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  • COVID-19
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Johanna Loomba; Suchetha Sharma;

    The goals of this electronic health record (EHR) query are to investigate: Amongst known COVID-19 patients, who is experiencing new Economic Instability? Amongst COVID-19 patients with documented Long COVID, who is experiencing new Economic Instability? The data source is the National COVID Cohort Collaborative (N3C) which hosts nearly 20 billion rows of medical record data, providing a rich resource for big data analysis.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Report . 2022
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Report . 2022
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Report . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Report . 2022
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Madeleine Jones; Aubrey Winger; Christian Wernz; Jonathan Michel; +9 Authors

    National COVID Cohort Collaborative (N3C) en- clave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites’ local electronic health records (EHRs), such as the principal diagnosis code for reported emergency department (ED) and inpatient (IP) visits. This means a principal diagnosis of COVID-19 can only be inferred by applying a time relationship between the visit dates and the record of infection and diagnosis. The purpose of this study is to perform a single-site sensitivity analysis modeled after an N3C study examining potential race-ethnicity based bias in hospitalization decisions during COVID-19 related ED visits. The analytic pipeline was first run in N3C, then reproduced locally with N3C data fields from a single-site, and finally run a third time using the additional principal diagnosis data. We find the effects of patient comorbidities and race-ethnicity groups on direct IP admittance to be consistent among the three cohorts with varying levels of statistical significance due to different sample sizes. Consortial contributors are in the process of being documented.

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    Other literature type . 2023
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Research . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      Other literature type . 2023
      License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Research . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Melissa A Haendel; Chris Chute; N3C Consortium (See attached document);

    Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Article . 2020
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Article . 2020
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Article . 2020
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Article . 2020
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2020
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      Article . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sexton, Mary Elizabeth; Waggoner, Jesse J; Carmola, Ludy R; Nguyen, Phuong-Vi; +16 Authors

    We describe rapid detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant using targeted spike single-nucleotide polymorphism polymerase chain reaction and viral genome sequencing. This case occurred in a fully vaccinated and boosted returning traveler with mild symptoms who was identified through community surveillance rather than clinical care.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Motelow, Joshua E.; Kahn, Stacie; Wilson, Patrick T.;

    As the pandemic continues to evolve, more cases of COVID-19 in pediatric patients are being detected. A 12-year-old boy with HbSC disease alpha-thalassemia trait presented to a pediatric emergency room with fever and weakness. His vital signs were notable for fever, tachypnea, and tachycardia. His physical exam was concerning for increased work of breathing. He tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by PCR although his hemoglobin level remained near his baseline. His chest radiograph showed a retrocardiac opacity concerning for evolving acute chest syndrome. He decompensated quickly requiring invasive mechanical ventilation and exchange transfusion. He received hydroxychloroquine, broad-spectrum antibiotics, and enoxaparin for DVT prophylaxis. Despite showing clinical signs of improvement, he became acutely hypoxemic and suffered a cardiac arrest. We believe this to be an unusual case of a pediatric patient with HbSC disease and COVID-19. We outline clearly the course of illness and treatments trialed, which can prove beneficial to providers facing similar challenges as this virus continues to strike areas around the world. Although children have significantly better outcomes than adults, providers must remain vigilant while treating any patient with a hemoglobinopathy in the setting of severe COVID-19.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Hindawi Publishing C...arrow_drop_down
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    Authors: Warmack, Pearl A. McElfish; Don E. Willis; Sumit K. Shah; Sharon Reece; Jennifer A. Andersen; Mario Schootman; Gloria Richard-Davis; James P. Selig; T. Scott;

    A cross-sectional survey design was used to assess Arkansas parents’/guardians’ intentions to vaccinate their child against COVID-19. Parents/guardians whose oldest child was age 0–11 years (n = 171) or 12–17 years (n = 198) were recruited between 12 July and 30 July 2021 through random digit dialing. Among parents/guardians with an age-eligible child, age 12–17, 19% reported their child had been vaccinated, and 34% reported they would have their child vaccinated right away. Among parents/guardians with a child aged 0–11, 33% of parents/guardians reported they would have their child vaccinated right away. Twenty-eight percent (28%) of parents/guardians whose oldest child was 12–17 and 26% of parents/guardians whose oldest child was 0–11 reported they would only have their child vaccinated if their school required it; otherwise, they would definitely not vaccinate them. For both groups, parents’/guardians’ education, COVID-19 vaccination status, and COVID-19 vaccine hesitancy were significantly associated with intentions to vaccinate their child. More than a third of parents/guardians whose child was eligible for vaccination at the time of the survey reported they intended to have them vaccinated right away; however, they had not vaccinated their child more than two months after approval. This finding raises questions about the remaining barriers constraining some parents/guardians from vaccinating their child.

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    Authors: Domar, Alice D.; Shah, Jaimin S.; Gompers, Annika; Meyers, Alison J.; +6 Authors

    Objective To compare the impact of the COVID-19 pandemic on the psychological health of infertility patients who have become pregnant to women who have not. Design Prospective cohort study from April to June 2020. Participants completed three questionnaires over this period. Setting A single large, university-affiliated infertility practice. Patient(s) 443 pregnant women and 1476 women still experiencing infertility who completed all three questionnaires. Intervention(s) None. Main Outcome Measure(s) Patient-reported primary stressor over three months of the first major COVID-19 surge. Further data on self-reported sadness, anxiety, loneliness and the use of personal use of coping strategies. Results Pregnant participants were significantly less likely to report taking an antidepressant (p<0.01) or anxiolytic medication (p<0.001), to have a prior diagnosis of depression (p<0.01), were more likely to cite COVID-19 as a top stressor (p<0.001) and overall were less likely to practice stress-relieving activities during the first surge. Conclusion Women pregnant following infertility treatment cited the pandemic as their top stressor and were more distressed about the pandemic than their non-pregnant counterparts but were less likely to be engaging in stress-relieving activities. Given the ongoing impact of the pandemic, infertility patients pregnant after treatment should be counseled and encouraged to practice specific stress-reduction strategies. Women pregnant after infertility treatment were more concerned about COVID-19 than patients continuing with infertility treatment but overall were less distressed.

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    Authors: Jia, Xiaolin; Cao, Shu; Lee, Alexandra S; Manohar, Monali; +39 Authors

    BACKGROUND Prolonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODS Adult SARS-CoV-2 reverse transcription PCR–positive (RT-PCR–positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1–3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTS Our cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSION We found that all disease severities had a similar risk of developing post–COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATION ClinicalTrials.gov, NCT04373148.FUNDING NIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

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    Authors: Levy, Joshua I.; Andersen, Kristian G.; Knight, Rob; Karthikeyan, Smruthi;

    A University of California San Diego researcher collects wastewater on campus for SARS-CoV-2 analysis and sequencing.

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    Authors: Adams, Elizabeth; Brickhouse, Tegwyn; Dugger, Roddrick; Bean, Melanie;

    Temporary expansion of the Child Tax Credit (CTC) during the COVID-19 pandemic provided additional monthly income for US families, with no restrictions on use, from July through December 2021. This study examined food security and children's dietary intake after three months of expanded CTC payments. Parents completed online surveys before and after three months of CTC payments. Among parents participating in the expansion, food and beverage purchases were the most common use of expanded CTC funds (45.9 percent), particularly in households with very low food security (63.0 percent). From before to midway through the CTC expansion, very low food security decreased from 12.7 percent to 5.6 percent, and simultaneously, food security increased from 57.4 percent to 66.4 percent. The CTC expansion was also associated with decreases in children's consumption of added sugar, sugar-sweetened beverages, and sweetened fruit beverages. No changes were observed in children's intake of other dietary components. Our findings suggest that the expanded CTC payments may have helped lessen food insecurity and supported reductions in children's intake of added sugar in participating households.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Johanna Loomba; Suchetha Sharma;

    The goals of this electronic health record (EHR) query are to investigate: Amongst known COVID-19 patients, who is experiencing new Economic Instability? Amongst COVID-19 patients with documented Long COVID, who is experiencing new Economic Instability? The data source is the National COVID Cohort Collaborative (N3C) which hosts nearly 20 billion rows of medical record data, providing a rich resource for big data analysis.

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    ZENODO
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    ZENODO
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    Authors: Madeleine Jones; Aubrey Winger; Christian Wernz; Jonathan Michel; +9 Authors

    National COVID Cohort Collaborative (N3C) en- clave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites’ local electronic health records (EHRs), such as the principal diagnosis code for reported emergency department (ED) and inpatient (IP) visits. This means a principal diagnosis of COVID-19 can only be inferred by applying a time relationship between the visit dates and the record of infection and diagnosis. The purpose of this study is to perform a single-site sensitivity analysis modeled after an N3C study examining potential race-ethnicity based bias in hospitalization decisions during COVID-19 related ED visits. The analytic pipeline was first run in N3C, then reproduced locally with N3C data fields from a single-site, and finally run a third time using the additional principal diagnosis data. We find the effects of patient comorbidities and race-ethnicity groups on direct IP admittance to be consistent among the three cohorts with varying levels of statistical significance due to different sample sizes. Consortial contributors are in the process of being documented.

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    Authors: Melissa A Haendel; Chris Chute; N3C Consortium (See attached document);

    Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sexton, Mary Elizabeth; Waggoner, Jesse J; Carmola, Ludy R; Nguyen, Phuong-Vi; +16 Authors

    We describe rapid detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant using targeted spike single-nucleotide polymorphism polymerase chain reaction and viral genome sequencing. This case occurred in a fully vaccinated and boosted returning traveler with mild symptoms who was identified through community surveillance rather than clinical care.

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