Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study.

BACKGROUND: Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. OBJECTIVE: This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). METHODS: The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. RESULTS: For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. CONCLUSIONS: These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.

Racial Composition of Past and Current Social Environments and Health Literacy.

BACKGROUND: Research is needed to understand the impact of social determinants of health on health literacy throughout the life course. This study examined how racial composition of multiple past and current social environments was related to adults' health literacy. METHODS: In this study, 546 adult patients at a primary care clinic in St. Louis, Missouri, completed a self-administered written questionnaire that assessed demographic characteristics and a verbally administered component that assessed health literacy with the Rapid Estimate of Adult Literacy in Medicine - Revised (REALM-R) and Newest Vital Sign (NVS), and self-reported racial composition of six past and four current social environments. Multilevel logistic regression models were built to examine the relationships between racial composition of past and current social environments and health literacy. RESULTS: Most participants identified as Black or multiracial (61%), had a high school diploma or less (54%), and household income <$20,000 (72%). About 56% had adequate health literacy based on REALM-R and 38% based on NVS. In regression models, participants with multiple past white environments (e.g., locations/conditions in which most of the people who live, go to school, work, and have leisure time are White) and (vs. 0 or 1) were more likely to have adequate health literacy based on REALM-R (adjusted odds ratio [aOR] = 1.79; 95% confidence interval [CI]: 1.04-3.07). Similarly, participants who had multiple past white social environments were more likely (aOR = 1.94, 95% CI: 1.15-3.27) to have adequate health literacy based on NVS than those who had not. The racial composition of current social environments was not significantly associated with health literacy in either model. CONCLUSIONS: Racial composition of past, but not current, educational and residential social environments was significantly associated with adult health literacy. The results highlight the importance of examining the impact of social determinants over the life course on health literacy. The findings suggest that policies ensuring equitable access to educational resources in school and community contexts is critical to improving equitable health literacy. [HLRP: Health Literacy Research and Practice. 2024;8(3):e130-e139.].

PLAIN LANGUAGE SUMMARY: We studied how the racial make-up of past and current places where people live, work, and go to school were related to their health literacy as adults. We found that the racial make-up of past places, but not current places, was related to health literacy. Our results show the need to study the impact of childhood places on health literacy.

Exploring Monoracial/Ethnic and Multiracial/Ethnic Classification in the Context of Mental Health Among High School Students.

OBJECTIVES: To explore the prevalence of Multiracial/ethnic identity and its association with mental health among high school students. METHODS: The 2021 national Youth Risk Behavior Survey (N=17,232) data were used. Respondents were classified as monoracial/ethnic or Multiracial/ethnic. RESULTS: Overall, 21.5% of students were Multiracial/ethnic. Multiracial/ethnic status was most prevalent among students who identify as American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and Hispanic or Latino. Logistic regression models showed Multiracial/ethnic classification was associated with persistent feelings of sadness or hopelessness among students identifying as American Indian or Alaska Native, Asian, Black, and White. Multiracial/ethnic Asian students had significantly higher odds of all four indicators of poor mental health compared with monoracial/ethnic Asian students. CONCLUSION: Multiracial/ethnic students constitute a heterogenous group. This study found important subgroup differences in indicators of mental health that might be missed when Multiracial/ethnic groups are considered in aggregate.

Race adjustments in clinical algorithms can help correct for racial disparities in data quality.

Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (P-value: <0.001) and area under the receiving operating characteristic curve among Black participants (P-value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.

Moral distress, coping mechanisms, and turnover intent among healthcare providers in British Columbia: a race and gender-based analysis.

BACKGROUND: This study explores intersectionality in moral distress and turnover intention among healthcare workers (HCWs) in British Columbia, focusing on race and gender dynamics. It addresses gaps in research on how these factors affect healthcare workforce composition and experiences. METHODS: Our cross-sectional observational study utilized a structured online survey. Participants included doctors, nurses, and in-home/community care providers. The survey measured moral distress using established scales, assessed coping mechanisms, and evaluated turnover intentions. Statistical analysis examined the relationships between race, gender, moral distress, and turnover intention, focusing on identifying disparities across different healthcare roles. Complex interactions were examined through Classification and Regression Trees. RESULTS: Racialized and gender minority groups faced higher levels of moral distress. Profession played a significant role in these experiences. White women reported a higher intention to leave due to moral distress compared to other groups, especially white men. Nurses and care providers experienced higher moral distress and turnover intentions than physicians. Furthermore, coping strategies varied across different racial and gender identities. CONCLUSION: Targeted interventions are required to mitigate moral distress and reduce turnover, especially among healthcare workers facing intersectional inequities.

The Burden of Melasma: Race, Ethnicity, and Comorbidities.

INTRODUCTION: In an effort to define the characteristics of populations affected by melasma, we utilized a large global health research network database from 108 health care organizations (TriNetx) to quantify the associations between race, ethnicity, and comorbidities. METHODS: We identified the cohort of all patients with melasma from the TriNetx database, and subsequently generated a control cohort. ICD-10 codes were used to identify the prevalence of various comorbidities associated with melasma. RESULTS: A total of 41,283 patients with melasma (93% female, mean [SD] age 48.8 [12.6] year) were identified. The most frequently associated risk factors included hypertension (25% of the melasma cohort) and hormonal contraception (24%). Rosacea (OR=5.1), atopic dermatitis (OR=3.3), lupus (OR=2.5), history of skin cancer (OR=2.5), history of internal malignancy (OR=2.1), and hormonal contraception use (OR=2.1) possessed the highest odds ratios for development of melasma (all P< 0.01). A statistically significant association was identified for melasma in Asian or Other/Unknown races (OR=2.0 and OR=1.7, P< 0.01), as well as Hispanic ethnicity (OR=1.3, P< 0.01). White, Black/African American, and Not Hispanic groups all revealed slightly lower odds (all 0.8, P< 0.01). CONCLUSION: This latest global update on the etiopathology of melasma further supports findings from prior epidemiologic study reporting preference in melanized phenotypes (Fitzpatrick skin type III-V), but less so in extreme skin types (I, II, VI). Increased associations with rosacea, atopic dermatitis, and history of cancer may emphasize the importance of treating concurrent inflammatory environments and the consideration of more frequent malignancy surveillance. J Drugs Dermatol. 2024;23(8):691-693.  doi:10.36849/JDD.8233.

Mortality due to falls by county, age group, race, and ethnicity in the USA, 2000-19: a systematic analysis of health disparities.

BACKGROUND: Fall-related mortality has increased rapidly over the past two decades in the USA, but the extent to which mortality varies across racial and ethnic populations, counties, and age groups is not well understood. The aim of this study was to estimate age-standardised mortality rates due to falls by racial and ethnic population, county, and age group over a 20-year period. METHODS: Redistribution methods for insufficient cause of death codes and validated small-area estimation methods were applied to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual fall-related mortality. Estimates from 2000 to 2019 were stratified by county (n=3110) and five mutually exclusive racial and ethnic populations: American Indian or Alaska Native (AIAN), Asian or Pacific Islander (Asian), Black, Latino or Hispanic (Latino), and White. Estimates were corrected for misreporting of race and ethnicity on death certificates using published misclassification ratios. We masked (ie, did not display) estimates for county and racial and ethnic population combinations with a mean annual population of less than 1000. Age-standardised mortality is presented for all ages combined and for age groups 20-64 years (younger adults) and 65 years and older (older adults). FINDINGS: Nationally, in 2019, the overall age-standardised fall-related mortality rate for the total population was 13·4 deaths per 100 000 population (95% uncertainty interval 13·3-13·6), an increase of 65·3% (61·9-68·8) from 8·1 deaths per 100 000 (8·0-8·3) in 2000, with the largest increases observed in older adults. Fall-related mortality at the national level was highest across all years in the AIAN population (in 2019, 15·9 deaths per 100 000 population [95% uncertainty interval 14·0-18·2]) and White population (14·8 deaths per 100 000 [14·6-15·0]), and was about half as high among the Latino (8·7 deaths per 100 000 [8·3-9·0]), Black (8·1 deaths per 100 000 [7·9-8·4]), and Asian (7·5 deaths per 100 000 [7·1-7·9]) populations. The disparities between racial and ethnic populations varied widely by age group, with mortality among younger adults highest for the AIAN population and mortality among older adults highest for the White population. The national-level patterns were observed broadly at the county level, although there was considerable spatial variation across ages and racial and ethnic populations. For younger adults, among almost all counties with unmasked estimates, there was higher mortality in the AIAN population than in all other racial and ethnic populations, while there were pockets of high mortality in the Latino population, particularly in the Mountain West region. For older adults, mortality was particularly high in the White population within clusters of counties across states including Florida, Minnesota, and Wisconsin. INTERPRETATION: Age-standardised mortality due to falls increased over the study period for each racial and ethnic population and almost every county. Wide variation in mortality across geography, age, and race and ethnicity highlights areas and populations that might benefit most from efficacious fall prevention interventions as well as additional prevention research. FUNDING: US National Institutes of Health (Intramural Research Program, National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; Intramural Research Program, National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research).

Risk factors underlying racial and ethnic disparities in tuberculosis diagnosis and treatment outcomes, 2011-19: a multiple mediation analysis of national surveillance data.

BACKGROUND: Despite an overall decline in tuberculosis incidence and mortality in the USA in the past two decades, racial and ethnic disparities in tuberculosis outcomes persist. We aimed to examine the extent to which inequalities in health and neighbourhood-level social vulnerability mediate these disparities. METHODS: We extracted data from the US National Tuberculosis Surveillance System on individuals with tuberculosis during 2011-19. Individuals with multidrug-resistant tuberculosis or missing data on race and ethnicity were excluded. We examined potential disparities in tuberculosis outcomes among US-born and non-US-born individuals and conducted a mediation analysis for groups with a higher risk of treatment incompletion (a summary outcome comprising diagnosis after death, treatment discontinuation, or death during treatment). We used sequential multiple mediation to evaluate eight potential mediators: three comorbid conditions (HIV, end-stage renal disease, and diabetes), homelessness, and four census tract-level measures (poverty, unemployment, insurance coverage, and racialised economic segregation [measured by Index of Concentration at the ExtremesRace-Income]). We estimated the marginal contribution of each mediator using Shapley values. FINDINGS: During 2011-19, 27 788 US-born individuals and 57 225 non-US-born individuals were diagnosed with active tuberculosis, of whom 27 605 and 56 253 individuals, respectively, met eligibility criteria for our analyses. We did not observe evidence of disparities in tuberculosis outcomes for non-US-born individuals by race and ethnicity. Therefore, subsequent analyses were restricted to US-born individuals. Relative to White individuals, Black and Hispanic individuals had a higher risk of not completing tuberculosis treatment (adjusted relative risk 1·27, 95% CI 1·19-1·35; 1·22, 1·11-1·33, respectively). In multiple mediator analysis, the eight measured mediators explained 67% of the disparity for Black individuals and 65% for Hispanic individuals. The biggest contributors to these disparities for Black individuals and Hispanic individuals were concomitant end-stage renal disease, concomitant HIV, census tract-level racialised economic segregation, and census tract-level poverty. INTERPRETATION: Our findings underscore the need for initiatives to reduce disparities in tuberculosis outcomes among US-born individuals, particularly in highly racially and economically polarised neighbourhoods. Mitigating the structural and environmental factors that lead to disparities in the prevalence of comorbidities and their case management should be a priority. FUNDING: US Centers for Disease Control and Prevention National Center for HIV, Viral Hepatitis, STD, and Tuberculosis Prevention Epidemiologic and Economic Modeling Agreement.

Racial and social inequities in medication use: A review of articles responding to the Journal of Managed Care + Specialty Pharmacy's Call to Action.

This article provides a summary of Viewpoint and Research articles responding to the 2020 Journal of Managed Care + Specialty Pharmacy Call to Action to address racial and social inequities in medication use. We find great heterogeneity in terms of topic, clinical condition examined, and health disparity addressed. Common recommendations across Viewpoint articles include the need to increase racial and ethnic diversity in clinical trial participants, the need to address drug affordability and health insurance literacy, and the need to incentivize providers and plans to participate in diversity initiatives, such as the better capture of information on social determinants of health (SDOH) in claims data to be able to address SDOH needs. Across research articles, we also find a large range of approaches and study designs, spanning from randomized controlled trials to surveys to observational studies. These articles identify disparities in which minoritized beneficiaries are shown to be less likely to receive medications and vaccines, as well as less likely to be adherent to medications, across a variety of conditions. Finally, we discuss Healthy People 2030 as a potential framework for future health disparity researchers.

Reassessing the Use of Race in Clinical Algorithms: An Interactive, Case-Based Session for Medical Students Using eGFR.

Introduction: Medical curricula implicitly teach that race has a biological basis. Clinical rotations reinforce this misconception as race-based algorithms are used to guide clinical decision-making. This module aims to expose the fallacy of race in clinical algorithms, using the estimated glomerular filtration rate (eGFR) equation as an example. Methods: We created a 60-minute module in consultation with nephrologists. The format was an interactive, case-based presentation with a didactic section. A third-year medical student facilitated the workshops to medical students. Evaluation included pre/post surveys using 5-point Likert scales to assess awareness regarding use of race as a biological construct. Higher scores indicated increased awareness. Results: Fifty-five students participated in the module. Pre/post results indicated that students significantly improved in self-perceived knowledge of the history of racism in medicine (2.6 vs. 3.2, p < .001), awareness of race in clinical algorithms (2.7 vs. 3.7, p < .001), impact of race-based eGFR on quality of life/treatment outcomes (4.5 vs. 4.8, p = .01), differences between race and ancestry (3.7 vs. 4.3, p < .001), and implications of not removing race from the eGFR equation (2.7 vs. 4.2, p < .001). Students rated the workshops highly for quality and clarity. Discussion: Our module expands on others' work to expose the fallacy of race-based algorithms and define its impact on health equity. Limitations include a lack of objective assessment of knowledge acquisition. We recommend integrating this module into preclinical and clinical curricula to discuss the use of race in medical literature and clinical practice.

Detecting Early-Stage Pressure Injury in Patients With Dark Skin Tones Using an Enhanced Physical Assessment Technique and Halogen Lighting.

PURPOSE: The purpose of this quality improvement project was to identify stage 1 pressure injuries (PIs) in patients with darker skin tones using an enhanced skin assessment (Skin Assessment for Dark Skin, SADS) and halogen lighting. PARTICIPANTS AND SETTING: This quality improvement project was conducted on 3 units at a large university teaching hospital in Southeast Michigan. The project was originally designed so that participating patients were identified by bedside nurses as having sufficient melanin to obscure blanching on the hand/forearm using regular lighting, but this goal was not met. Data analysis is based on patients who self-identified as African American or Black, Native Hawaiian or Pacific Islander, Asian, American Indian, and Native Alaskan. Bedside nurses participating in this project were also asked to complete a questionnaire describing their knowledge and experiences with this project and assessment of early-stage PI in patients with darker skin tones. APPROACH: The Iowa Model of Evidence-Based Practice was used to guide this quality improvement initiative. Following a trigger event, we reviewed pertinent literature and developed an enhanced technique for assessing patients with darker skin tones that involved use of halogen lighting (SADS) and documentation of findings. We selected units from our facility and taught nurses to apply the SADS approach. Data from the electronic medical record and a survey of participating nurses were used to compare findings before and after project implementation. Comparisons were based on descriptive data analysis. OUTCOMES: Following implementation of the enhanced physical assessment, the participating units experienced a 6% decrease in the total number of facility-acquired PIs during the implementation period. IMPLICATIONS FOR PRACTICE: The enhanced physical assessment, when paired with halogen lighting, enhanced identification of stage 1 PI in persons with dark skin. The assessment method was easy to teach, learn, and can be performed at the bedside as part of a shift assessment which routinely includes inspection of skin.

Racial and ethnic disparities in gallbladder cancer: A two-decade analysis of incidence and mortality rates in the US.

BACKGROUND: Gallbladder cancer (GBC) is an aggressive malignancy that is usually diagnosed at a late stage. Prior data showed increasing incidence of GBC in the US. However, little is known about race/ethnic-specific incidence and mortality trends of GBC per stage at diagnosis. Therefore, we aimed to conduct a time-trend analysis of GBC incidence and mortality rates categorized by race/ethnicity and stage-at-diagnosis. METHODS: Age-adjusted GBC incidence and mortality rates were calculated using SEER*Stat software from the United States Cancer Statistics database (covers ~98% of US population between 2001 and 2020) and NCHS (covers ~100% of the US population between 2000 and 2020) databases, respectively. Race/Ethnic groups were Non-Hispanic-White (NHW), Non-Hispanic-Black (NHB), Hispanic, Non-Hispanic-Asian/Pacific-Islander (NHAPI), and Non-Hispanic-American-Indian/Alaska-Native (NHAIAN). Stage-at-diagnoses were all stages, early, regional, and distant stages. Joinpoint regression was used to generate time-trends [annual percentage change (APC) and average APC (AAPC)] with parametric estimations and a two-sided t-test (p-value cut-off 0.05). RESULTS: 76,873 patients were diagnosed with GBC with decreasing incidence rates in all races/ethnicities except NHB who experienced an increasing trend between 2001 and 2014 (APC = 2.08, p < 0.01) and plateauing afterward (APC = -1.21, p = 0.31); (AAPC = 1.03, p = 0.03). Among early-stage tumors (9927 patients), incidence rates were decreasing only in Hispanic (AAPC = -4.24, p = 0.006) while stable in other races/ethnicities (NHW: AAPC = -2.61, p = 0.39; NHB: AAPC = -1.73, p = 0.36). For regional-stage tumors (29,690 patients), GBC incidence rates were decreasing only in NHW (AAPC = -1.61, p < 0.001) while stable in other races/ethnicities (NHB: AAPC = 0.73, p = 0.34; Hispanic: AAPC = -1.58, p = 0.24; NHAPI: AAPC = -1.22, p = 0.07). For distant-stage tumors (31,735 patients), incidence rates were increasing in NHB (AAPC = 2.72, p < 0.001), decreasing in Hispanic (AAPC = -0.64, p = 0.04), and stable in NHW (AAPC = 0.07, p = 0.84) and NHAPI (AAPC = 0.79, p = 0.13). There were 43,411 deaths attributed to GBC with decreasing mortality rates in all races/ethnicities except NHB who experienced a stable trend (AAPC = 0.25, p = 0.25). CONCLUSION: Nationwide data over the last two decades show that NHB patients experienced increasing GBC incidence between 2001 and 2014 followed by stabilization of the rates. This increase was driven by late-stage tumors and occurred in the first decade. NHB also experienced non-improving GBC mortality, compared to other race and ethnic groups who had decreasing mortality. This can be due to lack of timely-access to healthcare leading to delayed diagnosis and worse outcomes. Future studies are warranted to investigate contributions to the revealed racial and ethnic disparities, especially in NHB, to improve early detection.

Overall, sex-and race/ethnicity-specific prevalence of thyroid dysfunction in US adolescents aged 12-18 years.

Background: Thyroid dysfunction significantly affects the health and development of adolescents. However, comprehensive studies on its prevalence and characteristics in US adolescents are lacking. Methods: We investigated the prevalence of thyroid dysfunction in US adolescents aged 12-18 years using data from the National Health and Nutrition Examination Survey (NHANES) 2001-2002 and 2007-2012 cycles. Thyroid dysfunction was assessed using serum thyroid-stimulating hormone (TSH) and free thyroxine (fT4) measurements. We analyzed the prevalence across demographic subgroups and identified associated risk factors. Results: The study included 2,182 participants, representing an estimated 12.97 million adolescents. The group had a weighted mean age of 15.1 ± 0.06 years, with males constituting 51.4%. Subclinical hyperthyroidism emerged as the most prevalent thyroid dysfunction, affecting 4.4% of the population. From 2001-2002 to 2011-2012, subclinical hyperthyroidism remained consistent at 4.99% vs. 5.13% in the overall cohort. Subclinical and overt hypothyroidism was found in 0.41 and 1.03% of adolescents respectively, and overt hyperthyroidism was rare (0.04%). The prevalence of thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb) positivity in the overall population were 5.8 and 9.8%, respectively. Positivity for TgAb was risk factors for hypothyroidism, while older age, female and Black Americans were risk factors for hyperthyroidism. Female adolescents and adolescents with an older age were more likely to be positive for TPOAb and TgAb, while Black and Mexican Americans had a lower risk of TPOAb and TgAb positivity. Conclusion: Subclinical hyperthyroidism was the most common form of thyroid dysfunction, and its prevalence remained stable from 2001-2002 to 2011-2012. Notable disparities in the prevalence of hyperthyroidism and antibody positivity were observed among different age, sex and racial/ethnic groups.

Challenging Misconceptions about Race in Undergraduate Genetics.

Racial biases, which harm marginalized and excluded communities, may be combatted by clarifying misconceptions about race during biology lessons. We developed a human genetics laboratory activity that challenges the misconception that race is biological (biological essentialism). We assessed the relationship between this activity and student outcomes using a survey of students' attitudes about biological essentialism and color-evasive ideology and a concept inventory about phylogeny and human diversity. Students in the human genetics laboratory activity showed a significant decrease in their acceptance of biological essentialism compared with a control group, but did not show changes in color-evasive ideology. Students in both groups exhibited increased knowledge in both areas of the concept inventory, but the gains were larger in the human genetics laboratory. In the second iteration of this activity, we found that only white students' decreases in biological essentialist beliefs were significant and the activity failed to decrease color-evasive ideologies for all students. Concept inventory gains were similar and significant for both white and non-white students in this iteration. Our findings underscore the effectiveness of addressing misconceptions about the biological origins of race and encourage more research on ways to effectively change damaging student attitudes about race in undergraduate genetics education.

Fairness in Predicting Cancer Mortality Across Racial Subgroups.

Importance: Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so that existing racial disparities are not exacerbated. Objective: To evaluate whether racial bias exists in a predictive machine learning model that identifies 180-day cancer mortality risk among patients with solid malignant tumors. Design, Setting, and Participants: In this cohort study, a machine learning model to predict cancer mortality for patients aged 21 years or older diagnosed with cancer between January 2016 and December 2021 was developed with a random forest algorithm using retrospective data from the Mount Sinai Health System cancer registry, Social Security Death Index, and electronic health records up to the date when databases were accessed for cohort extraction (February 2022). Exposure: Race category. Main Outcomes and Measures: The primary outcomes were model discriminatory performance (area under the receiver operating characteristic curve [AUROC], F1 score) among each race category (Asian, Black, Native American, White, and other or unknown) and fairness metrics (equal opportunity, equalized odds, and disparate impact) among each pairwise comparison of race categories. True-positive rate ratios represented equal opportunity; both true-positive and false-positive rate ratios, equalized odds; and the percentage of predictive positive rate ratios, disparate impact. All metrics were estimated as a proportion or ratio, with variability captured through 95% CIs. The prespecified criterion for the model's clinical use was a threshold of at least 80% for fairness metrics across different racial groups to ensure the model's prediction would not be biased against any specific race. Results: The test validation dataset included 43 274 patients with balanced demographics. Mean (SD) age was 64.09 (14.26) years, with 49.6% older than 65 years. A total of 53.3% were female; 9.5%, Asian; 18.9%, Black; 0.1%, Native American; 52.2%, White; and 19.2%, other or unknown race; 0.1% had missing race data. A total of 88.9% of patients were alive, and 11.1% were dead. The AUROCs, F1 scores, and fairness metrics maintained reasonable concordance among the racial subgroups: the AUROCs ranged from 0.75 (95% CI, 0.72-0.78) for Asian patients and 0.75 (95% CI, 0.73-0.77) for Black patients to 0.77 (95% CI, 0.75-0.79) for patients with other or unknown race; F1 scores, from 0.32 (95% CI, 0.32-0.33) for White patients to 0.40 (95% CI, 0.39-0.42) for Black patients; equal opportunity ratios, from 0.96 (95% CI, 0.95-0.98) for Black patients compared with White patients to 1.02 (95% CI, 1.00-1.04) for Black patients compared with patients with other or unknown race; equalized odds ratios, from 0.87 (95% CI, 0.85-0.92) for Black patients compared with White patients to 1.16 (1.10-1.21) for Black patients compared with patients with other or unknown race; and disparate impact ratios, from 0.86 (95% CI, 0.82-0.89) for Black patients compared with White patients to 1.17 (95% CI, 1.12-1.22) for Black patients compared with patients with other or unknown race. Conclusions and Relevance: In this cohort study, the lack of significant variation in performance or fairness metrics indicated an absence of racial bias, suggesting that the model fairly identified cancer mortality risk across racial groups. It remains essential to consistently review the model's application in clinical settings to ensure equitable patient care.

Improvements in data completeness in health information systems reveal racial inequalities: longitudinal national data from hospital admissions in Brazil 2010-2022.

BACKGROUND: Race and ethnicity are important drivers of health inequalities worldwide. However, the recording of race/ethnicity in data systems is frequently insufficient, particularly in low- and middle-income countries. The aim of this study is to descriptively analyse trends in data completeness in race/color records in hospital admissions and the rates of hospitalizations by various causes for Blacks and Whites individuals. METHODS: We conducted a longitudinal analysis, examining hospital admission data from Brazil's Hospital Information System (SIH) between 2010 and 2022, and analysed trends in reporting completeness and racial inequalities. These hospitalization records were examined based on year, quarter, cause of admission (using International Classification of Diseases (ICD-10) codes), and race/color (categorized as Black, White, or missing). We examined the patterns in hospitalization rates and the prevalence of missing data over a period of time. RESULTS: Over the study period, there was a notable improvement in data completeness regarding race/color in hospital admissions in Brazil. The proportion of missing values on race decreased from 34.7% in 2010 to 21.2% in 2020. As data completeness improved, racial inequalities in hospitalization rates became more evident - across several causes, including assaults, tuberculosis, hypertensive diseases, at-risk hospitalizations during pregnancy and motorcycle accidents. CONCLUSIONS: The study highlights the critical role of data quality in identifying and addressing racial health inequalities. Improved data completeness has revealed previously hidden inequalities in health records, emphasizing the need for comprehensive data collection to inform equitable health policies and interventions. Policymakers working in areas where socioeconomic data reporting (including on race and ethnicity) is suboptimal, should address data completeness to fully understand the scale of health inequalities.