Susan R Hintz1, Jeffrey B Gould2, Mihoko V Bennett3, Erika E Gray4, Kimie J Kagawa5, Joseph Schulman6, Barbara Murphy7, Grace Villarin-Duenas7, Henry C Lee3. 1. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; California Perinatal Quality of Care Collaborative (CPQCC)-California Children's Services (CCS) High Risk Infant Follow-Up Quality of Care Initiative, Stanford, CA. Electronic address: srhintz@stanford.edu. 2. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; California Perinatal Quality of Care Collaborative (CPQCC)-California Children's Services (CCS) High Risk Infant Follow-Up Quality of Care Initiative, Stanford, CA; CPQCC, Stanford, CA. 3. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; CPQCC, Stanford, CA. 4. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; California Perinatal Quality of Care Collaborative (CPQCC)-California Children's Services (CCS) High Risk Infant Follow-Up Quality of Care Initiative, Stanford, CA. 5. California Department of Health Care Services, Children's Medical Services Branch, Sacramento, CA. 6. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; California Department of Health Care Services, Children's Medical Services Branch, Sacramento, CA. 7. CPQCC, Stanford, CA.
Abstract
OBJECTIVES: To determine rates and factors associated with referral to the California Children's Services high-risk infant follow-up (HRIF) program among very low birth weight (BW) infants in the California Perinatal Quality of Care Collaborative. STUDY DESIGN: Using multivariable logistic regression, we examined independent associations of demographic and clinical variables, neonatal intensive care unit (NICU) volume and level, and California region with HRIF referral. RESULTS: In 2010-2011, 8071 very low BW infants were discharged home; 6424 (80%) were referred to HRIF. Higher odds for HRIF referral were associated with lower BW (OR 1.9, 95% CI 1.5-2.4; ≤ 750 g vs 1251-1499 g), higher NICU volume (OR 1.6, 1.2-2.1; highest vs lowest quartile), and California Children's Services Regional level (OR 3.1, 2.3-4.3, vs intermediate); and lower odds with small for gestational age (OR 0.79, 0.68-0.92), and maternal race African American (OR 0.58, 0.47-0.71) and Hispanic (OR 0.65, 0.55-0.76) vs white. There was wide variability in referral among regions (8%-98%) and NICUs (<5%-100%), which remained after risk adjustment. CONCLUSIONS: There are considerable disparities in HRIF referral, some of which may indicate regional and individual NICU resource challenges and barriers. Understanding demographic and clinical factors associated with failure to refer present opportunities for targeted quality improvement initiatives.
OBJECTIVES: To determine rates and factors associated with referral to the California Children's Services high-risk infant follow-up (HRIF) program among very low birth weight (BW) infants in the California Perinatal Quality of Care Collaborative. STUDY DESIGN: Using multivariable logistic regression, we examined independent associations of demographic and clinical variables, neonatal intensive care unit (NICU) volume and level, and California region with HRIF referral. RESULTS: In 2010-2011, 8071 very low BW infants were discharged home; 6424 (80%) were referred to HRIF. Higher odds for HRIF referral were associated with lower BW (OR 1.9, 95% CI 1.5-2.4; ≤ 750 g vs 1251-1499 g), higher NICU volume (OR 1.6, 1.2-2.1; highest vs lowest quartile), and California Children's Services Regional level (OR 3.1, 2.3-4.3, vs intermediate); and lower odds with small for gestational age (OR 0.79, 0.68-0.92), and maternal race African American (OR 0.58, 0.47-0.71) and Hispanic (OR 0.65, 0.55-0.76) vs white. There was wide variability in referral among regions (8%-98%) and NICUs (<5%-100%), which remained after risk adjustment. CONCLUSIONS: There are considerable disparities in HRIF referral, some of which may indicate regional and individual NICU resource challenges and barriers. Understanding demographic and clinical factors associated with failure to refer present opportunities for targeted quality improvement initiatives.
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