scholarly journals Clinical genetics laboratories use divergent demographic frameworks across countries: comparing data structures for race, ethnicity, and ancestry on test requisition forms

2021 ◽  
Author(s):  
Alice B Popejoy ◽  
Julia Gimbernat-Mayol ◽  
Katherine C Anderson ◽  
Gillian Hooker
Keyword(s):  
Categorical Data ◽  
Clinical Laboratory ◽  
Demographic Data ◽  
Clinical Genetics ◽  
Clinical Genetic ◽  
Patient Demographics ◽  
Clinical Genetic Testing ◽  
Clinical Laboratory Test ◽  
The Us ◽  
Race Ethnicity

Purpose. The goal of this study is to investigate how population groups are represented on requisition forms for clinical genetic testing in different laboratories. Methods. Clinical laboratory test requisition forms (RFs) were obtained from 70 laboratories in the US, Canada, Europe, and Australia. Details about the laboratories and how RFs represent patient demographics were extracted and analyzed for trends between forms in the U.S. (N=213) and other countries (N=203). Results. Clinical genetics laboratories included in the analysis vary widely regarding the format of demographic data collected on test requisition forms. US-based laboratory RFs are more likely than those from other countries to include race or ethnicity. These are most often represented as categorical data, with multiple-choice options. RFs from laboratories in other countries do not include race, and those that include ethnicity most often provide a blank space for open-ended responses. Conclusions. These results are consistent with existing research on heterogeneity in the nomenclature and number of categories used to describe patient populations across clinical genetics laboratories in the US. It also suggests systemic differences in the way measures of diversity are conceptualized in the US compared to other countries.

scholarly journals Utility of RNA Sequencing Analysis in the Context of Genetic Testing

2020 ◽  
Vol 8 (4) ◽  
pp. 140-146
Author(s):  
Jackie Tahiliani ◽  
Jeanne Leisk ◽  
Kerry Aradhya ◽  
Karen Ouyang ◽  
Swaroop Aradhya ◽  
...  
Keyword(s):  
Genetic Counseling ◽  
Genetic Testing ◽  
Rna Sequencing ◽  
Clinical Laboratory ◽  
Sequencing Analysis ◽  
Sample Type ◽  
Clinical Genetic ◽  
Clinical Genetic Testing ◽  
Rna Analysis ◽  
Uncertain Significance

Abstract Purpose of Review RNA analysis is beginning to be integrated into clinical laboratory genomics, and a review of its current uses and limitations is warranted. Here, we summarize the scope and utility of RNA analysis in the context of clinical genetic testing, including considerations for genetic counseling. Recent Findings RNA analysis is a powerful approach for interpreting some variants of uncertain significance, for analyzing splicing alterations, for providing additional functional evidence for sequence and structural variants, and for discovering novel variants. However, a review of RNA sequencing methods has noted variability in both laboratory processes and findings. Genetic counseling related to RNA analysis has to take into account nonstandardized laboratory processes, sample-type limitations, and differences in variant-interpretation outcomes. Summary RNA analysis is an important complement to DNA testing, although limitations still exist. Maximizing the utility of RNA analysis will require appropriate patient referrals and standardization of laboratory processes as the practice continues to expand the ability to identify and resolve molecular diagnoses.

scholarly journals Communal data work: Data sharing and re-use in clinical genetics

2019 ◽  
Vol 25 (3) ◽  
pp. 511-525 ◽  
Author(s):  
Polyxeni Vassilakopoulou ◽  
Margunn Aanestad
Keyword(s):  
Genetic Testing ◽  
Empirical Data ◽  
Health Informatics ◽  
Clinical Genetics ◽  
Clinical Genetic ◽  
Data Interoperability ◽  
Clinical Genetic Testing ◽  
The World ◽  
Research Infrastructures ◽  
Research Findings

In this article, we examine work with communal data in the context of clinical genetic testing. Drawing from prior research on digital research infrastructures and from the analysis of our empirical data on genetic testing, we describe how data generated in laboratories distributed all over the world are shared and re-used. Our research findings point to six different human-driven activities related to expanding, disambiguating, sanitizing and assessing the relevance, validity and combinability of data. We contribute to research within Health Informatics with a framework that foregrounds human-driven activities for data interoperability.

Interpretation of Incidental Genetic Findings Localizing to Genes Associated With Cardiac Channelopathies and Cardiomyopathies

2021 ◽  
Author(s):  
Jordan E. Ezekian ◽  
Catherine Rehder ◽  
Priya S. Kishnani ◽  
Andrew P. Landstrom
Keyword(s):  
Genetic Diagnosis ◽  
Significant Risk ◽  
Clinical Genetics ◽  
Clinical Settings ◽  
Clinical Genetic ◽  
Diagnostic Significance ◽  
Genetic Sequencing ◽  
Clinical Genetic Testing ◽  
Cardiovascular Genetics ◽  
Clinical Presentations

Recent advances in next-genetic sequencing technology have facilitated an expansion in the use of exome and genome sequencing in the research and clinical settings. While this has aided in the genetic diagnosis of individuals with atypical clinical presentations, there has been a marked increase in the number of incidentally identified variants of uncertain diagnostic significance in genes identified as clinically actionable by the American College of Medical Genetics guidelines. Approximately 20 of these genes are associated with cardiac diseases, which carry a significant risk of sudden cardiac death. While identification of at-risk individuals is paramount, increased discovery of incidental variants of uncertain diagnostic significance has placed a burden on the clinician tasked with determining the diagnostic significance of these findings. Herein, we describe the scope of this emerging problem using cardiovascular genetics to illustrate the challenges associated with variants of uncertain diagnostic significance interpretation. We review the evidence for diagnostic weight of these variants, discuss the role of clinical genetics providers in patient care, and put forward general recommendations about the interpretation of incidentally identified variants found with clinical genetic testing.

scholarly journals CCMG practice guideline: laboratory guidelines for next-generation sequencing

2019 ◽  
Vol 56 (12) ◽  
pp. 792-800 ◽  
Author(s):  
Stacey Hume ◽  
Tanya N Nelson ◽  
Marsha Speevak ◽  
Elizabeth McCready ◽  
Ron Agatep ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Massively Parallel Sequencing ◽  
Clinical Genetics ◽  
Next Generation ◽  
Clinical Genetic ◽  
Inherited Disorders ◽  
Laboratory Service ◽  
Clinical Laboratories ◽  
Generation Sequencing

PurposeThe purpose of this document is to provide guidance for the use of next-generation sequencing (NGS, also known as massively parallel sequencing or MPS) in Canadian clinical genetic laboratories for detection of genetic variants in genomic DNA and mitochondrial DNA for inherited disorders, as well as somatic variants in tumour DNA for acquired cancers. They are intended for Canadian clinical laboratories engaged in developing, validating and using NGS methods.Methods of statement developmentThe document was drafted by the Canadian College of Medical Geneticists (CCMG) Ad Hoc Working Group on NGS Guidelines to make recommendations relevant to NGS. The statement was circulated for comment to the CCMG Laboratory Practice and Clinical Practice committees, and to the CCMG membership. Following incorporation of feedback, the document was approved by the CCMG Board of Directors.DisclaimerThe CCMG is a Canadian organisation responsible for certifying medical geneticists and clinical laboratory geneticists, and for establishing professional and ethical standards for clinical genetics services in Canada. The current CCMG Practice Guidelines were developed as a resource for clinical laboratories in Canada and should not be considered to be inclusive of all information laboratories should consider in the validation and use of NGS for a clinical laboratory service.

Epidemics

2018 ◽  
Author(s):  
Samuel K. Cohn, Jr.
Keyword(s):  
Sixteenth Century ◽  
Preventive Measures ◽  
Fifth Century ◽  
The Other ◽  
Fundamental Aspect ◽  
Late Nineteenth Century ◽  
Psychological Consequences ◽  
The Us ◽  
History Of ◽  
Race Ethnicity

This book challenges a dominant hypothesis in the study of epidemics. From an interdisciplinary array of scholars, a consensus has emerged: invariably, epidemics in past times provoked class hatred, blame of the ‘other’, or victimization of the diseases’ victims. It is also claimed that when diseases were mysterious, without cures or preventive measures, they more readily provoked ‘sinister connotations’. The evidence for these assumptions, however, comes from a handful of examples—the Black Death, the Great Pox at the end of the sixteenth century, cholera riots of the 1830s, and AIDS, centred almost exclusively on the US experience. By investigating thousands of descriptions of epidemics, reaching back before the fifth-century BCE Plague of Athens to the eruption of Ebola in 2014, this study traces epidemics’ socio-psychological consequences across time and discovers a radically different picture. First, scholars, especially post-AIDS, have missed a fundamental aspect of the history of epidemics: their remarkable power to unify societies across class, race, ethnicity, and religion, spurring self-sacrifice and compassion. Second, hatred and violence cannot be relegated to a time when diseases were mysterious, before the ‘laboratory revolution’ of the late nineteenth century: in fact, modernity was the great incubator of a disease–hate nexus. Third, even with diseases that have tended to provoke hatred, such as smallpox, poliomyelitis, plague, and cholera, blaming ‘the other’ or victimizing disease bearers has been rare. Instead, the history of epidemics and their socio-psychological consequences has been richer and more varied than scholars and public intellectuals have heretofore allowed.

scholarly journals African American Unemployment and the Disparity in Periviable Births

2021 ◽  
Author(s):  
Ralph Catalano ◽  
Deborah Karasek ◽  
Tim Bruckner ◽  
Joan A. Casey ◽  
Katherine Saxton ◽  
...  
Keyword(s):  
African American ◽  
African American Women ◽  
Unemployment Rate ◽  
Racial Disparity ◽  
Live Births ◽  
Percentage Point Increase ◽  
Point Increase ◽  
The Us ◽  
Race Ethnicity ◽  
Hispanic White

AbstractPeriviable infants (i.e., born before 26 complete weeks of gestation) represent fewer than .5% of births in the US but account for 40% of infant mortality and 20% of billed hospital obstetric costs. African American women contribute about 14% of live births in the US, but these include nearly a third of the country’s periviable births. Consistent with theory and with periviable births among other race/ethnicity groups, males predominate among African American periviable births in stressed populations. We test the hypothesis that the disparity in periviable male births among African American and non-Hispanic white populations responds to the African American unemployment rate because that indicator not only traces, but also contributes to, the prevalence of stress in the population. We use time-series methods that control for autocorrelation including secular trends, seasonality, and the tendency to remain elevated or depressed after high or low values. The racial disparity in male periviable birth increases by 4.45% for each percentage point increase in the unemployment rate of African Americans above its expected value. We infer that unemployment—a population stressor over which our institutions exercise considerable control—affects the disparity between African American and non-Hispanic white periviable births in the US.

scholarly journals The convergence of racial and income disparities in health insurance coverage in the United States

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
De-Chih Lee ◽  
Hailun Liang ◽  
Leiyu Shi
Keyword(s):  
Health Insurance ◽  
Low Income ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Combined Effect ◽  
Medical Expenditure ◽  
The Us ◽  
Race Ethnicity ◽  
Vulnerability Measure

Abstract Objective This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. Data source The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. Study design Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. Data collection/extraction methods We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findings While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health. Conclusion Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

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