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Quantifying the potential bias when directly comparing standardised mortality ratios for in-unit neonatal mortality
T Alun Evans1, Sarah E Seaton, Bradley N Manktelow
1Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
Directly comparing Standardised Mortality Ratios (SMRs) between neonatal units can be misleading due to population differences. While comparisons within similar units are useful, caution is needed when comparing different levels of neonatal care to avoid biased performance assessments.
Area of Science:
- Neonatal Medicine
- Healthcare Performance Measurement
- Biostatistics
Background:
- Standardised Mortality Ratio (SMR) is used to compare healthcare providers.
- Population differences between providers can bias direct SMR comparisons.
- This bias is a significant concern in neonatal care due to varying levels of service.
Purpose of the Study:
- To assess the potential bias in Standardised Mortality Ratio (SMR) comparisons in neonatal care.
- To evaluate the impact of differing patient populations on SMRs across neonatal units.
- To determine the reliability of SMRs for comparing different levels of neonatal intensive care.
Main Methods:
- Utilized data from The Neonatal Survey (TNS) for babies born at 24-31 weeks gestational age (2002-2011).
- Calculated risk-adjusted SMRs for 11 UK neonatal units.
- Re-estimated model parameters for each unit and applied them to others to simulate outcomes under identical mortality rates, then calculated SMR ratios.
Main Results:
- Analysis included 7243 infants.
- Ratios of SMRs ranged from 0.92-1.00 within Level 3 units and 0.79-1.56 within Level 2 units.
- Comparisons between units of different care levels showed ratios up to 1.68, indicating significant potential bias.
Conclusions:
- Direct SMR comparisons are likely biased when healthcare provider populations differ significantly.
- SMR comparisons are most reliable within Network Neonatal Units (Level 3).
- Caution is advised when comparing Local Neonatal Units (Level 2) or units of different types; tools to quantify bias are needed.
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