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Racial Groups

Racial Groups: A broad classification of human beings based on physical characteristics, such as skin color, facial features, and hair texture.
This term encompasses the diverse ethnic and cultural backgrounds that contribute to the rich tapestry of human diversity.
Researchers can enhance the accuracy and reproducibility of their studies on racial groups by leveraging PubCompare.ai, a leading AI platform that streamlines the identification of relevant protocols from literature, preprints, and patents.
PubCompare.ai's powerful tools enable researchers to easily compare protocols and identify the best approaches for their specific needs, improving the quality and efficiency of their findings.
With PubCompare.ai, researchers can optimize their workflow and elevate the rigor of their racial group research.

Most cited protocols related to «Racial Groups»

1
Standardized Neuropsychological Battery for Alzheimer's Research
Cited 289 times
Cognitive domain and test selection were based on a combination of methods evolving from regular meetings of the CTF. A subcommittee was formed to specifically undertake the design of the neuropsychological test battery, to bring essential issues to the larger group and to interface with the ADCs. Three overriding criteria governed decisions for selecting domains and tests. The first was the mandate for the UDS to initially focus on cognitive markers of aging and of dementia associated with AD, the second was to minimize burden on the ADCs and their subjects, and the third was to accommodate the continuity of measures that ADCs have previously collected. A fourth principle that emerged after an initial set of domains and tests was identified was the need to overlap with other ADC initiatives such as the Late Onset Alzheimer’s Disease (LOAD) Genetics study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Because of the need to focus on the cognitive continuum from aging without dementia, to MCI, to AD, cognitive domains were selected for their sensitivity to age-related change in cognition [17 (link)–29 (link)] sensitivity to the demonstrated primary cognitive impairments in AD [30 –36 (link)], ability to measure change over time and to stage AD [37 (link)], and ability to predict progression from MCI to AD [38 (link)–41 ]. Additional criteria for test selection included applicability of the measures to different educational levels, to diverse racial/ethnic minority groups and to Spanish-speaking populations. A Spanish translation of the UDS has been completed and is available on the NACC website (https://www.alz.washington.edu).
The minimization of burden, an issue of feasibility, had to figure centrally in test selection. Many ADCs have been conducting research for over 20 years. Well-established protocols and longitudinal research projects could be disrupted by the need to significantly alter assessment and enrollment methods, notwithstanding the added time burden for subjects and their study partners. Thus, with input from the ADCs, the CTF concluded that the neuropsychological battery should not add more than 30 minutes to existing protocols at each Center. One implication of this principle was that tests already in use by all or most ADCs would be high on the list of candidates for inclusion.
The CTF conducted several surveys of the ADCs to gather data about their ongoing assessment practices including, among other variables: 1) cognitive domains tested; 2) specific instruments and versions, for tests with multiple forms; 3) populations of subjects followed (i.e., disease and control groups; clinic and/or community samples); 4) frequency of subject visits. Once these data were acquired, the most commonly tested domains and the most commonly used specific measures were identified and comments and approval were solicited from the ADCs.
Weintraub S., Salmon D., Mercaldo N., Ferris S., Graff-Radford N.R., Chui H., Cummings J., DeCarli C., Foster N.L., Galasko D., Peskind E., Dietrich W., Beekly D.L., Kukull W.A, & Morris J.C. (2009). The Alzheimer’s Disease Centers’ Uniform Data Set (UDS): The Neuropsychological Test Battery. Alzheimer disease and associated disorders, 23(2), 91-101.
Publication 2009
Cognition Disease Progression Disorders, Cognitive Ethnic Minorities Hispanic or Latino Hypersensitivity Neuropsychological Tests Population Group Presenile Dementia Racial Groups SET Domain
2
Continuous Surveillance of Heart Failure Hospitalization
Cited 129 times
Beginning in 2005, the ARIC Study conducted continuous, retrospective surveillance of hospital discharges for HF for all residents age 55 years and older in four US communities: Forsyth County, North Carolina; the city of Jackson, Mississippi; eight northwest suburbs of Minneapolis, Minnesota; and Washington County, Maryland. In 2005, there were 31 hospitals serving the four ARIC communities. The combined population in 2005 for these regions was approximately 177,000 men and women 55 years of age or older. Because of the small number of hospitalizations in the sample among race/ethnic groups other than black or white (n=55), we categorized these as white for the purposes of these analyses.
Annual electronic discharge indices were obtained from all hospitals admitting residents from the four ARIC communities. Discharges meeting eligibility criteria were sampled from these files. A hospitalization was considered eligible for validation as a HF event based on its International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code, age, gender, race, and residence in the community surveillance area. Target primary or secondary hospital discharge diagnoses codes included: heart failure (428), rheumatic heart disease (398.91), hypertensive heart disease- with congestive heart failure (402.01, 402.11, 402.91), hypertensive heart disease and renal failure- with CHF (404.01, 404.03, 404.13, 404.91, 404.93), acute cor pulmonale (415.0), chronic pulmonary heart disease, unspecified (416.9), other primary cardiomyopathies (425.4), acute edema of lung, unspecified (518.4), dyspnea and respiratory abnormalities (786.0). Sampling probabilities were created to optimize variance estimates around event rate estimates with a pre-set maximum number of cases to be abstracted in 2005 of 1499 (See Supplemental Methods). This fixed number of abstractions was estimated and set based on a target number (n=500) of hospitalized events that could be investigated and validated considering available resources and time constraints. All analyses were weighted to account for the sampling probabilities.
Rosamond W.D., Chang P.P., Baggett C., Johnson A., Bertoni A.G., Shahar E., Deswal A., Heiss G, & Chambless L.E. (2012). Classification of Heart Failure in the Atherosclerosis Risk in Communities (ARIC) Study: A Comparison of Diagnostic Criteria. Circulation. Heart Failure, 5(2), 152-159.
Publication 2012
Cardiomyopathies, Primary Congenital Abnormality Cor Pulmonale Diagnosis Dyspnea Eligibility Determination Gender Heart Heart Diseases High Blood Pressures Hospitalization Kidney Failure Patient Discharge Pulmonary Edema Racial Groups Respiratory Rate Rheumatic Heart Disease Woman
3
Validating CKD-EPI Equation for GFR Estimation
Cited 102 times
CKD-EPI is a research group funded by the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) to address challenges in the study and care of CKD, including development and validation of improved GFR estimating equations by pooling data from research studies and clinical populations (hereafter referred to as “studies”)10 (link). The design and studies have been previously described and are briefly reviewed here10 (link). We developed and internally validated the CKD-EPI equation in a database of 10 studies with a total of 8254 participants, divided randomly into separate datasets for development (n=5504) and internal validation (n=2750). The equations were then externally validated in a separate dataset of 16 other studies with a total of 3896 participants. In the current report, we use the same dataset for development and internal validation. We use the same external validation dataset, with the addition of more data from Native Americans that were not available in the original report due to absence of creatinine calibration (herein referred to as “CKD-EPI validation dataset”) (N=4014)10 (link). In addition, we also evaluated the equations in three separate studies from outside of US and Europe; two are from Asia and one is from South Africa, each of which has been previously described9 (link), 11 (link), 12 (link) (herein referred to as ‘Non-US and Europe validation datasets’). The appendix tables 1 and 2 describe the distribution and race group for each study. GFR was measured using urinary clearance of iothalamate in the development dataset and iothalamate and other filtration markers in the external validation datasets, Serum creatinine values were calibrated to standardized creatinine measurements using the Roche enzymatic method (Roche-Hitachi P-Module instrument with Roche Creatininase Plus assay, Hoffman-La Roche, Ltd., Basel, Switzerland) at the Cleveland Clinic Research Laboratory (Cleveland, OH)13 (link), 14 (link).
Stevens L.A., Claybon M.A., Schmid C.H., Chen J., Horio M., Imai E., Nelson R.G., Van Deventer M., Wang H.Y., Zuo L., Zhang Y.(, & Levey A.S. (2010). Evaluation of the CKD-EPI Equation in Multiple Races and Ethnicities. Kidney international, 79(5), 555-562.
Publication 2010
American Indian or Alaska Native Biological Assay creatininase Creatinine Diabetes Mellitus Digestive System Enzymes Filtration Iothalamate Kidney Diseases Population Group Racial Groups Serum Urine
4
Developing and Validating Kidney Function Equations
Cited 78 times
We pooled data from research studies and clinical populations in which GFR was measured with the use of urinary or plasma clearance of exogenous filtration markers (Table S1 and Fig. S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org).5 (link),9 (link) For development of new equations, we used the data sets previously used for development of current equations (development data sets): CKD-EPI 2009 for eGFRcr (10 studies, 8254 participants) and CKD-EPI 2012 for eGFRcys and eGFRcr-cys (13 studies, 5352 participants)5 (link),9 (link) (Table S2). For external validation, we used a new data set (CKD-EPI 2021), consisting of the CKD-EPI 2012 external validation data set and new studies (12 studies [7 new], 4050 participants) (validation data set), to compare the performance of current and new equations. All the participants were 18 years of age or older. Race was reported by the participant in most studies (Table S3) and was categorized as Black or non-Black (hereafter, race groups), consistent with current equations and the purposes of these analyses. Measurement of creatinine, cystatin C, and GFR followed previously reported methods.5 (link),9 (link),21 (link),22
Inker L.A., Eneanya N.D., Coresh J., Tighiouart H., Wang D., Sang Y., Crews D.C., Doria A., Estrella M.M., Froissart M., Grams M.E., Greene T., Grubb A., Gudnason V., Gutiérrez O.M., Kalil R., Karger A.B., Mauer M., Navis G., Nelson R.G., Poggio E.D., Rodby R., Rossing P., Rule A.D., Selvin E., Seegmiller J.C., Shlipak M.G., Torres V.E., Yang W., Ballew S.H., Couture S.J., Powe N.R, & Levey A.S. (2021). New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race. The New England journal of medicine, 385(19), 1737-1749.
Publication 2021
Creatinine Filtration Plasma Population Group Post-gamma-Globulin Racial Groups
5
Racial Diversity in Medicare Home Health
Cited 62 times
The study population included all Medicare beneficiaries, 18 years and older, who received home health care in 2015 (4,243,090 people). Two data sources containing three race/ethnicity variables for our sample of Medicare beneficiaries were linked using the unique Chronic Conditions Warehouse (CCW) beneficiary identification number for the entire study population: The 2015 Medicare Beneficiary Summary File (MBSF) containing the Enrollment Database (EDB) race variable and Research Triangle Institute (RTI) race variable; and the 2015 Outcome and Assessment Information Set (OASIS) containing the ‘gold-standard’ self-reported race/ethnicity for all home health care patients. All three race variables (EDB, RTI, OASIS) were available for the entire study population.
During the initial home health care visit by a registered nurse or licensed physical therapist, as part of the standardized OASIS assessment, race/ethnicity data are obtained by self-report (a caregiver may answer if the patient is unable) and allows for multiple answers to be recorded. The directions for this question include the words “Mark all that apply” and the response choices are: 1) American Indian or Alaska Native, 2) Asian, 3) Black or African-American, 4) Hispanic or Laino, 5) Native Hawaiian or Pacific Islander, and 6) White.
For the purposes of this paper, and for consistency with the EDB and RTI race variable categories, beneficiaries who self-identified as either or both 1) Asian and 2) Native Hawaiian or Pacific Islander were classified as Asian American/Pacific Islander (AAPI). The vast majority (99.73%) of home health beneficiaries had only a single race/ethnicity recorded, and we restricted our study to this population. Details of the remaining 11,720 people (0.27% of study population) who identified with two or more racial/ethnic groups are included for the interested reader as a brief Appendix. Our final study sample consisted of 4,231,370 adult Medicare beneficiaries who received home health care in 2015. The study was approved by the Institutional Review Board of [replace with the authors’ academic institution].
Jarrín O.F., Nyandege A.N., Grafova I.B., Dong X, & Lin H. (2020). Validity of race and ethnicity codes in Medicare administrative data compared to gold-standard self-reported race collected during routine home health care visits. Medical care, 58(1), e1-e8.
Publication 2019
Adult African American Alaskan Natives American Indians Asian American Native Hawaiian and Pacific Islander Asian Persons Chronic Condition Ethics Committees, Research Ethnicity Gold Hispanics Native Hawaiians Pacific Islander Americans Patients Physical Therapist Racial Groups Registered Nurse

Most recents protocols related to «Racial Groups»

1
Fetal HC Classification Across Growth Curves
To determine whether classification differences were consistent across growth curves, we classified HC using three different growth curves. We used growth curves that are either commonly used (Hadlock and Intergrowth-21st) and/or were designed to be representative of the U.S. population (Intergrowth-21st and National Institute of Child Health and Human Development [NICHD]) to determine the proportion of female and male fetuses classified as having microcephaly (<3rd percentile, z-score < −1.88) or macrocephaly (>97th percentile, z-score >1.88) in the data subset of our sample described above. While the Society for Maternal-Fetal Medicine (SMFM) provides recommendations for standardizing the evaluation of fetal HC in the context of Zika virus exposure (5 ), there are no universal definitions for microcephaly and macrocephaly; the 3rd and 97th percentiles were chosen because they are commonly used and because the information provided in the NICHD curves does not allow direct calculation of other potential cutpoints for microcephaly and macrocephaly. All three evaluated growth curves are sex-neutral, using a single set of curves for both sexes.
We used cubic interpolation to calculate values of the 3rd and 97th percentiles for integer values of GA in days (17 (link)). NICHD percentiles were published separately for four specific race/ethnicity groups: Asian/Pacific Islander, Hispanic, Black non-Hispanic, and White non-Hispanic. There were no published NICHD percentiles that were nonspecific for race/ethnicity. We used the mean of the four race/ethnicity-specific values at each GA to create percentiles for a fifth group, deemed “Uncategorized.” For the NICHD analyses only, we excluded the small number of ultrasounds that could not be linked with maternal data. Women with a recorded race/ethnicity that did not fit in these categories, or whose maternal data were available but missing race/ethnicity data, were evaluated using the “Uncategorized” percentiles. Although there are limits of the reliability and precision of race/ethnicity data, the EHR was the only potential source of race/ethnicity data for this population and therefore the only way to evaluate our data compared to the US-based NICHD percentiles.
Brawley A.M., Schaefer E.W., Lucarelli E., Ural S.H., Chuang C.H., Hwang W., Paul I.M, & Daymont C. (2023). Differing prevalence of microcephaly and macrocephaly in male and female fetuses. Frontiers in Global Women's Health, 4, 1080175.
Publication 2023
Asian Persons Care, Prenatal Cuboid Bone Ethnicity Females Fetus Hispanics Macrocephaly Males Microcephaly Mothers Pacific Islander Americans Racial Groups Ultrasonography Woman Zika Virus
2
Genetic Ancestry Classification in POAG
We classified POAG cases and controls with a previously published algorithm developed in the VA (17 ) and applied to the MVP as previously described (13 ). Ancestry groups were defined using the Harmonized Ancestry and Race/Ethnicity (HARE) algorithm (18 (link)), which classifies an individual’s HARE group based on the correspondence of their self-identified race/ethnicity and genetically inferred ancestry.
Waksmunski A.R., Kinzy T.G., Cruz L.A., Nealon C.L., Halladay C.W., Anthony S.A., Greenberg P.B., Sullivan J.M., Wu W.C., Iyengar S.K., Crawford D.C., Peachey N.S, & Bailey J.N. (2023). Diversity is key for cross-ancestry transferability of glaucoma genetic risk scores in Hispanic Veterans in the Million Veteran Program. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 28, 413-424.
Publication 2023
Ethnicity Glaucoma, Primary Open Angle Racial Groups
3
Measuring Racial Misclassification Among NHPI
We measured racial misclassification using two items—the socially assigned race question from the Behavioral Risk Factor Surveillance System, Reactions to Race optional module and self-identified race [39 ]. After having reported their race and ethnicity, respondents were later given this prompt: “Earlier I asked you to self-identify your race. Now I will ask you how other people identify you and treat you. How do other people usually classify you in this country?” They could respond that they were Asian, Black, Hispanic/Latinx, American Indian or Alaska Native, NHPI, White, or some other group. Because the entire analytical sample was selected based on a self-identified race of NHPI, any respondents in this subsample who indicated their socially assigned race was also NHPI were coded as experiencing a match between their self-identified and socially assigned race. Those who selected they were perceived to be any other racial group were coded as experiencing racial misclassification.
Willis D.E., Kaholokula J.K., Andersen J.A., Selig J.P., Bogulski C.A., Scott A.J, & McElfish P.A. (2023). Racial Misclassification, Discrimination, Consciousness, and Self-Rated Health Among Native Hawaiian and Pacific Islander Adults in the USA. Journal of Racial and Ethnic Health Disparities.
Publication 2023
Alaskan Natives American Indians Asian Persons Behavioral Risk Factor Surveillance System Ethnicity Hispanics Latinx Racial Groups
4
Quantifying Racial Disparities in Air Pollution Exposure
We employed PWE to reduce multidimensional spatial exposure fields to a single dimension by emphasizing portions of the exposure field that impact areas with high population. For gridded coal PM2.5 source impacts, we calculated population-weighted exposure PWEu,y,d from each unit or group of units u in each year y for each demographic group d as:
PWEu,y,d=∑i=1I[Coal PM2.5]u,y,i×(Py,d,iPy,d,total), where i=1, 2,…, I denotes grid cell locations, Py,d,i is the population of a given demographic in each grid cell, and Py,d,total is the total population in the domain of the demographic group.
Annual grid cell population was calculated by spatially apportioning U.S. Census county populations to the 36-km HyADS grid. We used annual intercensal population estimates from 2000 to 2020.45 We assigned 1 April 2000 population estimates for year 1999 population. We calculated PWu,y,d for the following racial/ethnic groups (census names in parentheses): White (White alone), Black (Black or African-American population alone), Native (American Indian and Alaska Native population alone), Asian (Asian population alone), Pacific (Native Hawaiian and Other Pacific Islander population alone), and Hispanic (population of Hispanic origin and any race). Although county data and the HyADS 36-km grid are relatively coarse measures of air pollution and demographic spatial distribution, they are justified by the regional nature of coal power plant pollution.20 (link),43 (link),46 (link),47 (link) Recent findings, for example, have shown that cross-state EGU source impacts accounted for around half of total fatalities attributable to EGU emissions,20 (link),43 (link) likely because local contributions to sulfate PM2.5 from SO2 emissions are less likely due to elevated stack heights and the delay introduced by atmospheric processing of SO2 to sulfate.48 (link) HyADS uses a 36-km spatial grid; the average land area of counties in the contiguous United States is 1,555 km2 ,49 (link) which is of similar order as the 1,296-km2 area of a 36-km grid cell. Daouda et al. previously used a county-level analysis and a portion of the data set described here to quantify racial disparities in preterm birth outcomes attributable to EGU SO2 emissions.50 (link)
Henneman L.R., Rasel M.M., Choirat C., Anenberg S.C, & Zigler C. (2023). Inequitable Exposures to U.S. Coal Power Plant–Related : 22 Years and Counting. Environmental Health Perspectives, 131(3), 037005.
Publication 2023
African American Air Pollution Alaskan Natives American Indians Asian Americans Coal Grid Cells Hispanics Native Hawaiians Pacific Islander Americans Premature Birth Racial Groups Sulfates, Inorganic
5
Neighborhood Social Organization and Hypertension
To examine the study’s first objective, we estimate associations between neighborhood social organization exposure and hypertension risk by executing a series of random effects logistic models. We choose a random effects model because of the longitudinal and multilevel structure of the data. Recall that our neighborhood exposure measures are at the respondent level resulting in a two-level model—time (survey wave) nested within individuals. Here, we prefer the random effects model to the fixed effects model because of its ability to examine both time-invariant and time-varying variables, as well as our substantive interest in between-effects (i.e., racial/ethnic disparities) [59 ]. Our modeling strategy proceeds as follows: Model 1 presents the baseline racial/ethnic gap in hypertension risk, with Black adults as the reference group. Model 2 enters our neighborhood social organization measures (organizational participation and collective efficacy). Model 3 adjusts for Model 2 variables and includes residential socioeconomic disadvantage and co-ethnic density, while Model 4 controls for activity space versions of socioeconomic disadvantage and co-ethnic density. The full model (Model 5) adjusts for our individual-level controls. For ease of interpretation, we convert logistic regression coefficients to average marginal effects (AMEs) with 95% confidence intervals (CIs) derived from robust standard errors clustered at the individual level. We also report the intraclass correlation (ICC) for each model.
To address our second aim, we examine whether the hypertension effects of neighborhood social organization vary across racial/ethnic groups. Specifically, both neighborhood organizational participation and collective efficacy are interacted with race/ethnicity in separate fully adjusted random effects logistic models. The results are illustrated by plotting predicted probabilities of having hypertension by levels of neighborhood social organization with 95% CIs.
For our final objective, we explore sources of the racial/ethnic gaps in hypertension risk—between Black and White adults and Black and Latino adults—using Fairlie’s extension of the Blinder-Oaxaca decomposition technique for nonlinear models [60 ]. A common approach to assessing the contributing factors to racial/ethnic disparities in high blood pressure and other chronic diseases [61 (link), 62 (link)], decomposition methods construct a counterfactual reflecting how the Black-White gap in hypertension would change, for example, if Black adults had the same neighborhood and individual characteristics as White adults. To do so, we use estimates from group-specific logistic models and partition Black-White (and Black-Latino) differences into the part explained by observed characteristics and an unexplained part, which reflects group differences in unobserved characteristics. See S1 Appendix for a detailed description of our application of the nonlinear decomposition of hypertension disparities. We apply L.A.FANS panel survey weights to all analyses, which were executed using Stata 16 [63 ].
Sharp G, & Carpiano R.M. (2023). Neighborhood social organization exposures and racial/ethnic disparities in hypertension risk in Los Angeles. PLOS ONE, 18(3), e0282648.
Publication 2023
Adult Disease, Chronic Ethnicity High Blood Pressures Latinos Mental Recall Racial Groups

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More about "Racial Groups"

Racial Groups, also known as Ethnic Groups or Ethnic Backgrounds, refer to the diverse classification of human beings based on their physical characteristics, such as skin color, facial features, and hair texture.
This broad term encompasses the rich tapestry of human diversity, reflecting the various ethnic and cultural backgrounds that shape our world.
Researchers studying Racial Groups can enhance the accuracy and reproducibility of their studies by leveraging the power of PubCompare.ai, a leading AI platform that streamlines the identification of relevant protocols from literature, preprints, and patents.
PubCompare.ai's cutting-edge tools enable researchers to easily compare protocols and identify the best approaches for their specific needs, improving the quality and efficiency of their findings.
Whether you're using SAS version 9.4, SAS 9.4, SAS v9.4, Stata 15, Stata 14, Stata version 15, SAS software, Stata 12.0, Stata 13, or Stata 16, PubCompare.ai can help you optimize your workflow and elevate the rigor of your Racial Group research.
With its user-friendly interface and advanced AI capabilities, researchers can quickly locate relevant protocols, compare them side-by-side, and select the most appropriate methodologies to ensure the accuracy and reproducibility of their studies.
Embrace the diversity of Racial Groups and unlock the full potential of your research with the help of PubCompare.ai, the go-to platform for protocol identification and comparison.
Streamline your workflow, improve the quality of your findings, and contribute to the enriching tapestry of human diversity through your groundbreaking work.