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Last Posted: Nov 21, 2022
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The Social Vulnerability Metric (SVM) As A New Tool for Public Health.
Saulsberry Loren et al. Health services research 2022 11

The Social Vulnerability Metric (SVM) was constructed from U.S. zip-code level measures (2018) from survey data using multidimensional Item Response Theory and validated using outcomes including all-cause mortality (2016), COVID-19 vaccination (2021), and emergency department visits for asthma (2018). The correlation between SVM scores and national age-adjusted county all-cause mortality was r=0.68. This correlation demonstrated the SVM’s robust validity and outperformed the SVI with an almost four-fold increase in explained variance (46% versus 12%).

Asthma: From Phenotypes to Personalized Medicine
P Bakakos, J Per Med, November 6, 2022

Asthma is a heterogeneous disease of the airways with a high prevalence worldwide characterized by chronic inflammation. The aim of asthma management is the control of the disease, and the cornerstone of asthma treatment is inhaled corticosteroids. Asthma is no longer recognized as a unique manifestation, and the “one size fits all” approach may apply only in the treatment of mild asthma.

Measurement of Exhaled Volatile Organic Compounds as a Biomarker for Personalised Medicine: Assessment of Short-Term Repeatability in Severe Asthma
A Azim et al, J Per Medicine, September 29, 2022

The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering.

Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome
C Cervia et al, Nature Comms, January 25, 2022

In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling.

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.