Classifying hospitals as mortality outliers: logistic versus hierarchical logistic models.
The use of hierarchical logistic regression for provider profiling has been recommended due to the clustering of patients within hospitals, but has some associated difficulties. We assess changes in hospital outlier status based on standard logistic versus hierarchical logistic modelling of mortality. The study population consisted of all patients admitted to acute, non-specialist hospitals in England between 2007 and 2011 with a primary diagnosis of acute myocardial infarction, acute cerebrovascular disease or fracture of neck of femur or a primary procedure of coronary artery bypass graft or repair of abdominal aortic aneurysm. We compared standardised mortality ratios (SMRs) from non-hierarchical models with SMRs from hierarchical models, without and with shrinkage estimates of the predicted probabilities (Model 1 and Model 2). The SMRs from standard logistic and hierarchical models were highly statistically significantly correlated (r > 0.91, p = 0.01). More outliers were recorded in the standard logistic regression than hierarchical modelling only when using shrinkage estimates (Model 2): 21 hospitals (out of a cumulative number of 565 pairs of hospitals under study) changed from a low outlier and 8 hospitals changed from a high outlier based on the logistic regression to a not-an-outlier based on shrinkage estimates. Both standard logistic and hierarchical modelling have identified nearly the same hospitals as mortality outliers. The choice of methodological approach should, however, also consider whether the modelling aim is judgment or improvement, as shrinkage may be more appropriate for the former than the latter.
Dr. Foster Unit at Imperial College, Department of Primary Care and Public Health, Imperial College London, London, EC4Y 8EN, UK, roxana.alexandrescu@kcl.ac.uk.
Roxana
R
Alexandrescu
Alex
A
Bottle
Brian
B
Jarman
Paul
P
Aylin
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004739:England / Q000453:epidemiology; D005260:Female; D006333:Heart Failure / Q000145:classification* / Q000401:mortality*; D017052:Hospital Mortality / Q000639:trends*; D006779:Hospitals, Public / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D019984:Quality Indicators, Health Care; D012307:Risk Factors