Volume 40 | Number 3 | June 2005

Abstract List

Matthew L. Maciejewski Ph.D., Chuan‐Fen Liu M.P.H., Ph.D., Ann Derleth, Mary McDonell, Steve Anderson, Stephan D. Fihn


To evaluate the performance of different prospective risk adjustment models of outpatient, inpatient, and total expenditures of veterans who regularly use Veterans Affairs (VA) primary care.

Data Sources

We utilized administrative, survey and expenditure data on 14,449 VA patients enrolled in a randomized trial that gave providers regular patient health assessments.

Study Design

This cohort study compared five administrative data‐based, two self‐report risk adjusters, and base year expenditures in prospective models.

Data Extraction Methods

VA outpatient care and nonacute inpatient care expenditures were based on unit expenditures and utilization, while VA expenditures for acute inpatient care were calculated from a Medicare‐based inpatient cost function. Risk adjusters for this sample were constructed from diagnosis, medication and self‐report data collected during a clinical trial. Model performance was compared using adjusted and predictive ratios.

Principal Findings

In all expenditure models, administrative‐based measures performed better than self‐reported measures, which performed better than age and gender. The Diagnosis Cost Groups (DCG) model explained total expenditure variation (=7.2 percent) better than other models. Prior outpatient expenditures predicted outpatient expenditures best by far (=42 percent). Models with multiple measures improved overall prediction, reduced over‐prediction of low expenditure quintiles, and reduced under‐prediction in the highest quintile of expenditures.


Prediction of VA total expenditures was poor because expenditure variation reflected utilization variation, but not patient severity. Base year expenditures were the best predictor of outpatient expenditures and nearly the best for total expenditures. Models that combined two or more risk adjusters predicted expenditures better than single‐measure models, but are more difficult and expensive to apply.