Racism in obstetric care: a psychometric study of the Gendered Racial Microaggressions Scale among Global Majority birthing people in obstetric contexts.

In the United States, maternal health inequities disproportionately affect Global Majority (e.g., Asian, Black, and Hispanic) populations. Despite a substantial body of research underscoring the influence of racism on these inequities, little research has examined how experiences of gendered racial microaggressions during pregnancy and birth impact racially and ethnically diverse Global Majority pregnant and birthing people in obstetric hospital settings. We evaluated the psychometric properties of an adapted version of Lewis & Neville's Gendered Racial Microaggressions Scale, using data collected from 417 Global Majority birthing people. Findings from our study indicate that our adapted GRMS is a valid tool for assessing the experiences of gendered racial microaggressions in hospital-based obstetric care settings among Global Majority pregnant and birthing people whose preferred languages are English or Spanish. Item Response Theory (IRT) analysis demonstrated high construct validity of the adapted GRMS scale (Root Mean Square Error of Approximation = 0.1089 (95% CI 0.0921, 0.1263), Comparative Fit Index = 0.977, Standardized Root Mean Square Residual = 0.075, log-likelihood c2 = -85.6, df = 8). IRT analyses demonstrated that the unidimensional model was preferred to the bi-dimensional model as it was more interpretable, had lower AIC and BIC, and all items had large discrimination parameters onto a single factor (all discrimination parameters > 3.0). Given that we found similar response profiles among Black and Hispanic respondents, our Differential Item Functioning analyses support validity among Black, Hispanic, and Spanish-speaking birthing people. Inter-item correlations demonstrated adequate scale reliability, α = 0.97, and empirical reliability = 0.67. Pearsons correlations was used to assess the criterion validity of our adapted scale. Our scale's total score was significantly and positively related to postpartum depression and anxiety. Researchers and practitioners should seek to address instances of gendered racial microaggressions in obstetric settings, as they are manifestations of systemic and interpersonal racism, and impact postpartum health.

Development of a prediction model of postpartum hospital use using an equity-focused approach.

BACKGROUND: Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification. OBJECTIVE: This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach. STUDY DESIGN: We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use. RESULTS: The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications. CONCLUSION: The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.