International Journal for Quality in Health Care 11:29-35 (1999)
© 1999 International Society for Quality in Health Care
Inter-hospital comparison of mortality rates
0 Epidemiology Unit, Health Care Evaluation, Department of Human Services,
1 Department of Public Health and Community Medicine, University of Melbourne and
2 Centre for the Study of Clinical Practice, St Vincent's Hospital, Melbourne, Victoria, Australia
Correspondence to: MZ Ansari, Senior Clinical Epidemiologist, Epidemiology Unit, Health Care Evaluation, Department of Human Services, 18/120 Spencer Street, Melbourne, Victoria, Australia 3001
Objective. To compare crude and adjusted in-hospital mortality rates after prostatectomy between hospitals using routinely collected hospital discharge data and to illustrate the value and limitations of using comparative mortality rates as a surrogate measure of quality of care.
Methods. Mortality rates for non-teaching hospitals (n = 21) were compared to a single notional group of teaching hospitals. Patients' age, disease (comorbidity), length of stay, emergency admission, and hospital location were identified using ICD-9-CM coded Victorian hospital morbidity data from public hospitals collected between 1987/88 and 1994/95. Comparisons between hospitals were based on crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) derived using univariate and multivariate logistic regression. Model fit was evaluated using receiver operating characteristic curve, i.e. c statistic, Somer's D, Tau-a, and R2.
Results. The overall crude mortality rates between hospitals achieved borderline significance (
2 = 25.68; P = 0.21). On crude analysis of mortality rates, four hospitals were initially identified as 'low' outlier hospitals; after adjustment, none of these remained outside the 95% CI, whereas a new hospital emerged as a 'high' outlier (OR = 4.56; P = 0.05). The adjusted ORs between hospitals compared to the reference varied from 0.21 to 5.54, ratio = 26.38. The model provided a good fit to the data (c = 0.89, Somer's D = 0.78; Tau-a = 0.013; R2 = 0.24).
Conclusions. Regression adjustment of routinely collected data on prostatectomy from the Victorian Inpatient Minimum Database reduced variance associated with age and correlates of illness severity. Reduction of confounding in this way is a move in the direction of exploring differences in quality of care between hospitals. Collection of such information over time, together with refinement of data collection would provide indicators of change in quality of care that could be explored in more detail as appropriate in the clinical setting.
Keywords:administrative database, comorbidities, in-hospital death, inter-hospital comparison, quality of care, severity adjustment