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Ethnicity

Ethnicity refers to the shared cultural characteristics, including language, heritage, and practices, of a group of people.
It is a complex and multidimensional concept that can influence health outcomes and research protocils.
Ethnicity should be carefully considered when designing and reporting on studies to ensure maximal reproducibility and accuracy.
PubCompare.ai can help streamline this process by identifying the most effective research protocols from literature, preprints, and patents, while providing intelligent comparisons to optimize your ethnicity research.
Experince the power of PubCompare.ai today and achieve reliable, data-driven results.

Most cited protocols related to «Ethnicity»

1
UK Biobank Genotyping Protocol
Cited 503 times
Ethics approval for the UK Biobank study was obtained from the North West Centre for Research Ethics Committee (11/NW/0382). Blood samples were collected from participants on their visit to a UK Biobank assessment centre and the samples are stored at the UK Biobank facility in Stockport, UK7 (link). Over a period of 18 months samples were retrieved, DNA was extracted, and 96-well plates of 94 × 50-μl aliquots were shipped to Affymetrix Research Services Laboratory for genotyping. Special attention was paid in the automated sample retrieval process at UK Biobank to ensure that experimental units such as plates or timing of extraction did not correlate systematically with baseline phenotypes such as age, sex, and ethnic background, or the time and location of sample collection. Full details of the UK Biobank sample retrieval and DNA extraction process were described previously34 (link).
On receipt of DNA samples, Affymetrix processed samples on the GeneTitan Multi-Channel (MC) Instrument in 96-well plates containing 94 UK Biobank samples and two control samples from the 1000 Genomes Project25 (link). Genotypes were then called from the array intensity data, in units called ‘batches’ which consist of multiple plates. Across the entire cohort, there were 106 batches of 4,700 UK Biobank samples each (Supplementary Information, Supplementary Table 12). Following the earlier interim data release, Affymetrix developed a custom genotype calling pipeline that is optimized for biobank-scale genotyping experiments, which takes advantage of the multiple-batch design35 . This pipeline was applied to all samples, including the 150,000 samples that were part of the interim data release. Consequently, some of the genotype calls for these samples may differ between the interim data release and this final data release (see below).
Routine quality checks were carried out during the process of sample retrieval, DNA extraction36 , and genotype calling37 . Any sample that did not pass these checks was excluded from the resulting genotype calls. The custom-designed arrays contain a number of markers that had not been previously typed using Affymetrix genotype array technology. As such, Affymetrix also applied a series of checks to determine whether the genotyping assay for a given marker was successful, either within a single batch, or across all samples. Where these newly attempted assays were not successful, Affymetrix excluded the markers from the data delivery (see Supplementary Information for details).
Bycroft C., Freeman C., Petkova D., Band G., Elliott L.T., Sharp K., Motyer A., Vukcevic D., Delaneau O., O’Connell J., Cortes A., Welsh S., Young A., Effingham M., McVean G., Leslie S., Allen N., Donnelly P, & Marchini J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203-209.
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Publication 2018
Attention Biological Assay BLOOD Ethics Committees, Research Ethnicity Genome Obstetric Delivery Phenotype Specimen Collection
2
UK Biobank Genotyping and Quality Control
Cited 272 times
We analyzed data from the UK Biobank consisting of 152,729 samples typed at ≈800,000 SNPs. Using PLINK31 (see URLs), we removed 480 individuals marked for exclusion from genomic analyses based on missingness and heterozygosity filters and 1 individual who had withdrawn consent, leaving 152,248 samples (see URLs, UK Biobank Genotyping and QC). We restricted the SNP set to autosomal, biallelic SNPs with missingness ≤10% and we further excluded 65 autosomal SNPs found to have significantly different allele frequencies between the UK BiLEVE array and the UK Biobank array, leaving 707,524 SNPs (57,753 on chr1, 41,538 on chr5, 34,588 on chr10, 22,367 on chr15, and 18,349 on chr20). We identified 72 trios based on IBS0<0.001, sex of parents, and age of trio members (see URLs, Genotyping and QC). Of the 72 trio children, 69 self-reported British ethnicity, one self-reported Indian ethnicity, and one self-reported Caribbean ethnicity. The remaining trio child did not self-report any ethnicity, but her parents self-reported Irish and “Any other white background” as their ethnicities, so we included this trio child in the 70 European-ancestry trio children we used to benchmark phasing accuracy.
Loh P.R., Danecek P., Palamara P.F., Fuchsberger C., Reshef Y.A., Finucane H.K., Schoenherr S., Forer L., McCarthy S., Abecasis G.R., Durbin R, & Price A.L. (2016). Reference-based phasing using the Haplotype Reference Consortium panel. Nature genetics, 48(11), 1443-1448.
Publication 2016
Caribbean People Child Ethnicity Europeans Genome Heterozygote Parent Single Nucleotide Polymorphism TRIO protein, human
3
UK Biobank Genotyping and Quality Control
Cited 251 times
We analyzed data from the UK Biobank consisting of 152,729 samples typed at ≈800,000 SNPs. Using PLINK31 (see URLs), we removed 480 individuals marked for exclusion from genomic analyses based on missingness and heterozygosity filters and 1 individual who had withdrawn consent, leaving 152,248 samples (see URLs, UK Biobank Genotyping and QC). We restricted the SNP set to autosomal, biallelic SNPs with missingness ≤10% and we further excluded 65 autosomal SNPs found to have significantly different allele frequencies between the UK BiLEVE array and the UK Biobank array, leaving 707,524 SNPs (57,753 on chr1, 41,538 on chr5, 34,588 on chr10, 22,367 on chr15, and 18,349 on chr20). We identified 72 trios based on IBS0<0.001, sex of parents, and age of trio members (see URLs, Genotyping and QC). Of the 72 trio children, 69 self-reported British ethnicity, one self-reported Indian ethnicity, and one self-reported Caribbean ethnicity. The remaining trio child did not self-report any ethnicity, but her parents self-reported Irish and “Any other white background” as their ethnicities, so we included this trio child in the 70 European-ancestry trio children we used to benchmark phasing accuracy.
Loh P.R., Danecek P., Palamara P.F., Fuchsberger C., Reshef Y.A., Finucane H.K., Schoenherr S., Forer L., McCarthy S., Abecasis G.R., Durbin R, & Price A.L. (2016). Reference-based phasing using the Haplotype Reference Consortium panel. Nature genetics, 48(11), 1443-1448.
Publication 2016
Caribbean People Child Ethnicity Europeans Genome Heterozygote Parent Single Nucleotide Polymorphism TRIO protein, human
4
Comprehensive Lung Function Reference Values
Cited 231 times
Datasets were obtained from 73 centres (initial N=160,330). In France it is prohibited by law to record ethnicity; as a result 63,031 records known to be of mixed ethnic population could not be included in the final analyses. In the remaining datasets, ethnicity could not be traced in an additional 834 cases. In 805 cases data were discarded because they comprised subjects with suspected asthma. In 123 cases data could not be used to derive reference values because forced expiratory time was < 1 s. Records with transcription errors that could not be resolved, with missing values for sex, age, height, FEV1 or FVC, or where the FEV1/FVC ratio was >1.0 were discarded. Since virtually all data had been previously used in publications, there were very few errors. Datasets from India, Pakistan, Iran, Oman, the Philippines and South Africa were either too small in number for analysis, or could not be combined into groups with other sets (N=17,341). One dataset (N=3483) could not be used until the data had first been published by the authors. As the statistical analyses are sensitive to outliers, in subsequent analyses data points that yielded a z-score <−5.0 or >5.0 were identified as outliers (N=526) and excluded from further analyses. This left data on 31,856 males and 42,331 females aged 2.5–95 years (Figure 1, online supplement (OLS) tables E1, E2 and E3).
Quanjer P.H., Stanojevic S., Cole T.J., Baur X., Hall G.L., Culver B.H., Enright P.L., Hankinson J.L., Ip M.S., Zheng J, & Stocks J. (2012). MULTI-ETHNIC REFERENCE VALUES FOR SPIROMETRY FOR THE 3–95 YEAR AGE RANGE: THE GLOBAL LUNG FUNCTION 2012 EQUATIONS: Report of the Global Lung Function Initiative (GLI), ERS Task Force to establish improved Lung Function Reference Values. The European respiratory journal, 40(6), 1324-1343.
Publication 2012
Asthma Dietary Supplements Ethnicity Exhaling Females Males Transcription, Genetic
5
Global Burden of Disease 2019 Analysis
Cited 223 times
GBD 2019 estimated each epidemiological quantity of interest—incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs)—for 23 age groups; males, females, and both sexes combined; and 204 countries and territories that were grouped into 21 regions and seven super-regions. For GBD 2019, nine countries and territories (Cook Islands, Monaco, San Marino, Nauru, Niue, Palau, Saint Kitts and Nevis, Tokelau, and Tuvalu) were added, such that the GBD location hierarchy now includes all WHO member states. GBD 2019 includes subnational analyses for Italy, Nigeria, Pakistan, the Philippines, and Poland, and 16 countries previously estimated at subnational levels (Brazil, China, Ethiopia, India, Indonesia, Iran, Japan, Kenya, Mexico, New Zealand, Norway, Russia, South Africa, Sweden, the UK, and the USA). All subnational analyses are at the first level of administrative organisation within each country except for New Zealand (by Māori ethnicity), Sweden (by Stockholm and non-Stockholm), the UK (by local government authorities), and the Philippines (by province). In this publication, we present subnational estimates for Brazil, India, Indonesia, Japan, Kenya, Mexico, Sweden, the UK, and the USA; given space constraints, these results are presented in appendix 2. At the most detailed spatial resolution, we generated estimates for 990 locations. The GBD diseases and injuries analytical framework generated estimates for every year from 1990 to 2019.
Diseases and injuries were organised into a levelled cause hierarchy from the three broadest causes of death and disability at Level 1 to the most specific causes at Level 4. Within the three Level 1 causes—communicable, maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries—there are 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes (including 131 Level 3 causes that are not further disaggregated at Level 4; see appendix 1 sections 3.4 and 4.12 for the full list of causes). 364 total causes are non-fatal and 286 are fatal. For GBD 2019, 12 new causes were added to the modelling framework: pulmonary arterial hypertension, eye cancer, soft tissue and other extraosseous sarcomas, malignant neoplasm of bone and articular cartilage, and neuroblastoma and other peripheral nervous cell tumours at Level 3, and hepatoblastoma, Burkitt lymphoma, other non-Hodgkin lymphoma, retinoblastoma, other eye cancers, and two sites of osteoarthritis (hand and other joints) at Level 4.
Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. (2020). Lancet (London, England), 396(10258), 1204-1222.
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Publication 2020
Bone Cancer Burkitt Lymphoma Cancer of Eye Cartilages, Articular Cells Degenerative Arthritides Disabled Persons Ethnicity Females Hepatoblastoma Idiopathic Pulmonary Arterial Hypertension Infant, Newborn Injuries Joints Lymphoma, Non-Hodgkin, Familial Males Neuroblastoma Noncommunicable Diseases Nutrition Disorders Peripheral Nervous System Neoplasms Retinoblastoma Sarcoma Tissues

Most recents protocols related to «Ethnicity»

1
Systemic Inflammation Index and Cardiovascular Mortality
Mean (standard deviation) and quantity (percentage, %) are used to represent continuous and categorical variables, respectively. For continuous variables, Student’s t-test or one-way ANOVA were used. In addition, to compare the constituent ratios between each group, the chi-square test was performed. The SII index was divided into four groups: Q1 (4.056–349.500), Q2 (349.501–508.800), Q3 (508.801–736.154), and Q4 (376.155–11,700.000), with Q1 serving as the reference group. Cox regression analysis was used to examine the relationship between SII index and all-cause, cardiovascular, and cancer-related mortality in patients with CVD. First, model 1 was adjusted for age and sex. Second, model 2 was further adjusted for race/ethnicity, education level, marital status, family PIR, the complication of hypertension, and DM, smoker, and drinker. Finally, model 3 was further adjusted for BMI, waist circumference, mean energy intake, SBP, DBP, Hb, FBG, HbA1c, Alt, Ast, albumin, TC, TG, HDL-C, UA, BUN, Scr, and eGFR, as our final model. Then, using the above methods and models, we also explored the relationship between SII index and all-cause, CVD, and cancer-related mortality in patients with a composite of 5 CVD outcomes (CHF, CHD, angina pectoris, MI, and stroke). All statistical analyses were performed using the “survey”, “openxlsx”, “dplyr”, “reshape2”, and “do” packages of R version 3.6.4 (R Foundation for Statistical Computing, Vienna, Austria), Stata version 13.0 (Stata Corporation, College Station, TX, USA), and SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). Two-side P-value <0.05 was regarded as statistically significant.
Xiao S., Wang Z., Zuo R., Zhou Y., Yang Y., Chen T, & Liu N. (2023). Association of Systemic Immune Inflammation Index with All-Cause, Cardiovascular Disease, and Cancer-Related Mortality in Patients with Cardiovascular Disease: A Cross-Sectional Study. Journal of Inflammation Research, 16, 941-961.
Publication 2023
Albumins Angina Pectoris Cardiovascular System Cerebrovascular Accident EGFR protein, human Ethnicity High Blood Pressures Malignant Neoplasms neuro-oncological ventral antigen 2, human Patients Student Waist Circumference
2
Cardiometabolic Risk Factors in NHANES
The following covariates were considered in the study: age, sex, race/ethnicity, family poverty income ratio (PIR), education level, marital status, the complication of hypertension, and diabetes mellitus (DM), smoker, drinker, body mass index (BMI), waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean energy intake, hemoglobin (Hb), fast glucose (FBG), glycosylated hemoglobin (HbA1c), alanine transaminase (Alt), aspartate aminotransferase (Ast), albumin, total cholesterol (TC), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), uric acid (UA), blood urea nitrogen (BUN), serum creatinine (Scr), and estimated glomerular filtration rate (eGFR). Individuals who have smoked less than 100 cigarettes in their lifetime/smoked less than 100 cigarettes in their lifetime, do not smoke at all at present/smoked more than 100 cigarettes in their lifetime, and smoke some days or every day were defined as never smoke, former smokers, and now smokers, respectively. There are three categories of drinkers: current heavy alcohol consumption were defined as ≥3 drinks per day for females, ≥4 drinks per day for males, or binge drinking [≥4 drinks on same occasion for females, ≥5 drinks on same occasion for males] on 5 or more days per month; current moderate alcohol consumption were defined as ≥2 drinks per day for females, ≥3 drinks per day for males, or binge drinking ≥2 days per month. Those who did not meet the above criteria were classified as current mild alcohol user.21 (link) Hypertension was defined as an average systolic blood pressure more than 140 mmHg/diastolic blood pressure greater than 90 mmHg or self-reported use of antihypertensive medication. DM will be assessed by measures of blood glycohemoglobin, fasting plasma glucose, 2-hour glucose (Oral Glucose Tolerance Test), serum insulin in participants aged 12 years and over. Hb, FBG, HbA1c, Alt, Ast, albumin, TC, TG, HDL-C, UA, BUN, Scr, and eGFR were all determined in the laboratory. More information regarding the variables used is available at https://www.cdc.gov/nchs/nhanes/index.htm.
Xiao S., Wang Z., Zuo R., Zhou Y., Yang Y., Chen T, & Liu N. (2023). Association of Systemic Immune Inflammation Index with All-Cause, Cardiovascular Disease, and Cancer-Related Mortality in Patients with Cardiovascular Disease: A Cross-Sectional Study. Journal of Inflammation Research, 16, 941-961.
Publication 2023
Alanine Transaminase Albumins Alcohols Antihypertensive Agents BLOOD Cholesterol Creatinine Diabetes Mellitus Ethnicity Females Glomerular Filtration Rate Glucose Hemoglobin Hemoglobin, Glycosylated High Blood Pressures High Density Lipoprotein Cholesterol Index, Body Mass Insulin Males Oral Glucose Tolerance Test Plasma Pressure, Diastolic Serum Smoke Systolic Pressure Transaminase, Serum Glutamic-Oxaloacetic Triglycerides Urea Nitrogen, Blood Uric Acid Waist Circumference
3
Cigar Pack Color Influence on Flavor Perceptions
The Rutgers University Institutional Review Board deemed this online survey study exempt because of minimal participant risk. Participants provided consent before survey completion. The study followed the AAPOR reporting guideline.
We examined the association of cigar pack color with consumer flavor perceptions using data from 1 wave of the Rutgers Omnibus Study (a quarterly Amazon Mechanical Turk [mTurk] survey of US adults aged 18–45 years) collected in August 2022. Respondents were randomized to view a cigar with blue or purple packaging and asked if the cigar was flavored (yes or no) and, if yes, what flavor. We used multivariable logistic regression models to examine the association of condition (pack color) with cigar use, self-reported demographic characteristics (age, sex, and race and ethnicity), and flavor perceptions. Statistical significance was defined as P < .05 (2-tailed) and analyses were conducted in October 2022 using Stata/MP, version 17 (StataCorp).
Delnevo C.D, & Ganz O. (2023). Association of Flavor Perception With Blue vs Purple Cigar Packaging Among US Adults. JAMA network open, 6(2), e2254003.
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Publication 2023
Adult Ethics Committees, Research Ethnicity Flavor Enhancers
4
Hearing Loss and USOC Retention
We described differences in baseline demographics between our analytic sample and excluded individuals, and by hearing group using χ2 (categorical variables) and Kruskal–Wallis analysis of variance (continuous variables). Given the discrete nature of our data we estimated a discrete-time proportional hazard model using a cloglog model to estimate hazard ratios (HRs) (22 ) and 95% confidence intervals (CIs) for losing USOC. We verified the proportional hazard assumption by looking at the correlation between Schoenfeld residuals and survival times.
We performed unweighted and weighted (using 2011 enrollment weights) estimations to account for NHATS’ complex survey design. All main analyses were adjusted for participant’s baseline characteristics including: age, sex, race/ethnicity, marital status, education, household income, self-reported health, number of chronic conditions, dementia status, additional health coverage, and depression. All covariates were treated as time-invariant in our analyses. Survival functions by hearing group were estimated for the unweighted unadjusted model.
In secondary analyses, we explored the potential moderation effect of (i) depression and (ii) transportation barriers on the association between hearing groups and self-reported loss of USOC. These secondary analyses were driven by previous findings showing the importance of unmet transportation needs and depression as risk factors for loss of USOC. For the case of analyses pertaining to transportation barriers, a sample of N = 320 was used due to missing data.
Finally, as a sensitivity analysis we estimated our main model including all study participants who satisfied our inclusion criteria (community-dwelling, having a USOC, full set of covariates), but who were lost to follow-up during the study period. As participants in residential care facilities were assumed to have a USOC, we excluded at-risk participants who transitioned into residential care from this analysis. All our estimations were performed using Stata/SE 17.0.
Garcia Morales E., Assi L., Powell D., Luu K, & Reed N. (2023). The Association Between Self-Reported Hearing Loss and Loss of Usual Source of Health Care Among Older Medicare Beneficiaries: Evidence From the National Health and Aging Trends Study. Innovation in Aging, 7(2), igad002.
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Publication 2023
Chronic Condition Dementia Ethnicity Households Hypersensitivity Transitional Care
5
Sociodemographic Factors and USOC/HL Associations
Covariate selection was guided by previous literature on sociodemographic and health characteristics associated with having a USOC or HL (10 (link),11 (link)). These include baseline, age, race/ethnicity (White, Black, Hispanic, and other), sex, marital status (married/living with partner, and single/never married/divorced/widow), education (less than high school, high school diploma or equivalent, and some college or more), household income (under the poverty line, 100%–199% the poverty line, and ≥200% of the poverty line), number of chronic health conditions among heart attack, heart disease, high blood pressure, arthritis, osteoporosis, diabetes, lung disease, stroke, or cancer (0, 1–2, 3–5, or 6+), self-reported health status (Likert scale, 1 = Excellent, …, 5 = Poor), number of activities of daily living (ADLs) for which the respondent reported needing help (none, 1–2 ADLs, and 3≤ ADLs), dementia (probable, possible, and no dementia) (20 ), additional health coverage (Medigap/Medicare supplement, Medicaid, or Tricare), and depression status (based on Patient Health Questionnaire-2 scores ≥3) (21 (link)).
Despite being identified as a risk factor for loss of USOC, experiencing transportation barriers (reporting that a transportation problem restricted any activity participation in the month before the interview) was not included in the main analyses due to data availability, as a total of N = 1 804 participants had missing information.
Garcia Morales E., Assi L., Powell D., Luu K, & Reed N. (2023). The Association Between Self-Reported Hearing Loss and Loss of Usual Source of Health Care Among Older Medicare Beneficiaries: Evidence From the National Health and Aging Trends Study. Innovation in Aging, 7(2), igad002.
Full text: Click here
Publication 2023
Arthritis Cerebrovascular Accident Dementia Diabetes Mellitus Dietary Supplements Ethnicity Heart Diseases High Blood Pressures Hispanics Households Insurance, Medigap Lung Diseases Malignant Neoplasms Myocardial Infarction Osteoporosis Training Programs Widow

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More about "Ethnicity"

Ethnicity is a complex and multifaceted concept that encompasses the shared cultural characteristics, such as language, heritage, and practices, of a group of people.
This factor can have a significant impact on health outcomes and research protocols.
When designing and reporting on studies, it is crucial to carefully consider ethnicity to ensure maximum reproducibility and accuracy.
PubCompare.ai is a powerful tool that can help streamline the process of optimizing ethnicity research protocols.
The platform utilizes advanced AI technology to identify the most effective research protocols from literature, preprints, and patents, while providing intelligent comparisons to help you choose the best solutions.
By leveraging the insights gained from PubCompare.ai, researchers can achieve reliable, data-driven results that are tailored to their specific ethnicity-related research needs.
The platform's intuitive interface and user-friendly features make it easy to navigate, and it can be seamlessly integrated with popular statistical software like SAS version 9.4, Stata 15, and others.
Whether you're conducting epidemiological studies, clinical trials, or exploring the intersection of ethnicity and public health, PubCompare.ai can help you optimize your research protocols and ensure the maximal reproducibility and accuracy of your findings.
Experience the power of this innovative platform today and take your ethnicity research to new heights.