LitCovid in 2022: an information resource for the COVID-19 literature

Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation

BACKGROUND

Since the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications.

OBJECTIVE

This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience.

METHODS

We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths.

RESULTS

We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching.

CONCLUSIONS

Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available.

Education and pandemic SARS-CoV-2 infections in the German working population - the mediating role of working from home

OBJECTIVES

SARS-CoV-2 infections were unequally distributed during the pandemic, with those in disadvantaged socioeconomic positions being at higher risk. Little is known about the underlying mechanism of this association. This study assessed to what extent educational differences in SARS-CoV-2 infections were mediated by working from home.

METHODS

We used data of the German working population derived from the seroepidemiological study "Corona Monitoring Nationwide - Wave 2 (RKI-SOEP-2)" (N=6826). Infections were assessed by seropositivity against SARS-CoV-2 antigens and self-reports of previous PCR-confirmed infections from the beginning of the pandemic until study participation (November 2021 - February 2022). The frequency of working from home was assessed between May 2021 and January 2022.We used the Karlson-Holm-Breen (KHB) method to decompose the effect of education on SARS-CoV-2 infections.

RESULTS

Individuals with lower educational attainment had a higher risk for SARS-CoV-2 infection (adjusted prevalence ratio of low versus very high = 1.76, 95% confidence interval 1.08-2.88; P=0.023). Depending on the level of education, between 27% (high education) and 58% (low education) of the differences in infection were mediated by the frequency of working from home.

CONCLUSIONS

Working from home could prevent SARS-CoV-2 infections and contribute to the explanation of socioeconomic inequalities in infection risks. Wherever possible, additional capacities to work remotely, particularly for occupations that require lower educational attainment, should be considered as an important measure of pandemic preparedness. Limitations of this study are the observational cross-sectional design and that the temporal order between infection and working from home remained unclear.

Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response

Background

The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis.

Methods

NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning-based model built to identify common topics/themes within the publications.

Results and Conclusions

We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning-based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase.

What did we learn about changing behaviour during the COVID-19 pandemic? A systematic review of interventions to change hand hygiene and mask use behaviour

BACKGROUND

behaviour change interventions were central in the COVID-19 response and are vital for strengthening pandemic preparedness and resilience. To be effective, interventions must target specific behavioural determinants, but determinants are complex and multifaceted and there is a gap in robust, theory driven evidence on which behavioural determinants are most effective at changing mask usage and hand hygiene behaviour.

PURPOSE

to map available evidence on the types of hand hygiene and mask usage behaviour change interventions conducted during the COVID-19 pandemic and assess their effectiveness, feasibility and acceptability.

METHODS

we conducted a systematic review, searching four peer-reviewed databases for terms related to COVID-19, targeted behaviours (hand hygiene and mask usage) and interventions. Eligible studies were those which focused on adults or children in naturalistic, non-experimental settings; reported on an intervention designed to change hand hygiene and or mask usage to reduce COVID-19 transmission; provided clear outcome measures, including through self-report, proxy indicators or observation. Studies were excluded if they were purely qualitative, opinion pieces or based on secondary data alone; focused on health workers; measured intended rather than enacted behaviour; were conducted in laboratory or health care-based settings; involved infants; were published before the 11th of March 2020 (when COVID-19 was declared a pandemic) and published in a language other than English. There were no geographical limits set. Descriptive summaries were produced and the quality of evidence and reporting was evaluated. Studies were divided into three sub-groups according to the behaviour targeted and behaviour change techniques (BCTs) were mapped. Effect estimates were summarised and the relationship between BCTs and effect was explored. Feasibility and acceptability was summarised where reported. Due to the heterogeneity of studies included, meta-analysis could not be conducted.

FINDINGS

sixteen citations met the criteria, with sub-studies (two citations including multiple studies) totalling nineteen eligible studies. The majority were randomised controlled trials which targeted hand hygiene only and were conducted in high income nations, with none conducted in crisis settings. Due to the constraints of the pandemic, many interventions were delivered online. The quality of studies was low, with the majority demonstrating a medium risk of bias (Likert scale: low, medium, high). Whilst acceptability and feasibility was good, both were rarely evaluated. 'Natural consequences' was the most commonly used BCT group. Fourteen of the studies elicited positive or potentially positive effects in at least one intervention arm and/or targeted behaviour. Effective interventions typically targeted multiple individual BCTs, including 'Instruction on how to perform a behaviour', 'Information about health consequences', and group 'Reward and threat', through repeated engagement over a sustained period of time.

CONCLUSION

there is a substantial knowledge gap, particularly in low resource and crisis settings, and available evidence is of low quality. We must address these gaps to enable evidence-based practice and strengthen pandemic preparedness and resilience. Future research should include another systematic review which includes grey literature and different languages, as well as more robust evaluations which use implementation research to explore the impact of multiple BCTs in low resource and crisis settings. Evaluations should include assessments of acceptability, practicability, affordability and equity.

Assessment of search strategies in Medline to identify studies on the impact of long COVID on workability

Objectives

Studies on the impact of long COVID on work capacity are increasing but are difficult to locate in bibliographic databases, due to the heterogeneity of the terms used to describe this new condition and its consequences. This study aims to report on the effectiveness of different search strategies to find studies on the impact of long COVID on work participation in PubMed and to create validated search strings.

Methods

We searched PubMed for articles published on Long COVID and including information about work. Relevant articles were identified and their reference lists were screened. Occupational health journals were manually scanned to identify articles that could have been missed. A total of 885 articles potentially relevant were collected and 120 were finally included in a gold standard database. Recall, Precision, and Number Needed to Read (NNR) of various keywords or combinations of keywords were assessed.

Results

Overall, 123 search-words alone or in combination were tested. The highest Recalls with a single MeSH term or textword were 23 and 90%, respectively. Two different search strings were developed, one optimizing Recall while keeping Precision acceptable (Recall 98.3%, Precision 15.9%, NNR 6.3) and one optimizing Precision while keeping Recall acceptable (Recall 90.8%, Precision 26.1%, NNR 3.8).

Conclusions

No single MeSH term allows to find all relevant studies on the impact of long COVID on work ability in PubMed. The use of various MeSH and non-MeSH terms in combination is required to recover such studies without being overwhelmed by irrelevant articles.

PubMed and beyond: biomedical literature search in the age of artificial intelligence

Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.

Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research

The pandemic has led to the production and accumulation of various types of data related to coronavirus disease 2019 (COVID-19). To understand the features and characteristics of COVID-19 data, we summarized representative databases and determined the data types, purpose, and utilization details of each database. In addition, we categorized COVID-19 associated databases into epidemiological data, genome and protein data, and drug and target data. We found that the data present in each of these databases have nine separate purposes (clade/variant/lineage, genome browser, protein structure, epidemiological data, visualization, data analysis tool, treatment, literature, and immunity) according to the types of data. Utilizing the databases we investigated, we created four queries as integrative analysis methods that aimed to answer important scientific questions related to COVID-19. Our queries can make effective use of multiple databases to produce valuable results that can reveal novel findings through comprehensive analysis. This allows clinical researchers, epidemiologists, and clinicians to have easy access to COVID-19 data without requiring expert knowledge in computing or data science. We expect that users will be able to reference our examples to construct their own integrative analysis methods, which will act as a basis for further scientific inquiry and data searching.

Comprehensively identifying Long Covid articles with human-in-the-loop machine learning

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid Collection shows that (1) most Long Covid articles do not refer to Long Covid by any name, (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid Collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid.