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Last Posted: Sep 15, 2022
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Racial disparities in COVID-19 outcomes: Unwarranted statistical adjustments and the perpetuation of stereotypes
SP Massion et al, Lancet Regional Health, September 2022

By clarifying the hypothesized role of race in research investigations and employing appropriate statistical causal methodologies, researchers can avoid stigmatizing consequences and maintain the focus on the conditions and situations that may be the underlying causes of the observed disparities in SARS-CoV-2 infections and related outcomes. Identifying the underlying and modifiable factors that contribute to increased risk of SARS-CoV-2 infection among minoritized populations is essential for addressing disparities.

Genomics, Health Equity, and Global Health
J Osei et al, CDC Blog Post, September 12, 2022 Brand

The World Health Organization’s Science Council recently issued its first report on accelerating access to genomics for global health. The report makes a strong case for less-resourced countries to gain access to such technologies. The report makes clear that new genomic technologies should be available to all, not just a few privileged regions. The report calls for a robust roadmap for using genomic technologies to improve global health. A multi-faceted approach that fosters health equity and enhances capacity development will be crucial for this journey.

Social and scientific motivations to move beyond groups in allele frequencies: The TOPMed experience.
Nelson Sarah C et al. American journal of human genetics 2022 9 (9) 1582-1590

The NHLBI Trans-Omics for Precision Medicine (TOPMed) program considered the scientific and social implications of different approaches for adding stratified frequencies to the TOPMed BRAVO (Browse All Variants Online) variant server. We recommend a novel approach of presenting ancestry-specific allele frequencies using a statistical method based upon local genetic ancestry inference.

Subpopulation-specific machine learning prognosis for underrepresented patients with double prioritized bias correction
S Afrose et al, Comm Medicine, September 1, 2022

Biases exist in the widely accepted one-machine-learning-model-fits-all-population approach. We invent a bias correction method that produces specialized machine learning prognostication models for underrepresented racial and age groups. This technique may reduce potentially life-threatening prediction mistakes for minority populations.


Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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