OBJECTIVE: To examine the association of health, community integration, and economic status with subsequent mortality and life expectancy among persons with spinal cord injury. DESIGN: Cohort study. SETTING: Model Spinal Cord Injury Systems (MSCIS) hospitals. PARTICIPANTS: A total of 5947 persons injured since 1973 who were enrolled in the National Spinal Cord Injury Database and who were still alive and received an annual evaluation from November 1995 through March 2002. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Mortality was determined by routine follow-up supplemented by information from the Social Security Death Index. A logistic regression model based on the full set of predictor variables was developed to estimate the chance of dying in any given year. RESULTS: After adjusting for demographic characteristics and injury severity, health status indicators, measures of community integration, and economic status indicators all had relatively small but statistically significant effects (20%-70% increases) on the likelihood of dying during the next year. Inclusion of these factors may result in higher life expectancy estimates under highly favorable conditions. CONCLUSIONS: Whereas previous reports of the MSCIS data have identified the life expectancies associated with a particular set of demographic (eg, age, gender) and injury-related characteristics (level and completeness of injury; ventilator dependence), the current analysis suggests that consideration of health, economic, and psychosocial factors may make computations of life expectancy more accurate.
OBJECTIVE: To examine the association of health, community integration, and economic status with subsequent mortality and life expectancy among persons with spinal cord injury. DESIGN: Cohort study. SETTING: Model Spinal Cord Injury Systems (MSCIS) hospitals. PARTICIPANTS: A total of 5947 persons injured since 1973 who were enrolled in the National Spinal Cord Injury Database and who were still alive and received an annual evaluation from November 1995 through March 2002. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Mortality was determined by routine follow-up supplemented by information from the Social Security Death Index. A logistic regression model based on the full set of predictor variables was developed to estimate the chance of dying in any given year. RESULTS: After adjusting for demographic characteristics and injury severity, health status indicators, measures of community integration, and economic status indicators all had relatively small but statistically significant effects (20%-70% increases) on the likelihood of dying during the next year. Inclusion of these factors may result in higher life expectancy estimates under highly favorable conditions. CONCLUSIONS: Whereas previous reports of the MSCIS data have identified the life expectancies associated with a particular set of demographic (eg, age, gender) and injury-related characteristics (level and completeness of injury; ventilator dependence), the current analysis suggests that consideration of health, economic, and psychosocial factors may make computations of life expectancy more accurate.
Authors: Claire Z Kalpakjian; Bethlyn Houlihan; Michelle A Meade; Dunia Karana-Zebari; Allen W Heinemann; Marcel P Dijkers; Jane Wierbicky; Susan Charlifue Journal: Arch Phys Med Rehabil Date: 2011-01-31 Impact factor: 3.966
Authors: Robert M Shavelle; Michael J DeVivo; David J Strauss; David R Paculdo; Daniel P Lammertse; Steven M Day Journal: J Spinal Cord Med Date: 2006 Impact factor: 1.985
Authors: Robert M Shavelle; Michael J Devivo; David R Paculdo; Lawrence C Vogel; David J Strauss Journal: J Spinal Cord Med Date: 2007 Impact factor: 1.985