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PMERR > Projects > Study 5  


Risk for Adverse Outcomes after SCI: A Longitudinal Study. R01, National Institutes of Health
requested funding: $2,241,117; January 01, 2006 to December 31, 2011.

Spinal cord injury (SCI) heightens risk for adverse health outcomes and secondary conditions, including poor general health, the development of chronic health conditions such as pain and fatigue, and poor psychosocial adaptation including an elevated risk for depression. Although there are substantial individual differences in likelihood of developing these conditions, the risk factors that account for the variations are not fully understood, nor have they been systematically investigated.  The purpose of the proposed 10-year longitudinal study is to identify differential risk for 3 sets of adverse health outcomes using two empirical models to guide selection of risk variables.  The first general risk model highlights conceptual categories of risk variables from multiple levels including behavioral, psycho-social/environmental, and biographic/injury factors in predicting morbidity and mortality.  The second bi-dimensional behavioral model assesses the risk of poor health outcomes along independent risk and protective behavioral dimensions.  Preliminary data was collected 10 years prior to collection of the proposed follow-up outcome data from 1,391 adult participants with traumatic SCI of at least 1-year duration. These participants will complete a self-report battery of measures as will a new sample of over 1,000 cases.  Measures will include reassessment of risk factors and a detailed assessment of health outcomes.  General linear model will be used to explore the cross-sectional data as a prelude to confirmatory analyses of the longitudinal data using structural equation modeling (SEM).  The SEM will include confirmatory factor analysis to test the clarity of our conceptual categories and independence of our outcome measures, longitudinal change modeling to test the direct effects of time 1 predictors on time 2 outcomes, multiple groups modeling to evaluate hypotheses from our bi-dimensional model, and incomplete data modeling to identify and compensate for selective attrition.  Results of the study will enhance our understanding of the natural course of health decline and development of secondary conditions, lead to refined predictive models, and will serve as a basis for developing prevention strategies to limit and/or prevent these conditions among people with SCI.

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