Comparing self-reported health status and diagnosis-based risk adjustment to predict 1- and 2 to 5-year mortality.

Assessing the overall health status of a patient population is an important problem for medical plans and health care systems. Ensuring that the appropriate equipment, financial resources, and human resources are available for care of the patients requires careful planning. In this paper, we evaluate the ability of several types of patient-level risk adjustment data in a large database to predict mortality. Because the majority of health care services are consumed in the last 6 months of life (McCall 1984; Gaumer and Stavins 1992; Lubitz and Riley 1993), mortality prediction can be an important feature of health care budgeting and planning. Risk adjustment is widely used for predicting future costs and for other uses in medical plans and health care systems. Most risk adjustment schemes are based on patients' demographics and diagnoses. Diagnosis-based risk adjustment is based upon the fact that patients with certain groups of diagnoses have been found to have similar cost patterns. The ability of various diagnosis-based risk adjustment systems to predict cost has been evaluated in several articles (Lamers and van Vliet 1996; Rosen et al. 2001; Pietz, Byrne, and Petersen 2006). Research on the ability of diagnosis-based risk adjustment systems to predict clinical outcomes such as mortality is less well developed. Recently, we compared two widely used such systems, Adjusted Clinical Groups (ACGs) and Diagnostic Cost Groups (DCGs), to predict nursing home care and mortality in the same year as the diagnoses used (Petersen et al. 2005). We found that DCGs outperformed ACGs in the Department of Veterans Affairs (VA) data. Diagnosis-based risk adjustment systems capture only the provider's coding of the patient's medical condition and are dependent upon the patient having a health care encounter for which codes are recorded. Therefore, conditions that may significantly impact the risk of death may not be coded and thus not available for risk prediction. Another potential source of information about patients' health status is provided by health self-report. Health self-report may contain important medical information about the patient unknown to the provider, and may therefore supplement diagnostic information (Hornbrook and Goodman 1996). Also, because some veterans use the VA for only part of their health care needs, diagnostic information found in VA data may not provide a complete picture of the patient's health (Byrne et al. 2006). The ability of health self-report data to predict mortality has been the subject of considerable research (Idler and Angel 1990; Idler and Benyamini 1997; Inouye et al. 1998; Schoenman, Hayes, and Cheng 2001). In this article, we compare the ability of ACGs, DCGs, and 36-Item Short Form Health Survey for veterans (SF-36V) summary measures combined with age and gender to predict short- and long-term all-cause mortality. The SF-36V is a slightly modified short form 36 (SF-36) for veterans, described in detail later. We also compare the added value of combining SF-36V summary measures and individual scales to ACGs and DCGs. The SF-36V instrument measures entirely different information, such as limitations in physical activities and social functioning that may provide additional predictive power. The null hypothesis is that the SF-36V data contribute no additional health information. We are not aware of any research that compares these three sets of variables for prediction of long-term mortality. The VA health care system provides an opportunity for this type of study because we are able to ascertain vital status even for patients who have left the VA health care system (Cowper et al. 2002).