Does universal insurance influence disparities in high-quality hospital use for inpatient pediatric congenital heart defect care within the first year of diagnosis?

Background Healthcare disparities are an issue in the management of Congenital Heart Defects (CHD) in children. Although universal insurance may mitigate racial or socioeconomic status (SES) disparities in CHD care, prior studies have not examined these effects in the use of High-Quality Hospitals (HQH) for inpatient pediatric CHD care in the Military Healthcare System (MHS). To assess for racial and SES disparities in inpatient pediatric CHD care that may persist despite universal insurance coverage, we performed a cross-sectional study of the HQH use for children treated for CHD in the TRICARE system, a universal healthcare system for the U.S. Department of Defense. In the present work we evaluated for the presence of disparities, like those seen in the civilian U.S. healthcare system, among military ranks (SES surrogate) and races and ethnicities in HQH use for pediatric inpatient admissions for CHD care within a universal healthcare system (MHS). Methods We conducted a cross-sectional study using claims data from the U.S. MHS Data Repository from 2016 to 2020. We identified 11,748 beneficiaries aged 0 to 17 years who had an inpatient admission for CHD care from 2016 to 2020. The outcome variable was a dichotomous indicator for HQH utilization. In the sample, 42 hospitals were designated as HQH. Of the population, 82.9% did not use an HQH at any point for CHD care and 17.1% used an HQH at some point for CHD care. The primary predictor variables were race and sponsor rank. Military rank has been used as an indicator of SES status. Patient demographic information at the time of index admission post initial CHD diagnosis (age, gender, sponsor marital status, insurance type, sponsor service branch, proximity to HQH based on patient zip code centroid, and provider region) and clinical information (complexity of CHD, common comorbid conditions, genetic syndromes, and prematurity) were used as covariates in multivariable logistic regression analysis. Results After controlling for demographic and clinical ...