Inclusion Of Race Research Articles

A case for race and space in auto ownership modeling: A Los Angeles County study

Auto ownership behavior is driven by complex relationships that can vary dramatically across different traveler groups and communities. Differences in auto ownership among racial groups have been of particular interest, given ongoing efforts to advance equity in transportation outcomes. There are a number of studies documenting racial disparities in auto ownership associated with racial and ethnic residential clustering, termed “automobile mismatch.” Yet, these differences in auto ownership behavior by race and residential location are virtually never considered in models of travel behavior, despite calls for the consideration for race in transportation planning and decision making. This study aims to bridge the gap between understandings of the connections between race and space and transportation outcomes, using Los Angeles County as a case study. A series of auto ownership model specifications are used to investigate statistical connections between the racial and ethnic categories of residents, and neighborhoods, revealing systematic variations across racial and spatial dimensions. The composite model, which includes racial and spatial indicators, outperforms the base model, suggesting that the inclusion of race and space explains significantly more information on variations in auto ownership and provides a superior fit to the data. Our findings also suggest that the exclusion of racial and spatial indicators may lead to overestimation of certain effects, and may completely misrepresent the importance of certain household, individual-level, and built environment effects in explaining auto ownership preferences. Given the increasing attention to equity and representation in transportation outcomes, models that exclude considerations for race and space may be poorly positioned to support meaningful transportation equity analyses.

Abstract 13464: Considering Race and Ethnicity in Cardiovascular Risk Assessment

Background: Cardiovascular disease (CVD) remains one of the leading causes of mortality in American adults, with disparities among racial and ethnic groups due to differential healthcare access and systemic racism. To better characterize the validity and generalizability of the AHA/ACC’s 2019 guidelines for primary prevention of CVD and 2018 special report on the use of the risk assessment tool for ASCVD, we examined the racial and ethnic composition of the study populations used in the underlying cited works. Methods: We reviewed all the references cited in the guidelines for our primary outcome, inclusion of race and ethnicity information. In addition, data were collected on study design, location (international, US, or both), reported sex/gender of participants, and total participants. We further assessed discussion of race and ethnicity within the articles as: cursory (1-2 sentences), moderate (3-5 sentences), or extensive (>5 sentences). Finally, we pooled the racial distributions for all study populations and compared it to national rates in the US population of 76% White, 14% Black, 6 % Asian, 19% Hispanic. Results: Of the 58 cited articles, 12 (20.6%) reported racial and/or ethnic backgrounds of participants. Of those 12 articles, 7 (12% of total) included cursory discussions of race and ethnicity, while the remaining 5 included no such discussion. Only 3 (5%) reported both race and sex distributions, none of which allowed calculation of race by sex. The pooled racial averages were 20% Black, 1% Asian and 9% other, with 11% identifying ethnicity as Hispanic. Conclusion: The majority of the guideline source data provides no information regarding the racial and ethnic backgrounds of study participants. Studies that do address race and ethnicity show an overrepresentation of white individuals compared to the general US population and provide no data specifically addressing the risk to people who are neither white nor male. This leads to potential underestimation of risks and imprecise risk calculations for non-white populations due to small sample sizes in the original studies. It is crucial to include diverse participant groups in studies at population rates to ensure precise risk calculations, improve CVD prevention, and reduce mortality rates.

Utilizing a Machine Learning Approach to Define Racial Difference in Multi-Systemic Impact of Monoclonal Protein in Patient with Multiple Myeloma

Multiple myeloma (MM) is the second common hematologic malignancy characterized by the clonal proliferation of plasma cells. While the clinical outcomes of MM have been improved significantly, there remains an intriguing observation regarding the potential influence of race on disease biology and treatment response. In MM, the malignant plasma cell clone not only disrupts the local bone marrow microenvironment but also exerts a systemic impact on various physiological processes result in significant derangements in immune function, hematopoiesis, renal function, bone metabolism, and electrolyte homeostasis. Understanding the extent of this systemic impact and its potential variation between racial groups is crucial for optimizing patient care and treatment strategies. Here, we hypothesized that the systemic impact of the plasma cell clone does not differ significantly between African American and Caucasian patients. We employed a machine learning approach to capture the full spectrum of systemic effects comprehensively. Our novel model was trained to predict the magnitude of the plasma cell clone, as represented by serum monoclonal protein levels. By systematically comparing model performance with and without the inclusion of race as a variable, we sought to evaluate the potential influence of race on the systemic impact of the plasma cell clone. Method: A total of 171 patients with plasma cell dyscrasias including 1,472 observations were analyzed. where the upper limit of the observed M-spike was 3.5 gr/dL. Forty three clinical and lab variables as predictors of M-spike were fed into the machine learning model. Two lagged variables as the last two preceding M-spike values by the same subject were included. The random forest model was used, where regression forests are an ensemble of different regression trees and are used for nonlinear multiple regression. The goal of using a large number of trees was to train enough that each feature had a chance to appear in several models. The data was randomly split into a training set (80%) and a test set (20%), and a regression tree was built with the training set and then validated using the test set. Bootstrapping was used to generate a collection of data sets (n=500). Importance was measured by leaving a covariate out of models and comparing performance with its inclusion. Result: The training set used to develop the ML algorithm comprised 749 patient-based observations, which represented ~50% of the patient-based observations. The number of observations at each M-Spike value and the distribution of M-Spike values measured by SPEP are shown (Fig. 1). The residual distribution of the RF model indicated that nearly all M-spike values determined using the 43 variables distributed equally on either side of zero. The addition of race as variables did not significantly change the residual plots. The weighted value of each of the 43 independent variables was determined by individually removing a variable from the ML algorithm and measuring its effect on the mean squared error (MSE). As shown, removal of the first lagged M-spike, serum total protein, second-lagged M-spike, serum IgG, serum IgM, and serum IgA, had the greatest effects on the ML algorithm. The effect of race was then added to the ML algorithm. However, patient race had a low weighted value compared to the other variables included in the ML algorithm (Fig-2). M-spike values determined using the ML algorithm correlated highly with M-spike values determined using the laboratory measured SPEP values as indicated by the proximity of the Pearson and Spearman correlation coefficients to +1 . Using the 43 independent variables, the Pearson coefficient was 0.96 and the Spearman coefficient was 0.91, when M-spike values determined using the ML algorithm were compared to laboratory determined M-spike values. When race was added as a variable in the ML algorithm, the Pearson coefficient was 0.95 and the Spearman coefficient was 0.94 which is not significantly different. Conclusion: Here, we present the results of our machine learning analysis, which further supports our hypothesis and sheds light on the potential racial difference in the overall systemic impact of MM. Understanding the factors underlying racial disparities in MM will pave the way for more personalized and equitable treatment strategies, ultimately improving patient outcomes and overall disease management.

African American/Black race, apolipoprotein L1 , and serum creatinine among persons with HIV.

Accurate estimation of kidney function is critical among persons with HIV (PWH) to avoid under-dosing of antiretroviral therapies and ensure timely referral for kidney transplantation. Existing estimation equations for kidney function include race, the appropriateness of which has been debated. Given advancements in understanding of race and the necessity of accuracy in kidney function estimation, this study aimed to examine whether race, or genetic factors, improved prediction of serum creatinine among PWH. This cross-sectional study utilized data from the Center for AIDS Research Network of Integrated Clinical Systems cohort (2008-2018). The outcome was baseline serum creatinine. Ordinary least squares regression was used to examine whether inclusion of race or genetic factors [ apolipoprotein-L1 ( APOL1 ) variants and genetic African ancestry] improved serum creatinine prediction. A reduction in root mean squared error (RMSE) greater than 2% was a clinically relevant improvement in predictive ability. There were 4183 PWH included. Among PWH whose serum creatinine was less than 1.7 mg/dl, race was significantly associated with serum creatinine ( β = 0.06, SE = 0.01, P < 0.001) but did not improve predictive ability. African ancestry and APOL1 variants similarly failed to improve predictive ability. Whereas, when serum creatinine was at least 1.7 mg/dl, inclusion of race reduced the RMSE by 2.1%, indicating improvement in predictive ability. APOL1 variants further improved predictive ability by reducing the RMSE by 2.9%. These data suggest that, among PWH, inclusion of race or genetic factors may only be warranted at higher serum creatinine levels. Work eliminating existing healthcare disparities while preserving the utility of estimating equations is needed.

External Validation of the UTICalc with and Without Race for Pediatric Urinary Tract Infection

To validate externally the UTICalc, a popular clinical decision support tool used to determine the risk of urinary tract infections (UTIs) in febrile children, and compare its performance with and without the inclusion of race and at differing risk thresholds. We performed a retrospective, singlecenter case-control study of febrile children (2-24months) in an emergency department. Cases with culture-confirmed UTI were matched 1:1 to controls. We compared the performance of the original model which included race (version 1.0) to a revised model which did not consider race (version 3.0). We evaluated model performance at risk thresholds between 2% and 5%. We included 185 cases and 197 controls (median age 8.4months; IQR, 4.4-13.0months; 60.5% girls). When using UTICalc version 1.0, the model area under the receiver operator characteristic curve (AUROC) was 73.4% (95% CI 68.4%-78.5%), which was similar to the version 3.0 model (73.8%; 95% CI 68.7%-78.8%). When using a 2% risk threshold, the version 3.0 model demonstrated a sensitivity of 96.7% and a specificity of 25.0%, with declines in sensitivity and gains in specificity at higher risk thresholds. Version 1.0 of the UTICalc had 12 false negatives, of whom 10 were Black (83%); whereas version 3.0 had 6 false negatives, of whom 2 were Black (33%). Versions of the UTICalc with and without race had similar performance to each other with a slight decline from the original derivation sample. The removal of race did not adversely affect the accuracy of the UTICalc.

Race-Specific Spirometry Equations Do Not Improve Models of Dyspnea and Quantitative Chest CT Phenotypes

Race-specific spirometry reference equations are used globally to interpret lung function for clinical, research, and occupational purposes, but inclusion of race is under scrutiny. Does including self-identified race in spirometry reference equation formation improve the ability of predicted FEV1 values to explain quantitative chest CT abnormalities, dyspnea, or Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification? Using data from healthy adults who have never smoked in both the National Health and Nutrition Survey (2007-2012) and COPDGene study cohorts, race-neutral, race-free, and race-specific prediction equations were generated for FEV1. Using sensitivity/specificity, multivariable logistic regression, and random forest models, these equations were applied in a cross-sectional analysis to populations of individuals who currently smoke and individuals who formerly smoked to determine how they affected GOLD classification and the fit of models predicting quantitative chest CT phenotypes or dyspnea. Race-specific equations showed no advantage relative to race-neutral or race-free equations in models of quantitative chest CT phenotypes or dyspnea. Race-neutral reference equations reclassified up to 19%of Black participants into more severe GOLD classes, while race-neutral/race-free equations may improve model fit for dyspnea symptoms relative to race-specific equations. Race-specific equations offered no advantage over race-neutral/race-free equations in three distinct explanatory models of dyspnea and chest CT scan abnormalities. Race-neutral/race-free reference equations may improve pulmonary disease diagnoses and treatment in populations highly vulnerable to lung disease.

Assessing the predictive accuracy of the new vaginal birth after cesarean delivery calculator

The vaginal birth after cesarean delivery calculator by the Maternal-Fetal Medicine Units Network was created to help providers counsel patients on predicted success of trial of labor after cesarean delivery using individualized risk assessment. The inclusion of race and ethnicity as predictors of vaginal birth after cesarean delivery in the 2007 calculator was problematic and potentially exacerbated racial disparities in obstetrics. Thus, a modified calculator without race and ethnicity was published in June 2021. This study aimed to assess the accuracy of the 2007 and 2021 Maternal-Fetal Medicine Units vaginal birth after cesarean delivery calculators in predicting vaginal birth after cesarean delivery success among racial/ethnic minority patients receiving obstetrical care at a single urban tertiary medical center. All patients with 1 previous low transverse cesarean delivery who underwent a trial of labor at term with a vertex singleton gestation at an urban tertiary medical center from May 2015 to December 2018 were reviewed. Demographic and clinical data were collected retrospectively. Associations between maternal characteristics and success of vaginal birth after cesarean delivery were assessed using univariate and multivariable logistic regression. Predicted vaginal birth after cesarean delivery success rates using the Maternal-Fetal Medicine Units calculator were compared with actual outcomes (ie, successful trial of labor after cesarean delivery/vaginal birth after cesarean delivery vs repeated cesarean delivery) across each racial and ethnic group. A total of 910 patients met eligibility criteria and attempted trial of labor after cesarean delivery; 662 (73%) achieved vaginal birth after cesarean delivery. The rate of vaginal birth after cesarean delivery was highest among Asian women (81%) and lowest among Black women (61%). Univariate analyses demonstrated that success of vaginal birth after cesarean delivery was associated with maternal body mass index <30 kg/m2, history of vaginal delivery, and absence of indication of arrest of dilation or descent for previous cesarean delivery. Multivariate analyses evaluating predictors of vaginal birth after cesarean delivery reported in the 2021 calculator showed that maternal age, history of arrest disorder for previous cesarean delivery, and treated chronic hypertension were not significant in our patient population. Most patients who were of White, Asian, or "Other" race with a vaginal birth after cesarean delivery had a 2007 calculator-predicted probability of vaginal birth after cesarean delivery of >65%, whereas most Black and Hispanic patients with a vaginal birth after cesarean delivery were more likely to have a predicted probability of vaginal birth after cesarean delivery between 35% and 65% (P<.001). Most White, Asian, and Other-race patients with a repeated cesarean delivery had a 2007 calculator-predicted probability of vaginal birth after cesarean delivery of >65%, whereas most Black and Hispanic patients with a repeated cesarean delivery had a predicted probability of vaginal birth after cesarean delivery between 35% and 65%. Across all racial and ethnic groups, most patients with a vaginal birth after cesarean delivery had a 2021 calculator-predicted probability of vaginal birth after cesarean delivery of >65%. The inclusion of race/ethnicity in the 2007 Maternal-Fetal Medicine Units vaginal birth after cesarean delivery calculator underestimated predicted vaginal birth after cesarean delivery success rates among Black and Hispanic patients receiving obstetrical care at an urban tertiary medical center. Thus, we support the use of the 2021 vaginal birth after cesarean delivery calculator without race/ethnicity. Excluding race and ethnicity from vaginal birth after cesarean delivery counseling may be one way in whichproviders can ultimately contribute toward the reduction of racial and ethnic disparity in maternal morbidity in the United States. Further research is needed to understand the implications of treated chronic hypertension for the success of vaginal birth after cesarean delivery.

Prevalence of Statin Use for Primary Prevention of Atherosclerotic Cardiovascular Disease by Race, Ethnicity, and 10-Year Disease Risk in the US

The burden of atherosclerotic cardiovascular disease (ASCVD) in the US is higher among Black and Hispanic vs White adults. Inclusion of race in guidance for statin indication may lead to decreased disparities in statin use. To evaluate prevalence of primary prevention statin use by race and ethnicity according to 10-year ASCVD risk. This serial, cross-sectional analysis performed in May 2022 used data from the National Health and Nutrition Examination Survey, a nationally representative sample of health status in the US, from 2013 to March 2020 (limited cycle due to the COVID-19 pandemic), to evaluate statin use for primary prevention of ASCVD and to estimate 10-year ASCVD risk. Participants aged 40 to 75 years without ASCVD, diabetes, low-density lipoprotein cholesterol levels 190 mg/dL or greater, and with data on medication use were included. Self-identified race and ethnicity (Asian, Black, Hispanic, and White) and 10-year ASCVD risk category (5%-<7.5%, 7.5%-<20%, ≥20%). Prevalence of statin use, defined as identification of statin use on pill bottle review. A total of 3417 participants representing 39.4 million US adults after applying sampling weights (mean [SD] age, 61.8 [8.0] years; 1289 women [weighted percentage, 37.8%] and 2128 men [weighted percentage, 62.2%]; 329 Asian [weighted percentage, 4.2%], 1032 Black [weighted percentage, 12.7%], 786 Hispanic [weighted percentage, 10.1%], and 1270 White [weighted percentage, 73.0%]) were included. Compared with White participants, statin use was lower in Black and Hispanic participants and comparable among Asian participants in the overall cohort (Asian, 25.5%; Black, 20.0%; Hispanic, 15.4%; White, 27.9%) and within ASCVD risk strata. Within each race and ethnicity group, a graded increase in statin use was observed across increasing ASCVD risk strata. Statin use was low in the highest risk stratum overall with significantly lower rates of use among Black (23.8%; prevalence ratio [PR], 0.90; 95% CI, 0.82-0.98 vs White) and Hispanic participants (23.9%; PR, 0.90; 95% CI, 0.81-0.99 vs White). Among other factors, routine health care access and health insurance were significantly associated with higher statin use in Black, Hispanic, and White adults. Prevalence of statin use did not meaningfully change over time by race and ethnicity or by ASCVD risk stratum. In this study, statin use for primary prevention of ASCVD was low among all race and ethnicity groups regardless of ASCVD risk, with the lowest use occurring among Black and Hispanic adults. Improvements in access to care may promote equitable use of primary prevention statins in Black and Hispanic adults.

Inclusion of Race and Ethnicity With Neighborhood Socioeconomic Deprivation When Assessing COVID-19 Hospitalization Risk Among California Veterans Health Administration Users

Despite complexities of racial and ethnic residential segregation (hereinafter referred to as segregation) and neighborhood socioeconomic deprivation, public health studies, including those on COVID-19 racial and ethnic disparities, often rely on composite neighborhood indices that do not account for residential segregation. To examine the associations by race and ethnicity among California's Healthy Places Index (HPI), Black and Hispanic segregation, Social Vulnerability Index (SVI), and COVID-19-related hospitalization. This cohort study included veterans with positive test results for COVID-19 living in California who used Veterans Health Administration services between March 1, 2020, and October 31, 2021. Rates of COVID-19-related hospitalization among veterans with COVID-19. The sample available for analysis included 19 495 veterans with COVID-19 (mean [SD] age, 57.21 [17.68] years), of whom 91.0% were men, 27.7% were Hispanic, 16.1% were non-Hispanic Black, and 45.0% were non-Hispanic White. For Black veterans, living in lower-HPI (ie, less healthy) neighborhoods was associated with higher rates of hospitalization (odds ratio [OR], 1.07 [95% CI, 1.03-1.12]), even after accounting for Black segregation (OR, 1.06 [95% CI, 1.02-1.11]). Among Hispanic veterans, living in lower-HPI neighborhoods was not associated with hospitalization with (OR, 1.04 [95% CI, 0.99-1.09]) and without (OR, 1.03 [95% CI, 1.00-1.08]) Hispanic segregation adjustment. For non-Hispanic White veterans, lower HPI was associated with more frequent hospitalization (OR, 1.03 [95% CI, 1.00-1.06]). The HPI was no longer associated with hospitalization after accounting for Black (OR, 1.02 [95% CI, 0.99-1.05]) or Hispanic (OR, 0.98 [95% CI, 0.95-1.02]) segregation. Hospitalization was higher for White (OR, 4.42 [95% CI, 1.62-12.08]) and Hispanic (OR, 2.90 [95% CI, 1.02-8.23]) veterans living in neighborhoods with greater Black segregation and for White veterans in more Hispanic-segregated neighborhoods (OR, 2.81 [95% CI, 1.96-4.03]), adjusting for HPI. Living in higher SVI (ie, more vulnerable) neighborhoods was associated with greater hospitalization for Black (OR, 1.06 [95% CI, 1.02-1.10]) and non-Hispanic White (OR, 1.04 [95% CI, 1.01-1.06]) veterans. In this cohort study of US veterans with COVID-19, HPI captured neighborhood-level risk for COVID-19-related hospitalization for Black, Hispanic, and White veterans comparably with SVI. These findings have implications for the use of HPI and other composite neighborhood deprivation indices that do not explicitly account for segregation. Understanding associations between place and health requires ensuring composite measures accurately account for multiple aspects of neighborhood deprivation and, importantly, variation by race and ethnicity.

Risk Prediction and Machine Learning: A Case-Based Overview.

Risk Prediction and Machine Learning: A Case-Based Overview.

Sociodemographic disparities in ophthalmological clinical trials

IntroductionIn ophthalmology, clinical trials (CTs) guide the treatment of diseases such as diabetic retinopathy, myopia, age-related macular degeneration, glaucoma and keratoconus with distinct presentations, pathological characteristics and responses to treatment...

Abstract WMP25: Female And Minority Participants Are Underrepresented In Stroke Motor Recovery Trials Over The Last Decade

Introduction: Inclusion of diverse stroke patient populations is key for generalizable and clinically meaningful stroke rehabilitation clinical trials. The aim of this study was to assess 1) the reporting and inclusion of race, ethnicity, and gender in publications on motor recovery clinical trials post-stroke, and 2) the quality of the clinical trial. Methods: All stroke motor recovery clinical trials published in ten leading rehabilitation journals in the United States between 2012 and 2021 with at least one control group were identified. The percent of different racial minorities, ethnicities, and women enrolled in the trials was extracted and calculated for those trials with available data found in the manuscript or on clinicaltrials.gov. The quality of the trial was determined by the PEDro score. Results: 57 trials (total of 3004 participants) met the inclusion criteria and were included in the analysis. 97%, 37%, and 23% of the trials included reported enrollment by gender, race, and ethnicity, in the manuscript or on clinicaltrials.gov. Among trials reporting race, 59% of participants were White, followed by 30% Black, 1% Native American, 4% Asian and 10% Other. Among the trials that reported ethnicity, Hispanic or Latino participants were represented as 10% of the total participants. The average PEDro score was 7. Conclusions: While most stroke rehab trial publications were of good quality and reported data on sex, they did not report race/ethnicity, though this information was collected on clinicaltrials.gov. Improvement in the inclusion and reporting of race/ethnicity in stroke motor recovery trials is needed to allow for better generalizability in the future.

Cystatin C–Based Equation to Estimate GFR without the Inclusion of Race and Sex

BackgroundThe accuracy of estimation of kidney function with the use of routine metabolic tests, such as measurement of the serum creatinine level, has been controversial. The European Kidney Function Consortium (EKFC) developed a creatinine-based equation (EKFC eGFRcr) to estimate the glomerular filtration rate (GFR) with a rescaled serum creatinine level (i.e., the serum creatinine level is divided by the median serum creatinine level among healthy persons to control for variation related to differences in age, sex, or race). Whether a cystatin C–based EKFC equation would increase the accuracy of estimated GFR is unknown.MethodsWe used data from patients in Sweden to estimate the rescaling factor for the cystatin C level in adults. We then replaced rescaled serum creatinine in the EKFC eGFRcr equation with rescaled cystatin C, and we validated the resulting EKFC eGFRcys equation in cohorts of White patients and Black patients in Europe, the United States, and Africa, according to measured GFR, levels of serum creatinine and cystatin C, age, and sex.ResultsOn the basis of data from 227,643 patients in Sweden, the rescaling factor for cystatin C was estimated at 0.83 for men and women younger than 50 years of age and 0.83+0.005×(age–50) for those 50 years of age or older. The EKFC eGFRcys equation was unbiased, had accuracy that was similar to that of the EKFC eGFRcr equation in both White patients and Black patients (11,231 patients from Europe, 1093 from the United States, and 508 from Africa), and was more accurate than the Chronic Kidney Disease Epidemiology Collaboration eGFRcys equation recommended by Kidney Disease: Improving Global Outcomes. The arithmetic mean of EKFC eGFRcr and EKFC eGFRcys further improved the accuracy of estimated GFR over estimates from either biomarker equation alone.ConclusionsThe EKFC eGFRcys equation had the same mathematical form as the EKFC eGFRcr equation, but it had a scaling factor for cystatin C that did not differ according to race or sex. In cohorts from Europe, the United States, and Africa, this equation improved the accuracy of GFR assessment over that of commonly used equations. (Funded by the Swedish Research Council.)

Mixed heritage, mixed feelings: psychoanalytic parent infant psychotherapy during the coronavirus pandemic

ABSTRACT This paper is an account of Psychoanalytic Parent Infant Psychotherapy (PPIP) by a white therapist with a mixed heritage family during the coronavirus pandemic. It describes changes to the distant relationship between a mother and her infant son who appears at first to be developing an avoidant, dissociated defensive strategy to ward off painful projections from his traumatised parent. Necessary modifications in treatment due to working remotely contributed to several technical adjustments made. The paper attempts to consider the inclusion of race as a fully integrated aspect of working in a transcultural field, taking into account the ‘ghosts in society’. The specific trauma of racist abuse, with an emphasis on colourism that can be experienced by mixed heritage families is discussed. The key to the improved relationship between child and mother by the end of the Parent Infant Psychotherapy is postulated to have come from reflection on the therapist’s countertransference in regard to racism, that then enabled both therapist and patient/s to recognise and begin to work through experiences, thoughts and fantasies about belonging, heritage and racism.

The Use of Race in Risk Assessment Tools Contributes to Systemic Racism.

Many patients suffer from hip or knee osteoarthritis and elect to pursue total joint arthroplasty (TJA). Though perioperative risk is an inherent component of surgery, calculators that assess the risk of complications following TJA can help both surgeons and patients make informed decisions about the risk of surgery and aid in shared decision-making discussions. The inclusion of race in a risk calculator for readmission after TJA is flawed and unacceptable because a patient's race does not increase their risk of a complication after total joint replacement.

Personalized Surgical Transfusion Risk Prediction: Reply

We thank Burton et al.1 and Zapf et al.2 for their thoughtful comments on our research on personalized surgical transfusion risk prediction.3 Both letters raise important considerations regarding variable selection for predictive models in health care, which are worth discussing in further detail.As Burton et al. note, race and ethnicity were not included as input variables in our machine learning model for surgical transfusion risk; we would like to clarify that this was intentional for several reasons, which we explain here. First, the inclusion of race in predictive models has been well-described to contribute to inequity.4 One major limitation of machine learning is that a model can only learn from its training examples—in other words, real-world clinician behaviors. If such behaviors or the societal factors contextualizing that behavior are biased, the model will also be biased. The citation provided by Burton et al. is a perfect example of this5: in this study, researchers evaluated a model trained to predict healthcare utilization after hospital discharge, with the intention to allocate additional resources to patients predicted to have high utilization. Unfortunately, black patients had low utilization because they lacked access to care, which the model learned and perpetuated. Inclusion of race as an input variable in model development encourages machine learning models to explicitly encode such latent biases, and consequently the recommendations of such models will propagate systemic inequities in care.Second, although race is a frequently collected variable in many datasets, it serves as a proxy for often unmeasured variables such as socioeconomic status, access to care, illness severity (due to poor access to care and delayed presentation), and other social determinants of health.6 Thus, although the inclusion of race as a variable may improve model discrimination, it potentially does so for the wrong reasons. Given two individuals, identical except for their skin color, it seems unjust for one to have a “better” prediction based on the population averages of their racial group, which may be due to unmeasured variables not applicable to the specific individual.Third, to the best of our knowledge, there is little evidence that race itself contributes to risk for allogeneic blood transfusion after adjustment for disease burden, socioeconomic status, and other clinical variables that are known to contribute (e.g., hematocrit). We thank Burton et al. for bringing attention to the potential pitfalls of racial adjustment and the critical importance of fairness in predictive modeling. As machine learning is increasingly used for clinical decision support, model developers must be vigilant for potential sources of bias, which can be introduced at every step of model development and implementation.7,8 As a research community, we share a responsibility to ensure that the decision support tools we create do not exacerbate, and ideally help to reduce, the health disparities that are currently present in modern medicine.9Zapf et al. raise important points about the benefits and limitations of model development using large registry datasets versus institution-specific datasets. We agree that inclusion of surgeon and anesthesiologist identifiers may further improve predictive performance. Variation in transfusion risk can occur due to differences in surgeon technique or case complexity, and it would be appropriate to adjust for these; however, they can also occur due to differences in preference for discretionary transfusion, which may be less appropriate to adjust for. By training our models on a large national database, we captured the average transfusion behavior of U.S. physicians, which we believe, on average, to be appropriate. Further customizing model predictions based on individual behavior patterns risks encoding undesirable physician practice patterns into the model; nonetheless, we acknowledge that such adjustment might be necessary for widespread adoption. Our transfer learning approach (i.e., hospital-specific procedure-specific transfusion rate) could easily accommodate the addition of a surgeon- or anesthesiologist-specific adjustment, and it would be interesting to investigate such modifications in future work.Dr. Lou was supported by the National Institutes of Health (Bethesda, Maryland; 5T32GM108539-07) and the International Anesthesia Research Society (San Francisco, California).Dr. Kannampallil has consulting relationships with Pfizer Inc. (New York, New York) and Elsevier (Amsterdam, The Netherlands) that are unrelated to this work. Dr. Hall is the Consulting Director of the National Surgical Quality Improvement Program, American College of Surgeons (Chicago, Illinois). The other authors declare no competing interests.

Lack of Demographic Information in Total Hip Arthroplasty/Total Knee Arthroplasty Randomized Controlled Trial Publications

BackgroundThe impact of social determinants of health (SDOH) has been documented in orthopaedic literature. However, there is a lack of data on the inclusion of these variables in orthopaedic studies. Our aim was to investigate how many total hip arthroplasties and total knee arthroplasties randomized controlled trials report SDOH variables such as race, ethnicity, insurance, income, and education within the manuscript. MethodsA systematic review was conducted on a PubMed search for randomized controlled trials published from 2017 to 2019 in the Journal of Bone and Joint Surgery, Journal of Arthroplasty, Clinical Orthopaedics and Related Research, and Osteoarthritis and Cartilage. Data collected included publication year, type of surgery, and the inclusion of race, ethnicity, insurance, income, and education. ResultsOf the 72 manuscripts included in the study, 5.6% of the manuscripts mentioned race, 4.2% included race within the demographic table, and 1.4% included ethnicity in the demographic table. Overall, only 5 studies discussed any one of the variables studied and none included any SDOH variables in their multivariable regressions. There were no statistically significant differences on inclusion across journal year (P value = .78), journal name (P value = 1.00), or surgery type (P value = .555). ConclusionOur findings identify a major shortcoming in the inclusion of SDOH variables in total knee arthroplasty/total hip arthroplasty publications. Their exclusion may be indirectly perpetuating disparities if research that does not use representative patient samples is used in creating health policies and national standards. Level of EvidenceLevel V.

Reporting Race in Orthopaedic Journal Articles: Commentary on an article by Cody Crnkovic, BS, et al.: "Low Rates of Reporting Race, Ethnicity, and Socioeconomic Status in Studies Published in Top Orthopaedic Journals".

Commentary Whether or not it is proper to include race as a variable in clinical research studies has been a subject of debate for decades1. Crnkovic et al. lament the “underreporting” of race in articles published in Clinical Orthopaedics and Related Research and The Journal of Bone & Joint Surgery and call on the editors to remedy this perceived deficiency by requiring that race be recorded for all human research subjects. The authors quote from recent guidelines published in the Journal of the American Medical Association (JAMA) and from the recommendations of the International Committee of Medical Journal Editors (ICMJE) to support their view. In doing so, the authors greatly simplify a complex issue and mischaracterize the published guidelines. Thoughtful journal editors have long acknowledged that although race is a social construct without biological basis and that racialized medicine has been utilized repeatedly throughout history to mark a particular group as different, inferior, and defective, it is still sometimes necessary and helpful to collect meaningful data on race in order to identify, understand, and improve inequitable health outcomes2. The question is how best to do this. There is widespread agreement that when racial data are utilized in a study, there should be a valid reason. The JAMA guidelines state that “if race and ethnicity categories were collected for a study, the reasons that these were assessed also should be described.” The ICMJE guidelines state only that authors “should define how they determined race or ethnicity and justify their relevance.” Ioannidis et al. presented arguments for and against the inclusion of race in research and stated that “a large amount of research involving race variables has been, in hindsight, pedestrian and arguably lies among the greater waste of spurious, nonusable biomedical evidence.”3 He adds that “it is important to think carefully about the fundamental question. Why should race variables be used, if at all?” Some researchers, including Crnkovic et al., suggest that there is a universally applicable justification for including, even requiring, race data to be collected and reported. Knowledge of patient race is thought to be important for establishing the generalizability of the study results and for supporting research into inequities in health outcomes and processes. Although there are valid reasons to support the recruitment of diverse populations into research studies, the claim that it is important to know the racial makeup of a study population in order to assess the applicability to a reader’s own patients is questionable. The harm caused by the use of race as a cognitive framing device for clinical decision-making may be substantial4. The limitations of race as an explanatory variable are due both to its indistinct social construction and to its connection with other social determinants of health. The mutability of race is reflected in the JAMA guidelines, which recommend that authors include “an explanation of who identified participant race and ethnicity and the source of the classifications used (e.g., self-report or selection, investigator observed, database, electronic health record, survey instrument).” Cerdeña et al. stated that hypotheses that utilize racial labels “should make the definition and operationalisation of race explicit,” adding that “structural barriers to health that overlap with race should be considered, including socioeconomic status, discrimination, transportation, environmental exposures, criminal history, documentation status, English proficiency, and neighbourhood violence.”5 Winker noted that although the National Institutes of Health (NIH) requires race and ethnicity to be assessed, “the NIH does not require that authors consider thoroughly the implications of race in their study and measure other factors that might confound apparent associations of outcomes with race, such as socioeconomic status.”2 When considering race, Cooper et al. stated that to “resist racialized thinking, clinicians and researchers need to become better at appreciating social context and the potential personal and economic challenges that mediate susceptibility or lead to poor quality of care, and consider what all of that means for the analysis that contributes to a scientific report.”6 Simply reporting standard measures of socioeconomic status along with race does not accomplish this. There is a need for high-quality studies of what Ioannidis et al. called “the multifarious consequences of long-entrenched and continuously transformed racism” in medicine. Not every article published in The Journal of Bone & Joint Surgery will meet the high bar required, nor should they be expected to. Studies that seek to comment meaningfully on the effects of race should state a hypothesis, explain in detail how the race of subjects was determined, report relevant measures of socioeconomic status, consider alternative explanations for the findings, and place the results in the context of the health-care system. To do less adds to the cloud of unknowing that good journals try to dispel.

Kidney Function Assessment in African American Patients: A Narrative Review for Pharmacists.

Recent recognitions of longstanding societal inequity in kidney function assessments have prompted the call to eliminate race as part of the algorithm to assess estimated glomerular filtration rate (eGFR). Previous equations for eGFR estimation adopted race as part of the calculation. Incorporating race within eGFR equations results in overestimating and underestimating Black and nonblack patients, respectively. The inclusion of race is controversial. In September 2021, the National Kidney Foundation (NKF) and the American Society of Nephrology (ASN) combined task force recommended estimating the kidney function without using a race variable. The task force endorsed race-free creatinine-cystatin C equations to be more accurate than the creatinine-only equations. Before the application of NKF-ASN revised recommendations, major healthcare disparities influenced daily clinical practice. Those disparities include the delay in initiating medications that have reanl or cardio-protective effects, such as sodium-glucose cotransporter–2 inhibitors (SGLT-2i) and angiotensin-converting enzyme inhibitors (ACEIs). Clinical judgment should be employed when dose adjusting medications. Combining the eGFR with other clinical assessment tools such as urinary output, the expanded use of confirmatory tests, and the eGFR trend is suggested for a better kidney function assessment. Additionally, creatinine-cystatin C is recommended when feasible, and when institutions have the laboratory abilities.

Academic and disciplinary narratives that inhibit teaching for racial justice

ABSTRACT Within the United States’ academic system, faculty often confront academic norms and practices that contradict liberatory curricular and pedagogical practices. Using data from a critical race participatory action research study based in the United States, this study explores how academic and disciplinary narratives inhibit faculty consciousness and demonstrates how faculty members’ consciousness relate to the curricula and pedagogy they utilize. In this study, three psychology faculty members describe inhibiting academic and disciplinary narratives including: race only belongs in courses centered on issues of diversity, racist knowledge that is part of the cannon must persist, and the inclusion of race into a course requires extensive expertise. This study utilizes the framing of critical race conscience to analyze the academic and disciplinary narratives that inhibit faculty members’ consciousness and thus, the curricula and pedagogical practices they implement.