Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011 Oct;49(10):932-9.
doi: 10.1097/MLR.0b013e318215d5e2.

Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada

Peter C Austin  1 , Carl van Walraven, Walter P Wodchis, Alice Newman, Geoffrey M Anderson
Affiliations

Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada

Peter C Austin et al. Med Care. 2011 Oct.

Abstract

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Calibration: Observed vs. predicted probability of death across the centiles of risk
Figure 2
Figure 2
Calibration of Charlson and Elixhauser models
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Iezzoni LI, editor. Risk Adjustment for Measuring Health Outcomes. 2. Chicago, IL: Health Administration Press; 1997.
    1. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and Validation. Journal of Chronic Disease. 1987;40:373–383. - PubMed
    1. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. Journal of Clinical Epidemiology. 1992;45:613–619. - PubMed
    1. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care. 1998;36:8–27. - PubMed
    1. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care. 2005;43:1130–9. - PubMed

Publication types