Explaining racial and ethnic inequalities in postpartum allostatic load: Results from a multisite study of low to middle income woment
Bibliographic record
Abstract
BACKGROUND: Racial and ethnic inequalities in women's health are widely documented, but not for the postpartum period, and few studies examine whether neighborhood, psychosocial, and biological factors explain these gaps in women's health. METHODS: Using prospective longitudinal data collected from 1766 low to middle income women between 2008 and 2012 by the Community Child Health Network (CCHN), we tested the extent to which adjustment for neighborhood, economic, psychological, and medical conditions following a birth explained differences between African American, Latina, and White women in an indicator of physiological dysregulation allostatic load (AL), at one year postpartum as measured by 10 biomarkers: Body Mass Index, Waist Hip Ratio, systolic and diastolic blood pressure, high sensitivity C-reactive protein, Hemoglobin A1c, high-density lipoprotein and cholesterol ratio, and diurnal cortisol. RESULTS: Mean postpartum AL scores were 4.65 for African American, 4.57 for Latina and 3.86 for White women. Unadjusted regression estimates for high AL for African American women (with White as the reference) were 0.80 (SD = 0.11) and 0.53 (SD = 0.15) for Latina women. Adjustment for household poverty, neighborhood, stress, and resilience variables resulted in a reduction of 36% of the excess risk in high AL for African Americans versus Whites and 42% of the excess risk for Latinas compared to Whites. CONCLUSIONS: Racial and ethnic inequalities in AL were accounted for largely by household poverty with additional contributions by psychological, economic, neighbourhood and medical variables. There remained a significant inequality between African American, and Latina women as compared to Whites even after adjustment for this set of variables. Future research into health inequalities among women should include a fuller consideration of the social determinants of health including employment, housing and prepregnancy medical conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".