Assessing data from summary questions about earnings and income

In short surveys, or in surveys that prioritise other content domains, earnings and income are often elicited using small sets of summary questions. This contrasts with the detailed questions recommended for surveys that focus on earnings and income, that ask source by source. We evaluate earnings and income data collected with summary questions in a series of recent web-surveys: the Understanding Society COVID-19 Study. The fact that many COVID-19 Study respondents also contemporaneously answered the main annual Understanding Society survey provides individual- and household-level validation data. We find that measures of household earnings and income in the COVID-19 Study are noisier than those from the main annual Understanding Society survey, and that there is evidence of systematic under-reporting for household totals. However, for most measures and samples, we find that measurement errors in the COVID-19 Study are substantively uncorrelated with true values. We conclude that the COVID-19 Study collected valuable data on earnings and income, and more broadly, that summary questions on earnings or income can be a useful data collection tool. © 2023 The Author(s)

The Role of Soluble ACE2 as a Prognostic Marker in Severe COVID-19: A Brief Meta-Analysis

Background: The recently emerged novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has posed a serious threat to public health, and there is an urgent need to establish tools that can aid the clinician in the evaluation and management of high-risk patients. This meta-analysis aimed to investigate the potential of sACE2 (soluble angiotensin-converting enzyme 2) as a prognostic biomarker in COVID-19. Methods: A comprehensive search of PubMed/MEDLINE, Cochrane, and Google Scholar, was performed until May 26, 2021. Data extraction and quality assessment of the study were inde-pendently conducted by the authors. Finally, 6 studies were included in this meta-analysis. Results: ACE-2 serum or plasma levels were compared between COVID-19 patients and healthy controls. ACE-2 level was not significantly different between severe COVID-19 patients and healthy controls (SMD = 1.2;95% CI:-1.3-1.5;P = 0.86), severe and non-severe COVID-19 patients (SMD = 0.3;95% CI:-0.06-0.7;P = 0.1), and severe COVID-19 patients and healthy controls (SMD = 0.6;95% CI:-1.1-2.3;P = 0.5). Conclusions: We cautiously propose that circulating levels of ACE2 cannot be used as a biomarker to assess disease severity in COVID-19 patients. © 2022 Bentham Science Publishers.

Construction and evaluation of a domain-specific knowledge graph for knowledge discovery

Purpose: This study aims to evaluate a method of building a biomedical knowledge graph (KG). Design/methodology/approach: This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed. Findings: With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG. Originality/value: The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems. © 2022, Emerald Publishing Limited.

How Institutions Moderated the Pandemic's Economic Impact in EU Member States

We investigate how institutional quality affected the economic downturn in EU countries during the COVID‐19 pandemic (2020–21). Using quarterly panel data, we show that countries with a higher quality of governance and a higher score of economic freedom suffered markedly less. Importantly, institutions mattered more when the pandemic shock was larger. Thus, the pandemic highlights the asymmetric impact of seemingly symmetric exogenous shock on EU economies and raises important issues about the necessary reforms for short‐run resilience and long‐run convergence.

Role of the Clinical Data Managers During the COVID-19 Pandemic: An Overview.

BACKGROUND: The COVID-19 pandemic has significantly changed the implementation of clinical trials. A large focus has been directed on clinical trial design, timeline, and best practices. It has led clinical trial study teams to update the existing processes and perform a risk assessment to mitigate the impact of the COVID-19 pandemic according to ICH-GCP (Good Clinical Practice) requirements. Data management plays a crucial role in understanding the study team's needs and developing innovative solutions. The Clinical Data Manager (CDM) is a core clinical trial Study Team member, responsible for promptly collecting, managing, and delivering complete, highquality data. OBJECTIVE: The COVID-19 pandemic required the Clinical Data Manager (CDM) to respond to changing needs by adapting data collection tools, data review strategies, and data management processes to answer new questions and address new challenges. CDMs became responsible for identifying how the COVID-19 pandemic impacted current data management processes and documentation and implementing changes to reflect new ways of working. The present article reviews the impact of the COVID-19 pandemic on clinical trials and the solutions adopted by the Clinical Data manager. CONCLUSION: The collection of COVID-19-related data points provides a better understanding of patient safety during the pandemic and proactively fulfills the growing regulatory interests. Strategies and innovative solutions adopted by the Clinical Data Manager serve as guidance for the clinical research team during the crisis to make the trials more robust and patient-centered.