Cover: Misspecification Issues in Risk Adjustment and Construction of Outcome-Based Quality Indicators

Misspecification Issues in Risk Adjustment and Construction of Outcome-Based Quality Indicators

Published In: Health Services and Outcomes Research Methodology, v. 7, no. 1-2, June 2007, p. 39-56

Posted on 2007

by Yue Li, Andrew W. Dick, Laurent G. Glance, Xueya Cai, Dana B. Mukamel

Hospital report cards reporting risk-adjusted health outcomes are increasingly used to benchmark quality of care. However, risk adjustment methods that do not fully account for the interrelationship between quality, risks and outcomes may lead to biased quality measures. This study aims to determine whether the current approach based on logistic regression and observed-to-expected outcome comparisons (O-E difference or O/E ratio) provides unbiased measures of quality. We first provided a conceptual framework to demonstrate that O-E difference or O/E ratio is inconsistently specified when estimates are based on logistic risk adjustment models. To examine the misspecification issue empirically, risk adjustment was performed based on coronary artery bypass graft (CABG) surgery data from New York's Cardiac Surgery Reporting System, and quality indicators (QI) of different specifications were calculated for hospital profiling. Computer simulations further explored the issue of misspecified QIs. Results showed that risk-adjusted mortality rates (RAMR) calculated from different QIs identified the same hospital outliers based on 95% confidence intervals, but generated different rank orders for hospitals in both high-quality and low-quality tails of the quality distributions. Simulation results further showed that, compared to O-E and O/E, logistically transformed QIs were superior regarding their abilities to identify hospitals of true extreme rankings, especially when the outcome was less prevalent or the number of patients per hospital was small. Based on our findings, we recommend that analysts consider the use of logistically transformed QI prior to publicly releasing quality rankings using measures based on O-E or O/E.

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