To examine the influence of socioeconomic factors (SEF) defined by sex, college education, non-white race, insurance status and median area income, on self-reported disability in TBI-clinic patients.

SEF (e.g. race, insurance status) negatively influence mortality and short-term morbidity. However, the influence of SEF, particularly the community context, on long-term disability outcomes post-TBI remains underexplored.

Retrospective cohort study of TBI-clinic visits in UCI-NTBIC database (9/2022-9/2023). Inclusion criteria: ≥18yo, self-reported TBI history with known mechanism and reported neurologic symptom(s), completed SEF data. Disability defined as modified-Rankin-scale ≥2. Median area income determined from 2020 US Census data using patient’s home zip code. Descriptive statistics (Mann-Whitney/Fischer-test) and multivariable logistic-regression performed.

Of 66 patients evaluated (median-age 50, 41% women, 47% non-white, 73% college-educated, 21% uninsured/Medicaid, 78% mild TBI), 56% were disabled. There was no significant difference between disabled vs non-disabled patients with regard to age, sex, college educated level, non-white-race, insurance status on univariate analysis (all p>0.05). Median area income was significantly lower in those disabled (median [IQR], $90,147 [$78,046-$107,444.25]) vs. non-disabled (median [IQR], $116,283.50 [$97,469 - $133,088]). After adjusting for SEF, TBI type and time to clinic visit, median area income predicted disability (p = .003). For every $10,000 increase in median area income, odds of disability decreased by 34% (OR, 0.66; 95% CI, 0.49 - 0.85; p = .003).

In our small predominantly mild-TBI clinic cohort, lower median area income was associated with reported-disability, regardless of other SEF and TBI severity. The findings propose a compelling basis for augmenting socioeconomic support structures in lower income areas, which might contribute to ameliorating long-term disability outcomes for TBI survivors. Future studies will expand sample size, include controls and explore regional-community-level (e.g. area-deprivation-index, social-network) features.