Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management

Abstract

Background The interplay of host, agent, and environment implicated in traumatic brain injury (TBI) events is difficult to account for in hypothesis-driven research. Data-driven analysis of injury data can enable insight into injury events in novel ways. This research dissected complex and multidimensional data at the time of the TBI event by exploiting data mining and information visualization methods. Methods We drew upon population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. We applied a computational approach to categorize health records of 235,003 patients with TBI versus the same number of reference patients without TBI, individually matched based on sex, age, place of residence, and neighbourhood income quantile. We adopted the basic concepts of the Haddon Matrix (host, agent, environment) to organize emerging factors significantly related to TBI versus non-TBI events. To explore sex differences, the data of male and female patients with TBI were plotted on heatmaps and clustered using hierarchical clustering algorithms. Results Based on detected similarities, the computational technique yielded 34 factors on which individual TBI-event codes were loaded, allowing observation of a set of definable patterns within the host, the agent, and the environment. Differences in the patterns of host, agent and environment were found between male and female patients with TBI, which are currently not identified based on data from injury surveillance databases. The results were internally validated. Conclusions The study outlines novel areas for research relevant to TBI and offers insight into how computational and visual techniques can be applied to advance the understanding of TBI event. Results highlight unique aspects of sex differences of the host and agent at the injury event, as well as differences in exposure to adverse social and environmental circumstances, which can be a function of gender, aiding in future studies of injury prevention and gender-transformative care.

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

“…Hierarchical matrices can be used for the selection of appropriate interventions for individuals, which is useful to identify high costs and reduce costs for the member and insurer [ 79 ]. Gender-related aspects, as well as differences in exposure to adverse social and environmental circumstances, contribute to better prevention and development of gender-transformative care [ 80 ]. Such novel information and analyses have the potential to improve management [ 22 ].…”

Social determinants of health associated with psychological distress stratified by lifetime traumatic brain injury status and sex: Cross-sectional evidence from a population sample of adults in Ontario, Canada

“…This is problematic because early intervention and access to mental health services and supports are critical to prevent poor mental health and adverse TBI-related outcomes post-injury [ 6 ]. In planning health services and supports, it is critical to consider the social determinants of health (SDoH) to promote comprehensive access to care and prevent health inequities [ 14 ]. SDoH are social and economic factors that can positively or negatively influence both individual and population health and are globally recognized as some of the most important factors impacting health [ 15 ].…”