A Collie1, K-H Prang. 1. Institute for Safety Compensation and Recovery Research, Monash University, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Australia. Electronic address: alex.collie@monash.edu.
Abstract
BACKGROUND: The rate and extent of recovery after severe traumatic brain injury (TBI) is heterogeneous making prediction of likely healthcare service utilisation (HSU) difficult. Patterns of HSU derived from nomothetic samples do not represent the diverse range of outcomes possible within this patient group. Group-based trajectory model is a semi-parametric statistical technique that seeks to identify clusters of individuals whose outcome (however measured) follows a similar pattern of change over time. AIM: To identify and characterise patterns of HSU in the 5-year period following severe TBI. METHODS: Detailed healthcare treatment payments data in 316 adults with severe TBI (Glasgow Coma Scale score 3-8) from the transport accident compensation system in the state of Victoria, Australia was accessed for this analysis. A semi-parametric group-based trajectory analytical technique for longitudinal data was applied to monthly observation counts of HSU data to identify distinct clusters of participants' trajectories. Comparison between trajectory groups on demographic, injury, disability and compensation relevant outcomes was undertaken. RESULTS: Four distinct patterns (trajectories) of HSU were identified in the sample. The first trajectory group comprised 27% of participants and displayed a rapid decrease in HSU in the first year post-injury. The second group comprised 24% of participants and showed a sharp peak in HSU during the first 12 months post-injury followed by a decline over time. The third group comprised 32% of participants and showed a slight peak in HSU in the first few months post-injury and then a slow decline over time. The fourth group comprised 17% of participants and displayed a steady rise in HSU up to 30 months post-injury, followed by a gradual decline to a level consistent with that received in the first months post-injury. Significant differences were observed between groups on factors such as age, injury severity, and use of disability services. CONCLUSIONS: There is substantial variation in patterns of HSU following severe TBI. Idiographic analysis can provide rich information for describing and understanding the resources required to help people with TBI.
BACKGROUND: The rate and extent of recovery after severe traumatic brain injury (TBI) is heterogeneous making prediction of likely healthcare service utilisation (HSU) difficult. Patterns of HSU derived from nomothetic samples do not represent the diverse range of outcomes possible within this patient group. Group-based trajectory model is a semi-parametric statistical technique that seeks to identify clusters of individuals whose outcome (however measured) follows a similar pattern of change over time. AIM: To identify and characterise patterns of HSU in the 5-year period following severe TBI. METHODS: Detailed healthcare treatment payments data in 316 adults with severe TBI (Glasgow Coma Scale score 3-8) from the transport accident compensation system in the state of Victoria, Australia was accessed for this analysis. A semi-parametric group-based trajectory analytical technique for longitudinal data was applied to monthly observation counts of HSU data to identify distinct clusters of participants' trajectories. Comparison between trajectory groups on demographic, injury, disability and compensation relevant outcomes was undertaken. RESULTS: Four distinct patterns (trajectories) of HSU were identified in the sample. The first trajectory group comprised 27% of participants and displayed a rapid decrease in HSU in the first year post-injury. The second group comprised 24% of participants and showed a sharp peak in HSU during the first 12 months post-injury followed by a decline over time. The third group comprised 32% of participants and showed a slight peak in HSU in the first few months post-injury and then a slow decline over time. The fourth group comprised 17% of participants and displayed a steady rise in HSU up to 30 months post-injury, followed by a gradual decline to a level consistent with that received in the first months post-injury. Significant differences were observed between groups on factors such as age, injury severity, and use of disability services. CONCLUSIONS: There is substantial variation in patterns of HSU following severe TBI. Idiographic analysis can provide rich information for describing and understanding the resources required to help people with TBI.
Authors: Thi L Nguyen; Katharine S Baker; Liane Ioannou; Behrooz Hassani-Mahmooei; Stephen J Gibson; Alex Collie; Jennie Ponsford; Peter A Cameron; Belinda J Gabbe; Melita J Giummarra Journal: Int J Environ Res Public Health Date: 2020-10-07 Impact factor: 3.390