Notes

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The Survey of the Health of Wisconsin (SHOW) was established in 2008 by the University of Wisconsin (UW) School of Medicine and Public Health (SMPH) with the goals of 1) providing a timely and accurate picture of the health of the state residents; and 2) serving as an agile resource infrastructure for ancillary studies. Today SHOW continues to serve as a vital population health research infrastructure.

SHOW currently includes 5,846 adult and 980 minor participants recruited between 2008-2019 in four primary waves. WAVE I (2008-2013) includes annual statewide representative samples of 3,380 adults ages 21 to 74 years. WAVE II (2014-2016) is a triannual statewide sample of 1957 adults (age ≥18 years) and 645 children. WAVE III (2017) consists of follow-up of 725 adults from the WAVE I and baseline surveys of 222 children in selected households. WAVEs II and III include stool samples collected as part of an ancillary study in a subset of 784 individuals. WAVE IV consist of 517 adults and 113 children recruitres (physical, chemical, social) across the life course on cardiometabolic health, immunity, and aging related conditions.The extensive biorepository supports novel omics research into common biological mechanisms underlying numerous complex chronic conditions including inflammation, oxidative stress, metabolomics, and epigenetic modulation.Ancillary studies, such as the Wisconsin Microbiome Study, have expanded the utility of the study to examine human susceptibility to environmental exposures and opportunities for investigations of the role of microbiome in health and disease.Long-standing partnerships and recent participation among traditionally under-represented populations in biomedical research offer numerous opportunities to support community-driven health equity work.No biological samples were collected among children.The statewide sampling frame may limit generalizability to other regions in the United States.SARS-CoV-2 vaccines are powerful tools to combat the COVID-19 pandemic, but vaccine hesitancy threatens these vaccines' effectiveness. LY2157299 purchase To address COVID-19 vaccine hesitancy and ensure equitable distribution, understanding the extent of and factors associated with vaccine hesitancy is critical. We report the results of a large nationwide study conducted December 2020-January 2021 of 34,470 users from COVID-19-focused smartphone-based app How We Feel on their willingness to receive a COVID-19 vaccine. Nineteen percent of respondents expressed vaccine hesitancy, the majority being undecided. Vaccine hesitancy was significant among females, younger people, minority and low-income communities, healthcare and essential workers, rural residents, geographical regions with higher COVID-19 burden, those who did not use protective measures, and those who did not receive COVID-19 tests. Our findings support the need for targeted efforts to develop education and outreach programs to overcome vaccine hesitancy and improve equitable access, diversity, and inclusion in the national response to COVID-19.
Several states including Texas and Mississippi have lifted their mask mandates, sparking concerns that this policy change could lead to a surge in cases and hospitalizations.

To estimate the increase in incidence, hospitalizations, and deaths in Texas and Mississippi following the removal of mask mandates, and to evaluate the relative reduction of these outcomes if policy change is delayed by 90 days.

This study uses an age-stratified compartmental model parameterized to incidence data in Texas and Mississippi to simulate increased transmission following policy change in March or June 2021, and to estimate the resulting number of incidence, hospitalizations, and deaths.

The increase in incidence, hospitalizations, and deaths if mask mandates are lifted on March 14 compared to lifting on June 12.

If transmission is increased by 67% when mask mandates are lifted, we projected 11.39 (CrI 11.22 - 11.55) million infections, 170,909 (CrI 167,454 - 174,379) hospitalizations, and 5647 (5511 - 5804) deaths (Figure 1) in Texas from March 14 through the end of 2021. Delaying NPI lift until June reduces the average number of infections, hospitalizations, and deaths by 36%, 65%, and 62%, respectively. Proportionate differences were similar for the state of Mississippi. Peak hospitalization rates would be reduced by 79% and 63% in Texas and Mississippi, respectively.

Removal of mask mandates in March 2021 is premature. Delaying this policy change until June 2021, when a larger fraction of the population has been vaccinated, will avert more than half of the expected COVID-19 hospitalizations and deaths, and avoid an otherwise likely strain on healthcare capacity.
Removal of mask mandates in March 2021 is premature. Delaying this policy change until June 2021, when a larger fraction of the population has been vaccinated, will avert more than half of the expected COVID-19 hospitalizations and deaths, and avoid an otherwise likely strain on healthcare capacity.
Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non
drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects.

Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect.

We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients.

Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.
Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.
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