Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada

Background: Administrative healthcare databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions, or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting healthcare provider profiling, adequate risk-adjustment is necessary for conclusions about provider performance to be valid. There are limited validated methods for risk adjustment in ambulatory populations using administrative healthcare databases. Objectives: To examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict mortality in a general ambulatory population cohort. Research Design: Retrospective cohort constructed using population-based administrative data. Subjects: All 10,498,413 residents of Ontario, Canada between the ages of 20 and 100 years who were alive on their birthday in 2007. Subjects were randomly divided into derivation and validation samples. Measures: Death within 1 year of the subject's birthday in 2007. Results: A logistic regression model consisting of age, sex, and indicator variables for 28 of the 32 ADG categories had excellent discrimination: the c-statistic (equivalent to the area under the receiver operating characteristic curve) was 0.917 in both derivation and validation samples. Furthermore, the model showed very good calibration. In comparison, the use of the Charlson comorbidity index or the Elixhauser comorbidities resulted in a minor decrease in discrimination compared with the use of the ADGs. Conclusions: Logistic regression models using age, sex, and the John Hopkins ADGs were able to accurately predict 1-year mortality in a general ambulatory population of subjects.