and Fischer exact tests were used to compare demographic and baseline characteristics. Univariate and multivariate regression analyses were carried to adjust for confounding factors, including ethnicity, pre-existing conditions, smoking status, obesity, illicit drug use and insurance type. Results: Among 5,448,255 deliveries during the study period with income data, 1,218,989 deliveries were to women from the wealthiest median household income. These women were more likely to be older, Caucasian, and have private medical insurance (P < 0.05, all). They were less likely to smoke, have chronic hypertension, pre-gestational diabetes, and use illicit drugs (P < 0.05, all). They were less likely to develop complications including gestational hypertension (aOR 0.87 95 %CI 0.85-0.88), preeclampsia (aOR 0.88 95 %CI 0.86-0.89), eclampsia (aOR 0.81 95 %CI 0.66-0.99), gestational diabetes (aOR 0.91 95 %CI 0.89-0.92), preterm premature rupture of membranes (PPROM) (aOR 0.92 95 %CI 0.88-0.96), preterm birth (aOR 0.90 95 %CI 0.89-0.92), and placental abruption (aOR 0.89 95 %CI 0.85-0.93). They were less likely to have an intra-uterine fetal death (IUFD) (aOR 0.80 95 %CI 0.74-0.86), but more likely to deliver neonates with congenital anomalies (aOR 1.10 95 %CI 1.04-1.20). Conclusions: Higher SES predisposes to better pregnancy outcomes, even when controlled for confounding factors such as ethnicity and underlying baseline health status. Efforts are required in order to eliminate health disparities in pregnancy.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".