From race-based to race-conscious medicine: how anti-racist uprisings call us to act

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models

A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical literature to correct such disparities by removing race as a predictor (i.e., race-free models). Alternatively, a variety of machine learning methods have been proposed to constrain differences in model predictions between racial groups. In this work, we compare these approaches for equitable stroke risk prediction. We begin by proposing a discrete-time, neural network-based time-to-event model that incorporates a parity constraint designed to make predictions more similar between groups. Using harmonized data from Framingham Offspring, MESA, and ARIC studies, we develop both parity-constrained and unconstrained stroke risk prediction models, then compare their performance with race-free models in a held-out test set and a secondary validation set (REGARDS). Our evaluation includes both intra-group and inter-group performance metrics for right-censored time to event outcomes. Results illustrate a fundamental trade-off in which parity-constrained models must sacrifice intra-group calibration to improve inter-group discrimination performance, while the race-free models strike a balance between the two. Consequently, the choice of model must depend on the potential benefits and harms associated with the intended clinical use. All models as well as code implementing our approach are available in a public repository. More broadly, these results provide a roadmap for development of equitable clinical risk prediction models and illustrate both merits and limitations of a race-free approach.

Addressing systemic racism and its influence on health disparities through a foundational pharmacy course and a student-led symposium

OBJECTIVE

The purpose of this study is to determine the impact of integrating a student-led symposium within a pharmacy course on student knowledge, understanding, perceptions, confidence, and opinions of systemic racism (SR).

METHODS

First professional year (P1) students completed a group project that describes the historical context and impact of SR on health outcomes. The project included a written paper and presentation at a student-led symposium to faculty, staff, students, and community partners. Students were surveyed to assess the impact of the project on their knowledge, understanding, perceptions, confidence, and opinions of SR. Non-P1 student audience members were also surveyed on their opinions of the symposium.

RESULTS

Thirty P1 students participated in the pre-and post-symposium surveys (45 % response rate). An improvement was found for all survey items in the understanding, confidence, and opinions categories. Results showed an improvement in numerical and global scores for the pre- and post-knowledge-based assessments. Ten non-P1 post-symposium survey responses from audience members agreed that the symposium presentations were helpful, would result in a change to the services provided, and will help with individual written and/or verbal presentation skills when discussing SR.

CONCLUSION

This group project culminating in a student-led symposium offers a unique model to assess student outcomes in the context of a team project. This experience explores integration of SR topics within the core curriculum, and also offers a strategy to promote health equity and delivery of culturally competent care to students at an early stage in their academic career.

Rethinking the use of population descriptors in dermatology trials and beyond: disentangling race and ethnicity from skin color

IMPORTANCE

Race and ethnicity as population descriptors in research and clinical practice have often been a subject of debate, drawing heightened scrutiny in recent years. Criticism focuses on their oversimplification and misapplication, which fail to capture the complexity of human health and genetic diversity. There is growing recognition that these categories, rooted in outdated social constructs, do not accurately reflect biological differences.

OBSERVATIONS

Historically, race and ethnicity have been used as proxies for genetic variation and skin color, despite the understanding that these constructs are not biologically defined. The Skin of Color Society's second Meeting the Challenge Summit, attended by over 100 U.S. and international participants, highlighted several key themes: (1) the need for transparency in the rationale behind using population descriptors and decision-making processes; (2) recognizing the role of race and racism in dermatology; (3) exploring the intersection of dermatology, skin color, and cultural influences; (4) understanding the context of population descriptor usage; (5) developing improved, objective tools for classifying skin color; and (6) advancing research and creating guidelines.

CONCLUSIONS AND RELEVANCE

There is an urgent need to reconsider the use of race and ethnicity as population descriptors in dermatology research. Current systems, which conflate social identity with biological markers, perpetuate health disparities and limit the accuracy of clinical data. Moving forward, more specific descriptors such as skin color, alongside socially determined factors, will be crucial in achieving meaningful diversity and inclusivity in clinical research.

Pancreas transplant outcomes in patients with human immunodeficiency virus infection

There is limited information on access and outcomes of patients living with human immunodeficiency virus (HIV) (PLWH) who have undergone pancreas transplantation. We conducted a retrospective cohort study analyzing data from the United Network for Organ Sharing from July 1, 2001, to June 30, 2021. Recipients of pancreas transplant were stratified by HIV serostatus. Graft and patient survival were analyzed using Kaplan-Meier product limit estimates. Multivariable Cox proportional hazard models were generated to identify factors associated with increased mortality or graft loss. Fifty PLWH and 16 380 patients without HIV underwent pancreas (with kidney) transplantation. PLWH were more often male (P < .001), Black/African American (P = .009), and on Medicare (P = .004). There were no significant differences in waiting time (P = .159) or proportion of patients treated for rejection within 1 year of transplant (P = .189) between groups. There were no differences in pancreas graft survival (P = .964) and overall patient survival (P = .250) between the cohorts. Dialysis status was negatively associated with graft survival. Although PLWH were more likely to represent a historically marginalized population, their outcomes after pancreas transplant were similar to their HIV-negative counterparts.

Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity

Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health equity research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations. Data disaggregation underpins data equity, which uses community engagement to democratize data and develop better solutions for communities. Years of research on disaggregation show that researchers must collaborate closely with communities for adequate representation. However, despite generally positive support for this approach in health disparities research, data disaggregation faces methodological and political challenges. This review offers a framework for understanding data disaggregation in the context of data equity and highlights critical aspects of implementation, including challenges, opportunities, and recent policy and community-based efforts to address hurdles.

Co-creation of a Maternal Health Equity Quality Improvement Project With Black Maternal Health Stakeholders

In March 2021, the March of Dimes, U.S. Department of Health and Human Services, and Reproductive Health Impact convened a group of leading maternal health experts to offer guidance and ensure accountability for the design and implementation of the Maternal HealthCARE project, a quality improvement (QI) collaborative to address racial equity and the disparity gap in maternal health outcomes. The Black Maternal Health Stakeholder Group (BMHSG) provided recommendations and insights that served as the foundation of the QI project. From the BMHSG meeting, four themes emerged, which highlighted the underlying causes of maternal health disparities: racism, lack of accountability, poor data transparency, and inadequate patient-centered care. The BMHSG's recommendations provide actionable ways for hospitals to drive change and advance equity within their organizations. These insights offer a roadmap for QI work that places the experience and expertise of Black maternal health experts at the forefront, offering a promising way for hospitals to dismantle systemic and institutional racism in maternity care.

Online database of clinical algorithms with race and ethnicity

Some clinical algorithms incorporate an individual's race, ethnicity, or both as an input variable or predictor in determining diagnoses, prognoses, treatment plans, or risk assessments. Inappropriate use of race and ethnicity in clinical algorithms at the point of care may exacerbate health disparities and promote harmful practices of race-based medicine. Using database analysis primarily, we identified 42 risk calculators that use race and ethnicity as predictors, five laboratory test results with reference ranges that differed based on race and ethnicity, one therapy recommendation based on race and ethnicity, 15 medications with race- and ethnicity-based initiation and monitoring guidelines, and five medical devices with differential racial and ethnic performances. Information on these clinical algorithms is freely available at https://www.clinical-algorithms-with-race-and-ethnicity.org/ . This resource aims to raise awareness about the use of race and ethnicity in clinical algorithms and track progress toward eliminating their inappropriate use. The database is actively updated to include clinical algorithms that were missed and additional characteristics of these algorithms.

Advancing health equity within the Canadian health system

Canada's universal healthcare system aspires to provide equitable care for all citizens, yet persistent health disparities among Indigenous, Black, and other racialized communities reveal significant gaps in service and outcomes. This commentary explores how race-based data collection could address these inequities by identifying at-risk populations, informing targeted interventions, and improving accountability within healthcare systems. The discussion includes an overview of existing challenges, such as mistrust stemming from historical research abuses, varied provincial approaches, and ethical complexities related to privacy and data sovereignty. Examples from within Canada highlight the effectiveness of standardized data collection policies, while international lessons-particularly from the United Kingdom's incentivized data reporting and the United States' mandated approaches-underscore the importance of consistent data-gathering and careful monitoring. The commentary emphasizes that community engagement and leadership are essential to the development of culturally safe practices, and that legal and ethical frameworks must safeguard data from commodification or other forms of misuse. By integrating race-based data collection into policy reforms, embracing Indigenous data governance principles, providing mandatory training for health providers, and establishing robust monitoring systems, Canada can advance meaningful strategies to reduce health disparities. This approach promotes a more equitable healthcare environment, ensuring the principle of universal coverage benefits all communities.

Equitable machine learning counteracts ancestral bias in precision medicine

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. Therapeutics and outcomes remain hidden because we lack insights that could be gained from analyzing ancestrally diverse genomic data. To address this significant gap, we present PhyloFrame, a machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating functional interaction networks and population genomics data with transcriptomic training data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. Validation in fourteen ancestrally diverse datasets demonstrates that PhyloFrame is better able to adjust for ancestry bias across all populations. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. Analysis of performance in the most diverse continental ancestry group, African, illustrates how phylogenetic distance from training data negatively impacts model performance, as well as PhyloFrame's capacity to mitigate these effects. These results demonstrate how equitable artificial intelligence (AI) approaches can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

Diversity and Inclusion Within Datasets in Heart Failure: A Systematic Review

BACKGROUND

Heart failure (HF) is a life-threatening disease affecting 64 million people worldwide. Artificial intelligence (AI) technologies are being developed for use in HF to support early diagnosis and stratification of treatment. The performance characteristics of AI technologies are influenced by whether the data used during the AI lifecycle reflects the populations for which the AI is used.

OBJECTIVES

The aim of the study was to identify and characterize datasets used across the lifecycle of AI technologies for HF, focusing on data diversity and inclusivity.

METHODS

MEDLINE and Embase were systematically searched from January 1, 2012, until August 30, 2022, to identify articles relating to the development of AI in HF. Articles were independently screened by 2 reviewers to identify datasets. Dataset documentation was analyzed with a focus on accessibility, geographical origin, relevant metadata reporting, and dataset composition.

RESULTS

The 72 datasets identified represented 23 countries and over 2 million individuals. In total, 62 (86%) datasets reported "age," 61 (85%) reported sex or gender, 21 (29%) reported race and/or ethnicity, and 8 (11%) reported socioeconomic status. In the 21 datasets that reported race and/or ethnicity, 89% of individuals represented were reported within the "White" or "Caucasian" category. Only 20 (28%) datasets were fully accessible.

CONCLUSIONS

Reporting of sex, gender, and socioeconomic status in HF datasets is inconsistent. There is a need to generate datasets that are transparently reported and accessible. Although collecting and reporting demographic attributes is complex and needs to be undertaken with appropriate safeguards, it is also an essential step toward building equitable AI-based health technologies.

Fractured FRAX: Nurses' role in reckoning with racism in international osteoporosis fracture risk calculations

AIM

This paper will examine racism's role in calculating fracture risk via the use of race- and ethnicity-based calculations and offer guidance for nurses to mitigate the risk of further exacerbating race-based inequities in fracture care.

BACKGROUND

Using race adjustments in fracture risk calculation, such as in the FRAX, reflects the history of systemic racism in nursing and medicine, particularly in screening for and treating osteoporosis. Osteoporosis-related fractures, which result in increased costs, disability, and death, are a public health problem.

SOURCES OF EVIDENCE

The critique examines genetic and sociohistorical evidence to illuminate the fallacy that race has biological underpinnings and outline the implications of using race in fracture risk assessment.

DISCUSSION

Race-based risk calculation, including assessing fracture risk, is one of the mechanisms by which the medical and healthcare sectors perpetuate systemic racism.

IMPLICATIONS FOR NURSING PRACTICE AND POLICY

Nurses are called to action to address the contributing role of race-based algorithms, such as the FRAX, to unequal treatment. Nurses should advocate for the removal of race in these clinical decision-making tools and case studies. Additionally, nurses should advocate for their replacement with better tools that do not use race but rather measures of structural racism to calculate risk. Policy guidance should be issued so that race, a sociohistorical tool of categorization to preserve power structures, should no longer be used as an approximation of other more relevant and precise risk factors for fractures or other diseases.

CONCLUSION

Nurses are critical in cultivating and implementing antiracist approaches to remediate health inequities in screening and treatment of osteoporosis and other preventable diseases.

UroARC: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement

OBJECTIVE

To develop a surgical risk calculator for older adults undergoing suprapubic tube (SPT) placement that specifically factors in frailty, a key predictor of surgical risk in this vulnerable and heterogenous population.

METHODS

Medicare MedPAR, Outpatient, and Carrier files for beneficiaries undergoing SPT placement between 2014-2016 were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty, was used to calculate baseline frailty for each beneficiary. Stepwise regression models were used for each variable within the CFI and Charlson Comorbidity Index to determine the variables most highly predictive of postoperative complications. The most highly predictive variables were then combined into parsimonious categories. To ensure the prognostic accuracy for each outcome, calibration curves and tests of model fit, including C-statistics, Brier scores, and Spiegelhalter P-values were calculated.

RESULTS

A total of 26,999 beneficiaries were included. Among these, 39.1% were pre-frail, 36.8% were mildly frail, and 12.3% were moderately to severely frail. Thirteen prognostic variable categories were deemed highly predictive of postoperative complications of interest. All models demonstrated low Brier scores, indicating high model accuracy, and high C-statistic and Spiegelhalter P-values, consistent with excellent model discrimination and calibration, respectively. Excellent model fit was seen on calibration curves for each outcome.

CONCLUSION

UroARC is a novel surgical tool for older adults undergoing SPT placement that specifically factors in frailty. This risk calculator has high accuracy, calibration, and discrimination, and serves as a valuable resource to patients and clinicians for those undergoing consideration for SPT placement.

Incorporating area-level social drivers of health in predictive algorithms using electronic health record data

OBJECTIVES

The inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do. We argue that practitioners should consider the use of social indices and factors-a class of area-level measurements-given their accessibility, transparency, and quality.

RESULTS

We illustrate the process of using such indices in predictive algorithms, which includes the selection of appropriate indices for the outcome, measurement time, and geographic level, in a demonstrative example with the Kidney Failure Risk Equation.

DISCUSSION

Identifying settings where incorporating SDOH may be beneficial and incorporating them rigorously can help validate algorithms and assess generalizability.

Teaching Internal Medicine Residents to Critically Appraise the Role of Race in Pulmonary Function Testing

Introduction

Race-specific equations for spirometry reference values are one example of race-specific algorithms traditionally used in medicine. The American Thoracic Society now recommends use of race-neutral reference equations instead of race-specific equations. However, no published curricula on interpretation of spirometry using race-based compared to race-neutral reference equations exist. We developed a curriculum for internal medicine residents to address this gap and equip providers to interpret spirometry in a race-conscious fashion.

Methods

An internal medicine resident and an attending in pulmonary medicine developed the curriculum and invited other experts to review and edit the material. The internal medicine resident delivered an hour-long, interactive, slide-based, didactic presentation during a weekly, residency-wide videoconference to 45 participants. The presentation included the following components: (1) history of spirometry and race, (2) race-specific equations, (3) race-neutral equations, and (4) clinical implications. The presentation opened with a clinical case and small-group discussions. We conducted pre- and posttest surveys; the posttest survey was designed using the Kirkpatrick model to assess reaction, learning, and anticipated behavioral change. Mean score differences were evaluated for level 2 questions using Cohen's d effect size.

Results

Thirty-eight respondents completed the pretest survey, and 24 completed the posttest survey. Test scores significantly improved after session participation, with Cohen's d ranging from 0.27 to 1.17.

Discussion

This curriculum was successful in engaging participants in critically appraising race-based interpretations of pulmonary function testing. The structure of the curriculum could be repurposed to create didactic content on other examples of race-based clinical algorithms.

Structural Competency: A Faculty Development Workshop Series for Anti-racism in Medical Education

Introduction

In response to accreditation bodies requiring health disparities curricula, medical educators are tasked with incorporating structural competency, the understanding of how social and structural barriers like structural racism impact health, into their teaching. Most have not received training in this area, yet there remains a scarcity of faculty development curricula to address this gap. We describe the creation, implementation, and evaluation of a faculty development workshop series rooted in the framework of structural competency.

Methods

We delivered four 90-minute workshops at an urban academic medical center from March through April of 2021. Workshops were offered to interdisciplinary faculty. We evaluated this workshop series with a pre- and postintervention survey assessing attitudes and confidence, and a postintervention satisfaction survey. Data analysis was conducted using a paired t test.

Results

A total of 206 participants attended at least one workshop within the series, and 20 participants completed both pre- and postintervention surveys. Participants overwhelmingly recommended these workshops to their colleagues and had significant increases in overall attitudes (3.3 vs. 3.6, p = .001) and level of confidence (3.2 vs. 3.9, p < .001) incorporating structural competency.

Discussion

Our application of structural competency to faculty workshops and teaching tools feasibly engages faculty in instruction to incorporate concepts of structural racism and the downstream effects of social determinants of health into clinical teaching. It represents an innovative tool as we seek to enhance clinical teaching to improve care for racially and ethnically minoritized communities.

Online Database of Clinical Algorithms with Race and Ethnicity

Some clinical algorithms incorporate an individual's race, ethnicity, or both as an input variable or predictor in determining diagnoses, prognoses, treatment plans, or risk assessments. Inappropriate use of race and ethnicity in clinical algorithms at the point of care may exacerbate health disparities and promote harmful practices of race-based medicine. We identified 42 risk calculators that use race as a predictor, five laboratory test results with different reference ranges recommended for different races, one therapy recommendation based on race, 15 medications with guidelines for initiation and monitoring based on race, and four medical devices with differential racial performance. Information on these clinical algorithms are freely available at http://www.clinical-algorithms-with-race-and-ethnicity.org. This resource aims to raise awareness about the use of race in clinical algorithms and to track the progress made toward eliminating its inappropriate use. The database will be actively updated to include clinical algorithms based on race that were missed, along with additional characteristics of these algorithms.

Rethinking neglected tropical diseases: A shift towards more inclusive and equitable terminology

Neglected tropical diseases (NTD) refer to a group of 21 diseases that disproportionally affect impoverished communities in low- and middle-income countries (LMIC) [1]. NTD collectively impact 1.7 billion people, which is about 20% of the world's population [1]. Each year, NTD account for more than 200,000 deaths, with millions left disabled and disfigured due to insufficient access to care and affordable treatment, often leading to social exclusion, stigmatization and discrimination. Although the term NTD has successfully directed funding and resources towards these conditions and encouraged global partnerships and high-level policy initiatives, the term may also have unintended negative consequences. In this paper, we aim to explore the term NTD and stimulate a dialogue that re-evaluates its meaning into more inclusive and equitable language.

Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review

PURPOSE

There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digitised screen film mammography, 2D digital mammography and digital breast tomosynthesis) used in the development of AI technologies and present their characteristics, including their transparency of documentation, content, populations included and accessibility.

MATERIALS AND METHODS

MEDLINE and Google Dataset searches identified studies describing AI technology development and referencing breast imaging datasets up to June 2024. The characteristics of each dataset are summarised. In particular, the accompanying documentation was reviewed with a focus on diversity and inclusion of populations represented within each dataset.

RESULTS

254 datasets were referenced in the literature search, 190 were privately held, 36 had barriers which prevented access, and 28 were accessible. Most datasets originated from Europe, East Asia and North America. There was poor reporting of individuals' attributes: 32 (12 %) datasets reported race or ethnicity; 76 (30 %) reported female/male categories with only one dataset explicitly defining whether these categories represented sex or gender attributes.

CONCLUSION

Through this review, we demonstrate gaps in the data landscape for mammography, highlighting poor representation globally. To ensure datasets in breast imaging have maximum utility for researchers, their characteristics should be documented and limitations of datasets, such as their representativeness of populations and settings, should inform scientific efforts to translate data-driven insights into technologies and discoveries.

Mapping the institutionalization of racism in the research about race and traumatic brain injury rehabilitation: implications for Black populations

PURPOSE

Traumatic brain injury (TBI) is a chronic disease process and a public health concern that disproportionately impacts Black populations. While there is an abundance of literature on race and TBI outcomes, there is a lack of scholarship that addresses racism within rehabilitation care, and it remains untheorized. This article aims to illuminate how racism becomes institutionalized in the scientific scholarship that can potentially inform rehabilitation care for persons with TBI and what the implications are, particularly for Black populations.

MATERIAL AND METHODS

Applying Bacchi's What's the Problem Represented to be approach, the writings of critical race theory (CRT) are used to examine the research about race and TBI rehabilitation comparable to CRT in other disciplines, including education and legal scholarship.

RESULTS

A CRT examination illustrates that racism is institutionalized in the research about race and TBI rehabilitation through colourblind ideologies, meritocracy, reinforcement of a deficit perspective, and intersections of race and the property functions of whiteness. A conceptual framework for understanding institutional racism in TBI rehabilitation scholarship is presented.

CONCLUSIONS

The findings from this article speak to the future of TBI rehabilitation research for Black populations, the potential for an anti-racist agenda, and implications for research and practice.

UroARC: A novel surgical risk calculator for older adults undergoing pelvic organ prolapse and stress urinary incontinence surgery

INTRODUCTION

Surgeries for pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are commonly performed in older adults, many of whom are also frail. A surgical risk calculator for older adults undergoing POP/SUI surgeries that incorporates frailty, a factor known to increase the risk of surgical complications, would be helpful for preoperative counseling but currently does not exist.

MATERIALS AND METHODS

Medicare Carrier, Outpatient, and MedPAR files were examined for beneficiaries undergoing POP and SUI surgery between 2014 and 2016. A total of 15 POP/SUI categories were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty in Medicare data, and Charlson Comorbidity Index were deconstructed into their individual variables, and individual variables were entered into stepwise logistic regression models to determine which variables were most highly predictive of 30-day complications and 1-year mortality. To verify the prognostic accuracy for each model for surgical complications of interest, calibration curves and tests of model fit, including C-statistic, Brier scores, and Spiegelhalter p values, were determined.

RESULTS

In total, 108 479 beneficiaries were included. Among these, 4.7% had CFI scores consistent with mild to severe frailty (CFI≥0.25). A total of 13 prognostic variable categories were found to be most highly predictive of postoperative complications. Calibration curves for each outcome of interest showed models were well-fit. Most models demonstrated high c-statistic values (≥0.7) and high Spiegelhalter p values (≥0.9), indicating good model calibration and excellent discrimination, and low Brier scores (<0.02), indicating high model accuracy.

CONCLUSIONS

Urologic surgery for older Adults Risk Calculator serves as a novel surgical risk calculator that is readily accessible to both patients and clinicians that specifically factors in components of frailty. Furthermore, this calculator accounts for the heterogeneity of an aging population and can assist in individualized surgical decision-making for these common procedures.

A Framework for Considering the Value of Race and Ethnicity in Estimating Disease Risk

BACKGROUND

Accounting for race and ethnicity in estimating disease risk may improve the accuracy of predictions but may also encourage a racialized view of medicine.

OBJECTIVE

To present a decision analytic framework for considering the potential benefits of race-aware over race-unaware risk predictions, using cardiovascular disease, breast cancer, and lung cancer as case studies.

DESIGN

Cross-sectional study.

SETTING

NHANES (National Health and Nutrition Examination Survey), 2011 to 2018, and NLST (National Lung Screening Trial), 2002 to 2004.

PATIENTS

U.S. adults.

MEASUREMENTS

Starting with risk predictions from clinically recommended race-aware models, the researchers generated race-unaware predictions via statistical marginalization. They then estimated the utility gains of the race-aware over the race-unaware models, based on a simple utility function that assumes constant costs of screening and constant benefits of disease detection.

RESULTS

The race-unaware predictions were substantially miscalibrated across racial and ethnic groups compared with the race-aware predictions as the benchmark. However, the clinical net benefit at the population level of race-aware predictions over race-unaware predictions was smaller than expected. This result stems from 2 empirical patterns: First, across all 3 diseases, 95% or more of individuals would receive the same decision regardless of whether race and ethnicity are included in risk models; second, for those who receive different decisions, the net benefit of screening or treatment is relatively small because these patients have disease risks close to the decision threshold (that is, the theoretical "point of indifference"). When used to inform rationing, race-aware models may have a more substantial net benefit.

LIMITATIONS

For illustrative purposes, the race-aware models were assumed to yield accurate estimates of risk given the input variables. The researchers used a simplified approach to generate race-unaware risk predictions from the race-aware models and a simple utility function to compare models.

CONCLUSION

The analysis highlights the importance of foregrounding changes in decisions and utility when evaluating the potential benefit of using race and ethnicity to estimate disease risk.

PRIMARY FUNDING SOURCE

The Greenwall Foundation.

Impact of race-based calculations of eGFR on the management of muscle invasive bladder cancer

PURPOSE

The estimated glomerular filtration rate (eGFR) has historically been calculated with a race-coefficient multiplier (RCM); however, the RCM has been broadly criticized as inaccurate and a potential contributor to exacerbating disparities. We evaluated the impact of the RCM on eGFR and examined the 30-day post-cystectomy complications in a muscle-invasive bladder cancer cohort.

MATERIALS AND METHODS

We retrospectively analyzed patients diagnosed with MIBC who underwent cystectomy in the ACS NSQIP database from 2006 to 2020 using CPT and ICD codes. The eGFR was computed using the Modification of Diet in Renal Diseases equation which has RCM = 1.212 for black patients. Using the race data field, patients were categorized into Black and non-Black. The eGFR cut-off of 60 mL/min/1.73 m2 was chosen for patient stratification because it represents a key clinical threshold in the classification of chronic kidney disease and influences various care decisions such as chemotherapy choice. Subsequently, we examined the 30-day post-cystectomy cardiovascular and pulmonary (CV&P) complications in these patients stratified by their eGFR using descriptive statistics and a multivariable logistic regression model.

RESULTS

The application of the RCM to estimate eGFR in the Black cohort increased the mean eGFR from 57.8 to 70.0 ml/min/1.73 m2 (P = 0.001) which led to a 17.3% (45.6% vs 62.9%, P = 0.001) increase in the proportion of Black patients with eGFR≥60 ml/min/1.73 m2. The rate of CV&P complications post-cystectomy among this group of 17.3% of patients in the Black cohort was 7.6% compared to a 4.3% complication rate among a non-Black cohort matched for similar eGFR for whom RCM was not applied (P = 0.06). Black patients in this RCM-dependent category of eGFR≥60 mL/min/1.73 m2 had higher adjusted odds of developing 30-day post cystectomy CV&P complications compared to eGFR-matched non-Black patients (OR = 2.2, 95% CI = 1.13-4.31, P = 0.02).

CONCLUSION

In this study, we found that inclusion of RCM in the eGFR significantly increases the proportion of Black patients with eGFR≥60. This RCM might also be associated with higher post-cystectomy CV&P complications; therefore, future studies are needed to evaluate the implications of race-based algorithms on outcomes.

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 3 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.

The Racial Disparities in Maternal Mortality and Impact of Structural Racism and Implicit Racial Bias on Pregnant Black Women: A Review of the Literature

BACKGROUND

The maternal mortality rate (MMR) in the United States (USA) continues to increase despite medical advances and is exacerbated by stark racial disparities. Black women are disproportionately affected and are three times more likely to experience a pregnancy-related death (PRD) compared to Non-Hispanic White (NHW) women.

METHODS

A literature review was conducted to examine the racial disparities in the United States' MMR, specifically among pregnant Black women. PubMed and key organizations (World Health Organization, Center for Disease Control and Prevention, American College of Obstetricians and Gynecologists, Alliance for Innovation on Maternal Health, Association of American Medical Colleges, U.S. Census Bureau, and U.S. Congress) were searched for publications after 2014.

RESULT

Forty-two articles were reviewed to identify the role of structural racism, implicit biases, lack of cultural competence, and disparity education on pregnant Black women. This review highlights that maternal health disparities for Black women are further impacted by both structural racism and racial implicit biases. Cultural competence and educational courses targeting racial disparities among maternal healthcare providers (MHCP) are essential for the reduction of PRDs and pregnancy-related complications (PRC) among this target population. Additionally, quality and proper continuity of care require an increased awareness surrounding the risk of cardiovascular diseases for pregnant Black women.

CONCLUSIONS

The surging MMR for Black women is a public health crisis that requires a multi-tiered approach. Interventions should be implemented at the provider and healthcare institution level to dismantle implicit biases and structural racism. Improving patient-provider relationships through increased cultural competency and disparity education will increase patient engagement with the maternal healthcare (MHC) system.

The relationship between race and emergency medical services resuscitation intensity for those in refractory-arrest

Background

Previous studies have reported race-based health disparities in North America. It is unknown if emergency medical service (EMS) treatment of out-of-hospital cardiac arrest (OHCA) varies based on race. We sought to compare markers of resuscitation intensity among different racial groups.

Methods

Using data of adult EMS-treated OHCAs from the Trial of Continuous or Interrupted Chest Compressions During CPR, we analyzed data from participants for whom on-scene return of spontaneous circulation (ROSC) was not achieved. We fit multivariate regression models using a generalized estimating equation, to estimate the association between patient race (White vs. Black vs. "Other") and the following markers for resuscitation intensity: (1) resuscitation attempt duration; (2) intra-arrest transport; (3) number of epinephrine doses; (4) EMS arrival-to-CPR interval, and (5) 9-1-1 to first shock.

Results

From our study cohort of 5370 cases, the median age was 65 years old (IQR: 53-78), 2077 (39 %) were women, 2121 (39 %) were Black, 596 (11 %) were "Other race", 2653 (49 %) were White, and 4715 (88 %) occurred in a private location. With reference to White race, Black race was associated with a longer resuscitation attempt duration and a lower number of epinephrine doses; Black and "Other" race were both associated with a lower odds of intra-arrest transport.

Conclusion

We identified race-based differences in EMS resuscitation intensity for OHCA within a North American cohort, although 40% of race data was missing from this dataset. Future research investigating race-based differences in OHCA management may be warranted.

How do medical students deal with the topic of racism? A qualitative analysis of group discussions in Germany

BACKGROUND

Anti-racism is part of the medical professional ethos. Nevertheless, racism pervades medicine on individual, institutional, and structural levels. The concept of habitus helps to understand deficiencies in enacting anti-racism in practice. We use a habitus-based framework to analyse how medical students in Germany deal with the topic of racism. The research questions are: What are medical students' understandings of racism? How do they deal with the topic in discussions? What difficulties do they face in such discussions?

METHODS

In a qualitative-explorative research design, we conducted six online group discussions with 32 medical students from medical schools all over Germany. Data analysis combined qualitative methods from thematic qualitative content analysis and the documentary method.

RESULTS

We identified five typical ways of dealing with the topic of racism in discussions. The first one ('scientistic') orientates action towards the idea of medicine as an objective science, justifies the use of racial categories as scientific, and defines racism based on intention. The second ('pragmatic') orientates action towards tacit rules of clinical practice, justifies the use of racialised categories as practical and defines racism as an interpersonal problem. The third ('subjectivist') lacks a clear orientation of action for dealing with the topic of racism and instead displays uncertainty and subjectivism in understanding racialised categorisations as well as racism. The fourth ('interculturalist') orientates action towards an ideal of intercultural exchange, understands racialised categorisations as representing cultural differences and interprets racism as prejudice against cultures. The fifth ('critical') orientates action towards sociological scholarship, understands racialised categorisations as social constructs and views racism as a structural problem.

CONCLUSION

The results presented help to understand preconditions of enacting anti-racism in medicine and point to difficulties and learning needs. The heterogenous ways of dealing with the topic require a differentiated approach in medical education.

Health Care Perceptions and a Concierge-Based Transplant Evaluation for Patients With Kidney Disease

Importance

The kidney transplant (KT) evaluation process is particularly time consuming and burdensome for Black patients, who report more discrimination, racism, and mistrust in health care than White patients. Whether alleviating patient burden in the KT evaluation process may improve perceptions of health care and enhance patients' experiences is important to understand.

Objective

To investigate whether Black and White participants would experience improvements in perceptions of health care after undergoing a streamlined, concierge-based approach to KT evaluation.

Design, Setting, and Participants

This prospective cohort study from a single urban transplant center included Black and White English-speaking adults who were referred for KT and deemed eligible to proceed with the KT evaluation process. The patients responded to baseline and follow-up questionnaires. The study was conducted from May 2015 to June 2018. Questionnaires were collected before KT evaluation initiation (baseline) and after KT evaluation completion (follow-up). Data were analyzed from October 2022 to January 2024.

Exposure

Data were stratified by race (Black compared with White) and time (baseline compared with follow-up).

Main Outcomes and Measures

The main outcomes were experiences of discrimination in health care, perceived racism in health care, medical mistrust of health care systems, and trust in physician. Repeated-measures regression was used to assess race, time, and the race-by-time interaction as factors associated with each outcome.

Results

The study included 820 participants (mean [SD] age, 56.50 [12.93] years; 514 [63%] male), of whom 205 (25%) were Black and 615 (75%) were White. At baseline and follow-up, Black participants reported higher discrimination (119 [58%]; χ21 = 121.89; P < .001 and 77 [38%]; χ21 = 96.09; P < .001, respectively), racism (mean [SD], 2.73 [0.91]; t290.46 = 7.77; P < .001 and mean [SD], 2.63 [0.85]; t296.90 = 7.52; P < .001, respectively), and mistrust (mean [SD], 3.32 [0.68]; t816.00 = 7.29; P < .001 and mean [SD], 3.18 [0.71]; t805.00 = 6.43; P < .001, respectively) scores but lower trust in physician scores (mean [SD], 3.93 [0.65]; t818.00 = -2.01; P = .04 and mean [SD], 3.78 [0.65]; t811.00 = -5.42; P < .001, respectively) compared with White participants. All participants experienced statistically significant reductions in discrimination (Black participants: odds ratio, 0.27 [95% CI, 0.16-0.45]; P < .001; White participants: odds ratio, 0.37 [95% CI, 0.25-0.55]; P < .001) and medical mistrust in health care (Black participants: β [SE], -0.16 [0.05]; P < .001; White participants: β [SE], -0.09 [0.03]; P < .001), and Black participants reported lower perceived racism at follow-up (β [SE], -0.11 [0.05]; P = .04). There was a statistically significant race-by-time interaction outcome in which Black participants' trust in physicians was significantly lower at follow-up, but White participants reported no change.

Conclusions and Relevance

The findings of this cohort study of patients who underwent a streamlined, concierge-based KT evaluation process suggest that a streamlined approach to clinic-level procedures may improve patients' perceptions of the health care system but may not improve their trust in physicians. Future research should determine whether these factors are associated with KT outcome, type of KT received, and time to KT.

Through the Lens of Societal Norms and Experiences: Students' Conceptualization of Patient Case Data When Diversity is Apparent

OBJECTIVE

There are increasing calls to improve the representation of diversity within case-based learning materials, yet it is unclear how students interpret the inclusion of diversity data when synthesizing case information. The objective of this study was to determine factors that influence students' interpretation of written case data for visualization of a patient case.

METHODS

This was a qualitative study using interviews. Entry-to-practice pharmacy students from Dalhousie University in Canada were recruited to review 6 cases, each with varying representations of diversity (eg, race, sexual orientation, gender, relationship status, disability, or none). Students were prompted to state how they visualized the case patient and what factors influenced their perceptions. Interviews were audio-recorded and transcribed verbatim. A reflexive thematic analysis was conducted to interpret themes.

RESULTS

Interviews were conducted with 18 students. Students relied on 5 factors when interpreting case data in the presence of diversity. In addition to the case data itself, these included personal experience (relating to themselves or personal relationships), professional experience (through work or school), population stereotypes, and perceived societal norms.

CONCLUSION

This study found that students rely on their personal and professional experiences, perceptions, and social conditioning when interpreting the presence of diversity within learning materials. Findings support the notion that educators should deliberately and conscientiously expose students to a broad representation of diverse populations to increase students' knowledge and understanding of populations, and to create intentional time and space to challenge existing stereotypes that contribute to the inequities in health care.

From Simulation to Bedside: The Journey to Provide Equitable Patient Care

Simulation-based education (SBE) has revolutionized health care training by enhancing skills and addressing systemic issues. This article explores how SBE can bridge the gap between recognizing health care disparities and implementing actionable steps to address them. The immersive nature of SBE, combined with structured debriefing, sets the foundation for a "brave space" that fosters critical discussions on crucial topics, such as health equity. SBE enables health care professionals to develop cultural humility, confront biases, and practice upstander skills. This approach not only addresses hidden curricula but also integrates equity into clinical practice through practical scenarios and community engagement. Despite the potential benefits, challenges such as unintentional harm and the need for thoughtful implementation persist. To maximize effectiveness, SBE initiatives must be aligned with organizational goals and include interdisciplinary team commitment. Ongoing research and robust evaluation are essential to measure SBE's impact on health equity and patient outcomes. [Pediatr Ann. 2024;53(11):e414-e419.].

Race Dialogues and Potential Application in Clinical Environments: A Scoping Review

BACKGROUND

Race dialogues, conversations about race and racism among individuals holding different racial identities, have been proposed as one component of addressing racism in medicine and improving the experience of racially minoritized patients. Drawing on work from several fields, we aimed to assess the scope of the literature on race dialogues and to describe potential benefits, best practices, and challenges of conducting such dialogues. Ultimately, our goal was to explore the potential role of race dialogues in medical education and clinical practice.

METHODS

Our scoping review included articles published prior to June 2, 2022, in the biomedicine, psychology, nursing and allied health, and education literatures. Ultimately, 54 articles were included in analysis, all of which pertained to conversations about race occurring between adults possessing different racial identities. We engaged in an interactive group process to identify key takeaways from each article and synthesize cross-cutting themes.

RESULTS

Emergent themes reflected the processes of preparing, leading, and following up race dialogues. Preparing required significant personal introspection, logistical organization, and intentional framing of the conversation. Leading safe and successful race dialogues necessitated trauma-informed practices, addressing microaggressions as they arose, welcoming participation and emotions, and centering the experience of individuals with minoritized identities. Longitudinal experiences and efforts to evaluate the quality of race dialogues were crucial to ensuring meaningful impact.

DISCUSSION

Supporting race dialogues within medicine has the potential to promote a more inclusive and justice-oriented workforce, strengthen relationships amongst colleagues, and improve care for patients with racially minoritized identities. Potential levers for supporting race dialogues include high-quality racial justice curricula at every level of medical education and valuation of racial consciousness in admissions and hiring processes. All efforts to support race dialogues must center and uplift those with racially minoritized identities.

Race, ethnicity, and considerations for data collection and analysis in research studies

Research studies involving human subjects require collection of and reporting on demographic data related to race and ethnicity. However, existing practices lack standardized guidelines, leading to misrepresentation and biased inferences and conclusions for underrepresented populations in research studies. For instance, sometimes there is a misconception that self-reported racial or ethnic identity may be treated as a biological variable with underlying genetic implications, overlooking its role as a social construct reflecting lived experiences of specific populations. In this manuscript, we use the We All Count data equity framework, which organizes data projects across seven stages: Funding, Motivation, Project Design, Data Collection, Analysis, Reporting, and Communication. Focusing on data collection and analysis, we use examples - both real and hypothetical - to review common practice and provide critiques and alternative recommendations. Through these examples and recommendations, we hope to provide the reader with some ideas and a starting point as they consider embedding a lens of justice, equity, diversity, and inclusivity from research conception to dissemination of findings.

Race as a Risk Marker, Not a Risk Factor: Revising Race-Based Algorithms to Protect Racially Oppressed Patients

Emerging consensus in the medical and public health spheres encourages removing race and ethnicity from algorithms used in clinical decision-making. Although clinical algorithms remain appealing given their promise to lighten the cognitive load of medical practice and save time for providers, they risk exacerbating existing health disparities. Race is a strong risk marker of health outcomes, yet it is not a risk factor. The use of race as a factor in medical algorithms suggests that the effect of race is intrinsic to the patient or that its effects can be distinct or separated from other social and environmental variables. By contrast, incisive public health analysis coupled with a race-conscious perspective recognizes that race serves as a marker of countless other dynamic variables and that structural racism, rather than race, compromises the health of racially oppressed individuals. This perspective offers a historical and theoretical context for the current debates regarding the use of race in clinical algorithms, clinical and epidemiologic perspectives on "risk," and future directions for research and policy interventions that combat color-evasive racism and follow the principles of race-conscious medicine.

Addressing Health Disparities in Hypertension: A Comprehensive Medical Elective and Survey Study Among Medical Students and Professionals

Introduction

Medical education utilizes standard clinical practice and recommends clinical algorithms to inform trainee curricula. The use of race and ethnicity as a medical screening tool impacts medical outcomes by associating race with genetics without considering that race incorporates social, economic, and cultural variables that influence outcomes.

Methods

To evaluate underlying factors contributing to differences in hypertension prevalence, control, and treatment recommendations across race/ethnicities, a 2-week elective course was developed for third- and fourth-year medical students. In this elective course, students performed self-directed literature-based research on hypertension health disparities. We then developed three videos that addressed the racial/ethnic impact on hypertension prevalence and control and incorporated the students' research findings. The videos were presented at a lunch-and-learn session, open to medical students and health professionals, that was focused on healthcare inequities in hypertension. Pre- and post-session survey data was collected to assess how the discussion changed participant knowledge and impressions of the role race plays in hypertension prevalence, control, and treatment.

Results

Survey results denoted that 100% of lunch-and-learn participants increased their understanding of the impact of health inequities on hypertension. Overall, there were significant differences in knowledge gained and understanding of health disparities that influence hypertension treatment across participants from all genders and racial or ethnic groups. Notably, pre-session survey results indicated that participants tended to agree that treatment guidelines incorporating race improve equity in the treatment of hypertension whereas post-session results showed that participants were less likely to agree with this assertion.

Conclusions

Developing educational opportunities to discuss health inequities can influence perceptions of patient care.

Child Opportunity Index and Pediatric Extracorporeal Membrane Oxygenation Outcomes; the Role of Diagnostic Category

OBJECTIVES

To study the impact of social determinants of health (SDoH) on pediatric extracorporeal membrane oxygenation (ECMO) outcomes.

DESIGN, SETTING, AND PATIENTS

Retrospective study of children (< 18 yr) supported on ECMO (October 1, 2015 to March 1, 2021) using Pediatric Health Information System (44 U.S. children's hospitals). Patients were divided into five diagnostic categories: neonatal cardiac, pediatric cardiac, neonatal respiratory, pediatric respiratory, and sepsis. SDoH included the Child Opportunity Index (COI; higher indicates social advantage), race, ethnicity, payer, and U.S. region. Children without COI were excluded. Diagnostic category-specific clinical variables related to baseline health and illness severity were collected.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Children supported on ECMO experienced a 33% in-hospital mortality (2863/8710). Overall, children with lower COI, "other" race, Hispanic ethnicity, public insurance and from South or West regions had greater mortality. Associations between SDoH and ECMO outcomes differed between diagnostic cohorts. Bivariate analyses found that only pediatric cardiac patients had an association between COI or race and mortality. Multivariable logistic regression analyses examined relationships between SDoH, clinical variables and mortality within diagnostic categories. Pediatric cardiac patients had 5% increased odds of death (95% CI, 1.01-1.09) for every 10-point decrement in COI, while Hispanic ethnicity was associated with higher survival (adjusted odds ratio [aOR] 0.72 [0.57-0.89]). Children with heart disease from the highest COI quintile had less cardiac-surgical complexity and earlier cannulation. Independent associations with mortality were observed in sepsis for Black race (aOR 1.62 [1.06-2.47]) and other payer in pediatric respiratory patients (aOR 1.94 [1.23-3.06]).

CONCLUSIONS

SDoH are statistically associated with pediatric ECMO outcomes; however, associations differ between diagnostic categories. Influence of COI was observed only in cardiac patients while payer, race, and ethnicity results varied. Further research should investigate differences between diagnostic cohorts and age groups to understand drivers of inequitable outcomes.

Racial Disparities in Sports Cardiology: A Review

Importance

Racial disparities in cardiovascular health, including sudden cardiac death (SCD), exist among both the general and athlete populations. Among competitive athletes, disparities in health outcomes potentially influenced by social determinants of health (SDOH) and structural racism remain inadequately understood. This narrative review centers on race in sports cardiology, addressing racial disparities in SCD risk, false-positive cardiac screening rates among athletes, and the prevalence of left ventricular hypertrophy, and encourages a reexamination of race-based practices in sports cardiology, such as the interpretation of screening 12-lead electrocardiogram findings.

Observations

Drawing from an array of sources, including epidemiological data and broader medical literature, this narrative review discusses racial disparities in sports cardiology and calls for a paradigm shift in approach that encompasses 3 key principles: race-conscious awareness, clinical inclusivity, and research-driven refinement of clinical practice. These proposed principles call for a shift away from race-based assumptions towards individualized, health-focused care in sports cardiology. This shift would include fostering awareness of sociopolitical constructs, diversifying the medical team workforce, and conducting diverse, evidence-based research to better understand disparities and address inequities in sports cardiology care.

Conclusions and Relevance

In sports cardiology, inadequate consideration of the impact of structural racism and SDOH on racial disparities in health outcomes among athletes has resulted in potential biases in current normative standards and in the clinical approach to the cardiovascular care of athletes. An evidence-based approach to successfully address disparities requires pivoting from outdated race-based practices to a race-conscious framework to better understand and improve health care outcomes for diverse athletic populations.

The Construction and Meaning of Race Within Hypertension Guidelines: A Systematic Scoping Review

BACKGROUND

Professional society guidelines are evidence-based recommendations intended to promote standardized care and improve health outcomes. Amid increased recognition of the role racism plays in shaping inequitable healthcare delivery, many researchers and practitioners have critiqued existing guidelines, particularly those that include race-based recommendations. Critiques highlight how racism influences the evidence that guidelines are based on and its interpretation. However, few have used a systematic methodology to examine race-based recommendations. This review examines hypertension guidelines, a condition affecting nearly half of all adults in the United States (US), to understand how guidelines reference and develop recommendations related to race.

METHODS

A systematic scoping review of all professional guidelines on the management of essential hypertension published between 1977 and 2022 to examine the use and meaning of race categories.

RESULTS

Of the 37 guidelines that met the inclusion criteria, we identified a total of 990 mentions of race categories. Black and African/African American were the predominant race categories referred to in guidelines (n = 409). Guideline authors used race in five key domains: describing the prevalence or etiology of hypertension; characterizing prior hypertension studies; describing hypertension interventions; social risk and social determinants of health; the complexity of race. Guideline authors largely used race categories as biological rather than social constructions. None of the guidelines discussed racism and the role it plays in perpetuating hypertension inequities.

DISCUSSION

Hypertension guidelines largely refer to race as a distinct and natural category rather than confront the longstanding history of racism within and beyond the medical system. Normalizing race as a biological rather than social construct fails to address racism as a key determinant driving inequities in cardiovascular health. These changes are necessary to produce meaningful structural solutions that advance equity in hypertension education, research, and care delivery.

Race-Conscious Research Using Extracorporeal Life Support Organization Registry Data: A Narrative Review

Race-conscious research identifies health disparities with 1) rigorous and responsible data collection, 2) intentionality and considered analyses, and 3) interpretation of results that advance health equity. Individual registries must overcome specific challenges to promote race-conscious research, and this paper describes ways to achieve this with a focus on the international Extracorporeal Life Support Organization (ELSO) registry. This article reviews ELSO registry publications that studied race with outcomes to consider whether research outputs align with race-conscious concepts and describe the direction of associations reported. Studies were identified via secondary analysis of a comprehensive scoping review on ECMO disparities. Of 32 multicenter publications, two (6%) studied race as the primary objective. Statistical analyses, confounder adjustment, and inclusive, antibiased language were inconsistently used. Only two (6%) papers explicitly discussed mechanistic drivers of inequity such as structural racism, and five (16%) discussed race variable limitations or acknowledged unmeasured confounders. Extracorporeal Life Support Organization registry publications demonstrated more adverse ECMO outcomes for underrepresented/minoritized populations than non-ELSO studies. With the objective to promote race-conscious ELSO registry research outputs, we provide a comprehensive understanding of race variable limitations, suggest reasoned retrospective analytic approaches, offer ways to interpret results that advance health equity, and recommend practice modifications for data collection.

Equity in Pediatric Hospital-Based Safety and Quality Improvement

There are well-documented inequities in the quality of care and health outcomes of minoritized youth. Patient safety and quality improvement (QI) work with an equity focus has been identified as an important strategy to remedy these existing inequities. In this article, we will present evidence of inequities in pediatric hospital-based care, describe root causes with a focus on structural racism, highlight existing frameworks for applying equity principles to patient safety and QI, and provide best practices and recommendations on evaluating patient safety and QI data towards advancing equity in pediatric hospital-based care.

Race-Conscious Learning and Sociocultural Competence in an Academic Surgery Program: Diversity, Equity, and All-Inclusion Program

INTRODUCTION

Race-based associations in medicine are often taught and learned early in medical education. Students and residents enter training with implicit and explicit biases from their educational environments, further propagating biases in their practice of medicine. Health disparities described out of context can lead trainees to develop harmful stereotypes. Surgery leadership created a model to implement educational opportunities, resources, and outcomes in an academic Department of Surgery.

METHODS

An ad hoc committee of surgical faculty, residents, and medical students was assembled. Educational goals and objectives were established via Diversity, Equity & Inclusion (DEI) committee: 1) incorporate race-conscious awareness and learning into the academic surgery curriculum for residents and medical students, 2) cooperatively learn about race in clinical and surgical decision-making, 3) incorporate learning about social determinants of health that lead to racial and ethnic inequities, and 4) develop tailored learning in order to recognize and lessen health inequities.

PHASE I

DEI Committee formed of surgery faculty, residents, medical students, and support staff. Activities of the committee, goal development, a DEI mission statement, training, and education overview were formulated by committee members.

PHASE II

A strengths, weaknesses, opportunities, and threats analysis was created for assessment of diversity and inclusion, and race-conscious learning in the surgery clerkship and residency curriculum. Phase III: Baseline assessment to: 1) understand opinions on DEI in the Department of Surgery, 2) assess current representation within the department workforce, and 3) correlate workforce to the make-up of patient population served. Development and restructuring of the surgery education curriculum for medical students and residency created jointly with the Racism and Bias Task Force.

RESULTS

Educational programs have been implemented and delivered for: 1) appropriate inclusion of race-conscious learning such as image diversity, as well as race-based association, 2) social determinants of health in the care of patients, 3) racial disparities in surgical outcomes, 4) introduction of concepts on implicit bias, 5) opportunities for health equity rounds, and 6) inclusion in committees and leadership positions.

CONCLUSIONS

Awareness of clinical faculty and learners to race-conscious and antibias care is paramount to recognizing and addressing biases. Knowledge of sociocultural context may allow learners to develop a socioculturally sensitive approach for patient education, and to more broadly measure surgical outcomes. Race-conscious education should be implemented into teaching curriculum as well as professional development in attempts to close the gap in health-care equity.

What Does It Mean to Be White in Veterinary Medicine?

Race is a pseudo-scientific system that was invented to sort people by skin color into different categories. Race has no biologic basis, meaning the phenotype of skin color cannot accurately separate humans into distinct categories. Contemporary Western veterinary medicine was founded by white European men to treat livestock and working horses; 150+ years later, the majority of veterinary graduates are white women working on domesticated family pets. The history of veterinary medicine informs our current reality.

Disparities in Guideline Adherence for Febrile Infants in a National Quality Improvement Project

BACKGROUND

Interventions aimed to standardize care may impact racial and ethnic disparities. We evaluated the association of race and ethnicity with adherence to recommendations from the American Academy of Pediatrics' clinical practice guideline for febrile infants after a quality improvement (QI) intervention.

METHODS

We conducted a cross-sectional study of infants aged 8 to 60 days enrolled in a QI collaborative of 99 hospitals. Data were collected across 2 periods: baseline (November 2020-October 2021) and intervention (November 2021-October 2022). We assessed guideline-concordance through adherence to project measures by infant race and ethnicity using proportion differences compared with the overall proportion.

RESULTS

Our study included 16 961 infants. At baseline, there were no differences in primary measures. During the intervention period, a higher proportion of non-Hispanic white infants had appropriate inflammatory markers obtained (2% difference in proportions [95% confidence interval (CI) 0.7 to 3.3]) and documentation of follow-up from the emergency department (2.5%, 95% CI 0.3 to 4.8). A lower proportion of non-Hispanic Black infants (-12.5%, 95% CI -23.1 to -1.9) and Hispanic/Latino infants (-6.9%, 95% CI -13.8 to -0.03) had documented shared decision-making for obtaining cerebrospinal fluid. A lower proportion of Hispanic/Latino infants had appropriate inflammatory markers obtained (-2.3%, 95% CI -4.0 to -0.6) and appropriate follow-up from the emergency department (-3.6%, 95% CI -6.4 to -0.8).

CONCLUSIONS

After an intervention designed to standardize care, disparities in quality metrics emerged. Future guideline implementation should integrate best practices for equity-focused QI to ensure equitable delivery of evidence-based care.

Development of the UroARC Surgical Calculator: A Novel Risk Calculator for Older Adults Undergoing Surgery for Bladder Outlet Obstruction

PURPOSE

Bladder outlet obstruction (BOO) is common in older adults. Many older adults who pursue surgery have additional vulnerabilities affecting surgical risk, including frailty. A clinical tool that builds on frailty to predict surgical outcomes for the spectrum of BOO procedures would be helpful to aid in surgical decision-making but does not currently exist.

MATERIALS AND METHODS

Medicare beneficiaries undergoing BOO surgery from 2014 to 2016 were identified and analyzed using the Medicare MedPAR, Outpatient, and Carrier files. Eight different BOO surgery categories were created. Baseline frailty was calculated for each beneficiary using the Claims-Based Frailty Index (CFI). All 93 variables in the CFI and the 17 variables in the Charlson Comorbidity Index were individually entered into stepwise logistic regression models to determine variables most highly predictive of complications. Similar and duplicative variables were combined into categories. Calibration curves and tests of model fit, including C statistics, Brier scores, and Spiegelhalter P values, were calculated to ensure the prognostic accuracy for postoperative complications.

RESULTS

In total, 212,543 beneficiaries were identified. Approximately 42.5% were prefrail (0.15 ≤ CFI < 0.25), 8.7% were mildly frail (0.25 ≤ CFI < 0.35), and 1.2% were moderately-to-severely frail (CFI ≥0.35). Using stepwise logistic regression, 13 distinct prognostic variable categories were identified as the most reliable predictors of postoperative outcomes. Most models demonstrated excellent model discrimination and calibration with high C statistic and Spiegelhalter P values, respectively, and high accuracy with low Brier scores. Calibration curves for each outcome demonstrated excellent model fit.

CONCLUSIONS

This novel risk assessment tool may help guide surgical prognostication among this vulnerable population.