Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Use of race, ethnicity, and ancestry data in health research.

ABSTRACT: Race, ethnicity, and ancestry are common classification variables used in health research. However, there has been no formal agreement on the definitions of these terms, resulting in misuse, confusion, and a lack of clarity surrounding these concepts for researchers and their readers. This article examines past and current understandings of race, ethnicity, and ancestry in research, identifies the distinctions between these terms, examines the reliability of these terms, and provides researchers with guidance on how to use these terms. Although race, ethnicity, and ancestry are often treated synonymously, they should be considered as distinct terms in the context of health research. Researchers should carefully consider which term is most appropriate for their study, define and use the terms consistently, and consider how their classification may be used in future research by others. The classification should be self-reported rather than assigned by an observer wherever possible.

SUBMITTER: Lu C

PROVIDER: S-EPMC10022242 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Use of race, ethnicity, and ancestry data in health research.

Lu Clara C Ahmed Rabeeyah R Lamri Amel A Anand Sonia S SS

PLOS global public health 20220915 9

Race, ethnicity, and ancestry are common classification variables used in health research. However, there has been no formal agreement on the definitions of these terms, resulting in misuse, confusion, and a lack of clarity surrounding these concepts for researchers and their readers. This article examines past and current understandings of race, ethnicity, and ancestry in research, identifies the distinctions between these terms, examines the reliability of these terms, and provides researchers ...[more]

PMID: 36962630

Similar Datasets

The use of race, ethnicity and ancestry in human genetic research.

Project description:Post-Human Genome Project progress has enabled a new wave of population genetic research, and intensified controversy over the use of race/ethnicity in this work. At the same time, the development of methods for inferring genetic ancestry offers more empirical means of assigning group labels. Here, we provide a systematic analysis of the use of race/ethnicity and ancestry in current genetic research. We base our analysis on key published recommendations for the use and reporting of race/ethnicity which advise that researchers: explain why the terms/categories were used and how they were measured, carefully define them, and apply them consistently. We studied 170 population genetic research articles from high impact journals, published 2008-2009. A comparative perspective was obtained by aligning study metrics with similar research from articles published 2001-2004. Our analysis indicates a marked improvement in compliance with some of the recommendations/guidelines for the use of race/ethnicity over time, while showing that important shortfalls still remain: no article using 'race', 'ethnicity' or 'ancestry' defined or discussed the meaning of these concepts in context; a third of articles still do not provide a rationale for their use, with those using 'ancestry' being the least likely to do so. Further, no article discussed potential socio-ethical implications of the reported research. As such, there remains a clear imperative for highlighting the importance of consistent and comprehensive reporting on human populations to the genetics/genomics community globally, to generate explicit guidelines for the uses of ancestry and genetic ancestry, and importantly, to ensure that guidelines are followed.

| S-EPMC3237839 | biostudies-literature

Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: Experiences from the NHLBI TOPMed program.

Project description:How race, ethnicity, and ancestry are used in genomic research has wide-ranging implications for how research is translated into clinical care and incorporated into public understanding. Correlation between race and genetic ancestry contributes to unresolved complexity for the scientific community, as illustrated by heterogeneous definitions and applications of these variables. Here, we offer commentary and recommendations on the use of race, ethnicity, and ancestry across the arc of genetic research, including data harmonization, analysis, and reporting. While informed by our experiences as researchers affiliated with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, these recommendations are applicable to basic and translational genomic research in diverse populations with genome-wide data. Moving forward, considerable collaborative effort will be required to ensure that race, ethnicity, and ancestry are described and used appropriately to generate scientific knowledge that yields broad and equitable benefit.

| S-EPMC9481067 | biostudies-literature

Race, ethnicity, ancestry, and aspects that impact HLA data and matching for transplant.

Project description:Race, ethnicity, and ancestry are terms that are often misinterpreted and/or used interchangeably. There is lack of consensus in the scientific literature on the definition of these terms and insufficient guidelines on the proper classification, collection, and application of this data in the scientific community. However, defining groups for human populations is crucial for multiple healthcare applications and clinical research. Some examples impacted by population classification include HLA matching for stem-cell or solid organ transplant, identifying disease associations and/or adverse drug reactions, defining social determinants of health, understanding diverse representation in research studies, and identifying potential biases. This article describes aspects of race, ethnicity and ancestry information that impact the stem-cell or solid organ transplantation field with particular focus on HLA data collected from donors and recipients by donor registries or transplant centers.

| S-EPMC10978785 | biostudies-literature

Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort.

Project description:Using genome-wide genotypes, we characterized the genetic structure of 103,006 participants in the Kaiser Permanente Northern California multi-ethnic Genetic Epidemiology Research on Adult Health and Aging Cohort and analyzed the relationship to self-reported race/ethnicity. Participants endorsed any of 23 race/ethnicity/nationality categories, which were collapsed into seven major race/ethnicity groups. By self-report the cohort is 80.8% white and 19.2% minority; 93.8% endorsed a single race/ethnicity group, while 6.2% endorsed two or more. Principal component (PC) and admixture analyses were generally consistent with prior studies. Approximately 17% of subjects had genetic ancestry from more than one continent, and 12% were genetically admixed, considering only nonadjacent geographical origins. Self-reported whites were spread on a continuum along the first two PCs, indicating extensive mixing among European nationalities. Self-identified East Asian nationalities correlated with genetic clustering, consistent with extensive endogamy. Individuals of mixed East Asian-European genetic ancestry were easily identified; we also observed a modest amount of European genetic ancestry in individuals self-identified as Filipinos. Self-reported African Americans and Latinos showed extensive European and African genetic ancestry, and Native American genetic ancestry for the latter. Among 3741 genetically identified parent-child pairs, 93% were concordant for self-reported race/ethnicity; among 2018 genetically identified full-sib pairs, 96% were concordant; the lower rate for parent-child pairs was largely due to intermarriage. The parent-child pairs revealed a trend toward increasing exogamy over time; the presence in the cohort of individuals endorsing multiple race/ethnicity categories creates interesting challenges and future opportunities for genetic epidemiologic studies.

| S-EPMC4574246 | biostudies-literature

The use of race and ethnicity in sickle cell disease research.

Project description:This study explores practices surrounding the operationalization of ethno-racial categories (ERCs) as confounders in biomedical research, with a focus on sickle cell disease (SCD) as a model. ERCs, often aggregate labels encompassing diverse individuals which raises questions about their relevance as confounders. Given SCD's racialization as a "Black" disease, understanding ERC utilization is crucial. This study analyzed 1,105 SCD studies published globally. Data were collected on whether ERC adjustment was employed, regional variations in ERC-adjustment rates, labels used for ERCs, rationales provided for ERC matching, and methods used for ERC determination. 28% of the studies utilized ERC adjustment, with significant regional disparities (p < 0.001). Notably, Western studies showed higher rates of ERC adjustment compared to other regions. However, crucial details such as ERC labels and methodology were frequently missing. Commonly used labels included "African" or "Black." Only 7% of studies provided explicit rationales for ERC matching, and 70% did not specify the method used for ERC determination. The findings underscore the need to adhere to guidelines on ERC operationalization in biomedicine. The lack of standardized practices raises concerns about potential biases and misinterpretations in research outcomes. Adhering to clear guidelines can mitigate the risk of perpetuating racial stereotypes and inequalities while ensuring research integrity.

| S-EPMC11887362 | biostudies-literature

Race, ethnicity, and NIH research awards.

Project description:We investigated the association between a U.S. National Institutes of Health (NIH) R01 applicant's self-identified race or ethnicity and the probability of receiving an award by using data from the NIH IMPAC II grant database, the Thomson Reuters Web of Science, and other sources. Although proposals with strong priority scores were equally likely to be funded regardless of race, we find that Asians are 4 percentage points and black or African-American applicants are 13 percentage points less likely to receive NIH investigator-initiated research funding compared with whites. After controlling for the applicant's educational background, country of origin, training, previous research awards, publication record, and employer characteristics, we find that black applicants remain 10 percentage points less likely than whites to be awarded NIH research funding. Our results suggest some leverage points for policy intervention.

| S-EPMC3412416 | biostudies-literature

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

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

| S-EPMC11626588 | biostudies-literature

Estimating exposure to neighborhood crime by race and ethnicity for public health research.

Project description:BackgroundPolice-reported crime data (hereafter "crime") is routinely used as a psychosocial stressor in public health research, yet few studies have jointly examined (a) differences in crime exposure based on participant race and ethnicity, (b) differences in measures of crime exposure, and (c) considerations for how exposure to police is captured in police-recorded crime data. We estimate neighborhood exposure to crime and discuss the implications of structural differences in exposure to crime and police based on race and ethnicity.MethodsUsing GPS coordinates from 1188 participants in the Newborn Epigenetics Study, we estimated gestational exposure to crime provided by the Durham, North Carolina, Police Department within (a) 800 m and (b) the Census block group of residence. We controlled for non-overlapping spatial boundaries in crime, Census, residential, and police data to report crime spatial (crime per km2) and population (crime per 1000 people per km2) density.ResultsWe demonstrate dramatic disparities in exposure to crime based on participant race and ethnicity and highlight variability in these disparities based on the type of crime and crime measurement method chosen.ConclusionsPublic health researchers should give thoughtful consideration when using police-reported crime data to measure and model exposure to crime in the United States, as police-reported data encompasses joint exposure to police and crime in the neighborhood setting.

| S-EPMC8183080 | biostudies-literature

Accuracy of data entry of patient race/ethnicity/ancestry and preferred spoken language in an ambulatory care setting.

Project description:ObjectiveTo describe data collection methods and to audit staff data entry of patient self-reported race/ethnicity/ancestry and preferred spoken language (R/E/A/L) information.Data source/study settingLarge mixed payer outpatient health care organization in Northern California, June 2009.Study designSecondary analysis of an audit planned and executed by the Department of Clinical Services.Data collection/extraction methodsWe analyzed concordance between patient written responses and staff data entry.Principal findingsThe data entry accuracy rate across questions was high, ranging from 92 to 97 percent. Inaccuracies were due to human error (62 percent), flaws in system design (2 percent), or some combination of both (35 percent).ConclusionsThis study highlights the high accuracy of patient self-reported R/E/A/L data entry and identifies some areas for improvement in staff training and technical system design to facilitate further progress.

| S-EPMC3447251 | biostudies-literature

Underreporting of race/ethnicity in COVID-19 research.

Project description:ObjectivesAlthough racial/ethnic disparities in healthcare have long been recognized, recent discourse around structural racism will hopefully lead to improved transparency surrounding these issues. Despite the disproportionate impact of COVID-19 on racial/ethnic minorities, the extent and reliability of race reporting in COVID research is unclear.MethodsCOVID-19 research published in three top medical journals during the first wave of the COVID-19 pandemic was reviewed and assessed for race reporting and proportional representation.ResultsOf the 95 manuscripts that were identified, 56 reporting on 252,262 patients met eligibility. Thirty-five (62.5%) did not report race distribution and 15 (26.7%) did not report ethnicity. There was no difference based on journal (P = 0.87), study sponsor (P = 0.41), whether the study was retrospective or prospective (P = 0.33), or observational vs interventional (P = 0.11). Studies with ≥250 patients were more likely to report on race (OR 4.01, 95% CI: 1.12-14.37, P = 0.027), and North American (USA and Canada) studies were more likely than European studies (OR 7.88, 95% CI: 1.73-37.68, P = 0.006) to report on race. COVID-19 research mirrored USA COVID-19 racial incidence; however, both showed higher distribution of COVID-19 infection among Blacks and a smaller proportion of Whites compared to the USA population. This suggests that research is broadly representing infection rates and that social determinants of health are impacting racial distribution of infection.ConclusionsDespite increasing awareness of racial disparities and inequity, COVID-19 research during the first wave of the pandemic lacked appropriate racial/ethnicity reporting. However, research mirrored COVID-19 incidence in the USA, with an increased burden of infection among Black individuals.

| S-EPMC8221824 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data