Diagnostic Cost Groups (DCGs) and Concurrent Utilization among Patients with Substance Abuse Disorders


  • Amy K. Rosen,

  • Susan A. Loveland,

  • Jennifer J. Anderson,

  • Cheryl S. Hankin,

  • James N. Breckenridge,

  • Dan R. Berlowitz

This research was supported by grant number MPC 97-009, Office of Research and Development, Health Services Research and Development Service, Department of Veterans Affairs. An earlier version of this paper was presented at the Association of Health Services Research Annual Meeting (June 2000, Los Angeles, CA). The views expressed are solely those of the authors.

Amy K. Rosen, Ph.D., Senior Research Scientist, Center for Health Quality, Outcomes and Economic Research, Bedford VAMC/Edith Nourse Rogers Memorial (ENRM) Veterans Hospital (152), 200 Springs Road, Bedford MA 01730. Dr. Rosen is also Associate Professor of Public Health, Department of Health Services, Boston University School of Public Health. Susan A. Loveland, M.A.T., Jennifer J. Anderson, Ph.D., and Dan R. Berlowitz, M.D., M.P.H., are also with the Center for Health Quality, Outcomes and Economic Research. In addition, Drs. Anderson and Berlowitz are with Boston University School of Public Health, Department of Health Services. Cheryl S. Hankin, Ph.D., is with ALZA Corporation, Mountain View, CA. James N. Breckenridge, Ph.D., is with VA Palo Alto Healthcare System, Palo Alto, CA.


Objective. To assess the performance of Diagnostic Cost Groups (DCGs) in explaining variation in concurrent utilization for a defined subgroup, patients with substance abuse (SA) disorders, within the Department of Veterans Affairs (VA).

Data Sources. A 60 percent random sample of veterans who used health care services during Fiscal Year (FY) 1997 was obtained from VA administrative databases. Patients with SA disorders (13.3 percent) were identified from primary and secondary ICD-9-CM diagnosis codes.

Study Design. Concurrent risk adjustment models were fitted and tested using the DCG/HCC model. Three outcome measures were defined: (1) „service days” (the sum of a patient's inpatient and outpatient visit days), (2) mental health/substance abuse (MH/SA) service days, and (3) ambulatory provider encounters. To improve model performance, we ran three DCG/HCC models with additional indicators for patients with SA disorders.

Data Collection. To create a single file of veterans who used health care services in FY 1997, we merged records from all VA inpatient and outpatient files.

Principal Findings. Adding indicators for patients with mild/moderate SA disorders did not appreciably improve the R-squares for any of the outcome measures. When indicators were added for patients with severe SA who were in the most costly category, the explanatory ability of the models was modestly improved for all three outcomes.

Conclusions. Modifying the DCG/HCC model with additional markers for SA modestly improved homogeneity and model prediction. Because considerable variation still remained after modeling, we conclude that health care systems should evaluate „off-the-shelf” risk adjustment systems before applying them to their own populations.