Analysing differences in clinical outcomes between hospitals. Issue 4 (1st August 2003)

• Record Type: Journal Article
• Authors: Simpson, J M; Evans, N; Gibberd, R W; Heuchan, A M; Henderson-Smart, D J
• Other Names: group-author.
• Is Part Of: Quality & safety in health care. Volume 12:Issue 4(2003)
• Page Start: 257
• Page End: 262
• Publication Date: 2003-08-01
• Subjects: quality improvement -- case mix -- intraventricular haemorrhage -- shrinkage estimators
• DOI: 10.1136/qhc.12.4.257
• Languages: English
• ISSNs: 1475-3898

Abstract

Abstract : Objective: To examine the variation between hospitals in rates of severe intraventricular haemorrhage (IVH) in preterm babies adjusting for case mix and sampling variability. Design: Cross sectional study of pooled data from 1995 to 1997. Setting: 24 neonatal intensive care units (NICUs) in the Australian and New Zealand Neonatal Network. Participants: 5413 infants of gestational age 24–30 weeks. Main outcome measures: Crude rates of severe (grades 3 and 4) IVH and rates adjusted for case mix using logistic regression, and for sampling variability using shrinkage estimators. Results: The overall rate of severe IVH was 6.8%, but crude rates for individual units ranged from 2.9 to 21.4%, with interquartile range (IQR) 5.7–8.1%. Adjusting for the five significant predictor variables—gestational age at birth, 1 minute Apgar score, antenatal corticosteroids, transfer after birth, and sex—actually increased the variability in rates (IQR 5.9–9.7%). Shrinkage estimators, which adjust for differences in unit sizes and outcome rates, reduced the variation in rates (IQR 6.3–7.5%). Adjusting for case mix and using shrinkage estimators showed that one unit had a significantly higher adjusted rate than expected, while another was significantly lower. If all units could achieve an average rate equal to the 20th centile (5.74%), then 60 cases of severe IVH could be prevented in a 3 year period. Conclusions: The use of shrinkage estimators may have a greater impact on the variation in outcomes between hospitals than adjusting for case mix. Greater reductions in morbidity may be achieved by concentrating on the best rather than the worst performing hospitals.