-
PloS One 2022The analysis of existing institutional research proposal databases can provide novel insights into science funding parity. The purpose of this study was to analyze the...
PURPOSE
The analysis of existing institutional research proposal databases can provide novel insights into science funding parity. The purpose of this study was to analyze the relationship between race/ethnicity and extramural research proposal and award rates across a medical school faculty and to determine whether there was evidence that researchers changed their submission strategies because of differential inequities across submission categories.
METHOD
The authors performed an analysis of 14,263 biomedical research proposals with proposed start dates between 2010-2022 from the University of Michigan Medical School, measuring the proposal submission and award rates for each racial/ethnic group across 4 possible submission categories (R01 & Equivalent programs, other federal, industry, and non-profit).
RESULTS
Researchers from each self-identified racial/ethnic group (Asian, Black/African American, Hispanic/Latino) pursued a different proposal submission strategy than the majority group (White). The authors found that Black/African American researchers experienced negative award rate differentials across all submission categories, which resulted in the lowest R01 & Equivalent and Other Federal submission rates of any racial/ethnic group and the highest submission rate to non-profit sources. The authors did not find support for the hypothesis that researchers changed submission strategies in response to award rate inequalities across submission categories.
CONCLUSIONS
Biomedical researchers from different racial/ethnic groups follow markedly different proposal submission strategies within the University of Michigan Medical School. There is also a clear relationship between race/ethnicity and rates of proposal award. Black/African American and Asian researchers appear disadvantaged across all submission categories relative to White researchers. This study can be easily replicated by other academic research institutions, revealing opportunities for positive intervention.
Topics: Awards and Prizes; Biomedical Research; Ethnicity; Female; Humans; Pregnancy; Racial Groups; Research Personnel
PubMed: 35776730
DOI: 10.1371/journal.pone.0270612 -
JDR Clinical and Translational Research Apr 2022This article aims to examine the disparities in dental service utilization among 3 age groups: younger adults (20-49 y), middle-aged adults (50-64 y), and older adults...
OBJECTIVES
This article aims to examine the disparities in dental service utilization among 3 age groups: younger adults (20-49 y), middle-aged adults (50-64 y), and older adults (65+ y), among Whites, Hispanics, Blacks, Asians, American Indians or Alaska Natives (AIAN), and Native Hawaiian or other Pacific Islanders (NHOPI).
METHODS
Weighted logistic regression models were conducted to analyze 9 waves of cross-sectional survey data (2002-2018) from the Behavioral Risk Factor Surveillance System. We estimated age group- and race/ethnic-specific prevalences of dental service utilization adjusting sociodemographics and self-rated health for each wave and compared with crude analysis. Next, we performed linear regression analysis of the trend of adjusted prevalences over time and the average level by race/ethnicity and age groups.
RESULTS
Racial/ethnic disparities increased with age, even though the adjusted prevalences of dental service utilization were less apparent than the crude analysis. The all-wave average prevalence was 71%. Black older adults had the lowest level of dental service utilization (65%) as compared with the 2 highest groups: White older adults (79%) and Asian older adults (76%). The general younger adult populations had low prevalences, with the lowest among Asian younger adults (65%). AIAN and NHOPI individuals from all age groups tended to have average or below average prevalences. In addition, a decreasing trend of dental service utilization was observed among White individuals of all age groups (0.2%-0.3% lower per year, P < 0.01) and AIAN younger adults (0.5% lower per year, P < 0.01).
CONCLUSION
Health policy, federal funding, and community-based programs should address the needs of dental service utilization for racial/ethnic minorities including Blacks, AIANs, and NHOPIs.
KNOWLEDGE TRANSFER STATEMENT
Our study offers insights into our understanding of disparities in dental service utilization among minority racial/ethnic groups. As health policy, federal funding, and community-based programs seek to improve oral health, there is a need to address access to and utilization of dental service for Blacks, American Indians or Alaska Natives, and Native Hawaiian or other Pacific Islanders.
Topics: Aged; Cross-Sectional Studies; Dental Care; Hispanic or Latino; Humans; Middle Aged; Native Hawaiian or Other Pacific Islander; Racial Groups; United States
PubMed: 33938303
DOI: 10.1177/23800844211012660 -
Cancer Medicine Jul 2023There are well-established disparities in colorectal cancer (CRC) outcomes between White and Black patients; however, assessments of CRC disparities for other...
BACKGROUND
There are well-established disparities in colorectal cancer (CRC) outcomes between White and Black patients; however, assessments of CRC disparities for other racial/ethnic groups are limited.
METHODS
The Surveillance, Epidemiology, and End Results database identified patients aged 50-74 years with CRC adenocarcinoma from 2000 to 2019. Trends in age-adjusted incidence rates were computed by stage at diagnosis and subsite across five broad race/ethnic groups (White, Black, Asian/Pacific Islander [API], American Indian/Alaskan Native [AIAN], and Hispanic) and four API subgroups (East Asian, Southeast Asian, South Asian, and Pacific Islander) Multivariable logistic regression evaluated associations between race/ethnicity and diagnosis stage. Multivariable Cox proportional hazards models assessed differences in cause-specific survival (CSS).
RESULTS
Hispanic, AIAN, Southeast Asian, Pacific Islander, and Black patients were 3% to 28% more likely than Whites to be diagnosed with distant stage CRC, whereas East Asian and South Asians had similar or lower risk of distant stage CRC. From Cox regression analysis, Black, AIAN, and Pacific Islanders also experienced worse CSS, while East Asian and South Asian patient groups experienced better CSS. No significant differences in CSS were observed among Hispanic, Southeast Asian, and White patients. When stratified by stage, Black patients had worse CSS across all stages (early, hazard ratio (HR) = 1.38; regional, HR = 1.22; distant, HR: 1.07, p < 0.05 for all).
CONCLUSION
Despite advances in CRC screening, treatment and early detection efforts, marked racial/ethnic disparities in incidence, stage at diagnosis, and survival persist. Findings demonstrate the extent to which aggregating heterogenous populations masks significant variability in CRC outcomes within race/ethnic subgroups.
Topics: Aged; Humans; Middle Aged; Adenocarcinoma; Asian People; Colorectal Neoplasms; Ethnicity; Hispanic or Latino; Racial Groups; White People; Black or African American; East Asian People; Southeast Asian People; South Asian People; Pacific Island People; Health Status Disparities
PubMed: 37212502
DOI: 10.1002/cam4.6105 -
JCO Precision Oncology 2021
Topics: Black or African American; Genetic Phenomena; Humans; Male; Odds Ratio; Prostatic Neoplasms; Racial Groups
PubMed: 34746633
DOI: 10.1200/PO.21.00324 -
Public Health Genomics 2023Genetic screening for preventable adult-onset hereditary conditions has been proposed as a mechanism to reduce health disparities. Analysis of how race and ethnicity...
INTRODUCTION
Genetic screening for preventable adult-onset hereditary conditions has been proposed as a mechanism to reduce health disparities. Analysis of how race and ethnicity influence decision-making to receive screening can inform recruitment efforts and more equitable population screening design. A study at the University of Washington Medicine that invited unselected patients to participate in genetic screening for pathogenic variation in medically important genes provided an opportunity to evaluate these factors.
METHODS
We analyzed screening enrollee survey data to understand factors most important and least important in decision-making about screening overall and across different race and ethnicity groups. Electronic health record race and ethnicity and survey-reported race and ethnicity were compared to assist with interpretation. Comments provided about reasons for not enrolling in screening were analyzed using content analysis.
RESULTS
Overall, learning about disease risk and identifying risk early for prevention purposes were important factors in decision-making to receive screening, and regrets about screening and screening being against one's moral code were not viewed as important. Although racial identity was challenging to assign in all cases, compared to other enrollees, African-American and Asian enrollees considered test accuracy and knowing more about the test to be of greater importance. Three themes emerged related to nonparticipation: benefits do not outweigh risks, don't want to know, and challenges with study logistics.
CONCLUSION
Our results highlight important motivators for receiving screening and areas that can be addressed to increase screening interest and accessibility. This knowledge can inform future population screening program design including recruitment and education approaches.
Topics: Adult; Humans; Black or African American; Ethnicity; Genetic Testing; Socioeconomic Factors; Surveys and Questionnaires; Racial Groups; Decision Making
PubMed: 37604133
DOI: 10.1159/000531989 -
Brain and Cognition Feb 2021Studies examining the visual perception of face race have revealed mixed findings regarding the presence or direction of effects on early face-sensitive event-related...
Studies examining the visual perception of face race have revealed mixed findings regarding the presence or direction of effects on early face-sensitive event-related potential (ERP) components. Few studies have examined how early ERP components are influenced by individual differences in bottom-up and top-down processes involved in face perception, and how such factors might interact to influence early face-sensitive ERP components has yet to be investigated. Thus, the current study examined whether P100, N170, and P200 responses can be predicted by individual differences in own- and other-race face recognition, implicit racial bias, and their interaction. Race effects were observed in the P100, N170, and P200 responses. Other-race face recognition, implicit racial biases, and their interaction explained a significant amount of unique variability in N170 responses when viewing other-race faces. Responses to own-race faces were minimally influenced with only implicit racial bias predicting a significant amount of unique variability in N170 latency when viewing own-race faces. Face recognition, implicit racial bias, or their interaction did not predict P100 responses. The current findings suggest that face recognition abilities and its interaction with implicit racial bias modulate the early stages of other-race face processing.
Topics: Electroencephalography; Evoked Potentials; Facial Recognition; Humans; Pattern Recognition, Visual; Racial Groups; Racism
PubMed: 33360041
DOI: 10.1016/j.bandc.2020.105671 -
Human Genomics Jan 2015This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human... (Review)
Review
This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person's physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using "ancestry" (or biogeographical ancestry) to describe actual genetic variation, "race" to describe health disparity in societies characterized by racial categories, and "ethnicity" to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals' biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals.
Topics: Biomedical Research; Ethnicity; Genetic Markers; Genomics; Humans; Phenotype; Racial Groups; Self Report
PubMed: 25563503
DOI: 10.1186/s40246-014-0023-x -
Sleep Health Aug 2019This study assessed the associations between short and long sleep duration and prevalence of cardiometabolic outcomes in American Indians and Alaska Natives (AI/ANs) and...
OBJECTIVES
This study assessed the associations between short and long sleep duration and prevalence of cardiometabolic outcomes in American Indians and Alaska Natives (AI/ANs) and compared these associations to those evident among other race/ethnicities.
METHODS
We analyzed data from the 2013-2014 Behavioral Risk Factor Surveillance System. In total, 14,536 AI/ANs, 729,962 non-Hispanic whites, 71,765 blacks, and 59,472 Hispanics were included. Logistic regressions were conducted to compute unadjusted and adjusted odds ratios (OR) for the associations of interest.
RESULTS
Among AI/ANs, 38.6% reported sleeping <7 hours per night (short sleepers) while 39.3% reported 8+ hours of sleep (long sleepers). After adjusting for age and gender, both short and long sleep durations were associated with higher odds of reporting diabetes, stroke, coronary heart disease and heart attack in almost all race/ethnic groups. After multiple adjustments, the sleep-diabetes association was more pronounced (OR = 1.71 and OR = 1.56 for short and long sleepers, respectively) among AI/ANs than other race/ethnicities.
CONCLUSIONS
Future studies are warranted to examine race/ethnic variability in the association between sleep duration and cardiometabolic outcomes.
Topics: Adolescent; Adult; Aged; Alaska Natives; Behavioral Risk Factor Surveillance System; Cardiovascular Diseases; Ethnicity; Female; Humans; Indians, North American; Male; Metabolic Syndrome; Middle Aged; Prevalence; Racial Groups; Sleep; Time Factors; Young Adult
PubMed: 30987947
DOI: 10.1016/j.sleh.2019.02.003 -
JAMA Network Open Oct 2023Variants of uncertain significance (VUSs) are rampant in clinical genetic testing, frustrating clinicians, patients, and laboratories because the uncertainty hinders...
IMPORTANCE
Variants of uncertain significance (VUSs) are rampant in clinical genetic testing, frustrating clinicians, patients, and laboratories because the uncertainty hinders diagnoses and clinical management. A comprehensive assessment of VUSs across many disease genes is needed to guide efforts to reduce uncertainty.
OBJECTIVE
To describe the sources, gene distribution, and population-level attributes of VUSs and to evaluate the impact of the different types of evidence used to reclassify them.
DESIGN, SETTING, AND PARTICIPANTS
This cohort study used germline DNA variant data from individuals referred by clinicians for diagnostic genetic testing for hereditary disorders. Participants included individuals for whom gene panel testing was conducted between September 9, 2014, and September 7, 2022. Data were analyzed from September 1, 2022, to April 1, 2023.
MAIN OUTCOMES AND MEASURES
The outcomes of interest were VUS rates (stratified by age; clinician-reported race, ethnicity, and ancestry groups; types of gene panels; and variant attributes), percentage of VUSs reclassified as benign or likely benign vs pathogenic or likely pathogenic, and enrichment of evidence types used for reclassifying VUSs.
RESULTS
The study cohort included 1 689 845 individuals ranging in age from 0 to 89 years at time of testing (median age, 50 years), with 1 203 210 (71.2%) female individuals. There were 39 150 Ashkenazi Jewish individuals (2.3%), 64 730 Asian individuals (3.8%), 126 739 Black individuals (7.5%), 5539 French Canadian individuals (0.3%), 169 714 Hispanic individuals (10.0%), 5058 Native American individuals (0.3%), 2696 Pacific Islander individuals (0.2%), 4842 Sephardic Jewish individuals (0.3%), and 974 383 White individuals (57.7%). Among all individuals tested, 692 227 (41.0%) had at least 1 VUS and 535 385 (31.7%) had only VUS results. The number of VUSs per individual increased as more genes were tested, and most VUSs were missense changes (86.6%). More VUSs were observed per sequenced gene in individuals who were not from a European White population, in middle-aged and older adults, and in individuals who underwent testing for disorders with incomplete penetrance. Of 37 699 unique VUSs that were reclassified, 30 239 (80.2%) were ultimately categorized as benign or likely benign. A mean (SD) of 30.7 (20.0) months elapsed for VUSs to be reclassified to benign or likely benign, and a mean (SD) of 22.4 (18.9) months elapsed for VUSs to be reclassified to pathogenic or likely pathogenic. Clinical evidence contributed most to reclassification.
CONCLUSIONS AND RELEVANCE
This cohort study of approximately 1.6 million individuals highlighted the need for better methods for interpreting missense variants, increased availability of clinical and experimental evidence for variant classification, and more diverse representation of race, ethnicity, and ancestry groups in genomic databases. Data from this study could provide a sound basis for understanding the sources and resolution of VUSs and navigating appropriate next steps in patient care.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Child; Child, Preschool; Female; Humans; Infant; Infant, Newborn; Male; Middle Aged; Young Adult; American Indian or Alaska Native; Canada; Cohort Studies; Ethnicity; Genetic Testing; Genetic Diseases, Inborn; Racial Groups
PubMed: 37878314
DOI: 10.1001/jamanetworkopen.2023.39571 -
Health Services Research Apr 1995For decades data have been collected comparing health care in racial and ethnic groups. The use of such groups in health services research assumes that standard,... (Review)
Review
For decades data have been collected comparing health care in racial and ethnic groups. The use of such groups in health services research assumes that standard, reliable, and valid definitions of race and ethnicity exist and that these definitions are used consistently. In fact, race is a term often used, but ill defined. It can incorporate biological, social, and cultural characteristics of patients and can refer to both genetic and behavioral traits. Various investigators have reported differences between racial and ethnic groups in health status, disease manifestation and outcome, resource utilization, and health care access, often specifying neither a definition of race nor the measurement they used to classify their study populations. The role of race as an explanatory variable in health services research requires greater scrutiny than many researchers currently provide. Many studies use race as a proxy for other socioeconomic factors not collected in the research effort. This article explores the ambiguities about race as an explanatory variable that render such research difficult to interpret. We suggest that health services researchers focus on nonracial socioeconomic characteristics that might be both more informative and more useful in guiding policy formation.
Topics: Ethnicity; Health Services Research; Humans; Politics; Racial Groups; Social Class; Socioeconomic Factors
PubMed: 7721591
DOI: No ID Found