Missing in Action: African Ancestry Brain Research

Weinberger, Daniel R.; Dzirasa, Kafui; Crumpton-Young, Lesia

doi:10.1016/j.neuron.2020.07.008

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…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. Similar concerns have been raised about genetic risk studies based on specific populations or that do not include participants from other groups (eg, a genome-wide association study that reports a genetic association with a specific disease or disorder based solely on a population of European descent) . Use caution in interpreting or generalizing findings from studies of risk based on populations of individuals representing specific or limited racial and ethnic categories.…”

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

confidence: 93%

“…Similar concerns have been raised about genetic risk studies based on specific populations or that do not include participants from other groups (eg, a genome-wide association study that reports a genetic association with a specific disease or disorder based solely on a population of European descent). 22 Use caution in interpreting or generalizing findings from studies of risk based on populations of individuals representing specific or limited racial and ethnic categories.…”

Section: Concerns Sensitivities and Controversies In Health Care And Researchmentioning

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: 93%

Section: Concerns Sensitivities and Controversies In Health Care And Researchmentioning

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

show abstract

“…The Framingham Risk Score was originally developed from a cohort of US White, middle-class participants in the Framingham Heart Study, and it may not accurately estimate risk in other racial and ethnic populations. Similar concerns have been raised about genetic risk studies based on specific populations or that do not include participants from underrepresented groups (eg, a genome-wide association study that reports a genetic association with a specific disease or disorder based solely on a population of European descent) . Use caution in interpreting or generalizing findings from studies of risk based on populations of individuals representing specific or limited racial and ethnic categories.…”

Section: Section 11123 Race/ethnicitymentioning

confidence: 99%

The Reporting of Race and Ethnicity in Medical and Science Journals

Flanagin

Frey

Christiansen

et al. 2021

JAMA

257

220

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“…95 96 97 These issues are particularly prevalent in genome-wide association studies, as the body of previous research and genetic testing is chronically dominated by White populations. 98 99 100 Though race is not an ideal proxy, vulnerable populations including immigrants and those of low socioeconomic status tend to visit multiple health care facilities, resulting in health data that are more likely to be fragmented across different systems. In this way, models that rely on the quantity of encounter or the presence of an ordered test can adversely impact vulnerable populations.…”

Section: Discussionmentioning

confidence: 99%

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

2022

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Objective As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. Methods We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. Results From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. Discussion With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.

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Missing in Action: African Ancestry Brain Research

Abstract: Individuals of African ancestry have been starkly underrepresented in the pursuit of personalized medicine for brain illnesses. The African Ancestry Neuroscience Research Initiative will seek to generate much-needed brain gene and protein expression profiles for people of African ancestry.

Cited by 15 publications

References 10 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

The Reporting of Race and Ethnicity in Medical and Science Journals

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

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