Kenneth Pietz1, Laura A Petersen. 1. Houston Center for Quality of Care and Utilization Studies, Health Services Research and Development Service, the Michael E. DeBakey VA Medical Center (152), 2002 Holcombe Blvd., Houston, TX 77030, USA.
Abstract
OBJECTIVES: To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality. DATA SOURCES/STUDY SETTING: Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey. STUDY DESIGN: We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2-5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer-Lemeshow test. PRINCIPAL FINDINGS: The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2-5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2-5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2-5-year mortality. CONCLUSIONS: The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2-5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality.
OBJECTIVES: To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality. DATA SOURCES/STUDY SETTING: Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey. STUDY DESIGN: We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2-5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer-Lemeshow test. PRINCIPAL FINDINGS: The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2-5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2-5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2-5-year mortality. CONCLUSIONS: The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2-5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality.
Authors: A K Rosen; S Loveland; J J Anderson; J A Rothendler; C S Hankin; C C Rakovski; M A Moskowitz; D R Berlowitz Journal: Med Care Date: 2001-07 Impact factor: 2.983
Authors: L E Kazis; D R Miller; J Clark; K Skinner; A Lee; W Rogers; A Spiro; S Payne; G Fincke; A Selim; M Linzer Journal: Arch Intern Med Date: 1998-03-23
Authors: Elizabeth Brondolo; Leslie R M Hausmann; Juhee Jhalani; Melissa Pencille; Jennifer Atencio-Bacayon; Asha Kumar; Jasmin Kwok; Jahanara Ullah; Alan Roth; Daniel Chen; Robert Crupi; Joseph Schwartz Journal: Ann Behav Med Date: 2011-08
Authors: Bonnie K Lind; Mary M Gerkovich; Daniel C Cherkin; Richard A Deyo; Karen J Sherman; William E Lafferty Journal: J Altern Complement Med Date: 2012-10-04 Impact factor: 2.579
Authors: Peter C Austin; Matthew B Stanbrook; Geoffrey M Anderson; Alice Newman; Andrea S Gershon Journal: Ann Epidemiol Date: 2012-10-31 Impact factor: 3.797
Authors: Tara Lagu; Mark G Weiner; Christopher S Hollenbeak; Susan Eachus; Craig S Roberts; J Sanford Schwartz; Barbara J Turner Journal: J Gen Intern Med Date: 2008-05-09 Impact factor: 5.128