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Measuring QoL with SF-36 in Older Americans with TBI
Authors:Chengwu Yang  Anbesaw W Selassie  Rickey E Carter  Barbara C Tilley
Institution:(1) Pennsylvania State University College of Medicine, 600 Centerview Drive, Suite 3400H, Hershey, PA 17033, USA;(2) Medical University of South Carolina, 135 Cannon Street, Suite 302M, Charleston, SC 29425, USA;(3) Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA;(4) The University of Texas School of Public Health at Houston, 1200 Herman Pressler Drive, RAS E833, Houston, TX 77030, USA;
Abstract:This study demonstrated reliability and factor structure of the Medical Outcomes Study Short-Form Health Survey (SF-36) among older Americans with Traumatic brain injury (TBI), and evaluated effects of injury severity and race on SF-36's items and latent domains. A representative sample of 654 older, racially diverse patients with TBI was selected from the South Carolina Traumatic Brain Injury Follow-up Registry. Reliability and factor structure of SF-36 were evaluated using Cronbach’s alpha and confirmatory factor analysis (CFA). Multiple-indicator multiple-causes (MIMIC) models were used to study effects of injury severity and race on items (differential item functioning, DIF) and latent domains (population heterogeneity) of SF-36. SF-36 was reliable and its current eightfactor structure was confirmed. While TBI severity did not impact latent domain scores of SF-36, race did. Blacks had higher vitality and lower role-emotional scores than whites. The measurement model was invariant to injury severity and race (free of DIF), and DIF did not contribute to the differences of vitality and role-emotional between black and white older TBI patients. SF-36 was valid to measure quality of life (OoL) after TBI in racially diverse elderly population. Blacks tend to assert to strong coping behaviors in the presence of physical stress while admitting low performance due to emotional stress. In QoL research where the primary outcomes are usually composite scores from instruments, MIMIC models have advantages over conventional multivariable regression models in testing the validity of the instruments and assessing covariate effects on latent traits of instruments while controlling for DIF effects.
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