From acute injury to chronic comorbidity: Interrupted time series modeling of traumatic brain injury impact among post-9/11 veterans - Mustafa Ozmen; Shashank Vadlamani; James J. Gugger; Megan Amuan; Amanda Cheney; Ramon Diaz-Arrastia; Mary Jo Pugh; Eamonn Kennedy (PLoS ONE, 2025)
Traumatic brain injury (TBI) is associated with a variety of adverse health outcomes that display complex behavior over time. The objective of this study was to investigate both the early and late health impacts of TBI within a single framework. This study evaluated TBI associations among a cohort of post-9/11 Veterans with TBI documented between 2008 and 2017 in Veteran Health Administration (VHA) records. The cohort included 108,408 post-9/11 Veterans with any history of TBI documentation between 2008–2017 who were demographically matched with 108,408 TBI negative controls. Interrupted time series (ITS) models were used to fit the prevalence of comorbidities over time (±6 years from index date, i.e., date of first TBI). Three ITS measures were modeled for each comorbidity: 1) The incidence rate (IR) in the month of TBI index date, 2) The incidence rate ratio (IRR) between TBI and control groups in the month of index date, and 3) Long-term changes in year-over-year diagnosis rates, i.e., the annual incidence rate difference (IRD) before vs. after index date. Overall, TBI was associated with conditions related to somatic, cognitive, and psychological outcomes including headache, cognitive dysfunction, and PTSD. Neurological events were found to be elevated within the month of TBI documentation. Conditions with the largest IR were post-traumatic stress disorder (PTSD) (+29%, p < 0.001), headache (+22%, p < 0.001), and adjustment disorder (+22%, p < 0.001). Conditions with the highest IRR across TBI and control groups were cognitive dysfunction (474, p < 0.001), vestibular dysfunction (137, p < 0.001), and stroke (72, p < 0.001). Long term, the conditions with the highest IRD were substance use disorders (p < 0.001) and mental health conditions (p < 0.001). This work demonstrates how ITS modeling can help bridge traditional divides between early and late paradigms of TBI investigation to help inform research and care for Veterans living with TBI.