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Gastroenterology ; 162(7):S-383, 2022.
Article in English | EMBASE | ID: covidwho-1967304


Introduction: The SARS-CoV-2 pandemic highlighted the need for a way to predict progression to critical illness and ICU admission amongst infected patients. Previous liver disease is a known risk for progression to critical illness. Attempts to identify biomarkers for progression to critical illness suggest inflammatory markers and coagulation markers as useful. We used a machine learning approach to compare the admission liver panel and inflammatory biomarker assays in hospitalized COVID-19 patients with extant mild or severe hepatic disease who progressed to critical illness (ICU admission) versus those who were progression-free. Methods: We included data ed under IRB exemption from electronic medical records (EMR) for SARS-CoV-2 patients admitted to the hospital with chronic liver disease ICD-10-CM codes. Demographics, laboratory results and administrative data were archived and analyzed (SAS, Cary, NC). Generalized regression identified inflammatory and liver panel biomarkers assayed within 8h of hospital admission associated (p<.05) with progression to critical illness. Retained biomarkers underwent bootstrap forest analysis forming a receiver operating characteristic (ROC) that optimized area under ROC (AUROC) estimating model accuracy (precision). Continuous data summarized with median [IQR] were compared using Kruskal-Wallis Test. Discrete data summarized as counts or proportions were compared with chi-squared test. Two-tailed p<.05 was significant. Results: Out of the 4411 COVID-19 patients who were discharged between March 14, 2020 and September 30, 2021, 333 with a previous diagnosis chronic liver disease were included in this study. Demographics for this population are presented in Table 1. Statistical values for biomarkers and progression to critical illness are seen in table 1. Statistically significant markers are compared via explained variance and ROC curve in Figure 1. Although AST and D-dimer were statistically significant markers of progression to critical illness, when modelled as a predictive biomarker, they were not informative in the aggregated ensemble. Therefore, they were not included in the modeling analysis. Conclusion: Hypoalbuminemia, inflammatory markers, D-dimer, and AST were significantly associated with progression to critical illness. Indexing liver specific synthetic function (albumin) to CoV-2 evoked inflammatory markers improves explained variance for progression to critical illness. Alternative liver synthetic function biomarker (INR), ALT, and ALP were not a significant prognostic indicator for progression to severe illness. To our knowledge, this is debut of modeling hypoalbuminemia indexed with multiple routinely assayed inflammatory biomarkers for baseline risk assessment in COVID-19 patients with liver disease. (Table Presented) (Figure Presented)

AERA Open ; 8, 2022.
Article in English | Scopus | ID: covidwho-1685977


In spring 2020, many U.S. colleges and universities rapidly shifted to online instruction and implemented social distancing policies to respond to the COVID-19 pandemic. Students experienced unprecedented disruption of their interpersonal academic and social networks due to the loss of physical proximity. We used egocentric network analysis and latent profile analysis with survey data from April 2020 and conducted follow-up interviews in September 2020 to examine some of the pandemic’s immediate effects on student interpersonal network change. We found the disappearance of interpersonal network patterns featuring coworkers and academic ties, as well as reductions in students’ overall number of connections and the role diversity of their networks. Results suggest potential ongoing reduction of peer academic relationships, implying that institutional personnel may need to pay particular attention to academic connections in online spaces and to regenerating students’ academic networks when on-campus physical spaces may again be used to support learning. © The Author(s) 2022.

Stigma and Health ; : 10, 2022.
Article in English | Web of Science | ID: covidwho-1665684


Media coverage of coronavirus disease (COVID-19) has played a critical role throughout the pandemic: sharing news about the novel virus, policies and practices to mitigate it, and the race to create and distribute 4 vaccines. The media coverage, however, has been critiqued as stigmatizing. Although this critique is not new, there is limited understanding of how and why new stigmas emerge from exposure to media coverage. Drawing upon the model of stigma communication (Smith et al., 2019) and the attribution model of stigma (Corrigan et al., 2003), we investigated a novel model of stigma emergence that delineates two kinds of longitudinal processes: (a) a message-effects process, in which exposure to mediated messages about COVID-19 leads to public stigma through danger appraisal and (b) a coping process in which stress and rumination shape later perceptions of public stigma. To test the model, we tracked an emerging COVID-19 stigma with a two-wave survey of a prospective, longitudinal cohort living in one county in a mid-Atlantic state (N = 883). The results supported this model. The longitudinal processes of stigma emergence and implications for COVID-19 stigma are discussed.

Clinical Cancer Research ; 26(18 SUPPL), 2020.
Article in English | EMBASE | ID: covidwho-992098


Introduction: During the COVID-19 pandemic, the unemployment rate has sharply risen from 3.5% in February2020 to 13.3% in May 2020, a level not seen since the Great Depression. There are an estimated 21.0 millionunemployed adults in the United States. Employers are the most common source of health insurance amongworking-aged adults and their families. Thus, job loss may lead to loss of insurance and reduce access to cancerscreening, which can detect cancer at earlier, more treatable stages, and reduce cancer mortality. In this study, weexamined sequential associations between unemployment, health insurance, and cancer screening to informCOVID's potential longer-lasting impacts on early cancer detection. Methods: Up-to-date (UTD) and recent (past-year) breast (BC) and colorectal cancer (CRC) screening prevalence were computed among respondents aged 50-64 years in 2000-2018 National Health Interview Survey data.Respondents were grouped as unemployed (not working but looking BC n=852;CRC n=1,747) and employed(currently working BC n=19,013;CRC n= 36,566). A series of logistic regression models with predicted marginalprobabilities were used to estimate unemployed vs. employed unadjusted (PR) and adjusted prevalence ratios(aPR) and corresponding 95% Confidence Intervals (CI). Results: Unemployed adults were four times as likely to be uninsured as employed adults (41.4% v 10.0%, p-value<0.001). Unemployment was associated with lower UTD breast (67.8% vs 77.5%, p-value<0.001, PR=0.82, 95%CI0.77,0.87) and colorectal (49.4% and 60.1%, p-value<0.001, PR=0.86, 95%CI 0.80, 0.92) cancer screeningprevalence. These differences remained after adjusting for race/ethnicity, age, and sex, but were eliminated afteraccounting for health insurance. Patterns and magnitudes of PR and aPRs were similar for past-year CRC and BCscreening prevalence. Conclusion: Unemployment was adversely associated with guideline-recommended and potentially life-savingbreast and colorectal cancer screening. Compared to the employed, the unemployed disproportionately lackedhealth insurance, which accounted for their lower cancer screening utilization. Expanding and ensuring healthinsurance coverage after job loss may mitigate COVID-19's economic impacts on cancer screening.