Racial and Socioeconomic Disparities in Cardiovascular Outcomes of Preeclampsia Hospitalizations in the United States 2004-2019
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Bibliographic record
Abstract
Background: Preeclampsia is associated with higher in-hospital cardiovascular events and mortality with known disparities by race/ethnicity, but data on the interaction between income and these outcomes remain limited. Objectives: This study investigated racial and socioeconomic disparities in cardiovascular outcomes of preeclampsia at delivery hospitalizations. Methods: We analyzed National Inpatient Sample data using International Classification of Diseases-9th Revision/-10th Revision codes between 2004 and 2019. We identified a total of 2,436,991 delivery hospitalizations with preeclampsia/eclampsia as a primary diagnosis representing White (43.1%), Black (18.4%), Hispanic (18.7%), and Asian or Pacific Islander (A/PI; 3.3%) women. We stratified the population based on median household income (low income, medium income, and high income). Logistic regression and propensity-matched analysis were used for reporting outcomes adjusted for age, hospital region, and baseline comorbidities. Results: Black Hispanic, and A/PI women with preeclampsia had higher in-hospital mortality compared with White women across all groups of income. Hispanic women had lower odds of peripartum cardiomyopathy (PPCM) compared with White women. A significant interaction effect was observed with race/ethnicity and median household income for in-hospital mortality and PPCM with preeclampsia. Furthermore, high-income Black women had higher odds of PPCM, stroke, acute kidney injury, heart failure, cardiac arrhythmia, and venous thromboembolism compared with low-income White women. Conclusions: Women with preeclampsia experience significant racial/ethnic and socioeconomic disparities in inpatient mortality and cardiovascular outcomes at delivery. Across all income groups, Black, Hispanic, and A/PI women experience higher odds of in-hospital mortality compared with White women. Furthermore, high-income Black women had greater odds of many CV complications compared with low-income White women.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it