research papers of covid 19

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Published: 04 June 2021

Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

Israel Júnior Borges do Nascimento 1 , 2 ,
Dónal P. O’Mathúna 3 , 4 ,
Thilo Caspar von Groote 5 ,
Hebatullah Mohamed Abdulazeem 6 ,
Ishanka Weerasekara 7 , 8 ,
Ana Marusic 9 ,
Livia Puljak ORCID: orcid.org/0000-0002-8467-6061 10 ,
Vinicius Tassoni Civile 11 ,
Irena Zakarija-Grkovic 9 ,
Tina Poklepovic Pericic 9 ,
Alvaro Nagib Atallah 11 ,
Santino Filoso 12 ,
Nicola Luigi Bragazzi 13 &
Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume 21 , Article number: 525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.

Conclusions

In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Methodology

Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

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Acknowledgments

We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

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Israel Júnior Borges do Nascimento

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Dónal P. O’Mathúna

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Thilo Caspar von Groote

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Hebatullah Mohamed Abdulazeem

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Livia Puljak

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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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DOI : https://doi.org/10.1186/s12879-021-06214-4

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Coronavirus disease 2019 (COVID-19): A literature review

Affiliations.

1 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
2 Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan. Electronic address: [email protected].
3 Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
4 Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
5 School of Medicine, The University of Western Australia, Perth, Australia. Electronic address: [email protected].
6 Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia. Electronic address: [email protected].
7 Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia; Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA. Electronic address: [email protected].
8 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
9 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA. Electronic address: [email protected].
10 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
PMID: 32340833
PMCID: PMC7142680
DOI: 10.1016/j.jiph.2020.03.019

In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.

Keywords: 2019-nCoV; COVID-19; Novel coronavirus; Outbreak; SARS-CoV-2.

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COVID-19 pandemic and Internal Medicine Units in Italy: a precious effort on the front line. Montagnani A, Pieralli F, Gnerre P, Vertulli C, Manfellotto D; FADOI COVID-19 Observatory Group. Montagnani A, et al. Intern Emerg Med. 2020 Nov;15(8):1595-1597. doi: 10.1007/s11739-020-02454-5. Epub 2020 Jul 31. Intern Emerg Med. 2020. PMID: 32737837 Free PMC article. No abstract available.

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Introduction
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Article Information

Regions mapped in dark blue show counties included in the analysis (n = 268). Regions mapped in light blue show counties that submitted the wastewater data to NWSS, but were excluded from analysis (n = 462).

A, Graph shows smoothed spline-fit polymerase chain reaction (PCR) concentrations of SARS-CoV-2 for each sampling location as reported by the Centers for Disease Control and Prevention National Wastewater Surveillance System. B, Graph shows reported COVID-19 cases per 100 000 population. C, Graph shows wastewater SARS-CoV-2 percentile level. D, Graph shows COVID-19 hospital admissions per 100 000 population. Horizontal dashed lines in B and D show thresholds for high COVID-19 community level (reported COVID-19 case rate ≥200 per 100 000 population and reported hospitalization rate ≥10 new inpatient admissions per 100 000 population, respectively). E, Graph shows wastewater SARS-CoV-2 15-day percentage change. F, Graph shows state-level data for diagnostic laboratory tests per 100 000 population (solid black line shows reported tests from the state of California; dashed gray blue show estimates for all other US states; dashed vertical orange line represents the date when distribution of rapid home tests was announced by the Biden administration, January 19, 2022). The solid blue lines in panels A, B, C, D, and F show weighted mean values using each sewershed’s population served. Data for the most populous counties in US Census regions Midwest and Northeast are shown in eFigure 4 in Supplement 1 .

A, Graph shows smoothed spline-fit polymerase chain reaction (PCR) concentrations of SARS-CoV-2 for each sampling location as reported by the Centers for Disease Control and Prevention National Wastewater Surveillance System. When multiple sewersheds were sampled within a county, dashed gray lines in panel A represent individual sewersheds. B, Graph shows reported COVID-19 cases per 100 000 population. C, Graph shows wastewater SARS-CoV-2 percentile level. D, Graph shows COVID-19 hospital admissions per 100 000 population. Horizontal dashed lines in B and D show thresholds for high COVID-19 community level (reported COVID-19 case rate ≥200 per 100 000 population and reported hospitalization rate ≥10 new inpatient admissions per 100 000 population, respectively). E, Graph shows wastewater SARS-CoV-2 15-day percentage change. F, Graph shows state-level data for diagnostic laboratory tests per 100 000 population (solid blue lines show reported tests from the state of Texas; dashed gray lines show estimates for all other US states; dashed vertical orange line represents the date when distribution of rapid home tests was announced by the Biden administration, January 19, 2022). The solid blue lines in panels A, B, C, D, and F show weighted mean values using each sewershed’s population served. Data for the most populous counties in US Census regions Midwest and Northeast are shown in eFigure 4 in Supplement 1 .

Graphs show areas under the curve (AUCs) of wastewater percentile in reference to current reported COVID-19 cases (≥200 per 100 000 population) (A) and new hospital admissions in 2 weeks (≥10 per 100 000 population) (B). Shaded ribbons show bootstrapped 95% CIs for sensitivity at given specificity.

eTable. Sampled County Population and Sewershed Data, and Case and Hospitalization Rates by Quarters of 2022

eFigure 1. Diagnostic Testing and Reported New COVID-19 Cases in the US Between March 1, 2020, and September 30, 2022

eFigure 2. Selection of Counties Included in Analysis

eFigure 3. Changes in Wastewater Percentile Value as New Data Becomes Available, Wayne County, Michigan, as an Illustrative Example

eFigure 4. Time History of Wastewater Surveillance Data and Clinical Case Metrics From the Most Populous Counties in US Census Regions Midwest and Northeast Between January 2022 and September 2022

eFigure 5. Performance of Wastewater Percentile in Reference to Clinical Case Metrics in Small US Counties (n=230) Stratified by Calendar Quartile of 2022

eFigure 6. Performance of Wastewater Percentile in Reference to Clinical Case Metrics in Large US Counties (n=38) Stratified by Calendar Quartile of 2022

eFigure 7. Performance of 15-Day Wastewater Percent Change in Reference to Clinical Case Metrics Stratified by Calendar Quartile of 2022

eFigure 8. Performance of Combined Wastewater Metrics in Reference to Clinical Case Metrics Stratified by Calendar Quartile of 2022

eFigure 9. Performance of Current Reported COVID-19 Case Rates in Reference to Clinical Case Metrics Stratified by Calendar Quartile of 2022

eFigure 10. Performance of Current COVID-19 Hospital Admission Rate in Reference to Clinical Case Metrics Stratified by Calendar Quartile of 2022

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Varkila MRJ , Montez-Rath ME , Salomon JA, et al. Use of Wastewater Metrics to Track COVID-19 in the US. JAMA Netw Open. 2023;6(7):e2325591. doi:10.1001/jamanetworkopen.2023.25591

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Use of Wastewater Metrics to Track COVID-19 in the US

1 Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Palo Alto, California
2 Division of Nephrology, Department of Medicine, Stanford University, Palo Alto, California
3 Department of Health Policy, Stanford University, Stanford, California
4 US Renal Care, Plano, Texas
5 Department of Epidemiology and Population Health, Stanford University, Palo Alto, California

Question Are wastewater surveillance metrics reported to the Centers for Disease Control and Prevention’s National Wastewater Surveillance System associated with high community case and hospitalization rates of COVID-19 across US counties?

Findings In this cohort study with a time series analysis of 268 counties in 22 states from January to September 2022, SARS-CoV-2 wastewater metrics accurately reflected high clinical rates of disease early in 2022, but this association declined over time as home testing and vaccination increased.

Meaning These findings suggest that wastewater surveillance can provide an accurate assessment of county SARS-CoV-2 incidence and may be the best metric for monitoring amount of circulating virus as home testing increases and disease acuity decreases because of vaccination and treatment.

Importance Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence.

Objective To examine the association of county-level wastewater metrics with high case and hospitalization rates nationwide both before and after widespread use of at-home tests.

Design, Setting, and Participants This observational cohort study with a time series analysis was conducted from January to September 2022 in 268 US counties in 22 states participating in the US Centers for Disease Control and Prevention’s National Wastewater Surveillance System. Participants included the populations of those US counties.

Exposures County level of circulating SARS-CoV-2 as determined by metrics based on viral wastewater concentration relative to the county maximum (ie, wastewater percentile) and 15-day percentage change in SARS-CoV-2 (ie, percentage change).

Main Outcomes and Measures High county incidence of COVID-19 as evidenced by dichotomized reported cases (current cases ≥200 per 100 000 population) and hospitalization (≥10 per 100 000 population lagged by 2 weeks) rates, stratified by calendar quarter.

Results In the first quarter of 2022, use of the wastewater percentile detected high reported case (area under the curve [AUC], 0.95; 95% CI, 0.94-0.96) and hospitalization (AUC, 0.86; 95% CI, 0.84-0.88) rates. The percentage change metric performed poorly, with AUCs ranging from 0.51 (95% CI, 0.50-0.53) to 0.57 (95% CI, 0.55-0.59) for reported new cases, and from 0.50 (95% CI, 0.48-0.52) to 0.55 (95% CI, 0.53-0.57) for hospitalizations across the first 3 quarters of 2022. The Youden index for detecting high case rates was wastewater percentile of 51% (sensitivity, 0.82; 95% CI, 0.80-0.84; specificity, 0.93; 95% CI, 0.92-0.95). A model inclusive of both metrics performed no better than using wastewater percentile alone. The performance of wastewater percentile declined over time for cases in the second quarter (AUC, 0.84; 95% CI, 0.82-0.86) and third quarter (AUC, 0.72; 95% CI, 0.70-0.75) of 2022.

Conclusions and Relevance In this study, nationwide, county wastewater levels relative to the county maximum were associated with high COVID-19 case and hospitalization rates in the first quarter of 2022, but there was increasing dissociation between wastewater and clinical metrics in subsequent quarters, which may reflect increasing underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments. This study offers a strategy to operationalize county wastewater percentile to improve the accurate assessment of community SARS-CoV-2 infection prevalence when reliability of conventional surveillance data is declining.

Rapid determination of COVID-19 incidence within communities can guide screening at hospitals, residential facilities, schools, or communal gatherings; mobilize treatment supplies; and preserve hospital capacity. Public health agencies including the US Centers for Disease Control and Prevention (CDC) relied chiefly on rates of reported new COVID-19 cases and/or hospitalizations to estimate county levels of COVID-19. 1 As home testing becomes widespread, however, case counts are likely to substantially underestimate disease incidence, to a degree depending on at-home test availability, acceptance, and cost, as well as the severity of disease seen with circulating strains of SARS-CoV-2. 2 Similarly, since the introductions of vaccines and medications that reduce COVID-19 severity, 3 tracking hospitalization rates may produce unreliable estimates of disease incidence. The lack of accurate data regarding community infection prevalence leaves high-risk patients at particular risk.

Wastewater surveillance offers a potential solution to the problem of accurate SARS-CoV-2 surveillance because it is agnostic to symptomatic, diagnosed, or reported disease. High-resolution sequencing of wastewater can also identify emerging variants of concern 4 and estimate the effective reproductive number, 5 a key predictor of future transmission. For these reasons, many jurisdictions are investing in expanding wastewater surveillance, with over 70 countries and more than 3500 sites reporting data to a central dashboard. 6 Yet adoption of wastewater surveillance to inform public policy has not yet become widespread, in part because of challenges in interpreting results with shifting detection methods, virus strains, populations served, and wastewater dynamics. 7 To date, available data evaluating wastewater metrics against cases or hospitalizations in the US are geographically limited, evaluating a single sewershed 8 , 9 or a few sewersheds grouped regionally. 10

The CDC’s National Wastewater Surveillance System (NWSS) collates data from a majority of currently operating wastewater testing sites in the US. 11 The NWSS normalizes wastewater samples to wastewater flow and population size served and relies on viral gene copies per individual in the sewershed as the foundational data unit. This normalization addresses concerns about changes due to weather and differences in sewershed size. Despite normalization and generation of aggregate measures (eg, percentage change in normalized virus concentration in the last 15 days), no interpretation algorithm is provided to inform screening policy. Indeed, the NWSS specifically recommends that “point estimates of community infection based on wastewater measurements should not be used” 12 to shape policy, largely because the amount of virus shed by individuals with infection into the sewage system has not been well characterized. 13 Yet, with decreased institutional testing, lower disease virulence for a majority of the immunocompetent disease population, and, as of May 2023, CDC’s discontinuation of publicly shared case metrics, wastewater may be the best (and possibly only) way to understand the dynamics of circulating SARS-CoV-2 virus in communities.

Using data from the NWSS, we sought to evaluate how well national data on wastewater SARS-CoV-2 measures paralleled reported new COVID-19 cases and hospitalizations over time in the US. We hypothesized that the association of wastewater with COVID-19 disease metrics would be greater before widespread home COVID-19 testing and would decrease over time—that is, there would be attenuation of the association between viral transmission and formally reported new case and hospitalization rates. We aimed to determine whether selected wastewater surveillance metrics can be operationalized for future integration with other disease or socioeconomic vulnerability metrics to inform policy decisions regarding resource allocation to areas with high disease prevalence.

In this cohort study with a time series analysis, we obtained publicly available data from the NWSS spanning the Omicron variant dominant period of January to September 2022. 11 Because this work solely relies on publicly available data that do not carry any protected health information and we did not have access to codes or linkage that could enable individual identification, it is exempt from ethics review and the need for informed consent, as per Common Rule 45 CFR46.102. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines.

The NWSS reports data on wastewater from public health department–monitored sewersheds serving at least 3000 people. Excluded are sewersheds missing population estimates or that represent single institutions (eg, a university). The sewersheds quantify SARS-CoV2 in unconcentrated wastewater or sludge using either reverse-transcription quantitative polymerase chain reaction (672 sites) or reverse-transcription digital polymerase chain reaction (545 sites); irrespective of sample type and method, sites report virus concentrations per volume. 12

Publicly reported data include sewershed location identifiers and population served. In an effort to facilitate comparisons, NWSS-aggregated metrics include facility SARS-CoV-2 percentile (ie, an ordered rank of the current virus concentration relative to historic peak and nadir at that facility, hereafter referred to as wastewater percentile ), percentage change in normalized virus concentration in the prior 15 days (hereafter referred to as wastewater percentage change ), and percentage of wastewater samples with detectable virus in the prior 15 days. Our analysis focused on the first 2 NWSS-produced measures.

Because the wastewater percentile metric compares current with historical peak viral concentrations and because we aimed to compare data across sites, we restricted our analysis to sewersheds with available data in January 2022 (eFigure 1 in Supplement 1 ). Counties had to have submitted at least 1 week of data in January 2022 and have data available for more than 50% of the subsequent weeks to be further included in the analysis (eFigure 2 in Supplement 1 ). Implementing these criteria minimized fluctuation in percentile metric by date of assessment (eFigure 3 in Supplement 1 demonstrates similar percentile metrics for an example county for assessments in August vs October 2022).

We also restricted data to samples obtained from a treatment plant itself, rather than from pretreatment plant wastewater. In counties with more than 1 sewershed reporting to NWSS, we aggregated data from each sewershed to county level by creating a weighted average using each sewershed’s population served. Thus, the sewershed serving the largest population contributed the largest weight to the averaged county estimate.

In the primary analysis, we used 2 CDC community level indicators as our dependent variables: reported new COVID-19 cases per 100 000 and new inpatient admissions per 100 000. 14 We used publicly available time series data on aggregated counts of COVID-19 cases from state and local health departments, and hospital admissions from US Department of Health and Human Services Unified Hospital Data Surveillance System. 15 , 16 Consistent with CDC reporting practices, we computed aggregate counts of COVID-19 cases and hospitalizations per 100 000 population from the past 7 days at the midpoint of each week. When comparing wastewater metrics to hospitalizations, we lagged new inpatient admissions by 2 weeks. We defined high COVID-19 community level using CDC-defined thresholds: (1) reported case rate equal to or greater than 200 new COVID-19 cases per 100 000 population, and (2) reported hospitalization rate equal to or greater than 10 new inpatient admissions per 100 000 population.

We grouped data by calendar quarters (January to March, April to June, and July to September). We obtained county population data from the 2021 US Census. 17 To visually evaluate the association of our 2 wastewater metrics with clinical case metrics, we graphed these for 2022 for the most populous county from each US Census region. We also graphed the absolute wastewater concentrations within the county to visualize its association with the county-level wastewater metrics.

We then computed the sensitivity, specificity, and area under the receiver operating characteristic (AUC) by time period of wastewater metrics in identifying CDC thresholds for high COVID-19 case and hospitalization rates. We treated the wastewater metrics as the test of interest and the thresholds of cases and hospitalizations as comparative indicators of high COVID-19 community level. To determine whether combining both wastewater metrics was associated with high COVID-19 community levels, we used logistic regression accounting for wastewater percentile, percentage change, and the interaction of the 2 variables. Because our goal was to evaluate potential thresholds for high infection prevalence, we compared model predictive ability across probability cutoffs in which the sum of sensitivity and specificity was maximized (Youden index). 18

In a sensitivity analysis, we evaluated wastewater percentile performance in small vs large counties; large counties are defined as population equal to or greater than 500 000 by US Census Bureau. 17 We also conducted sensitivity analysis to test the association of current cases and hospitalization rates with rates of cases and hospitalizations lagged by 2 weeks, above CDC thresholds as our dependent variables.

Each county contributed data to the analysis for weeks during which wastewater data were available. Because not all counties consistently reported values, the number of counties included in the analysis varied per week. We assessed the association of the wastewater with clinical case metrics for each week stratified by calendar quarter. We computed bootstrapped 95% CIs for performance estimates using the bootstraps function. To account for within-county correlations in time series data, we performed resampling by county. Bootstrapped 95% CIs for figures were generated by calculating sensitivity at given specificity points using the ci.se function in R statistical software version 4.2.2 (R Project for Statistical Computing). We used R statistical packages epiR, rsample, and pROC to perform the analyses.

Among 730 wastewater treatment counties that submitted the analyzed metrics to NWSS during our study period, 268 counties across 22 states met our inclusion criteria ( Figure 1 and eFigure 2 in Supplement 1 ). The median (IQR) population of counties included in the analysis was 95 938 (44 697-294 772) residents ( Table ). Comparatively, the overall US county population median (IQR) is 25 752 (10 818-67 899) residents. Consistent with national data, reported new case and hospitalization incidence rates were high in the first and third quarters, and lower in the second. Also consistent with national data, in our sampled counties, new case incidence was at its highest since the start of the pandemic in the first quarter of 2022 (eFigure 1 in Supplement 1 ).

Plots of the available data for 2022 from the most populous counties in each US Census region demonstrate a direct association between wastewater percentile and absolute SARS-CoV-2 concentrations ( Figure 2 and Figure 3 ; eFigure 4 in Supplement 1 ). The 15-day percentage change variable fluctuated widely (eTable in Supplement 1 ). In the first quarter of 2022, facility wastewater percentile was closely associated with cases and hospitalizations. In contrast, the association was less evident in the third quarter when reported case and hospitalization rates were low, despite high levels of SARS-CoV2 in wastewater as indicated by facility percentile. In AUC analyses incorporating data from all counties, wastewater percentile was closely associated with high reported case (>200 per 100 000; AUC, 0.95; 95% CI, 0.94-0.96) and hospitalization rates (>10 per 100 000 lagged by 2 weeks; AUC, 0.86; 95% CI, 0.84-0.88) in the first quarter of 2022 ( Figure 4 ).

Among counties sharing wastewater data to NWSS since January 2021, wastewater percentile of 51% was the Youden index threshold for detecting cases exceeding 200 per 100 000 population (sensitivity, 0.82; 95% CI, 0.80-0.84; specificity, 0.93; 95% CI, 0.92-0.95) in the first quarter of 2022. Similarly, wastewater percentile of 54% was the Youden index threshold hospitalizations exceeding 10 per 100 000 population (sensitivity, 0.80; 95% CI, 0.77-0.83; specificity, 0.78; 95% CI, 0.76-0.81). In the overall analysis, AUC declined over the next 2 quarters (second quarter AUC, 0.84; 95% CI, 0.82-0.86; third quarter AUC, 0.72; 95% CI, 0.70-0.75) for the association of wastewater percentile with cases and hospitalizations ( Figure 4 ). Performance was similar in small and large counties: AUCs for the first quarter were 0.95 (95% CI, 0.94-0.96) for cases in small counties, 0.95 (95% CI, 0.93-0.98) for cases in large counties, 0.85 (95% CI, 0.82-0.87) for hospitalizations in small counties, and 0.94 (95% CI, 0.91-0.97) for hospitalizations in small large counties (eFigures 5 and 6 in Supplement 1 ).

The percentage change metric performed poorly, with AUCs ranging from 0.51 (95% CI, 0.50-0.53) to 0.57 (95% CI, 0.55-0.59) for reported new cases, and from 0.50 (95% CI, 0.48-0.52) to 0.55 (95% CI, 0.53-0.57) for hospitalizations across the 3 quarters (eFigure 7 in Supplement 1 ). Combining wastewater facility percentile, percentage change, and the interaction of the 2 variables in a logistic regression analysis yielded estimates of predictive performance similar to those found using the percentile metric alone (eFigure 8 in Supplement 1 ). AUC analyses examining the performance of clinical metrics as predictors of future clinical outcomes (ie, current case and hospitalization rates’ correlation with case and hospital admission rates lagged by 2 weeks) indicated an association (eFigures 9 and 10 in Supplement 1 ) for both clinical metrics in the first quarter of 2022. As with wastewater percentile, however, performance declined over the next 2 quarters.

To our knowledge, this cohort study with a time series analysis is the first to examine CDC-generated wastewater metrics from sewersheds located throughout the nation. We observed a direct association of a county’s SARS-CoV-2 wastewater concentration, relative to its maximal observed, with COVID-19 cases and hospitalizations for US counties during the first quarter of the 2022. When little home testing was being conducted, wastewater percentiles in all counties tracked quite closely with new cases per 100 000 population. However, the association of the wastewater percentile with COVID-19 reported cases decreased over the next 2 quarters, indicating an increasing dissociation between community viral prevalence and reports of infection to health departments. There was also increasing dissociation between wastewater and new hospitalization rates, perhaps indicative of lower rates of COVID-19–related hospitalization with heightened population immunity due to prior infection and vaccination, potentially lower virulence of evolving strains, and/or reduction in routine admissions testing in hospitals.

In the first quarter of 2022, we evaluated wastewater metrics against case metrics when many wastewater facilities were contributing data nationwide and when at-home testing, while increasing, was still not ubiquitous. 19 No nationally representative data exist on the relative use of home tests vs laboratory or point-of-care tests in the US, but from a large internet survey of more than 450 000 US adults, among persons with symptoms, there was a 4-fold increase in report of home COVID-19 tests, from 5% in the Delta-dominant period in fall 2021 to 20% in the Omicron-dominant period in winter 2022. 19 Results of home testing are rarely reported. In a recent analysis of self-testing data from October 2021 to May 2022, Ritchey et al 20 reported that results from only 3% of the nearly 400 million at-home tests produced by 4 US manufacturers were voluntarily reported to health authorities. Because we tracked the same facilities in the same counties over the subsequent 2 quarters, we postulate that it is the increasing use of at-home testing, rather than any changes in the association of SARS-CoV-2 incidence with shedding into the wastewater system, that led to our observed decline in the performance of wastewater percentile in detecting new cases.

In our analysis, the benchmark for evaluating data from facilities sharing data for at least 6 months is critical for interpretation, because the wastewater percentile metric places all newer data relative to the highest community prevalence of COVID-19 seen in the county. As more wastewater facilities come online, benchmarking remains a critical unresolved question. There are several ways to address lack of historical data, including imputation models and adopting references relative to data from neighboring established facilities. Furthermore, should there a surge in absolute viral concentration far exceeding that observed during Omicron variant circulation in 2022, the percentile metric may require recalibration or reassessment for thresholds correlating with high infection prevalence. Other potential improvements, such as selecting sewersheds that are better representative of counties sampled, could increase the yield of a sentinel surveillance system.

Since fall 2020, the CDC has invested over $100 million to support wastewater surveillance infrastructure in the US, with the largest share of the investment occurring in August 2022. 21 Although the utility of wastewater surveillance extends beyond COVID-19, national level data are collated and reported only for COVID-19. The NWSS makes substantial efforts to convert absolute viral concentration data into comparable measures across and within sites. As of September 2022, 1213 facilities representing 741 counties and 50 states were submitting data, which were updated weekly. 13 In our analysis, more than 99% of analyzed facilities had more than half of weeks covered from the past 3 quarters. Thus, timely nationwide data are available for an increasing number of US residents. Although a few counties publish and publicize their results 22 to raise public awareness, inform mask wearing, and promote social distancing, we lack a national strategy for the use of wastewater surveillance. If we presume the first quarter association we observed between wastewater percentile and new cases likely holds steady, then a wastewater percentile of 51% of maximum as generated by the NWSS can reflect high infection prevalence, regardless of reported case counts.

As a counterpoint, as COVID-19 infection evolves clinically for the largest share of the population, for whom there is a potentially lower risk for hospitalization and death with vaccination and the Omicron subvariants, some could argue that investments in capturing true prevalence of circulating disease are unnecessary. This is a reasonable trade-off to consider, but it needs to be contextualized with 2 important points. First, medically vulnerable populations, such organ transplant recipients, 23 persons receiving chemotherapy, 24 and persons receiving dialysis, 25 are suboptimally protected by vaccinations and remain at high risk for adverse health outcomes from COVID-19 infection. Thus, awareness of true disease prevalence could promote additional protective measures tailored to these populations and enable earlier treatment. For example, during periods of high disease circulation, universal asymptomatic testing could be offered in long-term care facilities, with nirmatrelvir-ritonavir treatment provided early to patients testing positive. Second, even among the general population, COVID-19 infection or reinfection has been shown to be associated with adverse health events, including symptoms of post–COVID-19 condition and hospitalizations. Moreover, there remain risks of waning immunity and worsening variants. In scenarios where home-based testing and underreporting of cases are common, wastewater surveillance may also enable better measurement of virulence by quantifying the denominator for the numerator of case counts.

Our research on wastewater was done during a period of marked flux in diagnostics, vaccination, and disease acuity. We postulate that wastewater surveillance is the most consistent measure of infection prevalence during this unstable time, especially given its sensitivity even in low-prevalence settings. 26 However, infection prevalence does not reflect disease acuity, and this may, in part, explain the smaller association of both wastewater percentile and case rates with high hospitalization rates (lagged by 2 weeks) in the second and third quarter of 2022. As vaccination, antiviral treatment, increasing population immunity, and changes in variants made infection less severe, fewer patients accessed diagnostic testing and were hospitalized despite continued shedding into the sewersheds. It is our expectation that, as COVID-19 settles into endemicity, decisions to test and report will reach a steady state (ie, that a stable proportion of cases will be tested and reported). When this happens, public health officials will be able to estimate total cases from reported cases. Notably, however, as of May 2023, the CDC has discontinued collated public sharing of COVID-19 cases by county but continues to update NWSS data. Furthermore, in a future pandemic, until stable testing rates are achieved and sufficient data on infection virulence are gathered, wastewater surveillance may be the best early signal of a local outbreak and as a method to monitor circulating variants. 27 - 29

We evaluated wastewater metrics against 2 commonly used outcomes of case and hospitalization rates and conclude that wastewater metrics likely provide the better estimate of infection prevalence as formal case testing declines. In the future, complementary measures, such as school attendance 30 or emergency department visits for influenza-like illness, could be integrated with wastewater metrics to better understand the clinical and public health impact of wastewater metrics.

Our analysis is limited by the need to rely on a subset of facilities with sufficient data to not only track back to a true community peak, but also to allow a relatively stable percentile value assigned to an absolute viral concentration over time. Newer facilities may experience substantial fluctuations in the association of absolute viral concentration with assigned percentile, unless they benchmark to a neighboring county reference and/or use imputed historical data. To facilitate potential public health adoption, we also only evaluated metrics available within NWSS, rather than generating de novo metrics using raw or normalized wastewater data. The counties in our wastewater cohort are larger than the average US county. Two factors may explain this. First, smaller counties may have fewer personnel or resources to devote to wastewater surveillance. Second, smaller counties are more likely rural, and households in rural counties are more likely to rely on individual septic systems rather than publicly owned treatment works. Although rural counties make up 97% of the landmass of the US and 63% of US counties, only 14% of the US population lives in these areas. 31 This important subset, however, is missed by wastewater surveillance that relies on public wastewater treatment works.

In summary, in this first analysis of wastewater metrics for SARS-CoV-2 incorporating data from the breadth of public health–monitored sewersheds in the US, found find that counties conducting wastewater surveillance and reporting data to the CDC NWSS in the US could use an aggregated measure of the percentage of maximum wastewater SARS-CoV-2 concentration to estimate county-level prevalence of COVID-19. Counties with a longer historical data record, tracking back to at least January 2022, will generally provide the most reliable estimates. We demonstrated that wastewater surveillance can be operationalized to fulfill the relevant public policy goals of public awareness of true SARS-CoV-2 incidence and implementation of additional actions specifically designed to protect medically vulnerable populations.

Accepted for Publication: June 10, 2023.

Published: July 26, 2023. doi:10.1001/jamanetworkopen.2023.25591

Corresponding Author: Meri R. J. Varkila, MD, Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, 3180 Porter Dr, Palo Alto, CA 94304 ( [email protected] ).

Author Contributions: Drs Varkila and Anand 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. Drs Parsonnet and Anand contributed equally.

Concept and design: Varkila, Montez-Rath, Salomon, Owens, Chertow, Parsonnet, Anand.

Acquisition, analysis, or interpretation of data: Varkila, Montez-Rath, Yu, Block, Chertow, Parsonnet.

Drafting of the manuscript: Varkila, Anand.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Varkila, Montez-Rath, Yu, Owens.

Obtained funding: Anand.

Administrative, technical, or material support: Block, Chertow.

Supervision: Montez-Rath, Block, Chertow, Parsonnet.

Conflict of Interest Disclosures: Dr Varkila reported receiving nonfinancial support from Ascend Clinical Laboratory and Abbott Laboratory (COVID-19 testing materials) during the conduct of the study. Dr Salomon reported receiving grants from Centers for Disease Control and Prevention to their institution through the Council of State and Territorial Epidemiologists during the conduct of the study. Dr Block reported being the Associate Chief Medical Officer at US Renal Care, Inc. Dr Chertow reported receiving personal fees from Akebia, Ardelyx, ReCor, Mineralys, Bayer, Vertex, Sanifit, Gilead, Reata, Satellite Healthcare, and AstraZeneca; stock options from CloudCath, Unicycive, Renibus, Outset, Miromatrix, and Durect; and grants from CSL Behring outside the submitted work. Dr Parsonnet reported receiving grants from Heluna Health, Max Planck Institute, and Gauss, Inc, outside the submitted work. Dr Anand reported receiving nonfinancial support from Abbott (study material kit) and Ascend Clinical (samples processing) and personal fees from Vera Therapeutics and HealthPals, Inc, consulting outside the submitted work No other disclosures were reported.

Funding/Support: This work is funded by grant 5U01AI169477 from the National Institutes of Health, National Institute of Allergy and Infectious Diseases. Dr Anand’s work is also funded by the Stanford Center for Innovation in Global Health and the Doris Duke Charitable Fund.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: Alexandra Boehm, PhD (Stanford University), Alexander Yu, MD (California Department of Public Health), and Jason Andrews, MD (Stanford University), assisted with interpretation and analysis of time series wastewater data. They were not compensated for this work.

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Why Are Some People Seemingly Immune to Covid-19? Scientists May Now Have an Answer

Researchers tracked the immune responses of 16 people intentionally exposed to SARS-CoV-2 and pinpointed a gene that seems to help resist the virus before it can take hold

Christian Thorsberg

Daily Correspondent

A female doctor in a mask and visor gives a nasal swab to a male patient.

More than four years after Covid-19 was declared a pandemic that has since totaled more than 775 million cumulative cases worldwide, scientists are shedding light on the specific immune responses that have made some people seemingly resistant to catching the virus.

New research emerging from the United Kingdom, conducted as part of the Covid-19 Human Challenge Study and the Human Cell Atlas project, has found that a combination of robust nasal cell defense and high activity of a particular gene work together to ward off the virus in some individuals before it can take hold.

The research, published last week in the journal Nature , provides clarity on the timeline of the human body’s immune response to SARS-CoV-2 and other infectious diseases.

“These findings shed new light on the crucial early events that either allow the virus to take hold or rapidly clear it before symptoms develop,” Marko Nikolić , the study’s senior author and an honorary consultant in respiratory medicine at University College London (UCL), says in a statement . “We now have a much greater understanding of the full range of immune responses, which could provide a basis for developing potential treatments and vaccines that mimic these natural protective responses.”

Conducted in 2021, the study began with the researchers spraying a low dosage of the original SARS-CoV-2 variant up the noses of 36 healthy adult volunteers who were both unvaccinated and had never had the virus before.

From this group, researchers collected 16 volunteers’ nasal and blood samples on multiple occasions—before exposure and several times in the following 28 days—to track the spread of the virus and the participants’ immune responses. Sequencing these samples, the team produced a data set containing more than 600,000 individual cells and their behaviors before, during and after exposure.

The volunteers’ responses fell into three distinct categories. Six people became ill and displayed symptoms; three people briefly tested positive for Covid-19 but were asymptomatic, known as a transient infection; and seven people consistently tested negative and displayed no symptoms, but built up an immune response to the virus—what the team called an abortive infection.

In these latter two groups, participants showed high baseline activity of a gene called HLA-DQA2, which helps to efficiently alert the immune system to potential threats.

“These cells will take a little bit of the virus and show it to immune cells and say: ‘This is foreign: You need to go and sort it out,’” Kaylee Worlock , a molecular biologist and post-doctoral research fellow at UCL, tells the Guardian ’s Hannah Devlin.

Another common trait among people in the two latter groups related to the production of interferon, or proteins that help bolster the body’s immune system. For these volunteers, interferon was produced in the blood before it appeared in the upper nasal region.

The people with transient and abortive responses developed a quick immune response—built up within about one day—inside their noses. Meanwhile, those who tested positive for Covid-19 took an average of five days to build up a nasal immune response.

Notably, the participants were not immune to getting Covid-19—some later caught the virus in the community, after the research concluded. And now, several other variants of SARS-CoV-2 are circulating—not just the original variant that was tested. But scientists say the research offers important clues to immune resistance.

“This study serves as a unique resource of previously uninfected SARS-CoV-2 participants due to its carefully controlled design and real understanding of ‘time zero’ for when the infection took place in order to measure the immune responses that follow,” José Ordovas-Montanes , an immunologist at the Harvard Stem Cell Institute who was not involved in the research, tells New Scientist ’s Sonali Roy.

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Christian Thorsberg | READ MORE

Christian Thorsberg is an environmental writer and photographer from Chicago. His work, which often centers on freshwater issues, climate change and subsistence, has appeared in Circle of Blue , Sierra magazine, Discover magazine and Alaska Sporting Journal .

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Here are the numbers: COVID-19 is ticking up in some places, but levels remain low

by The Associated Press

Here's a look at the state of COVID-19 in the U.S. as the Centers for Disease Control and Prevention establishes its latest advice on vaccinations .

About 300 COVID-19-associated deaths were occurring weekly in May, according to the most recent provisional CDC data . That's the lowest since the beginning of the pandemic. Nearly 26,000 people died from COVID-19 in the U.S. in the week ending Jan. 9, 2021—the highest weekly toll in the pandemic.

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DOI: 10.21716/tkfl.73.2
Corpus ID: 270633972

A Study on the Identity of Korean Teachers in the COVID-19

Published in Teaching Korean as a Foreign… 31 May 2024
Education, Linguistics

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Retraction Watch

Tracking retractions as a window into the scientific process

Paper recommending vitamin D for COVID-19 retracted four years after expression of concern

A paper that purported to find vitamin D could reduce the severity of COVID-19 symptoms has been retracted from PLOS ONE , four years after the journal issued an expression of concern about the research.

The article, “ Vitamin D sufficiency, a serum 25-hydroxyvitamin D at least 30 ng/mL reduced risk for adverse clinical outcomes in patients with COVID-19 infection ,” appeared online September 25, 2020. Michael F. Holick , a professor of medicine at Boston University and a proponent of the use of vitamin D, was the last author among a group of other researchers from the Tehran University of Medical Sciences in Iran.

Soon after publication, the paper gained traction on platforms like Twitter (now X) as evidence vitamin D could treat COVID-19 symptoms.

Amidst this discussion, Nick Brown , a science integrity researcher at Linnaeus University in Växjö, Sweden, and Gideon Meyerowitz-Katz , a research fellow at the University of Wollongong, Australia, pointed out potential flaws with the work, including the small sample size of the study and a lack of patient information such as how the patients died.

PLOS ONE posted an expression of concern for the article on October 14, 2020. According to the notice, “concerns were raised about the validity of results and conclusions reported in the article and about undisclosed competing interests.” The expression of concern also noted “statements in the article, including in its title and conclusions, that suggest a causal relationship between vitamin D levels and the clinical outcome of COVID-19 infections which is not supported by the data.”

The competing interests refer to Holick’s “non-financial interests based on his vitamin D research and other activities focused on vitamin D; contributions to an app that tracks vitamin D; and interests that include consultancies, funding support, and authorship of books related to vitamin D usage.” In 2018, the New York Times reported on Holick’s financial ties to the vitamin D industry.

“This project did not receive any grants. Prof. Holick did not have any funding support for this project,” Mohammad Ali Sahraian, the paper’s corresponding author, told Retraction Watch.

Regarding the competing interests, Holick said:

The app dminder.info is free. I do not derive any income from it. I am no longer a consultant for Quest Diagnostics and therefore it was not listed. I do not receive any book royalties related to any information that is in this publication. I do not have any conflict of interest regarding any aspect of the study design, the results, or conclusions.

The paper has been cited 189 times, according to Clarivate’s Web of Science, with the bulk of citations coming after the expression of concern.

On June 6, nearly four years later, PLOS ONE retracted the study. A statistical reviewer and members of the PLOS ONE Editorial Board found that the study design was inadequate to address the research question and the methods used were not detailed enough to reproduce the study, according to the retraction notice. “As such, we have concluded that the article’s conclusions are not supported by the reported data,” the notice said.

Sahraian said he and his colleagues were “very surprised” and “upset” by the retraction, which should “not be decided without any ethical problem.”

“It would have been preferable for these issues to have been identified and addressed during the pre-publication review process, rather than after the work had already been made public,” Sahraian told Retraction Watch. “However, we did not claim a causal role of vitamin D in the clinical outcome of COVID-19 infections. The current study was cross-sectional and only considered the association between COVID-19 and circulating vitamin D levels.”

Holick, Saharian, and the paper’s first author do not agree with the retraction, while others could not be reached, the notice states. Sahraian said they don’t agree because they provided detailed responses to the editorial board’s concerns on multiple occasions, but did not receive any response from the journal.

“I believe it is unfair, unethical, and inhumane to retract an article due solely to the faults of the editorial team rather than addressing the flaws inherent to the study itself,” he said.

David Knutson , head of communications at PLOS , said, “the authors’ comment that the communications in this case were one-sided, and the implication that our editorial decision was biased, are not true.”

PLOS completed an objective evaluation and communicated with the authors on multiple occasions, Knutston said. He acknowledged a “long gap in our communications with the authors between March 2021 and December 2023” and apologized for one instance in which PLOS did not update an author following their query about the retraction decision.

Meyerowitz-Katz said he has mixed feelings about this retraction. “It is to the credit of the editorial team that they even bothered to investigate, and retracting on the basis of low quality – rather than misconduct or similar – shows a commitment to integrity that few journals display,” he said.

But retracting it “at this point will have no impact whatsoever on the pollution of the literature – the damage has well and truly been done. I think that the PLOS ONE editorial team should explain exactly why it took an entire pandemic’s worth of time to come to a decision, given that the issues they cite as reasons to retract are the same ones I pointed out on Twitter in September 2020,” Meyerowitz-Katz said.

Brown said it would have been better if the retraction “didn’t take quite so long,” but given the expression of concern was issued rapidly, “I think the journal handled it overall fairly well.”

PLOS ONE cited an ethics case backlog for their slowness in retracting this paper, among other papers.

Knutson told us the case was “complex,” and the four year time period “reflects the time we required to complete a rigorous assessment and investigation,” as well as “some internal delays due to competing priorities.” He said:

Given the complexity of this case, the nature of the concerns, and potential clinical implications of the article and the concerns raised, we published an interim Expression of Concern soon after the concerns were raised to our attention.

Knutson said the competing interests mentioned in the expression of concern did not play a part in the retraction of the paper. “The retraction decision was based on concerns about the study design, methodological reporting, and the reliability of the article’s conclusions,” he said.

Like Retraction Watch? You can make a tax-deductible contribution to support our work , subscribe to our free daily digest or paid weekly update , follow us on Twitter , like us on Facebook , or add us to your RSS reader . If you find a retraction that’s not in The Retraction Watch Database , you can let us know here . For comments or feedback, email us at [email protected] .

Nagashree Vasudeva

Raju vaishya.

b Indraprastha Apollo Hospitals, New Delhi, India

Mohit Kumar Patralekh

c Safdarjung Hospital and Vardhman Mahavir Medical College, New Delhi, 110029, India

Background & aims

It has been just over a year since the Covid-19 pandemic started. The top 50 cited articles on this subject would help identify trends and focus on the research efforts.

We utilised e-utilities in PubMed to find publications on Covid-19 until the date of search on 7/2/21. The iCite website was used to find the top 50 citations of the output from the search strategy. We looked into their full text for the editorial dates, type of study, level of evidence, focus of the article and country of origin. We also counted the errata and comments on each of them.

The total number of citations of all 50 articles was 123,960, the highest being 10, 754 for a single article. Huang C was the most cited first author. They were published from week 4–17, with February being the month with most citations. Lancet was the most cited journal, having published 9 of the 50 articles. Majority belonged to level 3 of the evidence ladder and were retrospective studies. Thirty percent of them had an errata published and an average of 7 comments per article.

The top 50 most cited articles identify the most impactful studies on Covid-19, providing a resource to educators while identifying trends to guide research and publishing efforts. There has been an explosion of publications and an unprecedented rate and number of citations within the first year for any single condition in the literature.

1. Introduction

Covid-19 has affected humanity in a major way. An extremely dangerous virus, hitherto unknown to humanity, had to be studied and contained in order to overcome the pandemic. Research on Covid-19 had surged in the early days with an unprecedented surge in the publications on that specific topic. With vaccination drives in majorly affected countries, and the emergence of second and third waves, the interest on this topic in the scientific community has been sustained. Pubmed is the most commonly used and freely available database and most of the articles published on the Covid-19 topic in major journals were fast-tracked and made freely available for rapid dissemination of information and findings. Top 50 cited articles have been published in many areas of medicine. In fact there have been publications related to Covid-19 from an earlier period. The differences have been discussed in the discussion section of this manuscript. Due to the sheer volume of publications on this topic, there would be different outcome if two studies were done six months apart. We looked into the top 50 cited publications on this topic in the literature in the PubMed to analyse the trends and focus of research among the most cited articles.

A search was done on 7/2/21 with a search strategy of (COVID-19 OR SARS-CoV-2 OR “New Corona Virus” OR “coronavirus 2″ OR “new coronavirus”) AND ((“2020/01/01"[Date - Create]: “2021/01/01"[Date - Create]))

For all publications in 2020 which gave an output of 88337.

The strategy of (COVID-19 OR SARS-CoV-2 OR “New Corona Virus” OR “coronavirus 2″ OR “new coronavirus”) AND ((“2021/01/01"[Date - Create]: “3000"[Date - Create])) was used for all publications from 2021 which gave an output of 11855.

When this search strategy was fed through eUtilities, we got a total of 11853 + 87793 articles. The PMIDs of all these articles were fed into the iCite website for citations and related data, from which we got 95806 articles. Some articles were left out by the iCite website and were not processed.

The output from the iCite website was fed into Excel and analysed for citation numbers and other basic outcomes. The data from the iCite website includes information on Field Citation Ratio (FCR), expected citations, number of citations, PMIDs of all articles citing every article, total number of references and DOI (Digital Object Identifier) address for each of the article obtained from the search. We analysed this preceding data and present the results. We calculated citations per week, week number the article was published in the year and percentages where appropriate.

FCR is calculated by the number of citations received by a publication divided by the average number of citations received by publications within that field in the same year. PubMed page of each of the 50 articles was scanned to see the number of comments and errata published against each of them and noted.

The top 50 cited articles were selected from this output and full texts collected and analysed for the purpose of this paper. We looked into the following information from the full text of each manuscript: create date in PubMed, type of study, level of evidence, focus of the paper, month published, country it was published from, week of the year it was published.

We also looked into the following times (in days) of each article as given in their full text where applicable; (i) time from submission to accepting to publish, (ii) from acceptance to publication and (iii) from submission to publication. Any errata or comments on the articles on PubMed were also noted down. We collected the data and analysed it in an Excel database.

The total number of citations of the top 50 papers was 123,960. The top 50 cited publications were published between the weeks 4–17 of last year. Week five saw the most number of publications (8 in number) and most citations for publications, but publications from week four (6 publications) had the most citations per publication ( Table 1 ). February was the month with most publications of the top 50 cited and had the maximum total citations as well as citations per week among the four months these articles were published ( Table 2 ). Most publications were done in the month of February (19 in number) with a sum of citations of 53,204 for that month. However, the citations per publication was maximum for the month of January at 3862 per citation.

Table 1

Publications in Week number with Total Citations and citations/publication.

Cell highlighted with green and light orange indicates the highest value and lowest value in the corresponding column in all the tables where applicable.

Table 2

Publications according to the month of the year.

Most studies published were of level 3 evidence in the evidence pyramid with 27 (40%) in number. Studies of level 3 had the most citations per publication at 2930.35. Twelve of the thirteen retrospective analyses belonged to this category with a citation sum of 38,418. Since citations are a function of duration since publication, we looked at the sum of citations per publication per week (CPW). A level 2 study [ 1 ] had the highest at 200.223 followed by a level 3 study [ 2 ] with 151.02 CPW.

Cohort study was the commonest type of study with a citation sum of 51,574. Of them, 12 were of evidence level 3, and four of these belonged to evidence level 4. Nine of the articles were correspondence to the editor, making it the second commonest type of study.

Lancet was the most cited journal publishing on this topic ( Chart 1 .) Table 3 gives the numbers published by each journal with their impact factors.

Journals publishing with number of publications on the right and total citations on the left. Citations/publication of each journal were given in brackets.

Table 3

Journals publishing top 50 cited articles on Covid-19.

Totals ∗ Averages # ^Journal names are given using standard abbreviations.

A total of 24 journals published the 50 most cited articles on Covid-19. Half of these journals (12 in number) had an impact factor of >20 Table 3 . There were a total of 15 papers (30%) which published errata on PubMed. Of these, 12 articles were published in journals with an impact factor of 20 or higher.

China was the country with the most publications (31) and citations (92276) Chart 2 .

Most cited Countries Publishing on Covid-19. Numbers in brackets indicate number of publications and citations per week for that country.

Table 4 shows the Level of evidence with names of journals in each level of evidence that published on this topic. It is evident that the higher level of evidence studies were from the highest impact factor journals. The number of citations was also higher for these journals and they top each category of evidence. Standard abbreviations for the journals were used in the table. Level 3 had the highest number of citations and but citations per publication was highest for level 2 studies at 3709 followed by level 3 studies at 2930. Level 5 studies included opinions and Letters to the editors. Some level 5 articles received more number of citations than some level 1 and 2 articles. Huang C was the most cited first author at 10,754 citations followed by Guan WJ Table 5 .

Table 4

Level of Evidence with journals in each level along with the numbers and citations of publications.

Level of Evidence & Journal	Total Citations	Number of Articles	Citations per article


N Engl J Med	1767	1	1767

Ann Intern Med	1134	1	1134
Euro Surveill	1516	1	1516
JAMA	1432	1	1432
Lancet	10754	1	10754

BMJ	1122	1	1122
Cell	5381	2	2690.5
Clin Infect Dis	1133	1	1133
J Thromb Haemost	2698	2	1349
J Virol	1189	1	1189
JAMA	7619	2	3809.5
JAMA Intern Med	2090	1	2090
JAMA Neurol	1545	1	1545
Lancet	14657	3	4885.67
Lancet Respir Med	2748	1	2748
N Engl J Med	11253	2	5626.5
Nature	4466	1	4466
Radiology	1398	1	1398
Thromb Res	1308	1	1308

Int J Antimicrob Agents	1799	1	1799
Int J Environ Res Public Health	1136	1	1136
JAMA	8731	4	2182.75
Lancet	5337	3	1779
Lancet Oncol	1401	1	1401
Lancet Respir Med	2358	1	2358
N Engl J Med	9970	3	3323.33
Nat Microbiol	1351	1	1351
Nature	3459	2	1729.5
Science	1876	1	1876

Cell Res	1971	1	1971
Intensive Care Med	1321	1	1321
Lancet	3871	2	1935.5
Lancet Infect Dis	1357	1	1357
N Engl J Med	2812	2	1406

Table 5

Top 10 authors on Covid-19.

Table 6 shows the speciality-wise distribution of publications, citations and citations per week. Not surprisingly, Pulmonology was the speciality that topped the list. In fact, the first four entries in the table are expected to be high as major work on this topic was done in those fields. These were followed by molecular sciences and internal medicine.

Table 6

Publications and citations according to speciality.

The studies were analysed and categorised according to the focus of the study to give a comprehensive idea about the research trends, as shown in Table 7 . Majority of the papers describe the clinical data, which included the timeline of the disease, demographics of the patients, risk factor analysis, clinical features, blood and radiological investigations, treatment protocols used, prognostic factors, predictors of mortality, psychological impact and the outcomes.

Table 7

Publications according to the focus of the study.

One article was a consensus of the Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses to name the virus as 2019-nCoV and individual isolates as SARS-CoV-2. 4 studies detailed the diagnostic aspect of the disease. These included proving a diagnostic workflow of the disease, identification of the nCov-19 in body fluids and assessment of viral loads, analysing sensitivity and specificity of the RT-PCR and CT scans in the diagnosis. Twelve studies described the epidemiological characteristics of COVID-19. These studies described the aetiology and source of origin, modes of transmission, incubation period, timeline of the outbreak, epidemiologic curve and doubling time, stability of nCov19 in aerosols and other surfaces, tracking of the disease and geographical distribution of the outbreak. Some of the epidemiological studies focussed on the clinical data as well. The epidemiological data is very beneficial for the authorities to draft public health policies such as quarantine guidelines. Three studies focussed on providing various aspects of pathological findings. Two of them were post-mortem analyses detailing the histopathology of various organs, whereas the other study described the immune pathways and their dysregulation. Six studies carried out a detailed structural analysis of the virus. They provide insights into full-length genome sequencing, cell receptors, pathogenic mechanisms at the cellular level, phylogenetic origin and, antibody testing. This information identifies potential targets for developing diagnostic tests, vaccines, and anti-viral drugs, accelerating the countermeasure development. The remaining seven studies concentrated on therapeutic interventions. Various anti-viral agents were tested and compared to determine their applicability and efficacy. Three of them focussed on the coagulation profile abnormalities and stressed the importance of using anticoagulants in the treatment as the thrombotic phenomenon is associated with a worse prognosis.

Table 8 groups publications into clinical studies involving patients (clinical trials, case Series, Case Reports, RCTs), non-clinical publications (e.g. Correspondence letters, Reviews) and Basic Science studies (Lab studies, Non-human experimental research). Each of these categories have been classified according to the level of evidence in the table. Majority (60%) were clinical studies and the highest citations per article was seen for a clinical study at 2925.

Table 8

Publications grouped as clinical/non clinical and Basic sciences, each category classified according to the level of evidence.

Type of Study (Level of Evidence)	Total Citations	Number of articles	Citations per article


1	1767	1	1767
2	13320	3	4440
3	52037	17	3061
4	20628	9	2292

4	14914	8	1864.25
5	11332	7	1618.86

2	1516	1	1516
3	6570	3	2190
4	1876	1	1876

4. Discussion

As of Feb 9, 2021, the top 50 cited papers were cited 123,960 times on PubMed. There was a study looking into the top 50 cited papers on this subject [ 3 ]. But this was done in May 2020 which was very early during the pandemic. We feel now that sufficient time has passed since onset of the pandemic, (just over a year since the pandemic started), it is an appropriate time for a relook into this topic, especially with reference to citation numbers. ElHawary et al. [ 3 ] reported 63,849 citations for the top 50 cited articles which is about half of what we found about nine months after they studied. They searched Web of science (WOS), Google Scholar and Scopus for their top 50 citations. Pubmed search was not done in their study. They reported that over half of the publications were done in just three journals. Retrospective case series and correspondence/viewpoints formed the bulk of publications at 42% and 26% respectively.

In another study by Yuetian Yu [ 4 ], done in May 2020, scanning WOS database, 3626 publications were identified on this topic. Martinez-Perez et al. [ 5 ] found 14,335 publications between January and July 2020 with 42,374 citations from WOS. Senel et al. [ 6 ] reviewed literature on publications on coronavirus from 1980 to 2019 and found only 13,833 publications with a peak publication year of 2016 having 837 publications. This study may be considered as the baseline level of interest on coronavirus before the current pandemic. We found a total of 99,646 articles before we filtered the top 50 cited articles. Our study looked into some publication metrics of the articles which the previous publications did not include. These included, apart from general bibliometric data, like citations, journal and author data, clinically relevant data like focus of the paper, type of study, level of evidence of the study, speciality, month and week of publication, and country from which it was published. Most studies looked into WOS since citations are readily given in that database whereas for PubMed, it requires to use a different portal to get citation numbers which is not common knowledge.

Since most of the top cited studies we found were from the early stages of the Pandemic, one could expect that retrospective analysis is the type of study that would be the most commonly done as information was still needed to define various aspects of the disease. It could also be expected that lower level evidence studies in the evidence ladder would be done at this stage as higher level studies need greater understanding about the disease before they can be planned. Citations for studies done later take time to increase and catch up.

In a previous publication [ 7 ], we found 6831 total publications in the first 3 months of the pandemic and 1638 in the last week of the study alone from PubMed. This outbreak of Coronavirus has triggered an interest in publications and research that has never been seen on this subject. The publication numbers on Covid-19 have dwarfed those from any other subject during the pandemic. Irmak et al. [ 8 ] did the only study looking into the top 50 cited articles on PubMed in May 2020. They studied citations and co-citations and mapped them using R statistical software and Gephi softwares. Our study is different from theirs. We wanted to look into the top cited papers and analyse metric data as stated above.

Since the Pandemic originated in China, preliminary studies from china were the most cited studies and hence this country topped the citation numbers among countries at 92,276 which is 74.4% of the total number of citations of all the 50 publications 4 . Thirty one of the fifty publications originated from China. The maximum number of these top-cited articles belong to the speciality of Pulmonology. It is not surprising, as the COVID-19 disease is primarily a respiratory disease.

It has been reported in a study that more than 50% of the publications looked into had cited two high profile articles published in high impact factor journals even after the articles were retracted from publication [ 9 ]. A mechanism may need to be put in place to identify and prevent retracted articles from being cited in future studies. This may perhaps be included in the reference manager as a feature and/or included in scanning of manuscripts while submitting in the editorial manager of a journal.

We looked at the PubMed page of each article for the number of comments and errata Table 3 . Fifteen publications (30%) had at least one erratum published and of these, two articles had two errata on them. We are not aware of the average number errata published on PubMed, but 30% in the top 50 cited quality articles appears high. One study reported 19% studies containing errata among 127 studied [ 10 ]. They classified them into trivial, minor and major. Since errata are usually published after a time lag, for a fast evolving pandemic like the Covid-19, studies with major errata which could potentially change the conclusion of the study should be minimized so that further studies do not use any wrong conclusions. Their occurrence could be due to fast tracking of the articles on Covid-19 by most journals which reduces the reviewing times and also the deluge of submissions for publication [ 7 , 11 , 12 ].

We looked at the times related to publishing these articles ( Table 3 ). Not all journals give this data. From the data that was available, most journals appear to have fast tracked the publication process with an average time for submission to accepting at 10.9 days, acceptance to publication at 6.2 days and from submission to publication at just over two weeks (16.3 days).

The total number of comments published were 303 in all 50 publications with an average of 7.97 for each. Thirty eight of the top 50 cited had at least one comment published in PubMed. It indicates the level of interest the pandemic has evoked in the academic circles. It could also be due to free full text availability which encourages more researchers to be involved in the discussions.

All the articles in this study were published as open access and were freely available. Covid-19 publications in most major journals have been fast tracked and published open access for faster dissemination of knowledge and control of the pandemic. This could be one reason why the citation numbers have been so high. Shekhani et al. found open access provided a low magnitude but a significant correlation to high citation rate for manuscripts [ 13 ]. The most cited publication in the top 50, with a citation count of 10,754 in our study was by Huang C et al. [ 1 ].

5. Limitations

Limitations of our study include the fact that this study looked into a single database, namely PubMed. This has not been done in any of the previous studies. Most studies on citations looked into WOS. Although the citation number may be different from database to database, we believe the overall trends may be similar. But we do not have data to support this point. We could not compare with other studies to prove this because they were done at a different point of time.

6. Conclusions

There has been an explosion of publications on this topic and an unprecedented rate of citations within the first year for any condition in the literature. Chinese authors published on COVID-19 maximally, and Pulmonology was the medical speciality on which the articles were written and maximally by the Chinese authors. Majority of the publications focussed on the clinical data of the condition. The high-impact journals published these top-cited articles. The results identify impactful articles on Covid-19, providing a resource to educators while identifying trends that may be used to guide research and publishing efforts.

Declaration of competing interest

There is no conflict of interest to disclose for any of the authors.

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Public Trust in Government: 1958-2024

Public trust in the federal government, which has been low for decades, has increased modestly since 2023 . As of April 2024, 22% of Americans say they trust the government in Washington to do what is right “just about always” (2%) or “most of the time” (21%). Last year, 16% said they trusted the government just about always or most of the time, which was among the lowest measures in nearly seven decades of polling.

Date	.	Individual polls	Moving average
5/19/2024	PEW	22	22
6/11/2023	PEW	16	19
5/01/2022	PEW	20	20
4/11/2021	PEW	24	21
8/2/2020	PEW	20	24
4/12/2020	PEW	27	21
3/25/2019	PEW	17	17
12/04/2017	PEW	18	18
4/11/2017	PEW	20	19
10/04/2015	PEW	19	18
7/20/2014	CNN	14	19
2/26/2014	PEW	24	18
11/15/2013	CBS/NYT	17	20
10/13/2013	PEW	19	19
5/31/2013	CBS/NYT	20	20
2/06/2013	CBS/NYT	20	22
1/13/2013	PEW	26	23
10/31/2012	NES	22	19
10/19/2011	CBS/NYT	10	17
10/04/2011	PEW	20	15
9/23/2011	CNN	15	18
8/21/2011	PEW	19	21
2/28/2011	PEW	29	23
10/21/2010	CBS/NYT	22	23
10/01/2010	CBS/NYT	18	21
9/06/2010	PEW	24	23
9/01/2010	CNN	25	23
4/05/2010	CBS/NYT	20	23
4/05/2010	PEW	25	22
3/21/2010	PEW	22	24
2/12/2010	CNN	26	22
2/05/2010	CBS/NYT	19	21
1/10/2010	GALLUP	19	20
12/20/2009	CNN	20	21
8/31/2009	CBS/NYT	24	22
6/12/2009	CBS/NYT	20	23
12/21/2008	CNN	26	25
10/15/2008	NES	31	24
10/13/2008	CBS/NYT	17	24
7/09/2007	CBS/NYT	24	24
1/09/2007	PEW	31	28
10/08/2006	CBS/NYT	29	29
9/15/2006	CBS/NYT	28	30
2/05/2006	PEW	34	31
1/20/2006	CBS/NYT	32	33
1/06/2006	GALLUP	32	32
12/02/2005	CBS/NYT	32	32
9/11/2005	PEW	31	31
9/09/2005	CBS/NYT	29	30
6/19/2005	GALLUP	30	35
10/15/2004	NES	46	39
7/15/2004	CBS/NYT	40	41
3/21/2004	PEW	36	38
10/26/2003	GALLUP	37	36
7/27/2003	CBS/NYT	36	43
10/15/2002	NES	55	46
9/04/2002	GALLUP	46	46
9/02/2002	CBS/NYT	38	40
7/13/2002	CBS/NYT	38	40
6/17/2002	GALLUP	44	43
1/24/2002	CBS/NYT	46	46
12/07/2001	CBS/NYT	48	49
10/25/2001	CBS/NYT	55	54
10/06/2001	GALLUP	60	49
1/17/2001	CBS/NYT	31	44
10/31/2000	CBS/NYT	40	38
10/15/2000	NES	44	42
7/09/2000	GALLUP	42	39
4/02/2000	ABC/POST	31	38
2/14/2000	PEW	40	34
10/03/1999	CBS/NYT	30	36
9/14/1999	CBS/NYT	38	33
5/16/1999	PEW	31	33
2/21/1999	PEW	31	31
2/12/1999	ABC/POST	32	32
2/04/1999	GALLUP	33	34
1/10/1999	CBS/NYT	37	34
1/03/1999	CBS/NYT	33	37
12/01/1998	NES	40	33
11/15/1998	PEW	26	30
11/01/1998	CBS/NYT	24	26
10/26/1998	CBS/NYT	26	28
8/10/1998	ABC/POST	34	31
2/22/1998	PEW	34	35
2/01/1998	GALLUP	39	33
1/25/1998	CBS/NYT	26	32
1/19/1998	ABC/POST	31	32
10/31/1997	PEW	39	31
8/27/1997	ABC/POST	22	31
6/01/1997	GALLUP	32	26
1/14/1997	CBS/NYT	23	27
11/02/1996	CBS/NYT	25	27
10/15/1996	NES	33	28
5/12/1996	GALLUP	27	31
5/06/1996	ABC/POST	34	29
11/19/1995	ABC/POST	25	27
8/07/1995	GALLUP	22	22
8/05/1995	CBS/NYT	20	21
3/19/1995	ABC/POST	22	20
2/22/1995	CBS/NYT	18	21
12/01/1994	NES	22	21
10/29/1994	CBS/NYT	22	22
10/23/1994	ABC/POST	22	20
6/06/1994	GALLUP	17	19
1/30/1994	GALLUP	19	20
1/20/1994	ABC/POST	24	22
3/24/1993	GALLUP	22	25
1/17/1993	ABC/POST	28	25
1/14/1993	CBS/NYT	24	25
10/23/1992	CBS/NYT	22	25
10/15/1992	NES	29	25
6/08/1992	GALLUP	23	29
10/20/1991	ABC/POST	35	35
3/06/1991	CBS/NYT	47	42
3/01/1991	ABC/POST	45	46
1/27/1991	ABC/POST	46	40
12/01/1990	NES	28	33
10/28/1990	CBS/NYT	25	32
9/06/1990	ABC/POST	42	35
1/16/1990	ABC/POST	38	38
6/29/1989	CBS/NYT	35	39
1/15/1989	CBS/NYT	44	41
11/10/1988	CBS/NYT	44	43
10/15/1988	NES	41	41
1/23/1988	ABC/POST	39	40
10/18/1987	CBS/NYT	41	43
6/01/1987	ABC/POST	47	43
3/01/1987	CBS/NYT	42	44
1/21/1987	CBS/NYT	43	43
1/19/1987	ABC/POST	44	42
12/01/1986	NES	39	44
11/30/1986	CBS/NYT	49	43
9/09/1986	ABC/POST	40	44
1/19/1986	CBS/NYT	42	44
11/06/1985	CBS/NYT	49	43
7/29/1985	ABC/POST	38	42
3/21/1985	ABC/POST	37	40
2/27/1985	CBS/NYT	46	42
2/22/1985	ABC/POST	43	45
11/14/1984	CBS/NYT	46	44
10/15/1984	NES	44	41
12/01/1982	NES	33	39
11/07/1980	CBS/NYT	39	32
10/15/1980	NES	25	30
3/12/1980	CBS/NYT	26	27
11/03/1979	CBS/NYT	30	28
12/01/1978	NES	29	31
10/23/1977	CBS/NYT	33	32
4/25/1977	CBS/NYT	35	34
10/15/1976	NES	33	36
9/05/1976	CBS/NYT	40	35
6/15/1976	CBS/NYT	33	35
3/01/1976	GALLUP	33	34
2/08/1976	CBS/NYT	36	35
12/01/1974	NES	36	36
10/15/1972	NES	53	53
12/01/1970	NES	54	54
10/15/1968	NES	62	62
12/01/1966	NES	65	65
10/15/1964	NES	77	77
12/01/1958	NES	73	73

When the National Election Study began asking about trust in government in 1958, about three-quarters of Americans trusted the federal government to do the right thing almost always or most of the time.

Trust in government began eroding during the 1960s, amid the escalation of the Vietnam War, and the decline continued in the 1970s with the Watergate scandal and worsening economic struggles.

Confidence in government recovered in the mid-1980s before falling again in the mid-’90s. But as the economy grew in the late 1990s, so too did trust in government. Public trust reached a three-decade high shortly after the 9/11 terrorist attacks but declined quickly after. Since 2007, the shares saying they can trust the government always or most of the time have not been higher than 30%.

Today, 35% of Democrats and Democratic-leaning independents say they trust the federal government just about always or most of the time, compared with 11% of Republicans and Republican leaners.

Democrats report slightly more trust in the federal government today than a year ago. Republicans’ views have been relatively unchanged over this period.

Since the 1970s, trust in government has been consistently higher among members of the party that controls the White House than among the opposition party.

Republicans have often been more reactive than Democrats to changes in political leadership, with Republicans expressing much lower levels of trust during Democratic presidencies. Democrats’ attitudes have tended to be somewhat more consistent, regardless of which party controls the White House.

However, Republican and Democratic shifts in attitudes from the end of Donald Trump’s presidency to the start of Joe Biden’s were roughly the same magnitude.

Date	.	Democrat/Lean Dem	Republican/Lean Rep
5/19/2024	PEW	35	11
6/11/2023	PEW	25	8
5/1/2022	PEW	29	9
4/11/2021	PEW	36	9
8/2/2020	PEW	12	28
4/12/2020	PEW	18	36
3/25/2019	PEW	14	21
12/04/2017	PEW	15	22
4/11/2017	PEW	15	28
10/04/2015	PEW	26	11
7/20/2014	CNN	17	11
2/26/2014	PEW	32	16
11/15/2013	CBS/NYT	31	8
10/13/2013	PEW	27	10
5/31/2013	CBS/NYT	30	8
2/06/2013	CBS/NYT	34	8
1/13/2013	PEW	37	15
10/31/2012	NES	29	16
10/19/2011	CBS/NYT	13	8
10/04/2011	PEW	27	12
9/23/2011	CNN	20	11
8/21/2011	PEW	25	13
3/01/2011	PEW	34	24
10/21/2010	CBS/NYT	36	7
10/01/2010	CBS/NYT	27	13
9/06/2010	PEW	35	13
9/01/2010	CNN	31	18
4/05/2010	CBS/NYT	27	14
3/21/2010	PEW	32	13
2/12/2010	CNN	34	18
2/05/2010	CBS/NYT	31	9
1/10/2010	GALLUP	23	16
12/20/2009	CNN	25	16
8/31/2009	CBS/NYT	34	12
6/12/2009	CBS/NYT	35	10
12/21/2008	CNN	30	22
10/15/2008	NES	34	31
10/13/2008	CBS/NYT	12	19
7/09/2007	CBS/NYT	18	31
1/09/2007	PEW	22	43
10/08/2006	CBS/NYT	20	50
9/15/2006	CBS/NYT	20	44
2/05/2006	PEW	20	53
1/20/2006	CBS/NYT	23	51
1/06/2006	GALLUP	20	44
12/02/2005	CBS/NYT	19	52
9/11/2005	PEW	19	49
9/09/2005	CBS/NYT	21	42
6/19/2005	GALLUP	24	36
10/15/2004	NES	35	61
3/21/2004	PEW	24	55
10/26/2003	GALLUP	35	42
7/27/2003	CBS/NYT	25	51
10/15/2002	NES	52	63
9/04/2002	GALLUP	38	55
9/02/2002	CBS/NYT	32	52
7/13/2002	CBS/NYT	34	45
6/17/2002	GALLUP	33	55
1/24/2002	CBS/NYT	39	56
12/07/2001	CBS/NYT	39	60
10/25/2001	CBS/NYT	47	70
10/06/2001	GALLUP	52	68
1/17/2001	CBS/NYT	26	38
10/15/2000	NES	48	43
7/09/2000	GALLUP	42	41
4/02/2000	ABC/POST	38	24
2/14/2000	PEW	46	37
10/03/1999	CBS/NYT	31	27
9/14/1999	CBS/NYT	42	35
5/16/1999	PEW	36	30
2/21/1999	PEW	35	25
2/12/1999	ABC/POST	41	21
2/04/1999	GALLUP	38	29
1/10/1999	CBS/NYT	42	33
1/03/1999	CBS/NYT	37	29
12/01/1998	NES	45	35
11/19/1998	PEW	31	23
11/01/1998	CBS/NYT	28	22
10/26/1998	CBS/NYT	28	25
8/10/1998	ABC/POST	40	30
2/22/1998	PEW	42	28
2/01/1998	GALLUP	52	26
1/25/1998	CBS/NYT	31	22
10/31/1997	PEW	46	32
6/01/1997	GALLUP	39	25
1/14/1997	CBS/NYT	29	20
11/02/1996	CBS/NYT	31	20
10/15/1996	NES	40	27
5/12/1996	GALLUP	32	20
5/06/1996	ABC/POST	41	35
11/19/1995	ABC/POST	27	26
8/07/1995	GALLUP	24	21
8/05/1995	CBS/NYT	20	20
3/19/1995	ABC/POST	27	20
2/22/1995	CBS/NYT	18	19
12/01/1994	NES	26	18
10/29/1994	CBS/NYT	26	19
10/23/1994	ABC/POST	27	16
6/06/1994	GALLUP	23	11
1/30/1994	GALLUP	25	14
1/20/1994	ABC/POST	30	18
3/24/1993	GALLUP	32	11
1/17/1993	ABC/POST	32	25
1/14/1993	CBS/NYT	26	21
10/23/1992	CBS/NYT	17	31
10/15/1992	NES	31	34
6/08/1992	GALLUP	17	31
10/20/1991	ABC/POST	31	41
3/06/1991	CBS/NYT	40	56
3/01/1991	ABC/POST	41	52
12/01/1990	NES	26	32
10/28/1990	CBS/NYT	21	31
9/06/1990	ABC/POST	37	48
1/16/1990	ABC/POST	32	46
6/29/1989	CBS/NYT	27	45
1/15/1989	CBS/NYT	37	54
11/10/1988	CBS/NYT	36	58
10/15/1988	NES	35	51
1/23/1988	ABC/POST	31	51
10/18/1987	CBS/NYT	36	47
6/01/1987	ABC/POST	38	59
3/01/1987	CBS/NYT	34	54
1/21/1987	CBS/NYT	36	51
1/19/1987	ABC/POST	39	51
12/01/1986	NES	31	53
11/30/1986	CBS/NYT	37	63
9/09/1986	ABC/POST	30	51
1/19/1986	CBS/NYT	36	51
11/06/1985	CBS/NYT	42	59
7/29/1985	ABC/POST	30	48
3/21/1985	ABC/POST	29	49
2/22/1985	ABC/POST	30	62
11/14/1984	CBS/NYT	36	59
10/15/1984	NES	41	50
12/01/1982	NES	32	41
11/07/1980	CBS/NYT	40	42
10/15/1980	NES	31	23
3/12/1980	CBS/NYT	30	22
11/03/1979	CBS/NYT	32	28
12/01/1978	NES	33	26
10/23/1977	CBS/NYT	40	25
4/25/1977	CBS/NYT	37	34
10/15/1976	NES	30	42
9/05/1976	CBS/NYT	38	45
6/15/1976	CBS/NYT	36	36
3/01/1976	GALLUP	31	40
12/01/1974	NES	36	38
10/15/1972	NES	48	62
12/01/1970	NES	52	61
10/15/1968	NES	66	60
12/01/1966	NES	71	54
10/15/1964	NES	80	73
12/01/1958	NES	71	79

Date	.	Liberal Dem/Lean Dem	Cons-Moderate Dem/Lean Dem	Moderate-Lib Rep/Lean Rep	Conservative Rep/Lean Rep
5/19/2024	PEW	33	36	17	7
6/11/2023	PEW	23	27	14	4
5/1/2022	PEW	26	32	13	7
4/11/2021	PEW	31	40	16	5
8/2/2020	PEW	8	16	31	27
4/12/2020	PEW	12	22	37	37
3/25/2019	PEW	13	15	21	20
12/04/2017	PEW	15	16	26	20
4/11/2017	PEW	15	16	32	26
10/04/2015	PEW	28	25	14	9
7/20/2014	CNN	19	16	15	7
2/26/2014	PEW	31	33	21	13
11/15/2013	CBS/NYT	38	25	13	5
10/13/2013	PEW	25	27	16	7
5/31/2013	CBS/NYT	30	30	16	4
2/06/2013	CBS/NYT	35	34	9	7
1/13/2013	PEW	34	37	17	14
10/31/2012	NES	26	32	18	15
10/19/2011	CBS/NYT	9	13	11	7
10/04/2011	PEW	30	25	14	9
9/23/2011	CNN	30	16	11	11
8/21/2011	PEW	26	24	18	10
3/01/2011	PEW	36	33	32	18
10/21/2010	CBS/NYT	37	35	12	4
10/01/2010	CBS/NYT	34	22	10	16
9/06/2010	PEW	39	31	19	10
9/01/2010	CNN	36	30	28	11
4/05/2010	CBS/NYT	37	21	23	7
3/21/2010	PEW	36	31	19	11
2/12/2010	CNN	36	34	25	9
2/05/2010	CBS/NYT	31	32	13	7
1/10/2010	GALLUP	29	22	20	12
12/20/2009	CNN	31	23	18	13
8/31/2009	CBS/NYT	38	30	14	10
6/12/2009	CBS/NYT	42	34	13	8
12/21/2008	CNN	36	28	28	17
10/15/2008	NES	37	34	48	28
10/13/2008	CBS/NYT	16	12	26	12
7/09/2007	CBS/NYT	14	21	38	28
1/09/2007	PEW	15	25	41	45
10/08/2006	CBS/NYT	14	22	50	51
9/15/2006	CBS/NYT	11	23	44	44
2/05/2006	PEW	13	23	52	54
1/20/2006	CBS/NYT	27	21	52	50
1/06/2006	GALLUP	10	26	33	56
12/02/2005	CBS/NYT	16	21	60	47
9/11/2005	PEW	13	22	39	54
9/09/2005	CBS/NYT	12	26	46	41
6/19/2005	GALLUP	25	24	31	41
10/15/2004	NES	24	39	63	59
3/21/2004	PEW	23	24	53	56
10/26/2003	GALLUP	23	39	31	52
7/27/2003	CBS/NYT	21	27	55	47
10/15/2002	NES	53	56	66	61
9/04/2002	GALLUP	31	40	50	60
9/02/2002	CBS/NYT	32	32	55	53
7/13/2002	CBS/NYT	37	33	50	42
6/17/2002	GALLUP	30	36	59	55
1/24/2002	CBS/NYT	38	39	58	54
12/07/2001	CBS/NYT	34	43	61	58
10/06/2001	GALLUP	46	55	66	69
1/17/2001	CBS/NYT	33	24	41	33
10/15/2000	NES	58	52	54	44
7/09/2000	GALLUP	41	42	50	35
4/02/2000	ABC/POST	38	39	28	20
10/03/1999	CBS/NYT	26	33	29	24
9/14/1999	CBS/NYT	38	45	42	27
2/12/1999	ABC/POST	40	43	26	16
2/04/1999	GALLUP	36	40	33	27
1/10/1999	CBS/NYT	39	44	40	28
1/03/1999	CBS/NYT	34	39	31	26
12/01/1998	NES	45	46	39	34
11/01/1998	CBS/NYT	28	28	23	22
10/26/1998	CBS/NYT	30	28	22	26
8/10/1998	ABC/POST	38	35	24	27
2/01/1998	GALLUP	55	52	33	23
1/25/1998	CBS/NYT	24	31	24	19
6/01/1997	GALLUP	41	38	31	21
1/14/1997	CBS/NYT	30	28	25	14
11/02/1996	CBS/NYT	30	32	21	19
10/15/1996	NES	38	39	30	25
5/12/1996	GALLUP	25	35	25	18
5/06/1996	ABC/POST	41	41	39	33
11/19/1995	ABC/POST	26	27	26	28
8/07/1995	GALLUP	16	27	17	25
8/05/1995	CBS/NYT	21	19	19	23
3/19/1995	ABC/POST	24	28	22	17
2/22/1995	CBS/NYT	20	18	22	17
12/01/1994	NES	22	28	21	16
10/29/1994	CBS/NYT	26	27	23	15
10/23/1994	ABC/POST	32	25	22	11
6/06/1994	GALLUP	16	26	15	9
1/30/1994	GALLUP	20	27	18	12
1/20/1994	ABC/POST	26	31	25	10
1/17/1993	ABC/POST	30	33	28	22
1/14/1993	CBS/NYT	17	30	20	20
10/23/1992	CBS/NYT	20	15	30	32
10/15/1992	NES	26	33	37	31
6/08/1992	GALLUP	13	19	31	30
10/20/1991	ABC/POST	25	33	42	39
3/06/1991	CBS/NYT	46	39	57	56
3/01/1991	ABC/POST	39	41	54	50
12/01/1990	NES	27	26	31	33
9/06/1990	ABC/POST	34	39	49	45
1/16/1990	ABC/POST	28	34	50	39
6/29/1989	CBS/NYT	27	27	38	55
1/15/1989	CBS/NYT	33	38	56	54
11/10/1988	CBS/NYT	24	40	65	52
10/15/1988	NES	34	35	52	51
1/23/1988	ABC/POST	30	31	54	49
10/18/1987	CBS/NYT	34	37	47	49
6/01/1987	ABC/POST	34	41	60	55
1/21/1987	CBS/NYT	34	37	54	48
1/19/1987	ABC/POST	37	38	52	51
12/01/1986	NES	25	36	53	53
9/09/1986	ABC/POST	25	34	55	44
1/19/1986	CBS/NYT	34	38	51	52
11/06/1985	CBS/NYT	42	43	60	56
7/29/1985	ABC/POST	26	33	53	41
3/21/1985	ABC/POST	27	29	52	48
2/22/1985	ABC/POST	28	33	62	63
10/15/1984	NES	34	47	52	46
12/01/1982	NES	29	35	48	38
11/07/1980	CBS/NYT	38	42	44	41
10/15/1980	NES	34	28	28	18
3/12/1980	CBS/NYT	31	29	25	18
11/03/1979	CBS/NYT	34	31	28	26
12/01/1978	NES	38	33	24	24
10/23/1977	CBS/NYT	41	41	32	16
4/25/1977	CBS/NYT	41	38	33	36
10/15/1976	NES	27	34	49	41
9/05/1976	CBS/NYT	33	42	45	45
6/15/1976	CBS/NYT	35	35	39	34
12/01/1974	NES	36	40	39	40
10/15/1972	NES	44	53	62	66

Among Asian, Hispanic and Black adults, 36%, 30% and 27% respectively say they trust the federal government “most of the time” or “just about always” – higher levels of trust than among White adults (19%).

During the last Democratic administration, Black and Hispanic adults similarly expressed more trust in government than White adults. Throughout most recent Republican administrations, White Americans were substantially more likely than Black Americans to express trust in the federal government to do the right thing.

Date	.	Hispanic	Black	White	Asian
5/19/2024	PEW	30	27	19	36
6/11/2023	PEW	23	21	13	23
5/1/2022	PEW	29	24	16	37
4/11/2021	PEW	36	37	18	29
8/2/2020	PEW	28	15	18	27
4/12/2020	PEW	29	27	26
3/25/2019	PEW	28	9	17
12/04/2017	PEW	23	15	17
4/11/2017	PEW	24	13	20
10/04/2015	PEW	28	23	15
7/20/2014	CNN			9
2/26/2014	PEW	33	26	22
11/15/2013	CBS/NYT			12
10/13/2013	PEW	21	24	17
5/31/2013	CBS/NYT			15
2/06/2013	CBS/NYT		39	15
1/13/2013	PEW	44	38	20
10/31/2012	NES	38	38	16
10/19/2011	CBS/NYT	15	15	8
10/04/2011	PEW	29	25	17
9/23/2011	CNN			10
8/21/2011	PEW	28	35	15
3/01/2011	PEW	28	25	30
10/21/2010	CBS/NYT		40	15
10/01/2010	CBS/NYT			17
9/06/2010	PEW	37	37	20
9/01/2010	CNN			21
4/05/2010	CBS/NYT			18
3/21/2010	PEW	26	37	20
2/12/2010	CNN			22
2/05/2010	CBS/NYT			16
1/10/2010	GALLUP			16
12/20/2009	CNN		21	18
8/31/2009	CBS/NYT			21
6/12/2009	CBS/NYT			16
12/21/2008	CNN			22
10/15/2008	NES	34	28	30
10/13/2008	CBS/NYT			18
7/09/2007	CBS/NYT		11	25
1/09/2007	PEW	35	20	32
10/08/2006	CBS/NYT			31
9/15/2006	CBS/NYT			31
2/05/2006	PEW		26	36
1/20/2006	CBS/NYT		19	34
1/06/2006	GALLUP			33
12/02/2005	CBS/NYT			35
9/11/2005	PEW		12	32
9/09/2005	CBS/NYT		12	29
6/19/2005	GALLUP			32
10/15/2004	NES		34	50
3/21/2004	PEW		17	41
10/26/2003	GALLUP			39
7/27/2003	CBS/NYT		19	37
10/15/2002	NES		41	58
9/04/2002	GALLUP			46
9/02/2002	CBS/NYT			39
7/13/2002	CBS/NYT			39
6/17/2002	GALLUP			48
1/24/2002	CBS/NYT			48
12/07/2001	CBS/NYT			51
10/25/2001	CBS/NYT			60
10/06/2001	GALLUP			61
1/17/2001	CBS/NYT			33
10/15/2000	NES		32	46
7/09/2000	GALLUP			41
4/02/2000	ABC/POST			28
2/14/2000	PEW		36	40
10/03/1999	CBS/NYT			28
9/14/1999	CBS/NYT		30	39
5/16/1999	PEW		28	31
2/21/1999	PEW		32	31
2/12/1999	ABC/POST			32
2/04/1999	GALLUP			33
1/10/1999	CBS/NYT		37	35
1/03/1999	CBS/NYT		39	31
12/01/1998	NES	57	36	38
11/19/1998	PEW		27	26
11/01/1998	CBS/NYT		29	22
10/26/1998	CBS/NYT		26	25
8/10/1998	ABC/POST			33
2/22/1998	PEW		42	33
2/01/1998	GALLUP			36
1/25/1998	CBS/NYT			25
10/31/1997	PEW		39	38
6/01/1997	GALLUP		31	32
1/14/1997	CBS/NYT		15	24
11/02/1996	CBS/NYT	31	30	24
10/15/1996	NES		35	32
5/12/1996	GALLUP			24
5/06/1996	ABC/POST			34
11/19/1995	ABC/POST			26
8/07/1995	GALLUP			22
8/05/1995	CBS/NYT		24	19
3/19/1995	ABC/POST		27	21
2/22/1995	CBS/NYT		20	17
12/01/1994	NES		22	20
10/29/1994	CBS/NYT		16	22
10/23/1994	ABC/POST			21
6/06/1994	GALLUP			15
1/30/1994	GALLUP			17
1/20/1994	ABC/POST		34	21
3/24/1993	GALLUP			20
1/17/1993	ABC/POST		45	25
1/14/1993	CBS/NYT		22	24
10/23/1992	CBS/NYT		21	23
10/15/1992	NES	37	27	28
6/08/1992	GALLUP			23
10/20/1991	ABC/POST		29	36
3/06/1991	CBS/NYT		30	49
3/01/1991	ABC/POST		35	46
12/01/1990	NES	39	22	27
10/28/1990	CBS/NYT		26	25
9/06/1990	ABC/POST		39	43
1/16/1990	ABC/POST		35	38
6/29/1989	CBS/NYT		26	36
1/15/1989	CBS/NYT		33	46
11/10/1988	CBS/NYT		33	45
10/15/1988	NES		25	43
1/23/1988	ABC/POST		29	41
10/18/1987	CBS/NYT		32	41
6/01/1987	ABC/POST		34	49
3/01/1987	CBS/NYT		20	45
1/21/1987	CBS/NYT		27	46
1/19/1987	ABC/POST		31	47
12/01/1986	NES		21	42
11/30/1986	CBS/NYT		23	52
9/09/1986	ABC/POST		26	42
1/19/1986	CBS/NYT		22	45
11/06/1985	CBS/NYT		34	52
7/29/1985	ABC/POST		22	40
3/21/1985	ABC/POST		29	40
2/22/1985	ABC/POST		24	46
10/15/1984	NES		33	46
12/01/1982	NES		26	34
11/07/1980	CBS/NYT		30	40
10/15/1980	NES		26	25
3/12/1980	CBS/NYT		35	24
11/03/1979	CBS/NYT		36	29
12/01/1978	NES		29	29
10/23/1977	CBS/NYT		28	34
4/25/1977	CBS/NYT		34	35
10/15/1976	NES		22	35
6/15/1976	CBS/NYT		35	34
3/01/1976	GALLUP		23	34
12/01/1974	NES		19	38
10/15/1972	NES		32	56
12/01/1970	NES		40	55
10/15/1968	NES		62	61
12/01/1966	NES		65	65
10/15/1964	NES		77	77
12/01/1958	NES		62	74

Note: For full question wording, refer to the topline . White, Black and Asian American adults include those who report being one race and are not Hispanic. Hispanics are of any race. Estimates for Asian adults are representative of English speakers only.

Sources: Pew Research Center, National Election Studies, Gallup, ABC/Washington Post, CBS/New York Times, and CNN Polls. Data from 2020 and later comes from Pew Research Center’s online American Trends Panel; prior data is from telephone surveys. Details about changes in survey mode can be found in this 2020 report . Read more about the Center’s polling methodology . For analysis by party and race/ethnicity, selected datasets were obtained from searches of the iPOLL Databank provided by the Roper Center for Public Opinion Research .

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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Open Access

Peer-reviewed

Research Article

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
https://doi.org/10.1371/journal.pone.0263001
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.

Introduction

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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https://doi.org/10.1371/journal.pone.0263001.t001

The list contains only terms with at least 100 publications in 2020.

https://doi.org/10.1371/journal.pone.0263001.t002

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.

Open access.

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.

COVID-19-relatedness.

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;

Difference-in-differences

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.

Panel regression.

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.

Co-occurrence analysis

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)).

Publication growth

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.

https://doi.org/10.1371/journal.pone.0263001.g001

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.

https://doi.org/10.1371/journal.pone.0263001.g002

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.

Regression results

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.

https://doi.org/10.1371/journal.pone.0263001.t003

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.

https://doi.org/10.1371/journal.pone.0263001.t004

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.

Limitations

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.

Supporting information

S1 table. common trend assumption (cta) regression table..

Full regression table with all controls and interactions.

https://doi.org/10.1371/journal.pone.0263001.s001

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PubMed/NCBI
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15. Hook D, Porter S. The COVID Brain Drain; 2020. https://www.natureindex.com/news-blog/covid-brain-drain-scientific-research .
17. Page L, Brin S, Motwani R, Winograd T. The PageRank citation ranking: Bringing order to the web. Stanford InfoLab; 1999.

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