Racial and insurance disparities in hospital mortality for children undergoing congenital heart surgery

Pediatr Cardiol. 2012 Oct;33(7):1026-39. doi: 10.1007/s00246-012-0221-z. Epub 2012 Feb 15.


Many studies of racial and insurance disparities after congenital heart surgery have used limited regional data over short periods. This study examines the association of race and insurance with hospital mortality using a national hospitalization database spanning almost a decade. A retrospective, repeated cross-sectional analysis was performed. All the admissions from the Kids' Inpatient Database from 1997 through 2006 that fit a Risk Adjustment for Congenital Heart Surgery-1 category were examined. Multivariate logistic regression models examining hospital mortality, nonelective admission, and referral to high-mortality hospitals were constructed. Medicaid insurance [odds ratio (OR) 1.26, 95% confidence interval (CI) 1.09-1.46] and nonwhite race (OR 1.36, 95% CI 1.19-1.54) were independent risk factors for mortality. Furthermore, Medicaid insurance (OR 1.23, 95% CI 1.15-1.31) and nonwhite race (OR 1.26, 95% CI 1.19-1.34) were associated with nonelective admission for congenital heart surgery. Finally, children with Medicaid insurance (OR 1.18, 95% CI 1.10-1.27) and black children (OR 1.30, 95% CI 1.17-1.44) had higher odds of referral to high-mortality hospitals. Over the past decade, children undergoing congenital heart surgery continued to experience admission, referral, and survival disparities based on insurance and racial status.

MeSH terms

  • Cardiac Surgical Procedures / mortality*
  • Child, Preschool
  • Cross-Sectional Studies
  • Female
  • Health Services Research
  • Heart Defects, Congenital / ethnology*
  • Heart Defects, Congenital / mortality*
  • Heart Defects, Congenital / surgery*
  • Hospital Mortality*
  • Humans
  • Infant
  • Insurance, Health / statistics & numerical data*
  • Logistic Models
  • Male
  • Medicaid / statistics & numerical data
  • Retrospective Studies
  • Risk Factors
  • Statistics, Nonparametric
  • United States / epidemiology