Gender Differences in Outcomes after Traumatic Brain Injury among Service Members and Veterans.
Alison M CoganVirginia K McCaugheyJoel ScholtenPublished in: PM & R : the journal of injury, function, and rehabilitation (2019)
This scoping study describes the range of outcomes in traumatic brain injury (TBI) studies of military service members and veterans addressing gender differences. A secondary purpose is to identify differences in outcomes between male and female participants in such studies. We searched PubMed, CiNAHL, and PsycInfo databases for relevant articles. Two reviewers independently screened results. Of 822 unique titles and abstracts screened for eligibility, 55 full articles were reviewed, with 29 studies meeting full inclusion criteria. Twenty of the 29 included studies used retrospective designs and all but two used data collected from Veterans Affairs or Department of Defense health care settings. TBI was diagnosed by self-report, screening, and evaluation procedures, and medical record documentation. Ten different outcome categories were identified among the included studies. In general, female service members and veterans have not been well represented in TBI outcomes research. Evidence suggests that female veterans with mild TBI (mTBI) report more neurobehavioral symptoms and use more outpatient services than male veterans. Studies also indicate that female veterans with TBI are more frequently diagnosed with depression. Additional research is essential to support precision treatment recommendations for female veterans with TBI, as women represent a growing proportion of the patients served by the Veterans Health Administration. LEVEL OF EVIDENCE: IV.
Keyphrases
- traumatic brain injury
- healthcare
- mental health
- severe traumatic brain injury
- case control
- mild traumatic brain injury
- end stage renal disease
- electronic health record
- chronic kidney disease
- type diabetes
- metabolic syndrome
- depressive symptoms
- ejection fraction
- pregnant women
- climate change
- big data
- machine learning
- cross sectional
- prognostic factors
- physical activity
- newly diagnosed
- social media
- deep learning
- human health