Improved comorbidity adjustment for predicting mortality in Medicare populations.

As with any observational research, epidemiologic studies using data from administrative datasets have been criticized for incomplete control for confounding. Poorly measured patient characteristics, including comorbidities, can affect clinical decisions and may therefore bias results from etiologic studies using observational data (Walker 1996). However, it is widely acknowledged that in many situations, claims data may be the best source to study the outcomes of therapeutic and other clinical strategies in routine medical care of the elderly (Wang, Solomon et al. 2000; Ray et al. 2002). Improving comorbidity adjustment is therefore an important goal to increase the validity of findings from epidemiologic studies of older patients (Schneeweiss and Maclure 2000). Comorbidity scores aggregate relevant comorbidities into a single variable. The attraction of such scores is that they are easy to apply and widely accepted tools. They can simplify data analysis, particularly when multiple hypotheses will be tested or when comorbidities are considered as time-varying confounders. In small studies or in subgroup analyses with limited sample size, the fact that comorbidity scores reduce the number of variables that need to be adjusted will improve statistical inference. However, in either of these circumstances scores must correctly model the underlying functional relation between comorbidity and outcome in a specific study population. In an ideal world, one would have enough data and well-measured variables to completely adjust statistically for differences in risk. Such adjustments would be tailored to the outcome measure (e.g., death, readmission) and the population under study. In the real world, however, one may not have a large enough dataset, or all the data one would like. A second best alternative, then, is to use a comorbidity score that has preassigned weights from a plausibly relevant population. Given the widespread use of scores such as the Charlson Index and Chronic Disease Score, this paper puts forward an alternative that is based on mortality in the Medicare population. The construct “comorbidity” reflects the aggregate effect of all clinical conditions a patient might have, excluding the disease of primary interest (Greenfield and Nelson 1992; Last 1995). Because there is no gold standard, researchers have validated measures of comorbidity by how well they predict mortality. The predictive performance of comorbidity scores depends on several factors, including (1) the clinical conditions included in a score and their relative weights, (2) the distribution of comorbid conditions in the source population, (3) the endpoint of a study, for example, one-year mortality, and (4) the accuracy and completeness of the administrative data (Iezzoni 1997). The predictive performance of two scores can be validly compared when factors 2 to 4 are held constant. Several studies have explored the predictive validity of comorbidity measures in claims data (Roos et al. 1989; Romano, Roos, and Jollis 1993a, 1993b; Von Korff, Wagner, and Saunders 1992; Clark et al. 1995; Deyo, Cherkin, and Ciol 1992; D'Hoore, Sicotte, and Tilquin 1993; D'Hoore, Bouckaert, and Tilquin 1996; Ghali et al. 1996; Melfi et al. 1995; Poses et al. 1995). However, only a few studies sought to improve the performance of these scores (Clark et al. 1995; Zhang, Iwashyna, and Christakis 1999; Wang, Walker et al. 2000; Schneeweiss et al. 2001) and often only for specific subpopulations, for example, patients undergoing bypass surgery (Ghali et al. 1996). From earlier studies it has become clear that specific weights must be applied to elderly populations to achieve acceptable comorbidity adjustment in epidemiologic research using claims data (Schneeweiss and Maclure 2000; Ghali et al. 1996). We sought to improve and validate the improved performance of an existing and frequently used ICD-9 diagnosis-based comorbidity score and a drug use-based score in predicting one-year mortality in U.S. Medicare patients aged 65 years or older.