Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction.

Published

Journal Article

OBJECTIVE: To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. DESIGN: Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. SETTING: Models were replicated in 3 populations. PARTICIPANTS: Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Inpatient, outpatient, and total days of care in FY97. RESULTS: The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04). CONCLUSIONS: The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

Full Text

Duke Authors

Cited Authors

  • Warner, G; Hoenig, H; Montez, M; Wang, F; Rosen, A

Published Date

  • February 2004

Published In

Volume / Issue

  • 85 / 2

Start / End Page

  • 218 - 226

PubMed ID

  • 14966705

Pubmed Central ID

  • 14966705

International Standard Serial Number (ISSN)

  • 0003-9993

Digital Object Identifier (DOI)

  • 10.1016/s0003-9993(03)00768-8

Language

  • eng

Conference Location

  • United States