INTRODUCTION: Caused by a bump, blow, or jolt to the head, a traumatic brain injury (TBI) affects how the brain works. Determining the prevalence of TBI, including concussion, in the United States is difficult. Surveillance of TBI in the United States has historically relied on healthcare administrative datasets. The most recent numbers find that about 214,000 Americans are hospitalized and 69,000 Americans die from a TBI every year. However, these numbers likely undercount the true burden of TBI as they do not include people who seek care for their injuries outside of hospital settings or people who do not seek care. An alternative approach to TBI surveillance is to make use of national self-report surveys that ask respondents to report their experience with head injuries. The Centers for Disease Control and Prevention (CDC) has recently added TBI prevalence questions to several national surveys. The objective of this presentation is to summarize CDC’s recent efforts in TBI self-reporting.

METHODS: CDC added various 12-month and lifetime TBI prevalence questions to a series of nationally representative surveys (e.g., Porter Novelli’s ConsumerStyles survey, National Health Interview Survey, Youth Risk Behavior Survey). Each survey’s questions were slightly different, and they varied by time period assessed and whether they focused on adult or youth respondents.

RESULTS: Depending on the survey methodology and question wording used, 12-month prevalence of concussion/TBI among adults ranged from 3-12% while lifetime prevalence ranged from about 21-28%. Twelve-month prevalence of concussion/TBI among children and adolescents was about 10% while lifetime prevalence ranged from 7-14%.

CONCLUSION: These results demonstrate that TBI is a common health condition in the United States, and one that is likely consistently underestimated by traditional surveillance methods, which rely on hospital-based datasets. Allowing respondents to self-report their suspected concussions and TBIs resulted in larger prevalence estimates than those captured via traditional surveillance methods. Analysis of the various surveys shows that how the questions are asked, and what terminology is used (e.g., concussion vs. mild traumatic brain injury), affects the estimate. CDC has used the data collected to better refine the questions added to the surveys to ensure the most accurate prevalence estimates are being obtained. These data can be used to optimize and standardize data collection approaches across the field of TBI surveillance.

INTRODUCTION: Traumatic brain injury (TBI) is a major public health problem resulting in hospitalizations, morbidity and mortality globally. Despite the large proportion of elderly persons experiencing TBI, limited data exist at the population level.

OBJECTIVE: To apply quality indicators (QI) to examine TBI care quality for older persons (65 years and older).

METHODS: Provincial administrative health services data from publicly funded healthcare were used. We co-developed 12 QIs with healthcare partners and persons with lived experience, and measured care quality for patients 65+ years with TBI between 2016 and 2021. Age and gender adjusted incidence and QIs with 95% confidence limits were calculated. Variations in QI performance was explored according to age group, sex, geographic region, and income quintile.

RESULTS: A total of n=15,194 complex-mild and n=19,237 moderate/severe brain injury cases were identified between 2016-2021. The age and gender adjusted incidence rate for all severities of TBI increased with age. Older persons were more likely to get admitted to general rehab than specialized TBI rehab after discharged from acute care (8.35% vs 3.04% for persons with moderate to severe TBI). Higher ED visits rates in years 1 and 2 increased with age (156.8 per 100 PY in 80+ age group vs. 114.9 per 100 PY in 65-79 years age) group). The rate of falls in the first two years after moderate-severe TBI was higher among elderly patients (43.7 per 100 PY in 80+ age group vs. 30.6 per 100 PY in 65-79 year group).

CONCLUSION: This study establishes a foundation for quality-of-care assessments and monitoring disparity in care for older adults with TBI at a population level. Gaps were identified in receiving rehabilitation services after discharge from acute care, and follow-up with health professionals. Ensuring that older persons receive appropriate rehabilitation and community support to reduce falls is necessary to maintain independence in the community.

Current systems of classifying traumatic brain injury (TBI) frequently result in limitations to care. Clinical trials that use traditional classification schemes for TBI (e.g., mild, moderate, and severe) have failed to optimally translate to effective treatment and recovery in the real world, which may inhibit the discovery of effective therapies that improve outcomes based on more granular clinical profiles. Data-driven, alternative methods of classification may stratify TBI patient subpopulations more accurately for optimal identification and treatment. Data from the Centers for Disease Control and Prevention’s pilot National Concussion Surveillance System (n = 10,130 adults) were analyzed. Respondents who self-reported a head injury in the past 12 months were retained in the analysis (n = 1,364) and were queried for injury, outcome, and clinical characteristics. To identify potential TBI phenotypes among those reporting a head injury, respondents were grouped into clusters based upon 12 TBI signs and symptoms. Gower’s dissimilarity matrix was computed due to the nature of the binary input data (i.e., presence or absence of each sign or symptom). The partitioning around medoids (PAM) algorithm was used to cluster observations. To determine the association between outcomes and phenotypes, separate logistic regressions were run using the phenotype characterized by the least severity (e.g., Phenotype A [“cluster 1”]) as the reference group. The algorithm grouped the respondents into five clusters (TBI phenotypes A-E). Each TBI phenotype demonstrated unique clinical characteristics that corresponded to specific differences in outcomes and unique demographic profiles. Phenotype C represented more clinically severe TBIs with the highest prevalence of symptoms (i.e., >50% of respondents in this cluster self-reported 11 out of the 12 signs/symptoms) and a higher association with worse outcomes when compared to individuals in Phenotype A, a group with few TBI-related signs and symptoms: medical evaluation (odds ratio [OR] = 9.4, 95% confidence interval [CI] = 5.8-15.3), symptoms that were not currently resolved or resolved in 8+ days (OR = 10.6, 95% CI = 6.2-18.1), and more likely to report at least moderate, as compared to no or slight, impacts on social (OR = 54.7, 95% CI = 22.4-133.4) and work (OR = 25.4, 95% CI = 11.2-57.2) functioning. These results demonstrate that machine learning can be used to classify patients into unique TBI phenotypes. Further research might examine the utility of such classifications in supporting clinical diagnosis and patient recovery for this complex health condition.

Navigating care following the initial hospital discharge can be a complex and vulnerable time for individuals with Traumatic Brain Injury (TB). After acute hospitalization, individuals with TBI may be discharged to different postacute settings, not limited to: inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF), long-term acute care hospitals (LTCH), and home health (HH). When the right transitions are not made at the right times, individuals with TBI face poor outcomes such as hospital readmissions. Also, there exists disparity in these care transitions by gender, race/ethnicity, and living situation (rural versus urban settings). Our study objectives were, to examine care transitions patterns and differences for individuals with TBI in a 90-day post-hospitalization period, and to examine 30- and 90-day hospital readmission risk. This was a retrospective cohort study. We conducted secondary analysis of data from Virginia All-Payer Claims Database (APCD) for the years 2027-2021. Virginia APCD comprised of commercial, Medicare, Medicaid, etc. claims from about 5 million Virginia residents associated with health services (acute, postacute, and community-based) utilization. We selected records of individuals with TBI admitted to acute hospitals and followed their care transitions through postacute and community-based health services in 30- and 90-day follow-up period. We also calculated risk adjusted 30- and 90-day hospital readmission and examined if the hospital readmission risk is different by gender, race/ethnicity, locations, and type of postacute services they received, controlling for all the other covariates. Our analytical cohort comprised of 18,215 individuals with TBI with index acute hospitalization in the years of 2017-2021. The mean age of our cohort was 70.8 (18.7) years, 51% male, 33% non-white, and 5% living in rural locations. Only 61% (11,106) of our study cohort received any postacute care, with 30% going to SNFs, 18% to HHs, and 12% to IRFs. The unadjusted 30-day readmission rate was 3.6%, and 5% for 90-day hospital readmission. In the fully adjusted models, controlling for other covairtaes, we found higher likelihood of 30-day hospital readmission for those going to SNFs (OR=1.9, 95%CI=1.5-2.4), and IRFs (OR=2.9, 95%CI=2.3-3.8) relative to those without any postacute follow-up. Also, we found lower likelihood of 30-day hospital readmission for Blacks compared to non-Hispanic Whites (OR=0.68, 95%CI=0.50-0.93). For 90-day hospital readmission, we found higher likelihood with SNFs and IRFs discharges and lower likelihood for Blacks as compared to non-Hispanic Whites. Our study findings highlight need for equitable access to postacute care is an important consideration for individuals with TBI to maintain care continuity, and achieve desirable health outcomes, and more importantly avoidance of undesirable outcomes, such as hospital readmissions.

There is an apparent phenomenon where Black adult and pediatric athletes face disparities within their care, treatment, and recovery from mild traumatic brain injury (mTBI), or concussion while playing their sport. Previous literature has demonstrated that Black athletes who have experienced sports-related concussions (SRC) are less likely to receive formal concussion diagnoses, and subsequent referrals to tertiary concussion care. Additionally, Black athletes have demonstrated lowered access to clinical care, concussion knowledge and symptom identification, as well as intention to report injury and overall poorer psychosocial outcomes following injury. Overall, the current body of literature has identified that race is a salient social determinant of health for general mTBI and SRC – the axis of privilege and marginalization associated with race, can impact presentation for care, receiving diagnoses, symptom reporting and tracking, and the process of recovery or return to baseline functioning. However, these empirical findings do not elucidate why nor how these various factors compound. Thus, there is a need for a framework to conceptualize and create a clear theory for how these factors compound. Particularly, there is a need to encapsulate how sociocultural experiences of power, access, and biases can impact Black athletes experiencing mTBI. The authors use Ecological Systems Theory (EST; Bronfenbrenner, 1979) to create a novel organization-systems model of identified findings and theory that demonstrate and support racial disparities within general mTBI and SRC. A comprehensive literature search was employed to identify recent (published ≤10 years) empirical studies and theoretical perspectives on racial disparities in mTBI for Black patients and athletes. Using EST as a framework, the literature review examines and organizes these findings within the context of (1) historical and sociopolitical events and systems, (2) sociocultural ideologies and policy, (3) indirect and (4) direct community and cultural factors, as well as (5) person-centered social experiences and identities (social determinants of health). Ultimately, the organizational structure provides a clear thread on how macro-level policy and perceptions, can impact micro-level clinical care and decision-making for Black athletes and their experiences with mTBI.