Racial Health Inequities and Clinical Algorithms

Neal, Richard E; Morse, Michelle

doi:10.2215/cjn.01780221

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…However, there is concern about the use of race in clinical algorithms and some health-based risk scores and databases because of inapplicability to some groups and the potential for discrimination and inappropriate clinical decisions. For example, the use of race to estimate glomerular filtration rates among Black adults has become controversial for several reasons . Oversimplification of racial dichotomies can be harmful, such as in calculating kidney function, especially with racial inequities in kidney care.…”

Section: Concerns Sensitivities and Controversies In Health Care And ...mentioning

confidence: 99%

“…Oversimplification of racial dichotomies can be harmful, such as in calculating kidney function, especially with racial inequities in kidney care. In this context, health inequities among populations should be addressed rather than focusing solely on differences in racial categories (eg, Black vs White adults with kidney disease) . Another example is the Framingham Risk Score, which was originally developed from a cohort of White, middle-class participants in the US included in the Framingham Heart Study and may not accurately estimate risk in other racial and ethnic populations.…”

Section: Concerns Sensitivities and Controversies In Health Care And ...mentioning

confidence: 99%

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Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals

Flanagin

Frey

Christiansen

2021

JAMA

923

629

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The goal of this guidance is to provide recommendations and suggestions that encourage fairness, equity, consistency, and clarity in use and reporting of race and ethnicity in medical and science journals. As previously summarized, "terminology, usage, and word choice are critically important, especially when describing people and when discussing race and ethnicity. Inclusive language supports diversity and conveys respect. Language that imparts bias toward or against persons or groups based on characteristics or demographics must be avoided." 1 With the publication of an earlier version of this guidance, 1 comments were invited, and helpful assessments and comments were received from numerous reviewers, scholars, and researchers, who provided valuable feedback and represented diverse expertise and opinions. After thorough review of these comments (some of which did not agree with others) and additional research and discussion, the guidance was revised and updated, and additional formal review was obtained. In this Editorial, we present the updated guidance, and we sincerely thank the many reviewers for their contributions, each of whom are listed in the Acknowledgment at the end of this article.This guidance continues to acknowledge that race and ethnicity are social constructs as well as the important sensitivities and controversies related to use of these terms and associated nomenclature in medical and health research, education, and practice. Thus, for content published in medical journals, language and terminology must be accurate, clear, and precise, and must reflect fairness, equity, and consistency in use and reporting of race and ethnicity. The guidance also acknowledges that the reporting of race and ethnicity should not be considered in isolation and should be accompanied by reporting of other sociodemographic factors and social determinants, including concerns about racism, disparities, and inequities, and the intersectionality of race and ethnicity with these other factors.The guidance defines commonly used terms associated with race and ethnicity and acknowledges that these terms and definitions have changed, that some are out of date, and that the nomenclature will continue to evolve. Other topics addressed include relevant concerns and controversies in health care and research, including the intersectionality of ancestry and heritage, social determinants of health, and other socioeconomic, structural, institutional, cultural, and demographic factors; reporting of race and ethnicity in research articles; use of racial and ethnic collective or umbrella terms, capitalization, and abbreviations; listing racial and ethnic categories in alphabetical order vs order by majority; adjectival vs noun usage for categories of race and ethnicity; geo-Opinion EDITORIAL

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Section: Concerns Sensitivities and Controversies In Health Care And ...mentioning

confidence: 99%

Section: Concerns Sensitivities and Controversies In Health Care And ...mentioning

confidence: 99%

Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals

Flanagin

Frey

Christiansen

2021

JAMA

923

629

View full text Add to dashboard Cite

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“…By including factors that focus explicitly on minoritized populations and medical vulnerability, the MH SVI aims to fill key contextual gaps in the current SVI, but whether it fulfills this objective has not yet been assessed 16 . Despite the inclusion of indicators related to race and ethnicity, conflating race and structural racism or structural disadvantage can be, and often is, problematic 48–50 . Distinguishing between race and racism is not yet well operationalized and tools that explicitly measure racism are not widely adopted.…”

Section: Discussionmentioning

confidence: 99%

“…16 Despite the inclusion of indicators related to race and ethnicity, conflating race and structural racism or structural disadvantage can be, and often is, problematic. [48][49][50] Distinguishing between race and racism is not yet well operationalized and tools that explicitly measure racism are not widely adopted.…”

Section: Methodological Challenges: Underlying Data Source Index Cons...mentioning

confidence: 99%

Assessment of Population‐Level Disadvantage Indices to Inform Equitable Health Policy

Kaalund¹,

Thoumi²,

Bhavsar

et al. 2022

Milbank Quarterly

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The rapid uptake of disadvantage indices during the pandemic highlights investment in implementing tools that address health equity to inform policy. Existing indices differ in their design, including data elements, social determinants of health domains, and geographic unit of analysis. These differences can lead to stark discrepancies in place‐based social risk scores depending on the index utilized. Disadvantage indices are useful tools for identifying geographic patterns of social risk; however, indiscriminate use of indices can have varied policy implications and unintentionally worsen equity. Implementers should consider which indices are suitable for specific communities, objectives, potential interventions, and outcomes of interest. Context There has been unprecedented uptake of disadvantage indices such as the Centers for Disease Control and Prevention Social Vulnerability Index (SVI) to identify place‐based patterns of social risk and guide equitable health policy during the COVID‐19 pandemic. However, limited evidence around data elements, interoperability, and implementation leaves unanswered questions regarding the utility of indices to prioritize health equity. Methods We identified disadvantage indices that were (a) used three or more times from 2018 to 2021, (b) designed using national‐level data, and (c) available at the census‐tract or block‐group level. We used a network visualization to compare social determinants of health (SDOH) domains across indices. We then used geospatial analyses to compare disadvantage profiles across indices and geographic areas. Findings We identified 14 indices. All incorporated data from public sources, with half using only American Community Survey data (n = 7) and the other half combining multiple sources (n = 7). Indices differed in geographic granularity, with county level (n = 5) and census‐tract level (n = 5) being the most common. Most states used the SVI during the pandemic. The SVI, the Area Deprivation Index (ADI), the COVID‐19 Community Vulnerability Index (CCVI), and the Child Opportunity Index (COI) met criteria for further analysis. Selected indices shared five indicators (income, poverty, English proficiency, no high school diploma, unemployment) but varied in other metrics and construction method. While mapping of social risk scores in Durham County, North Carolina; Cook County, Illinois; and Orleans Parish, Louisiana, showed differing patterns within the same locations depending on choice of disadvantage index, risk scores across indices showed moderate to high correlation (rs 0.7‐1). However, spatial autocorrelation analyses revealed clustering, with discrepant distributions of social risk scores between different indices. Conclusions Existing disadvantage indices use varied metrics to represent place‐based social risk. Within the same geographic area, different indices can provide differences in social risk values and interpretations, potentially leading to varied public health or policy responses.

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“…The use of race in medicine and clinical research is a controversial issue and has been repeatedly challenged in recent publications (37,38), and guidance for researchers continues to evolve (39). Race is a complex social construct that reflects social determinants of health and systemic racism, among others, yet self-described race may contain information that helps to predict an individual's risk of cardiovascular outcomes.…”

Section: Statistical Analysesmentioning

confidence: 99%

Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study

Zelnick

Shlipak

Soliman

et al. 2021

CJASN

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Background and objectivesAtrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident AF. We compared a previously published prediction model with models developed using machine learning methods in a CKD population.Design, setting, participants, & measurementsWe studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro–B-type natriuretic peptide and high-sensitivity troponin T) and clinical data. We evaluated the utility of machine learning methods as well as a previously validated clinical prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology [CHARGE]-AF, which included 11 predictors, using original and re-estimated coefficients) to predict incident AF. Discriminatory ability of each model was assessed using the ten-fold cross-validated C-index; calibration was evaluated graphically and with the Grønnesby and Borgan test.ResultsMean (SD) age of participants was 57 (11) years, 55% were men, 38% were Black, and mean (SD) eGFR was 45 (15) ml/min per 1.73 m2; 259 incident AF events occurred during a median of 8 years of follow-up. The CHARGE-AF prediction equation using original and re-estimated coefficients had C-indices of 0.67 (95% confidence interval, 0.64 to 0.71) and 0.67 (95% confidence interval, 0.64 to 0.70), respectively. A likelihood-based boosting model using clinical variables only had a C-index of 0.67 (95% confidence interval, 0.64 to 0.70); adding N-terminal pro–B-type natriuretic peptide, high-sensitivity troponin T, or both biomarkers improved the C-index by 0.04, 0.01, and 0.04, respectively. In addition to N-terminal pro–B-type natriuretic peptide and high-sensitivity troponin T, the final model included age, non-Hispanic Black race/ethnicity, Hispanic race/ethnicity, cardiovascular disease, chronic obstructive pulmonary disease, myocardial infarction, peripheral vascular disease, use of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers, calcium channel blockers, diuretics, height, and weight.ConclusionsUsing machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro–B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.

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Racial Health Inequities and Clinical Algorithms

Cited by 8 publications

References 9 publications

Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals

Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals

Assessment of Population‐Level Disadvantage Indices to Inform Equitable Health Policy

Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study

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