Skip Navigation LinksHome > August 2008 - Volume 46 - Issue 8 > Estimating a Composite Measure of Hospital Quality From the...
Medical Care:
doi: 10.1097/MLR.0b013e31817893dc
Original Article

Estimating a Composite Measure of Hospital Quality From the Hospital Compare Database: Differences When Using a Bayesian Hierarchical Latent Variable Model Versus Denominator-Based Weights

Shwartz, Michael PhD*†; Ren, Justin PhD†; Peköz, Erol A. PhD†*; Wang, Xin PhD‡; Cohen, Alan B. ScD*†§; Restuccia, Joseph D. DrPH*†

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Abstract

Background: A single composite measure calculated from individual quality indicators (QIs) is a useful measure of hospital performance and can be justified conceptually even when the indicators are not highly correlated with one another.

Objective: To compare 2 basic approaches for calculating a composite measure: an extension of the most widely-used approach, which weights individual indicators based on the number of people eligible for the indicator (referred to as denominator-based weights, DBWs), and a Bayesian hierarchical latent variable model (BLVM).

Methods: Using data for 15 QIs from 3275 hospitals in the Hospital Compare database, we calculated hospital ranks using several versions of DBWs and 2 BLVMs. Estimates in 1 BLVM were driven by differences in variances of the QIs (BLVM1) and estimates in the other by differences in the signal-to-noise ratios of the QIs (BLVM2).

Results: There was a high correlation in ranks among all of the DBW approaches and between those approaches and BLVM1. However, a high correlation does not necessarily mean that the same hospitals were ranked in the top or bottom quality deciles. In general, large hospitals were ranked in higher quality deciles by all of the approaches, though the effect was most apparent using BLVM2.

Conclusions: Both conceptually and practically, hospital-specific DBWs are a reasonable approach for calculating a composite measure. However, this approach fails to take into account differences in the reliability of estimates from hospitals of different sizes, a big advantage of the Bayesian models.

© 2008 Lippincott Williams & Wilkins, Inc.

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