Efforts to Support the Mental Health and Well-being of Nursing Home Staff: Insights From Leaders.

OBJECTIVES: The sustained stress and trauma experienced by frontline nursing home (NH) staff throughout the COVID-19 pandemic has been described in health care literature and popular press. Yet, limited attention has been given to attempts to support NH staff. The objective of this study was to examine efforts to support the mental health and well-being of NH staff during the COVID-19 pandemic. DESIGN: Qualitative, multiple-case-study design that purposively sampled NHs from 3 groups based on the Centers for Medicare & Medicaid Services NH 5-star quality performance ratings [ie, high (4-5-star), medium (3-star), and low (1-2-star)]. SETTINGS AND PARTICIPANTS: Ninety-four US NH leaders participated in semistructured interviews via phone, between January 2021 and December 2022. METHODS: A 3-step rapid qualitative analysis process was used to conduct a thematic analysis. RESULTS: Five themes emerged as NH leaders described strategies used to address the mental health and well-being of their staff, including (1) efforts to address stressors in staff's personal lives (eg, risk of COVID-19 transmission to families, finances), (2) providing mental health services (eg, counseling, Employee Assistance Program) and resources (eg, staff self-care, mindfulness), (3) appreciation initiatives to combat negative media portrayals of NHs, (4) fostering an environment that supports mental health and well-being (eg, leadership initiatives to prioritize mental health, embedding training on burnout into standing meetings), and (4) modifying staff benefits (eg, expanding mental health coverage within staff insurance plan, paid time off). CONCLUSIONS: In light of concerns about NH staffing levels and the recently proposed minimum staffing levels, there is a need to design and evaluate initiatives to recruit and retain qualified NH staff. Insights into efforts implemented by NH leaders to improve mental health and well-being can inform the design of future efforts to improve staff retention.

Enhancing Clinical Decision Support in Nephrology: Addressing Algorithmic Bias Through Artificial Intelligence Governance.

There has been a steady rise in the use of clinical decision support (CDS) tools to guide Nephrology, as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools. In order to properly answer this question, it is necessary to understand how algorithmic bias arises. We break down three sources of bias encountered when using electronic health record data to develop CDS tools: (1) use of proxy variables, (2) observability concerns and (3) underlying heterogeneity. We discuss how answering the question of whether to include sensitive variables like race often hinges more on qualitative considerations than on quantitative analysis, dependent on the function that the sensitive variable serves. Based on our experience with our own institution's CDS governance group, we show how health system-based governance committees play a central role in guiding these difficult and important considerations. Ultimately, our goal is to foster a community practice of model development and governance teams that emphasizes consciousness about sensitive variables and prioritizes equity.

Skilled nursing facility staffing shortages: Sources, strategies, and impacts on staff who stayed.

OBJECTIVE: To examine skilled nursing facility (SNF) staffing shortages across job roles during the COVID-19 pandemic. We aimed to capture the perspectives of leaders on the breadth of staffing shortages and their implications on staff that stayed throughout the pandemic in order to provide recommendations for policies and practices used to strengthen the SNF workforce moving forward. STUDY SETTING AND DESIGN: For this qualitative study, we engaged a purposive national sample of SNF leaders (n = 94) in one-on-one interviews between January 2021 and December 2022. DATA SOURCE AND ANALYTIC SAMPLE: Using purposive sampling (i.e., Centers for Medicare & Medicaid quality rating, region, ownership) to capture variation in SNF organizations, we conducted in-depth, semi-structured qualitative interviews, guided a priori by the Institute of Medicine's Model of Healthcare System Framework. Interviews were conducted via phone, audio-recorded, and transcribed. Rigorous rapid qualitative analysis was used to identify emergent themes, patterns, and relationships. PRINCIPAL FINDINGS: SNF leaders consistently described staffing shortages spanning all job roles, including direct care (e.g., activities, nursing, social services), support services (e.g., laundry, food, environmental services), administrative staff, and leadership. Ascribed sources of shortages were multidimensional (e.g., competing salaries, family caregiving needs, burnout). The impact of shortages was felt by all staff that stayed. In addition to existing job duties, those remaining staff experienced re-distribution of essential day-to-day operational tasks (e.g., laundry) and allocation of new COVID-19 pandemic-related activities (e.g., screening). Cross-training was used to cover a wide range of job duties, including patient care. CONCLUSIONS: Policies are needed to support SNF staff across roles beyond direct care staff. These policies must address the system-wide drivers perpetuating staffing shortages (i.e., pay differentials, burnout) and leverage strategies (i.e., cross-training, job role flexibility) that emerged from the pandemic to ensure a sustainable SNF workforce that can meet patient needs.

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research.

The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivotal role in utilizing AI to foster a more efficient and equitable health-care system. However, to fulfil this role, nurses will require education that prepares them with the necessary skills and knowledge for the effective and ethical application of AI. This article proposes a framework for nurses which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity. By adopting this educational framework, nurses will be prepared to make substantial contributions to reducing health disparities and fostering a health-care system that is more efficient and equitable.

Psychosocial stressors associated with frailty in community-dwelling older adults in the United States.

BACKGROUND: Frailty is multifactorial; however, psychosocial stressors contributing to frailty are poorly understood. This study aimed to examine whether gender, race/ ethnicity, and education are associated with differential exposure to psychosocial stressors, determine psychosocial stressors contributing to frailty, and explore the mediating psychosocial stressors pathway. METHODS: This cross-sectional study involved 7679 community-dwelling older adults (≥65) from the Health and Retirement Study (2006 and 2008 waves). Psychosocial stressors such as loneliness, low subjective social status, financial strain, poor neighborhood cohesion, everyday discrimination, and traumatic life events were measured. Frailty was defined by the Fried phenotype measure. Multivariable logistic regressions were used to examine the association of gender, race/ethnicity, and education with psychosocial stressors, psychosocial stressors associated with frailty, and the mediating psychosocial stressors pathway. RESULTS: Females experienced greater financial strain but lower discrimination (both p < 0.05). Older adults who identified as Hispanic, Black, and racially or ethnically minoritized experienced low subjective social status, high financial strain, low neighborhood cohesion, and high discrimination than their White counterparts (all p < 0.05). Those with lower education experienced high loneliness, low subjective social status, high financial strain, low neighborhood cohesion but lower traumatic life events (all p < 0.05). Psychosocial stressors: High loneliness, low subjective social status, high financial strain, and low neighborhood cohesion (all p < 0.05) independently increased the odds of frailty. The mediating pathway of psychosocial stressors was not significant.  CONCLUSION: Disparities exist in exposure to psychosocial stressors associated with frailty. Multilevel interventions are needed to reduce the influence of psychosocial stressors on frailty.

A Qualitative Study of the Social Relationship Experiences Across the Life Course Among Black/African American Women Aging With HIV in the South.

ABSTRACT: Black/African American women continue to be disproportionately affected by HIV, facing multiple intersecting challenges that influence how they age and effectively manage their health. Supportive social relationships have been shown to help mitigate challenges and improve health in women with HIV, but little is known about Black/African American women's perceptions of social relationships. Guided by Life Course Theory, in-depth life history interviews were conducted with 18 Black/African American women aged 50+ years. In older adulthood, most important relationships among Black/African American women were with their adult children and grandchildren, intimate partners, God, and friends from the community. Factors that influenced relationships over time included: (a) a desire to build a community; (b) a need to empower oneself and give back; (c) yearning to engage the younger generation; and (d) battling HIV stigma. Older Black/African American women with HIV played a critical role in the education of the younger generation.

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare.

OBJECTIVE: The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion. MATERIALS AND METHODS: Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution. RESULTS: An Implementation Guide articulates evaluation criteria used during review of algorithmic technologies and details what evidence supports the implementation of ethical and quality principles for trustworthy health AI. Application of the processes described in the Implementation Guide can lead to algorithms that are safer as well as more effective, fair, and equitable upon implementation, as illustrated through 4 examples of technologies at different phases of the algorithmic lifecycle that underwent evaluation at our academic medical center. DISCUSSION: By providing clear descriptions/definitions of evaluation criteria and embedding them within standardized processes, we streamlined oversight processes and educated communities using and developing algorithmic technologies within our institution. CONCLUSIONS: We developed a scalable, adaptable framework for translating principles into evaluation criteria and specific requirements that support trustworthy implementation of algorithmic technologies in patient care and healthcare operations.

Structural determinants and cardiometabolic typologies related to frailty in community-dwelling older adults.

Frailty is a geriatric syndrome linked to adverse outcomes. Co-occurring cardiometabolic factors increase frailty risk; however, their distinct combinations (typologies) associated with frailty are unclear. We aimed to identify subgroups of older adults with distinct cardiometabolic typologies and characterize their relationship with structural determinants and frailty to inform tailored approaches to prevent and delay frailty. This study was cross-sectional design and included 7984 community-dwelling older adults (65+ years) enrolled in the Health and Retirement Study (2006 and 2008). Latent class analysis was performed using seven cardiometabolic indicators (abdominal obesity, obesity, low high-density lipoprotein; and elevated blood pressure, blood sugar, total cholesterol, C-reactive protein). Frailty was indicated by ≥3 features (weakness, slowness, fatigue, low physical activity, unintentional weight loss). Logistic regression was used to examine the relationship between structural determinants (gender, race/ethnicity, and education), cardiometabolic typologies, and frailty. Three cardiometabolic subgroups were identified: insulin-resistant (n = 3547), hypertensive dyslipidemia (n = 1246), and hypertensive (n = 3191). Insulin-resistant subgroup members were more likely to be female, non-Hispanic Black, and college non-graduates; hypertensive dyslipidemia subgroup members were more likely to be non-Hispanic Others and report high school education; and hypertensive subgroup members were more likely to be male and college educated (p≤.05). Frailty risk was higher for females, Hispanic or Non-Hispanic Black older adults, and those with lower education (p≤.001). Frailty risk was greater in the insulin-resistant compared to the other subgroups (both aOR=2.0, both p<.001). Findings highlight a need to design tailored interventions targeting cardiometabolic typologies to prevent and delay frailty.

Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review.

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from discriminating against individuals when using clinical algorithms in decision making. However, HHS did not provide specific guidelines on how covered entities should prevent discrimination. We conducted a scoping review of literature published during the period 2011-22 to identify health care applications, frameworks, reviews and perspectives, and assessment tools that identify and mitigate bias in clinical algorithms, with a specific focus on racial and ethnic bias. Our scoping review encompassed 109 articles comprising 45 empirical health care applications that included tools tested in health care settings, 16 frameworks, and 48 reviews and perspectives. We identified a wide range of technical, operational, and systemwide bias mitigation strategies for clinical algorithms, but there was no consensus in the literature on a single best practice that covered entities could employ to meet the HHS requirements. Future research should identify optimal bias mitigation methods for various scenarios, depending on factors such as patient population, clinical setting, algorithm design, and types of bias to be addressed.

A call to action to improve the completeness of older adult sexual and gender minority data in electronic health records.

Sexual and gender minority (SGM) older adults experience greater health disparities compared to non-SGM older adults. The SGM older adult population is growing rapidly. To address this disparity and gain a better understanding of their unique challenges in healthcare relies on accurate data collection. We conducted a secondary data analysis of 2018-2022 electronic health record data for older adults aged ≥50 years, in 1 large academic health system to determine the source, magnitude, and correlates of missing sexual orientation and gender identity (SOGI) data among hospitalized older adults. Among 153 827 older adults discharged from the hospital, SOGI data missingness was 67.6% for sexual orientation and 63.0% for gender identity. SOGI data are underreported, leading to bias findings when studying health disparities. Without complete SOGI data, healthcare systems will not fully understand the unique needs of SGM individuals and develop tailored interventions and programs to reduce health disparities among these populations.

Does cumulative psychosocial stress explain frailty disparities in community-dwelling older adults?

OBJECTIVE: Frailty is a leading predictor of adverse outcomes in older adults. Although disparities in frailty are well-documented, it is unclear whether psychosocial stressors explain these disparities. This study aimed to examine the potential mediating role of psychosocial stress. METHODS: This cross-sectional study included 7,679 community-dwelling older adults (≥ 65) from Health and Retirement Study in the US (2006 and 2008). We used six dichotomized psychosocial stressors: a) loneliness, b) discrimination, c) financial strain, d) low subjective status, e) poor neighborhood cohesion, and f) traumatic life events to compute cumulative psychosocial stress. The Fried frailty phenotype defined frailty based on three features: slowness, poor strength, weight loss, fatigue, and low physical activity. Multivariable regressions were used to examine the structural determinants (gender, education, race, and ethnicity) frailty relationship and test whether cumulative psychosocial stress has a mediating role. RESULTS: The frailty prevalence was 22%. Females, Hispanics, Blacks, and those with less education had higher odds of frailty (p<.01). Race and ethnic minorities and non-college graduates experienced greater cumulative psychosocial stress relative to their White and college graduate counterparts (p<.05), respectively. Greater cumulative psychosocial stress was associated with increased odds of frailty (p < .001); however, it did not mediate the structural determinants and frailty relationship. CONCLUSION: Contrary to expectations, cumulative psychosocial stress did not mediate the relationship between structural determinants and frailty. Rather, high cumulative psychosocial stress was independently associated with frailty. Further research should examine other psychosocial mediators to inform interventions to prevent/delay frailty.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Predictors of cognitive gains during inpatient rehabilitation for older adults with traumatic brain injury.

INTRODUCTION: Traumatic brain injury (TBI) among older adults is increasing and can affect cognition. To effectively meet the rehabilitation needs of older adults, a clearer picture is needed of patient-, clinical-, and facility-level characteristics that affect cognitive recovery during inpatient rehabilitation facility (IRF) stays. OBJECTIVE: To identify patient, clinical, and facility factors associated with cognitive recovery among older adults with TBI who received IRF care. DESIGN: Secondary data analysis. SETTING: Uniform Data System for Medical Rehabilitation-participating IRFs in the United States. PATIENTS: Patients were 65 to 99 years of age at IRF admission for TBI. Participants received IRF care between 2002 and 2018 (N = 137,583); 56.3% were male; 84.2% were white; mean age was 78.7 years. MAIN OUTCOME MEASURE: Change in Functional Independence Measure Cognitive Score (FIM-Cognitive) from IRF admission to discharge, categorized as favorable (FIM-cognitive score gains ≥3 points) or poor (FIM-cognitive score gains <3 points) cognitive outcomes. INTERVENTIONS: Not applicable. RESULTS: Patients had greater odds of favorable cognitive recovery if they were female (adjusted odds ratio [aOR] 1.05, 95% confidence interval [CI] 1.05-1.08), had higher motor functioning at IRF admission (aOR 1.03, 95% CI 1.03-1.04), longer length of stay (aOR 1.07, 95% CI 1.06-1.07), or received care at a freestanding IRF (vs. hospital rehab unit) (aOR 1.57, 95% CI 1.52-1.61). Patients who were older (aOR 0.99, 95% CI 0.98-0.99), Black (aOR 0.79, 95% CI 0.75-0.83), Hispanic or Latino (aOR 0.97, 95% CI 0.91-1.02), or were part of another racial or ethnic group (aOR 0.85, 95% CI 0.81-0.90) (vs. White), had high-cost comorbid conditions (aOR 0.71, 95% CI 0.65-0.76), or who had higher cognitive functioning at IRF admission (aOR 0.90, 95% CI 0.90-0.91) had lower odds of favorable cognitive recovery. CONCLUSIONS: Patient (age, sex, race, ethnicity), clinical (level of functioning at IRF admission, length of stay) and facility (e.g., freestanding IRF) factors contributed to the cognitive recoveries of older adults during IRF stays.

Gendered Consequences of COVID-19 Among Professional Tennis Players.

The COVID-19 pandemic increased the risk of travelling, working, and participating in public events. To test whether there were gendered differences in the response to COVID-19, we examine the behavior of male and female professional tennis players. We use data from major tennis tournaments which included a rather large number of athletes withdrawing from play. After controlling for past performance, wealth, and other relevant player attributes, we find that female tennis players were more likely to withdraw. This suggests that high-earning women may have greater risk aversion, especially related to COVID-19, than their male counterparts. Importantly, women were more risk-averse when it comes to international travel.

Do heavy-duty and passenger vehicle emissions standards reduce per capita emissions of oxides of nitrogen? Evidence from Europe.

Oxides of nitrogen are among the most dangerous emissions to human health and to the environment. In European nations, road transportation contributes to approximately 40% of emissions of oxides of nitrogen with the dominant share coming from passenger and freight transport. To help mitigate emissions of oxides of nitrogen, the European Union (EU) has implemented vehicular emissions standards. This paper studies the effect of EU vehicular emissions standards on per capita emissions of oxides of nitrogen in European nations during the period 2000 to 2017, both for on-road vehicular emissions and at the economy level. To do this, pollution is modelled as a byproduct of economic production. After controlling for economic growth, historical per capita levels of emissions of oxides of nitrogen, and a series of geographic and technological factors, it is determined that the vehicular emissions standards put in place by the EU decrease per capita levels of emissions of oxides of nitrogen. More precisely, reducing the heavy duty emissions standard by 1 g/kWh leads to as much as a 7% reduction in per capita on-road emissions of oxides of nitrogen. Reducing the passenger vehicle emissions standards for both diesel and gasoline engines enhances this effect, resulting in an even greater reduction in per capita emissions of oxides of nitrogen. These results further suggest that any rebound effect taking place is outweighed by the reduction in emissions of oxides of nitrogen from lowering emissions standards.

The Association Between Cardiometabolic Risk Factors and Frailty in Older Adults: A Systematic Review.

Background and Objectives: Enhanced management and prevention of frailty depend on our understanding of the association between potentially modifiable risk factors and frailty. However, the associations between potentially modifiable cardiometabolic risk factors and frailty are not clear. The purpose of this review was to appraise and synthesize the current evidence examining the associations between the cardiometabolic risk factors and frailty. Research Design and Methods: Multiple databases, including MEDLINE (via PubMed), Embase (via Elsevier), and Web of Science (via Clarivate), were searched extensively. Studies that examined cardiometabolic risk factors and frailty as main predictors and outcome of interest, respectively, among older adults (≥60 years) were included. The Joanna Briggs Institute critical appraisal tools were used to evaluate the quality of studies. PRISMA (2020) guided this review, and findings were synthesized without meta-analysis. This systematic review was registered in PROSPERO (CRD42021252565). Results: Twelve studies met the eligibility criteria and were included in the review. Abdominal obesity, hyperglycemia, and multiple co-occurring cardiometabolic risk factors were associated with the increased likelihood of frailty in older adults. There was inconsistency across the studies regarding the associations between dyslipidemia, elevated blood pressure, and frailty. Discussion and Implications: Understanding the association between cardiometabolic risk factors and frailty can have translational benefits in developing tailored interventions for the prevention and management of frailty. More studies are needed to validate predictive and clinically significant associations between single and specific combinations of co-occurring cardiometabolic risk factors and frailty.

Telemedicine versus in-Person Primary Care: Impact on Visit Completion Rate in a Rural Appalachian Population.

INTRODUCTION: The use of telemedicine increased during the global Coronavirus disease 2019 (COVID-19) pandemic. Rural populations often struggle with adequate access to care while simultaneously experiencing multiple health disparities. Yet, telemedicine use during the COVID-19 pandemic has been understudied on its effect on visit completion in rural populations. The primary purpose of this study is to understand how telemedicine delivery of family medicine care affects patient access and visit completion rates in a rural primary care setting. METHODS: We performed a retrospective cohort study on primary care patient visits at an academic family medicine clinic that serves a largely rural population. We gathered patient demographic and visit type and completion data on all patients seen in the West Virginia University Department of Family Medicine between January 2019 and November 2020. RESULTS: The final sample included 110,999 patient visits, including 13,013 telemedicine visit types. Our results show that telemedicine can increase completion rates by about 20% among a sample of all ages and a sample of adults only. Working-aged persons are more likely to complete telemedicine visits. Older persons with higher risk scores are more likely to complete their visits if they use telemedicine. CONCLUSIONS: Telemedicine can be a tool to improve patient access to primary care in rural populations. Our findings suggest that telemedicine may facilitate access to care for difficult-to-reach patients, such as those in rural areas, as well as those who have rigid work schedules, live longer distances from the clinic, have complex health problems, and are from areas of higher poverty and/or lower education.

Sex and Racial/Ethnic Differences in Within-Stay Readmissions During Inpatient Rehabilitation Among Patients With Traumatic Brain Injury.

OBJECTIVE: The aim of the study was to determine the association of sex and race/ethnicity with acute hospital readmissions ("within-stay readmissions") during inpatient rehabilitation facility care versus patients discharged home without a within-stay readmission among traumatic brain injury patients. DESIGN: The study used a secondary analysis ( N = 210,440) of Uniform Data System for Medical Rehabilitation data using multiple logistic regression. RESULTS: Within-stay readmissions occurred for 11.79% of female and 11.77% of male traumatic brain injury patients. Sex-specific models identified insurance, comorbidities, and complications factored differently in likelihood of within-stay readmissions among female than male patients but association of all other factors were similar per group. Within-stay readmissions differences were more pronounced by race/ethnicity: White, 11.63%; Black, 11.32%; Hispanic/Latino, 9.78%; and other, 10.61%. Descriptive bivariate analysis identified racial/ethnic patients with within-stay readmissions had greater days from traumatic brain injury to inpatient rehabilitation facility admission (White, 17.66; Black, 21.70; Hispanic/Latino, 23.81; other, 20.66) and lower admission cognitive and motor function. Factors differed across models predicting within-stay readmissions for race/ethnic groups; age, admission motor and cognitive function, complications, and length of stay were consistent across groups. CONCLUSIONS: This study demonstrates disparities by race/ethnicity for inpatient rehabilitation facility within-stay readmissions among traumatic brain injury patients and factors predictive of this potentially preventable outcome by sex and race/ethnicity. Findings could inform care planning and quality improvement efforts for TBI patients.