Inter-hospital comparison of mortality rates.
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. statistic, Somer's D, Tau-a, and R2.


RESULTS
The overall crude mortality rates between hospitals achieved borderline significance (alpha2=31.31; d.f.=21; P=0.06); these differences were no longer significant after adjustment (chi2=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.
Ansari
M Z
MZ
Epidemiology Unit, Health Care Evaluation, Department of Human Services, Melbourne, Victoria, Australia.
Ackland
M J
MJ
Jolley
D J
DJ
Carson
N
N
McDonald
I G
IG
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019985:Benchmarking; D002391:Catchment Area, Health; D017052:Hospital Mortality; D006779:Hospitals, Public / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data; D011468:Prostatectomy / Q000401:mortality; D011787:Quality of Health Care; D012189:Retrospective Studies; D014739:Victoria / Q000453:epidemiology