Caregiver Characteristics of Adults with Acute Traumatic Brain Injury in the United States and Latin America

Objectives: To compare characteristics of caregivers of adults with acute traumatic brain injury (TBI) in the U.S. and Latin America (Mexico and Colombia). Design: Secondary data analysis of two cohorts. Cohort 1: English-speaking caregivers of adults with TBI in the U.S. (n = 80). Cohort 2: Spanish-speaking caregivers of adults with TBI in Mexico or Colombia (n = 109). Results: Similarities between the U.S. and Latin American caregiver groups, respectively, were: predominantly women (81.3%, 81.7%, respectively); spouses/domestic partners (45%, 31.2%); and motor vehicle accident (41.5%, 48.6%) followed by fall etiologies (40%, 21.1%). Differences between U.S. and Latin American caregivers were: age (49.5 years, 41.5 years, p < 0.001); employment status ((X-5(2) = 59.63, p < 0.001), full-time employment (63.7%, 25.7%), homemaker (2.5%, 31.2%), and retired (17.5%, 1.8%)); violence-related etiology (2.5%, 15.6%); and severity of depressive symptoms (M = 7.9, SD = 5.8; M = 5.8, SD = 5.7; p = 0.014). Conclusions: TBI caregivers in the U.S. were older and employed full-time or retired more often than those in Latin America. Violence-related etiology was nearly five times more common in Latin America, raising concerns for potential implications of post-traumatic stress and family adjustment after injury. Although both groups likely could use mental health support, this was particularly true of the U.S. cohort, maybe due to differential demographics, mechanisms of injury, or family and community support.Data collection was supported by NIDILRR (grant numbers: Kessler 90DPTB0003; NTX-TBIMS 90DPTB0013; JFK 90DPTB0014) and Grant #R21TW009746 from the Fogarty International Center of the National Institutes of Health and in part by the Department of Veterans Affairs. Additional support for coauthors was provided by NIDILRR (grant numbers: Spaulding/Harvard TBIMS: 90DPTB0011; TIRR 90DPTB0016)

An Umbrella Review of Self-Management Interventions for Health Conditions With Symptom Overlap With Traumatic Brain Injury

OBJECTIVE: To synthesize evidence for the effectiveness of self-management interventions for chronic health conditions that have symptom overlap with traumatic brain injury (TBI) in order to extract recommendations for self-management intervention in persons with TBI. DESIGN: An umbrella review of existing systematic reviews and/or meta-analyses of randomized controlled trials or nonrandomized studies targeting self-management of chronic conditions and specific outcomes relevant to persons with TBI. METHOD: A comprehensive literature search of 5 databases was conducted using PRISMA guidelines. Two independent reviewers conducted screening and data extraction using the Covidence web-based review platform. Quality assessment was conducted using criteria adapted from the Assessing the Methodological Quality of Systematic Reviews-2 (AMSTAR-2). RESULTS: A total of 26 reviews met the inclusion criteria, covering a range of chronic conditions and a range of outcomes. Seven reviews were of moderate or high quality and focused on self-management in persons with stroke, chronic pain, and psychiatric disorders with psychotic features. Self-management interventions were found to have positive effects on quality of life, self-efficacy, hope, reduction of disability, pain, relapse and rehospitalization rates, psychiatric symptoms, and occupational and social functioning. CONCLUSIONS: Findings are encouraging with regard to the effectiveness of self-management interventions in patients with symptoms similar to those of TBI. However, reviews did not address adaptation of self-management interventions for those with cognitive deficits or for populations with greater vulnerabilities, such as low education and older adults. Adaptations for TBI and its intersection with these special groups may be needed

Natural Selection Affects Multiple Aspects of Genetic Variation at Putatively Neutral Sites across the Human Genome

A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations

Development and Validation of a Functionally Relevant Comorbid Health Index in Adults Admitted to Inpatient Rehabilitation for Traumatic Brain Injury

Several studies have characterized comorbidities among individuals with traumatic brain injury (TBI); however, there are few validated TBI comorbidity indices. Widely used indices (e.g., Elixhauser Comorbidity Index [ECI]) were developed in other patient populations and anchor to mortality or healthcare utilization, not functioning, and notably exclude conditions known to co-occur with TBI. The objectives of this study were to develop and validate a functionally relevant TBI comorbidity index (Fx-TBI-CI) and to compare prognostication of the Fx-TBI-CI with the ECI. We used data from the eRehabData database to divide the sample randomly into a training sample (N = 21,292) and an internal validation sample (N = 9166). We used data from the TBI Model Systems National Database as an external validation sample (N = 1925). We used least absolute shrinkage and selection operator (LASSO) regression to narrow the list of functionally relevant conditions from 39 to 12. In internal validation, the Fx-TBI-CI explained 14.1% incremental variance over an age and sex model predicting the Functional Independence Measure (FIM) Motor subscale at inpatient rehabilitation discharge, compared with 2.4% explained by the ECI. In external validation, the Fx-TBI-CI explained 4.9% incremental variance over age and sex and 3.8% over age, sex, and Glasgow Coma Scale score,compared with 2.1% and 1.6% incremental variance, respectively, explained by the ECI. An unweighted Sum Condition Score including the same conditions as the Fx-TBI-CI conferred similar prognostication. Although the Fx-TBI-CI had only modest incremental variance over demographics and injury severity in predicting functioning in external validation, the Fx-TBI-CI outperformed the ECI in predicting post-TBI function

Distal and Proximal Predictors of Rehospitalization Over 10 Years Among Survivors of TBI: A National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems Study

Objective: To describe the rates and causes of rehospitalization over a 10-year period following a moderate-severe traumatic brain injury (TBI) utilizing the Healthcare Cost and Utilization Project (HCUP) diagnostic coding scheme. Setting: TBI Model Systems centers. Participants: Individuals 16 years and older with a primary diagnosis of TBI. Design: Prospective cohort study. Main measures: Rehospitalization (and reason for rehospitalization) as reported by participants or their proxies during follow-up telephone interviews at 1, 2, 5, and 10 years postinjury. Results: The greatest number of rehospitalizations occurred in the first year postinjury (23.4% of the sample), and the rates of rehospitalization remained stable (21.1%-20.9%) at 2 and 5 years postinjury and then decreased slightly (18.6%) at 10 years postinjury. Reasons for rehospitalization varied over time, but seizure was the most common reason at 1, 2, and 5 years postinjury. Other common reasons were related to need for procedures (eg, craniotomy or craniectomy) or medical comorbid conditions (eg, diseases of the heart, bacterial infections, or fractures). Multivariable logistic regression models showed that Functional Independence Measure (FIM) Motor score at time of discharge from inpatient rehabilitation was consistently associated with rehospitalization at all time points. Other factors associated with future rehospitalization over time included a history of rehospitalization, presence of seizures, need for craniotomy/craniectomy during acute hospitalization, as well as older age and greater physical and mental health comorbidities. Conclusion: Using diagnostic codes to characterize reasons for rehospitalization may facilitate identification of baseline (eg, FIM Motor score or craniotomy/craniectomy) and proximal (eg, seizures or prior rehospitalization) factors that are associated with rehospitalization. Information about reasons for rehospitalization can aid healthcare system planning. By identifying those recovering from TBI at a higher risk for rehospitalization, providing closer monitoring may help decrease the healthcare burden by preventing rehospitalization