Racial and Socioeconomic Disparities in Cardiovascular Outcomes of Preeclampsia Hospitalizations in the United States 2004-2019
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".