Dataset Information

Properties of a scale of concern for COVID-19: Exploratory analysis in a Peruvian sample.

ABSTRACT: OBJECTIVE:Concern about getting sick could help prevent disease. The aim of this study was to analyze the psychometric properties of a COVID-19 concern scale (EPCov-19). MATERIALS AND METHODS:This is an instrument-based study and information was collected from 224 people of Peruvian nationality in the context of social isolation. The items of the Cancer Concern Scale were adapted for this study. RESULTS:A scale with satisfactory psychometric properties was obtained. With a matrix of polychoric correlations, values higher than the standard were obtained in all 6 items (r >0.3) and the reliability was acceptable (?=0.866; 95% CI=0.83 - 0.89). Parallel analysis suggested unidimensionality of the EPCov-19, the variance explained was 79.7% and saturations were higher than 0.4. Goodness-of-fit índices were satisfactory (CFI=0.995; GFI=0.997; TLI=0.991; and RMSEA=0.059, 95% CI=0.012 - 0.077). CONCLUSIONS:This is a valid and reliable instrument for measuring concern about the spread of COVID-19 and can be used in future studies.

SUBMITTER: Ruiz Mamani PG

PROVIDER: S-EPMC7346827 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

[Properties of a scale of concern for COVID-19: Exploratory analysis in a Peruvian sample].

Ruiz Mamani Percy G PG Morales-García Wilter C WC White Michael M Marquez-Ruiz Mayte S MS

Medicina clinica 20200709 12

<h4>Objective</h4>Concern about getting sick could help prevent disease. The aim of this study was to analyze the psychometric properties of a COVID-19 concern scale (EPCov-19).<h4>Materials and methods</h4>This is an instrument-based study and information was collected from 224 people of Peruvian nationality in the context of social isolation. The items of the Cancer Concern Scale were adapted for this study.<h4>Results</h4>A scale with satisfactory psychometric properties was obtained. With a ...[more]

PMID: 32718717

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Dataset Information

Properties of a scale of concern for COVID-19: Exploratory analysis in a Peruvian sample.

Publications

[Properties of a scale of concern for COVID-19: Exploratory analysis in a Peruvian sample].

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