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Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management.
Mollayeva, Tatyana; Tran, Andrew; Chan, Vincy; Colantonio, Angela; Escobar, Michael D.
Afiliación
  • Mollayeva T; KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada. tatyana.mollayeva@utoronto.ca.
  • Tran A; Department of Occupational Science & Occupational Therapy, University of Toronto, Toronto, Ontario, Canada. tatyana.mollayeva@utoronto.ca.
  • Chan V; Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. tatyana.mollayeva@utoronto.ca.
  • Colantonio A; Acquired Brain Injury Research Lab, University of Toronto, Toronto, Ontario, Canada. tatyana.mollayeva@utoronto.ca.
  • Escobar MD; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, Ontario. tatyana.mollayeva@utoronto.ca.
BMC Med Res Methodol ; 22(1): 30, 2022 01 30.
Article en En | MEDLINE | ID: mdl-35094688
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.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Lesiones Traumáticas del Encéfalo / Visualización de Datos Tipo de estudio: Prognostic_studies País/Región como asunto: America do norte Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Lesiones Traumáticas del Encéfalo / Visualización de Datos Tipo de estudio: Prognostic_studies País/Región como asunto: America do norte Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2022 Tipo del documento: Article