BackgroundRacial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use (PHU) 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 (SSDOH), and some include race, which may result in biased risk stratification. ObjectiveOur objective was to develop a risk prediction model of PHU incorporating novel SSDOH using an equity approach. Study DesignWe conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for liveborn infants in New York City. We included deliveries in 2016-2017 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 PHU as at least one readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on ICD-10 diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetric complications, and severe maternal morbidity. Structural determinants of health were racial-economic segregation at the zip code level using the index of concentration at the extremes (ICE) and publicly available indices of the neighborhood built/natural (BNE) and social/economic (SE) environment. We used four statistical and machine learning algorithms to predict Composite PHU, and an ensemble approach to predict Cause-specific PHU. We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in PHU. ResultsThe overall incidence of PHU was 5.7%; the incidence among Black, Hispanic and White people was 8.8%, 7.4%, and 3.3%, respectively. Most common diagnoses for hospital use were general perinatal reasons (17.5%), hypertension/eclampsia (12.0%), non-gynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with LASSO selection retained 22 predictor variables and achieved an auROC of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected SSDOH features included ICE, insurance payor, depressive symptoms, and trimester entering prenatal care. The Cause-specific PHU model selected six of the 14 outcome diagnoses: acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection, achieving an auROC of 0.75 in training, 0.77 in the test data, and 0.75 in the validation data using 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 using the Composite PHU model resulted in the greatest reduction in racial-ethnic disparities in PHU, compared to the Cause-specific PHU model or a standard approach to identifying high-risk individuals with common pregnancy complications. ConclusionA Composite PHU prediction model incorporating SSDOH can be used at delivery discharge to identify persons at risk for PHU..
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Jul 1, 2024
Haojian Luo + 7
Open Access