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The impact of the COVID-19 pandemic on scientific research in the life sciences
Roles Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing
Affiliation AXES, IMT School for Advanced Studies Lucca, Lucca, Italy
Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing
* E-mail: [email protected]
Affiliation Chair of Systems Design D-MTEC, ETH Zürich, Zurich, Switzerland
- Massimo Riccaboni,
- Luca Verginer
- Published: February 9, 2022
- Reader Comments
The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy.
Citation: Riccaboni M, Verginer L (2022) The impact of the COVID-19 pandemic on scientific research in the life sciences. PLoS ONE 17(2): e0263001. https://doi.org/10.1371/journal.pone.0263001
Editor: Florian Naudet, University of Rennes 1, FRANCE
Received: April 28, 2021; Accepted: January 10, 2022; Published: February 9, 2022
Copyright: © 2022 Riccaboni, Verginer. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The processed data, instructions on how to process the raw PubMed dataset as well as all code are available via Zenodo at https://doi.org/10.5281/zenodo.5121216 .
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
The COVID-19 pandemic has mobilized the world scientific community in 2020, especially in the life sciences [ 1 , 2 ]. In the first three months after the pandemic, the number of scientific papers about COVID-19 was fivefold the number of articles on H1N1 swine influenza [ 3 ]. Similarly, the number of clinical trials related to COVID-19 prophylaxis and treatments skyrocketed [ 4 ]. Thanks to the rapid mobilization of the world scientific community, COVID-19 vaccines have been developed in record time. Despite this undeniable success, there is a rising concern about the negative consequences of COVID-19 on clinical trial research, with many projects being postponed [ 5 – 7 ]. According to Evaluate Pharma, clinical trials were one of the pandemic’s first casualties, with a record number of 160 studies suspended for reasons related to COVID-19 in April 2020 [ 8 , 9 ] reporting a total of 1,200 trials suspended as of July 2020. As a consequence, clinical researchers have been impaired by reduced access to healthcare research infrastructures. Particularly, the COVID-19 outbreak took a tall on women and early-career scientists [ 10 – 13 ]. On a different ground, Shan and colleagues found that non-COVID-19-related articles decreased as COVID-19-related articles increased in top clinical research journals [ 14 ]. Fraser and coworker found that COVID-19 preprints received more attention and citations than non-COVID-19 preprints [ 1 ]. More recently, Hook and Porter have found some early evidence of ‘covidisation’ of academic research, with research grants and output diverted to COVID-19 research in 2020 [ 15 ]. How much should scientists switch their efforts toward SARS-CoV-2 prevention, treatment, or mitigation? There is a growing consensus that the current level of ‘covidisation’ of research can be wasteful [ 4 , 5 , 16 ].
Against this background, in this paper, we investigate if the COVID-19 pandemic has induced a shift in biomedical publications toward COVID-19-related scientific production. The objective of the study is to show that scientific articles listing covid-related Medical Subject Headings (MeSH) when compared against covid-unrelated MeSH have been partially displaced. Specifically, we look at several indicators of scientific production in the life sciences before and after the start of the COVID-19 pandemic: (1) number of papers published, (2) impact factor weighted number of papers, (3) opens access, (4) number of publications related to clinical trials, (5) number of papers listing grants, (6) number of papers listing grants existing before the pandemic. Through a natural experiment approach, we analyze the impact of the pandemic on scientific production in the life sciences. We consider COVID-19 an unexpected and unprecedented exogenous source of variation with heterogeneous effects across biomedical research fields (i.e., MeSH terms).
Based on the difference in difference results, we document the displacement effect that the pandemic has had on several aspects of scientific publishing. The overall picture that emerges from this analysis is that there has been a profound realignment of priorities and research efforts. This shift has displaced biomedical research in fields not related to COVID-19.
The rest of the paper is structured as follows. First, we describe the data and our measure of relatedness to COVID-19. Next, we illustrate the difference-in-differences specification we rely on to identify the impact of the pandemic on scientific output. In the results section, we present the results of the difference-in-differences and network analyses. We document the sudden shift in publications, grants and trials towards COVID-19-related MeSH terms. Finally, we discuss the findings and highlight several policy implications.
Materials and methods
The present analysis is based primarily on PubMed and the Medical Subject Headings (MeSH) terminology. This data is used to estimate the effect of the start of the COVID 19 pandemic via a difference in difference approach. This section is structured as follows. We first introduce the data and then the econometric methodology. This analysis is not based on a pre-registered protocol.
Selection of biomedical publications.
We rely on PubMed, a repository with more than 34 million biomedical citations, for the analysis. Specifically, we analyze the daily updated files up to 31/06/2021, extracting all publications of type ‘Journal Article’. For the principal analysis, we consider 3,638,584 papers published from January 2019 to December 2020. We also analyze 11,122,017 papers published from 2010 onwards to identify the earliest usage of a grant and infer if it was new in 2020. We use the SCImago journal ranking statistics to compute the impact factor weighted number (IFWN) of papers in a given field of research. To assign the publication date, we use the ‘electronically published’ dates and, if missing, the ‘print published’ dates.
Medical subject headings.
We rely on the Medical Subject Headings (MeSH) terminology to approximate narrowly defined biomedical research fields. This terminology is a curated medical vocabulary, which is manually added to papers in the PubMed corpus. The fact that MeSH terms are manually annotated makes this terminology ideal for classification purposes. However, there is a delay between publication and annotation, on the order of several months. To address this delay and have the most recent classification, we search for all 28 425 MeSH terms using PubMed’s ESearch utility and classify paper by the results. The specific API endpoint is https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi , the relevant scripts are available with the code. For example, we assign the term ‘Ageusia’ (MeSH ID D000370) to all papers listed in the results of the ESearch API. We apply this method to the whole period (January 2019—December 2020) and obtain a mapping from papers to the MeSH terms. For every MeSH term, we keep track of the year they have been established. For instance, COVID-19 terms were established in 2020 (see Table 1 ): in January 2020, the WHO recommended 2019-nCoV and 2019-nCoV acute respiratory disease as provisional names for the virus and disease. The WHO issued the official terms COVID-19 and SARS-CoV-2 at the beginning of February 2020. By manually annotating publications, all publications referring to COVID-19 and SARS-CoV-2 since January 2020 have been labelled with the related MeSH terms. Other MeSH terms related to COVID-19, such as coronavirus, for instance, have been established years before the pandemic (see Table 2 ). We proxy MeSH term usage via search terms using the PubMed EUtilities API; this means that we are not using the hand-labelled MeSH terms but rather the PubMed search results. This means that the accuracy of the MeSH term we assign to a given paper is not perfect. In practice, this means that we have assigned more MeSH terms to a given term than a human annotator would have.
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The list contains only terms with at least 100 publications in 2020.
Clinical trials and publication types.
We classify publications using PubMed’s ‘PublicationType’ field in the XML baseline files (There are 187 publication types, see https://www.nlm.nih.gov/mesh/pubtypes.html ). We consider a publication to be related to a clinical trial if it lists any of the following descriptors:
- D016430: Clinical Trial
- D017426: Clinical Trial, Phase I
- D017427: Clinical Trial, Phase II
- D017428: Clinical Trial, Phase III
- D017429: Clinical Trial, Phase IV
- D018848: Controlled Clinical Trial
- D065007: Pragmatic Clinical Trial
- D000076362: Adaptive Clinical Trial
- D000077522: Clinical Trial, Veterinary
In our analysis of the impact of COVID-19 on publications related to clinical trials, we only consider MeSH terms that are associated at least once with a clinical trial publication over the two years. We apply this restriction to filter out MeSH terms that are very unlikely to be relevant for clinical trial types of research.
We proxy the availability of a journal article to the public, i.e., open access, if it is available from PubMed Central. PubMed Central archives full-text journal articles and provides free access to the public. Note that the copyright license may vary across participating publishers. However, the text of the paper is for all effects and purposes freely available without requiring subscriptions or special affiliation.
We infer if a publication has been funded by checking if it lists any grants. We classify grants as either ‘old’, i.e. existed before 2019, or ‘new’, i.e. first observed afterwards. To do so, we collect all grant IDs for 11,122,017 papers from 2010 on-wards and record their first appearance. This procedure is an indirect inference of the year the grant has been granted. The basic assumption is that if a grant number has not been listed in any publication since 2010, it is very likely a new grant. Specifically, an old grant is a grant listed since 2019 observed at least once from 2010 to 2018.
Note that this procedure is only approximate and has a few shortcomings. Mistyped grant numbers (e.g. ‘1234-M JPN’ and ‘1234-M-JPN’) could appear as new grants, even though they existed before, or new grants might be classified as old grants if they have a common ID (e.g. ‘Grant 1’). Unfortunately, there is no central repository of grant numbers and the associated metadata; however, there are plans to assign DOI numbers to grants to alleviate this problem (See https://gitlab.com/crossref/open_funder_registry for the project).
Impact factor weighted publication numbers (IFWN).
In our analysis, we consider two measures of scientific output. First, we simply count the number of publications by MeSH term. However, since journals vary considerably in terms of impact factor, we also weigh the number of publications by the impact factor of the venue (e.g., journal) where it was published. Specifically, we use the SCImago journal ranking statistics to weigh a paper by the impact factor of the journal it appears in. We use the ‘citation per document in the past two years’ for 45,230 ISSNs. Note that a journal may and often has more than one ISSN, i.e., one for the printed edition and one for the online edition. SCImago applies the same score for a venue across linked ISSNs.
For the impact factor weighted number (IFWN) of publication per MeSH terms, this means that all publications are replaced by the impact score of the journal they appear in and summed up.
To measure how closely related to COVID-19 is a MeSH term, we introduce an index of relatedness to COVID-19. First, we identify the focal COVID-19 terms, which appeared in the literature in 2020 (see Table 1 ). Next, for all other pre-existing MeSH terms, we measure how closely related to COVID-19 they end up being.
Our aim is to show that MeSH terms that existed before and are related have experienced a sudden increase in the number of (impact factor weighted) papers.
Intuitively we can read this measure as: what is the probability in 2020 that a COVID-19 MeSH term is present given that we chose a paper with MeSH term i ? For example, given that in 2020 we choose a paper dealing with “Ageusia” (i.e., Complete or severe loss of the subjective sense of taste), there is a 96% probability that this paper also lists COVID-19, see Table 1 .
Note that a paper listing a related MeSH term does not imply that that paper is doing COVID-19 research, but it implies that one of the MeSH terms listed is often used in COVID-19 research.
In sum, in our analysis, we use the following variables:
- Papers: Number of papers by MeSH term;
- Impact: Impact factor weighted number of papers by MeSH term;
- PMC: Papers listed in PubMed central by MeSH term, as a measure of Open Access publications;
- Trials: number of publications of type “Clinical Trial” by MeSH term;
- Grants: number of papers with at least one grant by MeSH term;
- Old Grants: number of papers listing a grant that has been observed between 2010 and 2018, by MeSH term;
The difference-in-differences (DiD) method is an econometric technique to imitate an experimental research design from observation data, sometimes referred to as a quasi-experimental setup. In a randomized controlled trial, subjects are randomly assigned either to the treated or the control group. Analogously, in this natural experiment, we assume that medical subject headings (MeSH) have been randomly assigned to be either treated (related) or not treated (unrelated) by the pandemic crisis.
Before the COVID, for a future health crisis, the set of potentially impacted medical knowledge was not predictable since it depended on the specifics of the emergency. For instance, ageusia (loss of taste), a medical concept existing since 1991, became known to be a specific symptom of COVID-19 only after the pandemic.
Specifically, we exploit the COVID-19 as an unpredictable and exogenous shock that has deeply affected the publication priorities for biomedical scientific production, as compared to the situation before the pandemic. In this setting, COVID-19 is the treatment, and the identification of this new human coronavirus is the event. We claim that treated MeSH terms, i.e., MeSH terms related to COVID-19, have experienced a sudden increase in terms of scientific production and attention. In contrast, research on untreated MeSH terms, i.e., MeSH terms not related to COVID-19, has been displaced by COVID-19. Our analysis compares the scientific output of COVID-19 related and unrelated MeSH terms before and after January 2020.
In our case, some of the terms turn out to be related to COVID-19 in 2020, whereas most of the MeSH terms are not closely related to COVID-19.
Thus β 1 identifies the overall effect on the control group after the event, β 2 the difference across treated and control groups before the event (i.e. the first difference in DiD) and finally the effect on the treated group after the event, net of the first difference, β 3 . This last parameter identifies the treatment effect on the treated group netting out the pre-treatment difference.
For the DiD to have a causal interpretation, it must be noted that pre-event, the trends of the two groups should be parallel, i.e., the common trend assumption (CTA) must be satisfied. We will show that the CTA holds in the results section.
To specify the DiD model, we need to define a period before and after the event and assign a treatment status or level of exposure to each term.
Before and after.
The pre-treatment period is defined as January 2019 to December 2019. The post-treatment period is defined as the months from January 2020 to December 2020. We argue that the state of biomedical research was similar in those two years, apart from the effect of the pandemic.
Treatment status and exposure.
The treatment is determined by the COVID-19 relatedness index σ i introduced earlier. Specifically, this number indicates the likelihood that COVID-19 will be a listed MeSH term, given that we observe the focal MeSH term i . To show that the effect becomes even stronger the closer related the subject is, and for ease of interpretation, we also discretize the relatedness value into three levels of treatment. Namely, we group MeSH terms with a σ between, 0% to 20%, 20% to 80% and 80% to 100%. The choice of alternative grouping strategies does not significantly affect our results. Results for alternative thresholds of relatedness can be computed using the available source code. We complement the dichotomized analysis by using the treatment intensity (relatedness measure σ ) to show that the result persists.
In this work, we estimate a random effects panel regression where the units of analysis are 28 318 biomedical research fields (i.e. MeSH terms) observed over time before and after the COVID-19 pandemic. The time resolution is at the monthly level, meaning that for each MeSH term, we have 24 observations from January 2019 to December 2020.
The outcome variable Y it identifies the outcome at time t (i.e., month), for MeSH term i . As before, P t identifies the period with P t = 0 if the month is before January 2020 and P t = 1 if it is on or after this date. In (3) , the treatment level is measure by the relatedness to COVID-19 ( σ i ), where again the γ 1 identifies pre-trend (constant) differences and δ 1 the overall effect.
In total, we estimate six coefficients. As before, the δ l coefficient identifies the DiD effect.
Verifying the Common Trend Assumption (CTA).
We show that the CTA holds for this model by comparing the pre-event trends of the control group to the treated groups (COVID-19 related MeSH terms). Namely, we show that the pre-event trends of the control group are the same as the pre-event trends of the treated group.
To investigate if the pandemic has caused a reconfiguration of research priorities, we look at the MeSH term co-occurrence network. Precisely, we extract the co-occurrence network of all 28,318 MeSH terms as they appear in the 3.3 million papers. We considered the co-occurrence networks of 2018, 2019 and 2020. Each node represents a MeSH term in these networks, and a link between them indicates that they have been observed at least once together. The weight of the edge between the MeSH terms is given by the number of times those terms have been jointly observed in the same publications.
Medical language is hugely complicated, and this simple representation does not capture the intricacies, subtle nuances and, in fact, meaning of the terms. Therefore, we do not claim that we can identify how the actual usage of MeSH terms has changed from this object, but rather that it has. Nevertheless, the co-occurrence graph captures rudimentary relations between concepts. We argue that absent a shock to the system, their basic usage patterns, change in importance (within the network) would essentially be the same from year to year. However, if we find that the importance of terms changes more than expected in 2020, it stands to reason that there have been some significant changes.
To show that that MeSH usage has been affected, we compute for each term in the years 2018, 2019 and 2020 their PageRank centrality [ 17 ]. The PageRank centrality tells us how likely a random walker traversing a network would be found at a given node if she follows the weights of the empirical edges (i.e., co-usage probability). Specifically, for the case of the MeSH co-occurrence network, this number represents how often an annotator at the National Library of Medicine would assign that MeSH term following the observed general usage patterns. It is a simplistic measure to capture the complexities of biomedical research. Nevertheless, it captures far-reaching interdependence across MeSH terms as the measure uses the whole network to determine the centrality of every MeSH term. A sudden change in the rankings and thus the position of MeSH terms in this network suggests that a given research subject has risen as it is used more often with other important MeSH terms (or vice versa).
We then compare the growth for each MeSH i term in g i (2019), i.e. before the the COVID-19 pandemic, with the growth after the event ( g i (2020)).
Changes in output and COVID-19 relatedness
Before we show the regression results, we provide descriptive evidence that publications from 2019 to 2020 have drastically increased. By showing that this growth correlates strongly with a MeSH term’s COVID-19 relatedness ( σ ), we demonstrate that (1) σ captures an essential aspect of the growth dynamics and (2) highlight the meteoric rise of highly related terms.
We look at the year over year growth in the number of the impact weighted number of publications per MeSH term from 2018 to 2019 and 2019 to 2020 as defined in the methods section.
Fig 1 shows the yearly growth of the impact weighted number of publications per MeSH term. By comparing the growth of the number of publications from the years 2018, 2019 and 2020, we find that the impact factor weighted number of publications has increased by up to a factor of 100 compared to the previous year for Betacoronavirus, one of the most closely related to COVID-19 MeSH term.
Each dot represents, a MeSH term. The y axis (growth) is in symmetric log scale. The x axis shows the COVID-19 relatedness, σ . Note that the position of the dots on the x-axis is the same in the two plots. Below: MeSH term importance gain (PageRank) and their COVID-19 relatedness.
Fig 1 , first row, reveals how strongly correlated the growth in the IFWN of publication is to the term’s COVID-19 relatedness. For instance, we see that the term ‘Betacoronavirus’ skyrocketed from 2019 to 2020, which is expected given that SARS-CoV-2 is a species of the genus. Conversely, the term ‘Alphacoronavirus’ has not experienced any growth given that it is twin a genus of the Coronaviridae family, but SARS-CoV-2 is not one of its species. Note also the fast growth in the number of publications dealing with ‘Quarantine’. Moreover, MeSH terms that grew significantly from 2018 to 2019 and were not closely related to COVID-19, like ‘Vaping’, slowed down in 2020. From the graph, the picture emerges that publication growth is correlated with COVID-19 relatedness σ and that the growth for less related terms slowed down.
To show that the usage pattern of MeSH terms has changed following the pandemic, we compute the PageRank centrality using graph-tool [ 18 ] as discussed in the Methods section.
Fig 1 , second row, shows the change in the PageRank centrality of the MeSH terms after the pandemic (2019 to 2020, right plot) and before (2018 to 2019, left plot). If there were no change in the general usage pattern, we would expect the variance in PageRank changes to be narrow across the two periods, see (left plot). However, PageRank scores changed significantly more from 2019 to 2020 than from 2018 to 2019, suggesting that there has been a reconfiguration of the network.
To further support this argument, we carry out a DiD regression analysis.
Common trends assumption
As discussed in the Methods section, we need to show that the CTA assumption holds for the DiD to be defined appropriately. We do this by estimating for each month the number of publications and comparing it across treatment groups. This exercise also serves the purpose of a placebo test. By assuming that each month could have potentially been the event’s timing (i.e., the outbreak), we show that January 2020 is the most likely timing of the event. The regression table, as noted earlier, contains over 70 estimated coefficients, hence for ease of reading, we will only show the predicted outcome per month by group (see Fig 2 ). The full regression table with all coefficients is available in the S1 Table .
The y axis is in log scale. The dashed vertical line identifies January 2020. The dashed horizontal line shows the publications in January 2019 for the 0–20% group before the event. This line highlights that the drop happens after the event. The bands around the lines indicate the 95% confidence interval of the predicted values. The results are the output of the Stata margins command.
Fig 2 shows the predicted number per outcome variable obtained from the panel regression model. These predictions correspond to the predicted value per relatedness group using the regression parameters estimated via the linear panel regression. The bands around the curves are the 95% confidence intervals.
All outcome measures depict a similar trend per month. Before the event (i.e., January 2020), there is a common trend across all groups. In contrast, after the event, we observe a sudden rise for the outcomes of the COVID-19 related treated groups (green and red lines) and a decline in the outcomes for the unrelated group (blue line). Therefore, we can conclude that the CTA assumption holds.
Table 3 shows the DiD regression results (see Eq (3) ) for the selected outcome measures: number of publications (Papers), impact factor weighted number of publications (Impact), open access (OA) publications, clinical trial related publications, and publications with existing grants.
Table 3 shows results for the discrete treatment level version of the DiD model (see Eq (4) ).
Note that the outcome variable is in natural log scale; hence to get the effect of the independent variable, we need to exponentiate the coefficient. For values close to 0, the effect is well approximated by the percentage change of that magnitude.
In both specifications we see that the least related group, drops in the number of publications between 10% and 13%, respectively (first row of Tables 3 and 4 , exp(−0.102) ≈ 0.87). In line with our expectations, the increase in the number of papers published by MeSH term is positively affected by the relatedness to COVID-19. In the discrete model (row 2), we note that the number of documents with MeSH terms with a COVID-19 relatedness between 20 and 80% grows by 18% and highly related terms by a factor of approximately 6.6 (exp(1.88)). The same general pattern can be observed for the impact weighted publication number, i.e., Model (2). Note, however, that the drop in the impact factor weighted output is more significant, reaching -19% for COVID-19 unrelated publications, and related publications growing by a factor of 8.7. This difference suggests that there might be a bias to publish papers on COVID-19 related subjects in high impact factor journals.
By looking at the number of open access publications (PMC), we note that the least related group has not been affected negatively by the pandemic. However, the number of COVID-19 related publications has drastically increased for the most COVID-19 related group by a factor of 6.2. Note that the substantial increase in the number of papers available through open access is in large part due to journal and editorial policies to make preferentially COVID research immediately available to the public.
Regarding the number of clinical trial publications, we note that the least related group has been affected negatively, with the number of publications on clinical trials dropping by a staggering 24%. At the same time, publications on clinical trials for COVID-19-related MeSH have increased by a factor of 2.1. Note, however, that the effect on clinical trials is not significant in the continuous regression. The discrepancy across Tables 3 and 4 highlights that, especially for trials, the effect is not linear, where only the publications on clinical trials closely related to COVID-19 experiencing a boost.
It has been reported [ 19 ] that while the number of clinical trials registered to treat or prevent COVID-19 has surged with 179 new registrations in the second week of April 2020 alone. Only a few of these have led to publishable results in the 12 months since [ 20 ]. On the other hand, we find that clinical trial publications, considering related MeSH (but not COVID-19 directly), have had significant growth from the beginning of the pandemic. These results are not contradictory. Indeed counting the number of clinical trial publications listing the exact COVID-19 MeSH term (D000086382), we find 212 publications. While this might seem like a small number, consider that in 2020 only 8,485 publications were classified as clinical trials; thus, targeted trials still made up 2.5% of all clinical trials in 2020 . So while one might doubt the effectiveness of these research efforts, it is still the case that by sheer number, they represent a significant proportion of all publications on clinical trials in 2020. Moreover, COVID-19 specific Clinical trial publications in 2020, being a delayed signal of the actual trials, are a lower bound estimate on the true number of such clinical trials being conducted. This is because COVID-19 studies could only have commenced in 2020, whereas other studies had a head start. Thus our reported estimates are conservative, meaning that the true effect on actual clinical trials is likely larger, not smaller.
Research funding, as proxied by the number of publications with grants, follows a similar pattern, but notably, COVID-19-related MeSH terms list the same proportion of grants established before 2019 as other unrelated MeSH terms, suggesting that grants which were not designated for COVID-19 research have been used to support COVID-19 related research. Overall, the number of publications listing a grant has dropped. Note that this should be because the number of publications overall in the unrelated group has dropped. However, we note that the drop in publications is 10% while the decline in publications with at least one grant is 15%. This difference suggests that publications listing grants, which should have more funding, are disproportionately COVID-19 related papers. To further investigate this aspect, we look at whether the grant was old (pre-2019) or appeared for the first time in or after 2019. It stands to reason that an old grant (pre-2019) would not have been granted for a project dealing with the pandemic. Hence we would expect that COVID-19 related MeSH terms to have a lower proportion of old grants than the unrelated group. In models (6) in Table 4 we show that the number of old grants for the unrelated group drops by 13%. At the same time, the number of papers listing old grants (i.e., pre-2019) among the most related group increased by a factor of 3.1. Overall, these results suggest that COVID-19 related research has been funded largely by pre-existing grants, even though a specific mandate tied to the grants for this use is unlikely.
The scientific community has swiftly reallocated research efforts to cope with the COVID-19 pandemic, mobilizing knowledge across disciplines to find innovative solutions in record time. We document this both in terms of changing trends in the biomedical scientific output and the usage of MeSH terms by the scientific community. The flip side of this sudden and energetic prioritization of effort to fight COVID-19 has been a sudden contraction of scientific production in other relevant research areas. All in all, we find strong support to the hypotheses that the COVID-19 crisis has induced a sudden increase of research output in COVID-19 related areas of biomedical research. Conversely, research in areas not related to COVID-19 has experienced a significant drop in overall publishing rates and funding.
Our paper contributes to the literature on the impact of COVID-19 on scientific research: we corroborate previous findings about the surge of COVID-19 related publications [ 1 – 3 ], partially displacing research in COVID-19 unrelated fields of research [ 4 , 14 ], particularly research related to clinical trials [ 5 – 7 ]. The drop in trial research might have severe consequences for patients affected by life-threatening diseases since it will delay access to new and better treatments. We also confirm the impact of COVID-19 on open access publication output [ 1 ]; also, this is milder than traditional outlets. On top of this, we provide more robust evidence on the impact weighted effect of COVID-19 and grant financed research, highlighting the strong displacement effect of COVID-19 on the allocation of financial resources [ 15 ]. We document a substantial change in the usage patterns of MeSH terms, suggesting that there has been a reconfiguration in the way research terms are being combined. MeSH terms highly related to COVID-19 were peripheral in the MeSH usage networks before the pandemic but have become central since 2020. We conclude that the usage patterns have changed, with COVID-19 related MeSH terms occupying a much more prominent role in 2020 than they did in the previous years.
We also contribute to the literature by estimating the effect of COVID-19 on biomedical research in a natural experiment framework, isolating the specific effects of the COVID-19 pandemic on the biomedical scientific landscape. This is crucial to identify areas of public intervention to sustain areas of biomedical research which have been neglected during the COVID-19 crisis. Moreover, the exploratory analysis on the changes in usage patterns of MeSH terms, points to an increase in the importance of covid-related topics in the broader biomedical research landscape.
Our results provide compelling evidence that research related to COVID-19 has indeed displaced scientific production in other biomedical fields of research not related to COVID-19, with a significant drop in (impact weighted) scientific output related to non-COVID-19 and a marked reduction of financial support for publications not related to COVID-19 [ 4 , 5 , 16 ]. The displacement effect is persistent to the end of 2020. As vaccination progresses, we highlight the urgent need for science policy to re-balance support for research activity that was put on pause because of the COVID-19 pandemic.
We find that COVID-19 dramatically impacted clinical research. Reactivation of clinical trials activities that have been postponed or suspended for reasons related to COVID-19 is a priority that should be considered in the national vaccination plans. Moreover, since grants have been diverted and financial incentives have been targeted to sustain COVID-19 research leading to an excessive entry in COVID-19-related clinical trials and the ‘covidisation’ of research, there is a need to reorient incentives to basic research and otherwise neglected or temporally abandoned areas of biomedical research. Without dedicated support in the recovery plans for neglected research of the COVID-19 era, there is a risk that more medical needs will be unmet in the future, possibly exacerbating the shortage of scientific research for orphan and neglected diseases, which do not belong to COVID-19-related research areas.
Our empirical approach has some limits. First, we proxy MeSH term usage via search terms using the PubMed EUtilities API. This means that the accuracy of the MeSH term we assign to a given paper is not fully validated. More time is needed for the completion of manually annotated MeSH terms. Second, the timing of publication is not the moment the research has been carried out. There is a lead time between inception, analysis, write-up, review, revision, and final publication. This delay varies across disciplines. Nevertheless, given that the surge in publications happens around the alleged event date, January 2020, we are confident that the publication date is a reasonable yet imperfect estimate of the timing of the research. Third, several journals have publicly declared to fast-track COVID-19 research. This discrepancy in the speed of publication of COVID-19 related research and other research could affect our results. Specifically, a surge or displacement could be overestimated due to a lag in the publication of COVID-19 unrelated research. We alleviate this bias by estimating the effect considering a considerable time after the event (January 2020 to December 2020). Forth, on the one hand, clinical Trials may lead to multiple publications. Therefore we might overestimate the impact of COVID-19 on the number of clinical trials. On the other hand, COVID-19 publications on clinical trials lag behind, so the number of papers related COVID-19 trials is likely underestimated. Therefore, we note that the focus of this paper is scientific publications on clinical trials rather than on actual clinical trials. Fifth, regarding grants, unfortunately, there is no unique centralized repository mapping grant numbers to years, so we have to proxy old grants with grants that appeared in publications from 2010 to 2018. Besides, grant numbers are free-form entries, meaning that PubMed has no validation step to disambiguate or verify that the grant number has been entered correctly. This has the effect of classifying a grant as new even though it has appeared under a different name. We mitigate this problem by using a long period to collect grant numbers and catch many spellings of the same grant, thereby reducing the likelihood of miss-identifying a grant as new when it existed before. Still, unless unique identifiers are widely used, there is no way to verify this.
So far, there is no conclusive evidence on whether entry into COVID-19 has been excessive. However, there is a growing consensus that COVID-19 has displaced, at least temporally, scientific research in COVID-19 unrelated biomedical research areas. Even though it is certainly expected that more attention will be devoted to the emergency during a pandemic, the displacement of biomedical research in other fields is concerning. Future research is needed to investigate the long-run structural consequences of the COVID-19 crisis on biomedical research.
S1 table. common trend assumption (cta) regression table..
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COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses
- 1 The Department of Cerebrovascular Diseases, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China.
- 2 State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, PR China.
- 3 College of Life Sciences, Wuhan University, Wuhan, PR China.
- PMID: 32257431
- PMCID: PMC7113610
- DOI: 10.1016/j.jare.2020.03.005
The coronavirus disease 19 (COVID-19) is a highly transmittable and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in Wuhan, China and spread around the world. Genomic analysis revealed that SARS-CoV-2 is phylogenetically related to severe acute respiratory syndrome-like (SARS-like) bat viruses, therefore bats could be the possible primary reservoir. The intermediate source of origin and transfer to humans is not known, however, the rapid human to human transfer has been confirmed widely. There is no clinically approved antiviral drug or vaccine available to be used against COVID-19. However, few broad-spectrum antiviral drugs have been evaluated against COVID-19 in clinical trials, resulted in clinical recovery. In the current review, we summarize and comparatively analyze the emergence and pathogenicity of COVID-19 infection and previous human coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). We also discuss the approaches for developing effective vaccines and therapeutic combinations to cope with this viral outbreak.
Keywords: COVID-19; Coronaviruses; Origin; Outbreak; Spread.
© 2020 THE AUTHORS. Published by Elsevier BV on behalf of Cairo University.
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- v.25(15); 2020 Apr 16
Coronavirus disease (COVID-19): a scoping review
1 School of Public Health, Lanzhou University, Lanzhou, China
2 These authors contributed equally to this work and share first authorship
3 Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
4 Institute of Global Health, University of Geneva, Geneva, Switzerland
5 Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
6 Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
7 College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
8 School of Public Health, Chengdu Medical College, Chengdu, China
9 Department of Respiratory Diseases, Children’s Hospital of Chongqing Medical University, Chongqing, China
10 Chongqing Key Laboratory of Pediatrics, Chongqing, China
11 The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
12 The First Hospital of Lanzhou University, Lanzhou, China
Yangqin xun, yaolong chen.
13 World Health Organization (WHO) Collaborating Centre for Guideline Implementation and Knowledge Translation, Lanzhou, China
14 Guideline International Network Asia, Lanzhou, China
15 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou, China
16 Lanzhou University, an affiliate of the Cochrane China Network, Lanzhou, China
on behalf of the COVID-19 evidence and recommendations working group
17 The study collaborators are acknowledged at the end of the article
In December 2019, a pneumonia caused by a novel coronavirus (SARS-CoV-2) emerged in Wuhan, China and has rapidly spread around the world since then.
This study aims to understand the research gaps related to COVID-19 and propose recommendations for future research.
We undertook a scoping review of COVID-19, comprehensively searching databases and other sources to identify literature on COVID-19 between 1 December 2019 and 6 February 2020. We analysed the sources, publication date, type and topic of the retrieved articles/studies.
We included 249 articles in this scoping review. More than half (59.0%) were conducted in China. Guidance/guidelines and consensuses statements (n = 56; 22.5%) were the most common. Most (n = 192; 77.1%) articles were published in peer-reviewed journals, 35 (14.1%) on preprint servers and 22 (8.8%) posted online. Ten genetic studies (4.0%) focused on the origin of SARS-CoV-2 while the topics of molecular studies varied. Nine of 22 epidemiological studies focused on estimating the basic reproduction number of COVID-19 infection (R 0 ). Of all identified guidance/guidelines (n = 35), only ten fulfilled the strict principles of evidence-based practice. The number of articles published per day increased rapidly until the end of January.
The number of articles on COVID-19 steadily increased before 6 February 2020. However, they lack diversity and are almost non-existent in some study fields, such as clinical research. The findings suggest that evidence for the development of clinical practice guidelines and public health policies will be improved when more results from clinical research becomes available.
A new type of coronavirus (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) that began in Wuhan, China in late 2019 has spread across the world since then. The virus has caused an outbreak of viral pneumonia, which has been named Coronavirus disease (COVID-19). As of 24:00 on 6 February 2020, over 31,000 cases and 636 deaths had been confirmed in China [ 1 ]. Furthermore, more than 1,770,000 cases had been diagnosed in 213 countries, areas or territories as at 13 April 2020 [ 2 ]. On 23 January 2020, Chinese authorities imposed a lockdown of Wuhan [ 3 ]. On 30 January 2020, the World Health Organization (WHO) declared the outbreak a Public Health Emergency of International Concern (PHEIC) [ 4 ] and on 11 March 2020, a pandemic [ 5 ].
The WHO [ 6 - 9 ], the United States (US) Centers for Disease Control and Prevention (CDC) [ 10 , 11 ], the European Centre for Disease Prevention and Control (ECDC) [ 12 , 13 ] as well as Chinese researchers have issued several guidance documents or guidelines to help address the outbreaks. Meanwhile, many scientific journals have rapidly published a number of articles, comments, editorials and perspectives related to COVID-19. It may however be challenging for the global research community to find all the available evidence: many of the first studies on COVID-19 were published in Chinese, and because of the rapidly developing situation, the latest studies are often available on websites or preprint servers only [ 14 ].
Scoping reviews are regarded as a valid tool to map the available evidence on a given topic, to clarify the characteristics of body of literature, to organise the key concepts and their relationship and to analyse knowledge gaps [ 15 ]. The methodology continues to be developed, and a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRSIMA) extension for Scoping Reviews (PRISMA-SCR) including reporting guidance was published in 2018 [ 16 ]. Given the urgency of the COVID-19 epidemic and the need to understand and access information about it, a scoping review was considered suitable for the situation. We therefore conducted this scoping review to help identify research gaps related to this new viral disease and propose recommendations for future research on COVID-19.
We performed a systematic search of MEDLINE via PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data and China Biology Medicine (CBM) on 27 February 2020 with the terms “COVID-19” OR “SARS-CoV-2” OR “2019 novel coronavirus” OR “2019-nCoV” OR “Wuhan coronavirus” OR “novel coronavirus” OR “Wuhan seafood market pneumonia virus” OR “Wuhan virus”, published between 1 December 2019 and 6 February 2020 (see Supplement S1 for details of search strategies). Because of potential delays in indexing of databases, we also searched selected infectious disease journals ( Supplementary Table S1 ). We also searched Google Scholar; the official websites of WHO ( https://www.who.int/ ), US CDC ( https://www.cdc.gov/ ), ECDC ( https://www.ecdc.europa.eu/en ), Public Health England (PHE) ( https://www.gov.uk/government/organisations/public-health-england ); some preprint servers, including BioRxiv ( https://www.biorxiv.org/ ), ChemRxiv ( https://chemrxiv.org/ ), medRxiv ( https://www.medrxiv.org/ ) and SSRN ( https://www.ssrn.com/index.cfm/en/ ); and reference lists of the identified articles to find reports of additional studies.
Inclusion and exclusion criteria
We included all literature related to COVID-19 published in English and Chinese between 1 December 2019 and 6 February 2020 without restrictions, including guidance/guidelines, reviews, clinical studies, basic research, epidemiological studies and comments. Documents and guidance/guidelines posted by international organisations, government institutions, associations and societies were also included. We excluded news reports that were not published in scientific journals, and articles where we failed to access full text despite contacting the authors.
Article selection and data extraction
Two reviewers (ML and XL) screened all titles, abstracts and full texts independently and solved disagreements by consensus or consultation with a third reviewer. Then the following information was extracted: (i) title, (ii) first author, (iii) whether peer-reviewed or not, (iv) journal, (v) publication or posted date, (vi) first author’s country (or international organisation), (vii) type of article/study and (viii) topic. The details are shown in Supplementary Table S2 .
We conducted a descriptive analysis of the characteristics of the included literature. We described the source where we found the article, publication date, type of article/study, and topic of article/study or guidance/guideline on COVID-19 to examine the existing gaps in research. We categorised the literature into guidance/guidelines and consensus statements, reviews, clinical studies (including randomised controlled trials and observational studies), basic research, epidemiological studies, editorial comments on COVID-19 and other categories if identified. We conducted this scoping review in accordance with the PRISMA-ScR Checklist [ 16 ] ( Supplementary Table S3 ).
We identified 1,511 records, 280 of which were excluded as duplicates. Title and abstract screening were conducted for the remaining 1,231 articles, 989 of which were excluded because of being unrelated to COVID-19. For two articles, we failed to access the full text after contacting the authors. We retrieved the full texts of the 242 remaining articles. After further screening and supplementary searching of articles published or posted between 31 January 2020 and 6 February 2020, we identified an additional 42 articles and a total of 249 articles were included in the review ( Figure 1 ).
Flowchart of selection process for the scoping review of coronavirus disease (COVID-19) articles/studies and results, 1 December 2019–6 February 2020
CBM: China Biology Medicine; CNKI: China National Knowledge Infrastructure.
Characteristics of included articles/studies
Of the 249 included articles/studies, 147 (59.0%) were from China. The article/study type varied vastly, which we broadly characterised into 11 types ( Table 1 ). Of these, guidance/guidelines and consensuses statements were the most common (n = 56; 22.5%).
SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; WHO: World Health Organization.
a Includes the websites of WHO, United States Centers for Disease Control and Prevention (US CDC), European Centre for Disease Prevention and Control (ECDC) and Public Heath England (PHE), and preprint servers.
b Other than cross-sectional studies.
c Includes reviews and correspondence that discussed the characteristics of the virus in general.
d Other than traditional Chinese medicine.
Sources of articles/studies
Of all included articles/studies, 192 (77.1%) were published in peer-reviewed journals, 35 (14.1%) were posted on preprint servers and 22 (8.8%) were published on the official websites of public health organisations. The journal with the highest number of articles was The Lancet, with 13 (6.8%) published articles. Of preprint articles, most (n = 28) were posted on BioRxiv. Articles published on official websites were mainly COVID-19 guidance/guidelines, including 10 WHO interim guidance documents, nine US CDC interim guidelines/guidance documents, two ECDC guidance documents and one Communicable Diseases Network Australia (CNDA) guideline.
Figure 2 shows the cumulative number of articles published daily between 10 January 2020 and 6 February 2020. As at 6 February 2020, the number of articles on COVID-19 had been steadily increasing. Of the 192 articles that were published in peer-reviewed journals, the highest number of journal publications on a single day was on 30 January, with 24 articles (12.5%). For the 35 preprints, the number posted per day rose steadily from 19 January 2020 to 6 February 2020.
Cumulative number of coronavirus disease (COVID-19)-related articles/studies included in the scoping review, 10 January–6 February 2020 (n = 249)
Type of article/study
The types of articles/studies published on each day are shown in Figure 3 . The daily number of guidance/guidelines peaked between 29 January and 3 February whereas the number of published reviews showed an increasing trend since 29 January 2020. Only one systematic review was identified [ 17 ]. We found no randomised controlled studies or cohort studies.
Number of coronavirus disease (COVID-19)-related articles/studies published per day according to type, 10 January–6 February 2020 (n = 249)
a Including cross-sectional studies.
The different types of articles/studies focused on different topics. The basic research could be divided broadly into two categories: 21 genetic studies and 12 molecular biology studies. Ten genetic studies traced the origin of SARS-CoV-2 and tried to determine the possible virus reservoir. Among these, most suggested that SARS-CoV-2 evolved from a bat-CoV, namely bat-SL-CoVZC45, bat-SL-CoVZXC21, bat-SL-CoVZX45 and bat-CoV-RaTG13 as potential candidates [ 18 - 26 ]. However, Ji et al. [ 18 ] found snakes to be the most probable reservoir for SARS-CoV-2 while Guo et al. [ 26 ] suggested mink could be a candidate reservoir. Of the molecular studies, five [ 27 - 31 ] showed that the key receptor of SARS-CoV-2 is angiotensin converting enzyme 2 (ACE2), which is highly expressed in lung type II alveolar cells (AT2) [ 27 ], positive cholangiocytes [ 29 ], upper oesophagus, stratified epithelial cells and absorptive enterocytes from ileum and colon [ 30 ]. The other studies included an assessment of the cross-reactivity of anti-SARS-CoV antibodies with SARS-CoV-2 spike protein [ 32 ], and SARS-CoV-2 main proteases [ 33 , 34 ].
The main topic of epidemiological studies was the estimation of the transmissibility of COVID-19. The value of the basic reproduction number (R 0 ) varied across studies [ 35 - 43 ], however, all estimated it to be higher than one, which indicates the potential for sustained human-to-human transmission. According to the nine articles [ 35 - 43 ], R 0 ranges between 2.2 and 3.9. Some studies showed that the transmissibility of SARS-CoV-2 is comparable to [ 37 , 44 ] or even higher [ 39 ] than SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). In addition, studies focused on the disease burden associated with COVID-19 [ 45 ] and the global patterns of disease dispersion [ 46 , 47 ].
Most reviews on COVID-19 gave a brief summary of the clinical features [ 48 - 51 ] and the characteristics of SARS-CoV-2 [ 52 - 54 ], as well as recommendations on how to prevent and control [ 55 - 60 ] this novel pneumonia. A systematic review [ 17 ] explored the possibility of using lopinavir/ritonavir (LPV/r) to treat COVID-19, with the results supporting the use of LPV/r as a part of an experimental regimen for COVID-19 pneumonia treatment. Clinical features were reported in 21 studies [ 48 - 51 , 61 - 77 ]. The main symptoms of patients with COVID-19 at onset were found to be fever and cough, with a reduced lymphocyte count, which is similar to previous beta coronavirus infections [ 78 , 79 ].
Seventeen of the 56 editorials, comments and letters [ 80 - 96 ] were first reports or comments on the situation of the COVID-19 epidemic. Some [ 97 - 101 ] also briefly introduced the general information and characteristics of the new virus. The mapping of article/study type and topics, as well as associated gaps, is shown in Table 2 .
a Other than cross-sectional studies.
b Includes perspectives, case-control study and investigation protocols.
c Other than traditional Chinese medicine.
d Guidance/guideline or consensus statement: guidance for laboratory biosafety, caring and travellers, and national capacity review tools; review: reviews on human resources of healthcare, the causes and counter-measures of Wuhan ‘stigma’, and public health; letter: outbreak assessment; epidemiology study: studies on disease burden, the number of unreported cases, and infection fatality; editorial: journal’s opinion on matters related to COVID-19, and incidence rate estimation; cross-sectional study: hazard vulnerability analyses, epidemiology reports, and studies on public attitudes and perception; other: investigation protocol.
Guidance/guidelines and consensus statements
Of the 56 published guidance/guidelines and consensuses statements, 35 were guidance/guidelines. Nine of the 35 addressed the treatment and management of COVID-19 infection, eight addressed prevention and five addressed diagnostics. Ten of the guidance/guidelines were interim guidance documents issued by the WHO, including those on COVID-19 prevention, surveillance, assessment, care, management and mask use [ 6 - 9 , 102 - 107 ]. The US CDC published nine interim guidance/guidelines documents for evaluating, preventing and managing the new coronavirus [ 10 , 11 , 108 - 114 ]. In addition, ECDC published two guidance documents about COVID-19 patient care and the management of persons having had contact with SARS-CoV-2 cases [ 12 , 13 ]. Chinese researches also published 14 rapid-advice guidance/guidelines documents on diagnosis, prevention and management of COVID-19, all of which were interim guidance/guidelines documents developed by hospitals [ 115 - 128 ].
Only eight of the guidance documents/guidelines formed a guideline development group (GDG) [ 129 ]; the recommendations of 15 guidance documents/guidelines, including six developed by the WHO, were difficult to distinguish. Only ten guidance/guidelines fulfilled the strict principles of evidence-based practice and cited reference documents, which were mainly epidemic reports, government documents, and indirect evidence related to SARS-CoV or MERS-CoV [ 6 , 7 , 105 , 116 - 118 , 120 , 122 , 125 , 126 ]. Only two guidelines, both developed by Chinese researchers, were graded using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach [ 116 , 117 ]. Among the 35 guidance/guidelines, one [ 115 ] was completely on Traditional Chinese medicine and one [ 116 ] covered Chinese medicine. One Australian guideline [ 130 ] was adapted from SARS-CoV guidelines.
Our scoping review shows that while the number of articles on COVID-19 has been constantly increasing, as at 6 February, there were still clear gaps in several study types and research fields. We identified that some study types, in particular randomised controlled trials and cohort studies, were still non-existent before 6 February. According to a preliminary search of the Cochrane Network database up to 10 April 2020, the number of randomised controlled trials (RCTs) (n = 8) and observational studies (n = 42) still remains low [ 131 ].
We also found that there were only a few studies on clinical practice, making it difficult to develop clinical practice guidelines and health policies. The reason for the gaps in this area may be the rapid development of the outbreak and limited understanding of the new virus and the disease caused by it. Moreover, it takes time to conduct clinical research. When facing a public health emergency with a previously unknown cause, researchers should conduct studies on whether some clinical practice and public health interventions from other public health emergencies can be used as indirect evidence. However, we identified no such studies in our review.
We found that 14% of the studies related to COVID-19 were posted on preprint servers. This approach of sharing research as quickly as possible is very reasonable, especially in the case of such public health emergency. Previous studies have shown that preprints can accelerate progress in handling outbreaks of infectious disease [ 132 , 133 ].
The research topics in different types of articles/studies had both similarities and differences. Basic research was mostly focused on exploring the origin and reservoirs of the new virus, while epidemiological studies mainly focused on its transmissibility. Reviews and reports provided more general information of the virus and the outbreak, while guidance/guidelines included recommendations on how to prevent and control it.
Clinical practice guidelines are statements that include recommendations intended to optimise patient care that are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options [ 134 ]. Clinical practice guidelines can inform healthcare workers' actions [ 134 ], and, especially when public health emergencies occur, rapid advice guidelines can guide clinicians in terms of how to perform related work [ 135 ]. After the outbreak of COVID-19, the WHO, US CDC and ECDC released guidance/guidelines as soon as possible, as did several Chinese institutions. However, most of these documents did not establish formal guideline development groups, and they did not fulfil the strict principles of evidence-based practice. For example, most guidance/guidelines did not grade the quality of evidence and strength of recommendations, and thus owed to the emerging crisis, such guidance/guidelines need to be considered with these limitations in mind. In 2007, the WHO published guidance about the process of developing rapid advice guidelines [ 129 ], stating that when a public health emergency occurs, a rapid review is needed and the development time should not exceed 6 months [ 135 ]. However, considering the limited time to set up panels, this could be a challenge for guidance/guideline developers. Nonetheless, we still expect guidance/guideline developers to establish formal development groups and fulfil the evidence-based practice principles.
Our scoping review can help researchers identify research gaps so as to conduct research to fill these gaps. For example, in the current situation, a systematic review to estimate the incubation period or research on new drugs or treatments, would be of great importance. This scoping review has several strengths. We performed a systematic search of a comprehensive set of sources, including databases, preprint servers, and official websites of international organisations and associations at the early stage of the pandemic. Furthermore, our large sample size is sufficient to illustrate the state of research and identify research gaps related to COVID-19 at the onset of the pandemic.
This study also has some limitations. Because of the delay in indexing, some articles published as at 6 February 2020 may not have been identified. Also, because our retrieval time was only until this date, articles published or posted after this date, of which there have been many, have not been included in the analysis. As some preprints, guidance/guidelines and disease control plans are constantly updated, the publication date we extracted may not be the time of their first publication time. Also, we did not assess the quality of the included literature because of diversity of the types of included articles. Another limitation of our study was that it only included articles published in English and Chinese, which could introduce publication bias. However, as the epidemic was most heavily affecting China until early February, it is reasonable to expect that literature published in English and Chinese up until this point in time covered the majority of the available knowledge. Finally, we were unable to access the full texts of two articles despite contacting the authors. However, compared with the total number of articles included in the review, we anticipate that the exclusion of these two articles is unlikely to have a major impact.
This scoping review shows the state of literature published or posted online related to COVID-19 as at 6 February 2020. The number of articles in this field has steadily increased since the outbreak became evident. However, the types of studies lacked diversity, especially clinical studies. More clinical research is needed, but in the rapidly evolving global pandemic, we encourage researchers to continuously review the latest literature, to take into account the latest available evidence and avoid overlapping work, and to improve evidence for the development of clinical practice guidelines and public health policies.
Funding statement: 2020 Key R & D project of Gansu Province; Special funding for prevention and control of emergency of COVID-19 from Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province (No. GSEBMKT-2020YJ01).
The members of the COVID-19 evidence and recommendations working group: Xiao Liu (Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China); Nan Yang (Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China); Shuya Lu (Sichuan Provincial People’s hospital, Chengdu, China ); Peipei Du ( School of Public Health, Chengdu Medical College, Chengdu, China); Yanfang Ma (Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China); Zijun Wang (Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China); Qianling Shi (The First School of Clinical Medicine, Lanzhou University, Lanzhou, China); Hairong Zhang (Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China); Qiangqiang Guo (School of Public Health, ShanXi Medical University, Taiyuan,China); Yuting Yang (Children's Hospital of Chongqing Medical University, Chongqing, China); Bo Yang (Children's Hospital of Chongqing Medical University, Chongqing, China); Shouyuan Wu (School of Public Health, Lanzhou University, Lanzhou, China); Xiaoqin Wang (Michael G. DeGroote Institute for Pain Research and Care, McMaster University, Hamilton, Ontario, Canada).
Conflict of interest: None declared.
Authors’ contributions: All authors have read and agree to the published version of the manuscript. Conceptualisation, YC and XW; methodology, ML, XL and JE; software, YL, MR and JW; data extraction, QW, SZ, MR, XZ, LW, QZ and SY; formal analysis, XL and ML; resources, ML and WL; writing—original draft preparation, ML, XL, WM and XQ; writing—review and editing, YX, XY, YC, XW, SY, XF, WM, JE, EL and XQ; visualisation, ML and XL; supervision, YC and XW; project administration, YC; funding acquisition, YC.
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- 14 November 2023
How our memories of COVID-19 are biased — and why it matters
You have full access to this article via your institution.
The COVID-19 vaccination polarized opinion — and our memories. Credit: Arindam Shivaani/NurPhoto via Getty
Lives are still being lost to COVID-19 every day. And for many left with debilitating after-effects of the disease, it remains a very real, immediate experience. But for many others, the circumstances of the pandemic are becoming a matter of memory. These memories might still be fresh and painful, or more distant and neutralized by the passage of time. Either way, they are almost undoubtedly unreliable.
This is not, in itself, a surprise: that different people can have very different memories of the same past events, and that pre-existing biases can influence these memories, is an established facet of human psychology. But a series of studies reported in a paper 1 this month in Nature shows that our impressions of the COVID-19 pandemic’s severity, as well as of measures taken to limit the disease’s spread, are reliably skewed by a related factor: our vaccination status.
The results give pause for thought as countries exercise their collective memories to examine how authorities handled the pandemic and what should be done differently next time. “When looking back, we should all be aware that we have biased memories,” says Cornelia Betsch at the University of Erfurt in Germany, an author of the Nature paper. “You could be right or wrong. I could be right or wrong. Or, most likely, we’re all wrong.”
Can giant surveys of scientists fight misinformation on COVID, climate change and more?
Betsch and her colleagues’ project involved surveying more than 10,000 people across 11 countries. For one study, they resurveyed German adults who had been asked in summer 2020 or winter 2020–21 to estimate their risk of SARS-CoV-2 infection, asking them to recall their earlier answers. They embarked on the project in late 2022, after a journalist commented during a conference that people who opposed vaccination seemed to be shifting their narrative of the pandemic. The authors’ analysis revealed that unvaccinated individuals who identified strongly with their unvaccinated status were more likely to remember their earlier estimation of the risk as lower than it actually was. Conversely, and more markedly, those who had been vaccinated overestimated their earlier perception of their risk of catching the disease.
As with any study, there are caveats. The data were collected online, and most of the countries sampled are wealthy and in the Northern Hemisphere. The study did not evaluate the effect of the different pandemic policies enacted in different regions. The researchers also surveyed only adults. At this stage, there is no way of knowing how children will remember the pandemic when they are older — or how those memories might colour their decisions should another pandemic occur when they are adults.
Memory bias has been observed in other politically charged settings, including recall of COVID-19 vaccine misinformation 2 , the campaign surrounding Ireland’s 2018 referendum on legalizing abortion 3 and the 2021 US Capitol riots 4 . Such bias feeds polarization. Communication is difficult when shared memories diverge. It can influence discussions at every level: within families, in the media and within governments and other authorities.
Pioneers of mRNA COVID vaccines win medicine Nobel
The conclusions of the latest study are highly relevant to investigations such as the ongoing inquiry into the United Kingdom’s handling of COVID-19, a process that has been garnering headlines in the past weeks. Those overseeing such investigations must recognize that personal recollections are clouded by bias. In drawing conclusions about which pandemic interventions were warranted or effective and which were not, it is imperative that investigators rely as much as possible on hard data and evidence.
Many of the conflicts we struggle with today stem from how we view past events now, rather than how we experienced them then. The divergence in our collective memory is also likely to be a significant factor in future pandemics, determining, for example, whether individuals are willing to comply with the associated public-health mandates. How to counter these effects in the future must be a subject for more research today.
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Sprengholz, P., Henkel, L., Böhm, R. & Betsch, C. Nature https://doi.org/10.1038/s41586-023-06674-5 (2023).
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Dvir Y , Ryan C , Lee J. School Closures and ED Visits for Suicidality in Youths Before and During the COVID-19 Pandemic. JAMA Netw Open. 2023;6(11):e2343001. doi:10.1001/jamanetworkopen.2023.43001
School Closures and ED Visits for Suicidality in Youths Before and During the COVID-19 Pandemic
- 1 Division of Child and Adolescent Psychiatry, Department of Psychiatry, University of Massachusetts Chan Medical School/UMass Memorial Medical Center, Worcester
- 2 Department of Psychiatry, University of Massachusetts Chan Medical School/UMass Memorial Medical Center, Worcester
- 3 Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
Emergency department (ED) visits for suspected suicide attempts among persons aged 12 to 25 years increased after May 2020. 1 The public health response to the COVID-19 pandemic included widespread school closures. 2 Schools offer well-documented support for youths and families. 2 , 3 Describing the association between pandemic-related school closures and suicidality may inform future public health policy. Our study analyzed rates of youth ED suicidality visits (EDSVs), defined as visits for suicide and self-injury attempts, alongside patterns of school closure for Massachusetts and Texas.
The UMass Chan Medical School institutional review board indicated that oversight and informed consent were not required for this cohort study because the study team did not access private identifiable information. This study followed the STROBE reporting guideline.
Massachusetts and Texas were compared because of differences in school closure patterns. Data examining kindergarten through grade 12 school operations in academic years 2020-2021 and 2021-2022 were publicly available through Burbio’s School Opening Tracker. 4 We used in-person index (IPI), weighing virtual instruction at 0%, hybrid instruction (2-3 d/wk in person) at 50%, and traditional instruction (5 d/wk in person) at 100%. A higher value indicates more in-person education. The IPI was plotted by month from September 2020 to June 2022.
Rates of EDSVs in age groups 12 to 17 years and 18 to 25 years from March 2019 to September 2022 were acquired from the Massachusetts Department of Public Health and Texas Department of State Health Services. Given that school closures affected youths aged 12 to 17 years, those aged 18 to 25 years represented a control group. We adjusted raw data for state population and calculated monthly differences in EDSV rates between age groups. The autocorrelation function of the difference series over time exhibited characteristics of white noise, allowing for statistical tests with the assumption of independence. We used a 1-sample t test to examine differences in EDSV rates between age groups before and during the pandemic and a 2-sample t test to compare rates between states. A 2-sided test at a .05 level was used. Statistical analyses were conducted using R statistical software version 4.2.2 (R Project for Statistical Computing).
IPI plots ( Figure 1 ) for Massachusetts and Texas show that Texas schools resumed in-person instruction with greater than 80% IPI beginning November 2020 while the Massachusetts IPI remained low (<40%) until May 2021. Both states are outliers compared with the all-state mean.
Before the pandemic (March 2019 to February 2020), mean (SD) monthly EDSV counts for individuals ages 12 to 27 years were 115 (21) visits in Massachusetts and 505 (82) visits in Texas. Between March and August 2020, schools were universally closed (school shutdown) and reopened starting September 2020. In academic year 2020-2021 and 2021-2022, mean (SD) counts were 176 (33) and 189 (43) visits, respectively, in Massachusetts and 756 (126) and 754 (122) visits, respectively, in Texas. Before the pandemic, there were no significant gaps by age group or state as confirmed by a 1-sample t test on the difference in EDSV rates between the 2 age groups in Massachusetts and Texas ( Figure 2 ). Starting September 2020, youths aged 12 to 17 years had significantly higher rates of EDSVs in Massachusetts ( t = 8.11; df = 23; P < .001) and Texas ( t = 4.02; df = 23; P < .001) ( Figure 2 ). Examining difference between states by a 2-sample t test revealed that the disparity between age groups 12 to 17 and 18 to 25 years in Massachusetts significantly expanded after September 2020 compared with Texas ( t = 2.96; df = 46; P < .001) ( Figure 2 ).
This cohort study found an association between longer school closures in the public health response to the COVID-19 pandemic and increases in youth suicidality. Limitations of the study include comparing only 2 states without considering other factors that may be associated with suicidality, which is outside the scope of this article. These data revealed an association between school closures and youth mental health, calling for further investigation such that in future pandemics and other disasters, policy regarding school closures may better align with the mental health needs of youths.
Accepted for Publication: October 3, 2023.
Published: November 10, 2023. doi:10.1001/jamanetworkopen.2023.43001
Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Dvir Y et al. JAMA Network Open .
Corresponding Author: Yael Dvir, MD, Division of Child and Adolescent Psychiatry, Department of Psychiatry, University of Massachusetts Chan Medical School/UMass Memorial Medical Center, 55 Lake Ave N, Worcester, MA 01655 ( [email protected] ).
Author Contributions: Drs Dvir and Lee had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: Lee.
Drafting of the manuscript: All authors.
Critical review of the manuscript for important intellectual content: Lee.
Statistical analysis: Lee.
Administrative, technical, or material support: Ryan.
Conflict of Interest Disclosures: None reported.
Data Sharing Statement: See the Supplement .
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