Predicting use of case management support services for adolescents and adults living in community following brain injury: A longitudinal Canadian database study with implications for life care planning
Journal Articles
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
OBJECTIVE: To determine factors associated with case management (CM) service use in people with traumatic brain injury (TBI), using a published model for service use. DESIGN: A retrospective cohort, with nested case-control design. Correlational and logistic regression analyses of questionnaires from a longitudinal community data base. STUDY SAMPLE: Questionnaires of 203 users of CM services and 273 non-users, complete for all outcome and predictor variables. Individuals with TBI, 15 years of age and older. Out of a dataset of 1,960 questionnaires, 476 met the inclusion criteria. METHODOLOGY: Eight predictor variables and one outcome variable (use or non-use of the service). Predictor variables considered the framework of the Behaviour Model of Health Service Use (BMHSU); specifically, pre-disposing, need and enabling factor groups as these relate to health service use and access. RESULTS: Analyses revealed significant differences between users and non-users of CM services. In particular, users were significantly younger than non-users as the older the person the less likely to use the service. Also, users had less education and more severe activity limitations and lower community integration. Persons living alone are less likely to use case management. Funding groups also significantly impact users. CONCLUSIONS: This study advances an empirical understanding of equity of access to health services usage in the practice of CM for persons living with TBI as a fairly new area of research, and considers direct relevance to Life Care Planning (LCP). Many life care planers are CM and the genesis of LCP is CM. The findings relate to health service use and access, rather than health outcomes. These findings may assist with development of a modified model for prediction of use to advance future cost of care predictions.