research paper covid 19

• Research article
• Open access
• 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.

World Health Organization. Timeline - COVID-19: Available at: https://www.who.int/news/item/29-06-2020-covidtimeline . Accessed 1 June 2021.

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available at: https://coronavirus.jhu.edu/map.html . Accessed 1 June 2021.

Anzai A, Kobayashi T, Linton NM, Kinoshita R, Hayashi K, Suzuki A, et al. Assessing the Impact of Reduced Travel on Exportation Dynamics of Novel Coronavirus Infection (COVID-19). J Clin Med. 2020;9(2):601.

Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395–400. https://doi.org/10.1126/science.aba9757 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Fidahic M, Nujic D, Runjic R, Civljak M, Markotic F, Lovric Makaric Z, et al. Research methodology and characteristics of journal articles with original data, preprint articles and registered clinical trial protocols about COVID-19. BMC Med Res Methodol. 2020;20(1):161. https://doi.org/10.1186/s12874-020-01047-2 .

EPPI Centre . COVID-19: a living systematic map of the evidence. Available at: http://eppi.ioe.ac.uk/cms/Projects/DepartmentofHealthandSocialCare/Publishedreviews/COVID-19Livingsystematicmapoftheevidence/tabid/3765/Default.aspx . Accessed 1 June 2021.

NCBI SARS-CoV-2 Resources. Available at: https://www.ncbi.nlm.nih.gov/sars-cov-2/ . Accessed 1 June 2021.

Gustot T. Quality and reproducibility during the COVID-19 pandemic. JHEP Rep. 2020;2(4):100141. https://doi.org/10.1016/j.jhepr.2020.100141 .

Article   PubMed   PubMed Central   Google Scholar  

Kodvanj, I., et al., Publishing of COVID-19 Preprints in Peer-reviewed Journals, Preprinting Trends, Public Discussion and Quality Issues. Preprint article. bioRxiv 2020.11.23.394577; doi: https://doi.org/10.1101/2020.11.23.394577 .

Dobler CC. Poor quality research and clinical practice during COVID-19. Breathe (Sheff). 2020;16(2):200112. https://doi.org/10.1183/20734735.0112-2020 .

Article   Google Scholar  

Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med. 2010;7(9):e1000326. https://doi.org/10.1371/journal.pmed.1000326 .

Lunny C, Brennan SE, McDonald S, McKenzie JE. Toward a comprehensive evidence map of overview of systematic review methods: paper 1-purpose, eligibility, search and data extraction. Syst Rev. 2017;6(1):231. https://doi.org/10.1186/s13643-017-0617-1 .

Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of Reviews. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.1 (updated September 2020). Cochrane. 2020. Available from www.training.cochrane.org/handbook .

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions version 6.1 (updated September 2020). Cochrane. 2020; Available from www.training.cochrane.org/handbook .

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. The impact of different inclusion decisions on the comprehensiveness and complexity of overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):18. https://doi.org/10.1186/s13643-018-0914-3 .

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. A decision tool to help researchers make decisions about including systematic reviews in overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):29. https://doi.org/10.1186/s13643-018-0768-8 .

Hunt H, Pollock A, Campbell P, Estcourt L, Brunton G. An introduction to overviews of reviews: planning a relevant research question and objective for an overview. Syst Rev. 2018;7(1):39. https://doi.org/10.1186/s13643-018-0695-8 .

Pollock M, Fernandes RM, Pieper D, Tricco AC, Gates M, Gates A, et al. Preferred reporting items for overviews of reviews (PRIOR): a protocol for development of a reporting guideline for overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):335. https://doi.org/10.1186/s13643-019-1252-9 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open Med. 2009;3(3):e123–30.

Krnic Martinic M, Pieper D, Glatt A, Puljak L. Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks. BMC Med Res Methodol. 2019;19(1):203. https://doi.org/10.1186/s12874-019-0855-0 .

Puljak L. If there is only one author or only one database was searched, a study should not be called a systematic review. J Clin Epidemiol. 2017;91:4–5. https://doi.org/10.1016/j.jclinepi.2017.08.002 .

Article   PubMed   Google Scholar  

Gates M, Gates A, Guitard S, Pollock M, Hartling L. Guidance for overviews of reviews continues to accumulate, but important challenges remain: a scoping review. Syst Rev. 2020;9(1):254. https://doi.org/10.1186/s13643-020-01509-0 .

Covidence - systematic review software. Available at: https://www.covidence.org/ . Accessed 1 June 2021.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

Borges do Nascimento IJ, et al. Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis. J Clin Med. 2020;9(4):941.

Article   PubMed Central   Google Scholar  

Adhikari SP, Meng S, Wu YJ, Mao YP, Ye RX, Wang QZ, et al. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis Poverty. 2020;9(1):29. https://doi.org/10.1186/s40249-020-00646-x .

Cortegiani A, Ingoglia G, Ippolito M, Giarratano A, Einav S. A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. J Crit Care. 2020;57:279–83. https://doi.org/10.1016/j.jcrc.2020.03.005 .

Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol. 2020;109(5):531–8. https://doi.org/10.1007/s00392-020-01626-9 .

Article   CAS   PubMed   Google Scholar  

Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(6):577–83. https://doi.org/10.1002/jmv.25757 .

Lippi G, Lavie CJ, Sanchis-Gomar F. Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): evidence from a meta-analysis. Prog Cardiovasc Dis. 2020;63(3):390–1. https://doi.org/10.1016/j.pcad.2020.03.001 .

Lippi G, Henry BM. Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19). Eur J Intern Med. 2020;75:107–8. https://doi.org/10.1016/j.ejim.2020.03.014 .

Lippi G, Plebani M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chim Acta. 2020;505:190–1. https://doi.org/10.1016/j.cca.2020.03.004 .

Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta. 2020;506:145–8. https://doi.org/10.1016/j.cca.2020.03.022 .

Ludvigsson JF. Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 2020;109(6):1088–95. https://doi.org/10.1111/apa.15270 .

Lupia T, Scabini S, Mornese Pinna S, di Perri G, de Rosa FG, Corcione S. 2019 novel coronavirus (2019-nCoV) outbreak: a new challenge. J Glob Antimicrob Resist. 2020;21:22–7. https://doi.org/10.1016/j.jgar.2020.02.021 .

Marasinghe, K.M., A systematic review investigating the effectiveness of face mask use in limiting the spread of COVID-19 among medically not diagnosed individuals: shedding light on current recommendations provided to individuals not medically diagnosed with COVID-19. Research Square. Preprint article. doi : https://doi.org/10.21203/rs.3.rs-16701/v1 . 2020 .

Mullins E, Evans D, Viner RM, O’Brien P, Morris E. Coronavirus in pregnancy and delivery: rapid review. Ultrasound Obstet Gynecol. 2020;55(5):586–92. https://doi.org/10.1002/uog.22014 .

Pang J, Wang MX, Ang IYH, Tan SHX, Lewis RF, Chen JIP, et al. Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel coronavirus (2019-nCoV): a systematic review. J Clin Med. 2020;9(3):623.

Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al. Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med Infect Dis. 2020;34:101623. https://doi.org/10.1016/j.tmaid.2020.101623 .

Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. AJR Am J Roentgenol. 2020;215(1):87–93. https://doi.org/10.2214/AJR.20.23034 .

Sun P, Qie S, Liu Z, Ren J, Li K, Xi J. Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: a single arm meta-analysis. J Med Virol. 2020;92(6):612–7. https://doi.org/10.1002/jmv.25735 .

Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91–5. https://doi.org/10.1016/j.ijid.2020.03.017 .

Bassetti M, Vena A, Giacobbe DR. The novel Chinese coronavirus (2019-nCoV) infections: challenges for fighting the storm. Eur J Clin Investig. 2020;50(3):e13209. https://doi.org/10.1111/eci.13209 .

Article   CAS   Google Scholar  

Hwang CS. Olfactory neuropathy in severe acute respiratory syndrome: report of a case. Acta Neurol Taiwanica. 2006;15(1):26–8.

Google Scholar  

Suzuki M, Saito K, Min WP, Vladau C, Toida K, Itoh H, et al. Identification of viruses in patients with postviral olfactory dysfunction. Laryngoscope. 2007;117(2):272–7. https://doi.org/10.1097/01.mlg.0000249922.37381.1e .

Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC. The many estimates of the COVID-19 case fatality rate. Lancet Infect Dis. 2020;20(7):776–7. https://doi.org/10.1016/S1473-3099(20)30244-9 .

Wolkewitz M, Puljak L. Methodological challenges of analysing COVID-19 data during the pandemic. BMC Med Res Methodol. 2020;20(1):81. https://doi.org/10.1186/s12874-020-00972-6 .

Rombey T, Lochner V, Puljak L, Könsgen N, Mathes T, Pieper D. Epidemiology and reporting characteristics of non-Cochrane updates of systematic reviews: a cross-sectional study. Res Synth Methods. 2020;11(3):471–83. https://doi.org/10.1002/jrsm.1409 .

Runjic E, Rombey T, Pieper D, Puljak L. Half of systematic reviews about pain registered in PROSPERO were not published and the majority had inaccurate status. J Clin Epidemiol. 2019;116:114–21. https://doi.org/10.1016/j.jclinepi.2019.08.010 .

Runjic E, Behmen D, Pieper D, Mathes T, Tricco AC, Moher D, et al. Following Cochrane review protocols to completion 10 years later: a retrospective cohort study and author survey. J Clin Epidemiol. 2019;111:41–8. https://doi.org/10.1016/j.jclinepi.2019.03.006 .

Tricco AC, Antony J, Zarin W, Strifler L, Ghassemi M, Ivory J, et al. A scoping review of rapid review methods. BMC Med. 2015;13(1):224. https://doi.org/10.1186/s12916-015-0465-6 .

COVID-19 Rapid Reviews: Cochrane’s response so far. Available at: https://training.cochrane.org/resource/covid-19-rapid-reviews-cochrane-response-so-far . Accessed 1 June 2021.

Cochrane. Living systematic reviews. Available at: https://community.cochrane.org/review-production/production-resources/living-systematic-reviews . Accessed 1 June 2021.

Millard T, Synnot A, Elliott J, Green S, McDonald S, Turner T. Feasibility and acceptability of living systematic reviews: results from a mixed-methods evaluation. Syst Rev. 2019;8(1):325. https://doi.org/10.1186/s13643-019-1248-5 .

Babic A, Poklepovic Pericic T, Pieper D, Puljak L. How to decide whether a systematic review is stable and not in need of updating: analysis of Cochrane reviews. Res Synth Methods. 2020;11(6):884–90. https://doi.org/10.1002/jrsm.1451 .

Lovato A, Rossettini G, de Filippis C. Sore throat in COVID-19: comment on “clinical characteristics of hospitalized patients with SARS-CoV-2 infection: a single arm meta-analysis”. J Med Virol. 2020;92(7):714–5. https://doi.org/10.1002/jmv.25815 .

Leung C. Comment on Li et al: COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(9):1431–2. https://doi.org/10.1002/jmv.25912 .

Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. Response to Char’s comment: comment on Li et al: COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(9):1433. https://doi.org/10.1002/jmv.25924 .

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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

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

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Thilo Caspar von Groote

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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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Virology, transmission, and pathogenesis of SARS-CoV-2

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What you need to know

SARS-CoV-2 is genetically similar to SARS-CoV-1, but characteristics of SARS-CoV-2—eg, structural differences in its surface proteins and viral load kinetics—may help explain its enhanced rate of transmission

In the respiratory tract, peak SARS-CoV-2 load is observed at the time of symptom onset or in the first week of illness, with subsequent decline thereafter, indicating the highest infectiousness potential just before or within the first five days of symptom onset

Reverse transcription polymerase chain reaction (RT-PCR) tests can detect viral SARS-CoV-2 RNA in the upper respiratory tract for a mean of 17 days; however, detection of viral RNA does not necessarily equate to infectiousness, and viral culture from PCR positive upper respiratory tract samples has been rarely positive beyond nine days of illness

Symptomatic and pre-symptomatic transmission (1-2 days before symptom onset), is likely to play a greater role in the spread of SARS-CoV-2 than asymptomatic transmission

A wide range of virus-neutralising antibodies have been reported, and emerging evidence suggests that these may correlate with severity of illness but wane over time

Since the emergence of SARS-CoV-2 in December 2019, there has been an unparalleled global effort to characterise the virus and the clinical course of disease. Coronavirus disease 2019 (covid-19), caused by SARS-CoV-2, follows a biphasic pattern of illness that likely results from the combination of an early viral response phase and an inflammatory second phase. Most clinical presentations are mild, and the typical pattern of covid-19 more resembles an influenza-like illness—which includes fever, cough, malaise, myalgia, headache, and taste and smell disturbance—rather than severe pneumonia (although emerging evidence about long term consequences is yet to be understood in detail). 1 In this review, we provide a broad update on the emerging understanding of SARS-CoV-2 pathophysiology, including virology, transmission dynamics, and the immune response to the virus. Any of the mechanisms and assumptions discussed in the article and in our understanding of covid-19 may be revised as further evidence emerges.

What we know about the virus

SARS-CoV-2 is an enveloped β-coronavirus, with a genetic sequence very similar to SARS-CoV-1 (80%) and bat coronavirus RaTG13 (96.2%). 2 The viral envelope is coated by spike (S) glycoprotein, envelope (E), and membrane (M) proteins ( fig 1 ). Host cell binding and entry are mediated by the S protein. The first step in infection is virus binding to a host cell through its target receptor. The S1 sub-unit of the S protein contains the receptor binding domain that binds to the peptidase domain of angiotensin-converting enzyme 2 (ACE 2). In SARS-CoV-2 the S2 sub-unit is highly preserved and is considered a potential antiviral target. The virus structure and replication cycle are described in figure 1 .

(1) The virus binds to ACE 2 as the host target cell receptor in synergy with the host’s transmembrane serine protease 2 (cell surface protein), which is principally expressed in the airway epithelial cells and vascular endothelial cells. This leads to membrane fusion and releases the viral genome into the host cytoplasm (2). Stages (3-7) show the remaining steps of viral replication, leading to viral assembly, maturation, and virus release

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Coronaviruses have the capacity for proofreading during replication, and therefore mutation rates are lower than in other RNA viruses. As SARS-CoV-2 has spread globally it has, like other viruses, accumulated some mutations in the viral genome, which contains geographic signatures. Researchers have examined these mutations to study virus characterisation and understand epidemiology and transmission patterns. In general, the mutations have not been attributed to phenotypic changes affecting viral transmissibility or pathogenicity. The G614 variant in the S protein has been postulated to increase infectivity and transmissibility of the virus. 3 Higher viral loads were reported in clinical samples with virus containing G614 than previously circulating variant D614, although no association was made with severity of illness as measured by hospitalisation outcomes. 3 These findings have yet to be confirmed with regards to natural infection.

Why is SARS-CoV-2 more infectious than SARS-CoV-1?

SARS-CoV-2 has a higher reproductive number (R 0 ) than SARS-CoV-1, indicating much more efficient spread. 1 Several characteristics of SARS-CoV-2 may help explain this enhanced transmission. While both SARS-CoV-1 and SARS-CoV-2 preferentially interact with the angiotensin-converting enzyme 2 (ACE 2) receptor, SARS-CoV-2 has structural differences in its surface proteins that enable stronger binding to the ACE 2 receptor 4 and greater efficiency at invading host cells. 1 SARS-CoV-2 also has greater affinity (or bonding) for the upper respiratory tract and conjunctiva, 5 thus can infect the upper respiratory tract and can conduct airways more easily. 6

Viral load dynamics and duration of infectiousness

Viral load kinetics could also explain some of the differences between SARS-CoV-2 and SARS-CoV-1. In the respiratory tract, peak SARS-CoV-2 load is observed at the time of symptom onset or in the first week of illness, with subsequent decline thereafter, which indicates the highest infectiousness potential just before or within the first five days of symptom onset ( fig 2 ). 7 In contrast, in SARS-CoV-1 the highest viral loads were detected in the upper respiratory tract in the second week of illness, which explains its minimal contagiousness in the first week after symptom onset, enabling early case detection in the community. 7

After the initial exposure, patients typically develop symptoms within 5-6 days (incubation period). SARS-CoV-2 generates a diverse range of clinical manifestations, ranging from mild infection to severe disease accompanied by high mortality. In patients with mild infection, initial host immune response is capable of controlling the infection. In severe disease, excessive immune response leads to organ damage, intensive care admission, or death. The viral load peaks in the first week of infection, declines thereafter gradually, while the antibody response gradually increases and is often detectable by day 14 (figure adapted with permission from https://www.sciencedirect.com/science/article/pii/S009286742030475X ; https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30230-7/fulltext )

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) technology can detect viral SARS-CoV-2 RNA in the upper respiratory tract for a mean of 17 days (maximum 83 days) after symptom onset. 7 However, detection of viral RNA by qRT-PCR does not necessarily equate to infectiousness, and viral culture from PCR positive upper respiratory tract samples has been rarely positive beyond nine days of illness. 5 This corresponds to what is known about transmission based on contact tracing studies, which is that transmission capacity is maximal in the first week of illness, and that transmission after this period has not been documented. 8 Severely ill or immune-compromised patients may have relatively prolonged virus shedding, and some patients may have intermittent RNA shedding; however, low level results close to the detection limit may not constitute infectious viral particles. While asymptomatic individuals (those with no symptoms throughout the infection) can transmit the infection, their relative degree of infectiousness seems to be limited. 9 10 11 People with mild symptoms (paucisymptomatic) and those whose symptom have not yet appeared still carry large amounts of virus in the upper respiratory tract, which might contribute to the easy and rapid spread of SARS-CoV-2. 7 Symptomatic and pre-symptomatic transmission (one to two days before symptom onset) is likely to play a greater role in the spread of SARS-CoV-2. 10 12 A combination of preventive measures, such as physical distancing and testing, tracing, and self-isolation, continue to be needed.

Route of transmission and transmission dynamics

Like other coronaviruses, the primary mechanism of transmission of SARS-CoV-2 is via infected respiratory droplets, with viral infection occurring by direct or indirect contact with nasal, conjunctival, or oral mucosa, when respiratory particles are inhaled or deposited on these mucous membranes. 6 Target host receptors are found mainly in the human respiratory tract epithelium, including the oropharynx and upper airway. The conjunctiva and gastrointestinal tracts are also susceptible to infection and may serve as transmission portals. 6

Transmission risk depends on factors such as contact pattern, environment, infectiousness of the host, and socioeconomic factors, as described elsewhere. 12 Most transmission occurs through close range contact (such as 15 minutes face to face and within 2 m), 13 and spread is especially efficient within households and through gatherings of family and friends. 12 Household secondary attack rates (the proportion of susceptible individuals who become infected within a group of susceptible contacts with a primary case) ranges from 4% to 35%. 12 Sleeping in the same room as, or being a spouse of an infected individual increases the risk of infection, but isolation of the infected person away from the family is related to lower risk of infection. 12 Other activities identified as high risk include dining in close proximity with the infected person, sharing food, and taking part in group activities 12 The risk of infection substantially increases in enclosed environments compared with outdoor settings. 12 For example, a systematic review of transmission clusters found that most superspreading events occurred indoors. 11 Aerosol transmission can still factor during prolonged stay in crowded, poorly ventilated indoor settings (meaning transmission could occur at a distance >2 m). 12 14 15 16 17

The role of faecal shedding in SARS-CoV-2 transmission and the extent of fomite (through inanimate surfaces) transmission also remain to be fully understood. Both SARS-CoV-2 and SARS-CoV-1 remain viable for many days on smooth surfaces (stainless steel, plastic, glass) and at lower temperature and humidity (eg, air conditioned environments). 18 19 Thus, transferring infection from contaminated surfaces to the mucosa of eyes, nose, and mouth via unwashed hands is a possible route of transmission. This route of transmission may contribute especially in facilities with communal areas, with increased likelihood of environmental contamination. However, both SARS-CoV-1 and SARS-CoV-2 are readily inactivated by commonly used disinfectants, emphasising the potential value of surface cleaning and handwashing. SARS-CoV-2 RNA has been found in stool samples and RNA shedding often persists for longer than in respiratory samples 7 ; however, virus isolation has rarely been successful from the stool. 5 7 No published reports describe faecal-oral transmission. In SARS-CoV-1, faecal-oral transmission was not considered to occur in most circumstances; but, one explosive outbreak was attributed to aerosolisation and spread of the virus across an apartment block via a faulty sewage system. 20 It remains to be seen if similar transmission may occur with SARS-CoV-2.

Pathogenesis

Viral entry and interaction with target cells.

SARS-CoV-2 binds to ACE 2, the host target cell receptor. 1 Active replication and release of the virus in the lung cells lead to non-specific symptoms such as fever, myalgia, headache, and respiratory symptoms. 1 In an experimental hamster model, the virus causes transient damage to the cells in the olfactory epithelium, leading to olfactory dysfunction, which may explain temporary loss of taste and smell commonly seen in covid-19. 21 The distribution of ACE 2 receptors in different tissues may explain the sites of infection and patient symptoms. For example, the ACE 2 receptor is found on the epithelium of other organs such as the intestine and endothelial cells in the kidney and blood vessels, which may explain gastrointestinal symptoms and cardiovascular complications. 22 Lymphocytic endotheliitis has been observed in postmortem pathology examination of the lung, heart, kidney, and liver as well as liver cell necrosis and myocardial infarction in patients who died of covid-19. 1 23 These findings indicate that the virus directly affects many organs, as was seen in SARS-CoV-1 and influenzae.

Much remains unknown. Are the pathological changes in the respiratory tract or endothelial dysfunction the result of direct viral infection, cytokine dysregulation, coagulopathy, or are they multifactorial? And does direct viral invasion or coagulopathy directly contribute to some of the ischaemic complications such as ischaemic infarcts? These and more, will require further work to elucidate.

Immune response and disease spectrum ( figure 2 )

After viral entry, the initial inflammatory response attracts virus-specific T cells to the site of infection, where the infected cells are eliminated before the virus spreads, leading to recovery in most people. 24 In patients who develop severe disease, SARS-CoV-2 elicits an aberrant host immune response. 24 25 For example, postmortem histology of lung tissues of patients who died of covid-19 have confirmed the inflammatory nature of the injury, with features of bilateral diffuse alveolar damage, hyaline-membrane formation, interstitial mononuclear inflammatory infiltrates, and desquamation consistent with acute respiratory distress syndrome (ARDS), and is similar to the lung pathology seen in severe Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). 26 27 A distinctive feature of covid-19 is the presence of mucus plugs with fibrinous exudate in the respiratory tract, which may explain the severity of covid-19 even in young adults. 28 This is potentially caused by the overproduction of pro-inflammatory cytokines that accumulate in the lungs, eventually damaging the lung parenchyma. 24

Some patients also experience septic shock and multi-organ dysfunction. 24 For example, the cardiovascular system is often involved early in covid-19 disease and is reflected in the release of highly sensitive troponin and natriuretic peptides. 29 Consistent with the clinical context of coagulopathy, focal intra-alveolar haemorrhage and presence of platelet-fibrin thrombi in small arterial vessels is also seen. 27 Cytokines normally mediate and regulate immunity, inflammation, and haematopoiesis; however, further exacerbation of immune reaction and accumulation of cytokines in other organs in some patients may cause extensive tissue damage, or a cytokine release syndrome (cytokine storm), resulting in capillary leak, thrombus formation, and organ dysfunction. 24 30

Mechanisms underlying the diverse clinical outcomes

Clinical outcomes are influenced by host factors such as older age, male sex, and underlying medical conditions, 1 as well as factors related to the virus (such as viral load kinetics), host-immune response, and potential cross-reactive immune memory from previous exposure to seasonal coronaviruses ( box 1 ).

Risk factors associated with the development of severe disease, admission to intensive care unit, and mortality

Underlying condition.

Hypertension

Cardiovascular disease

Chronic obstructive pulmonary disease

Presentation

Higher fever (≥39°C on admission)

Dyspnoea on admission

Higher qSOFA score

Laboratory markers

Neutrophilia/lymphopenia

Raised lactate and lactate dehydrogenase

Raised C reactive protein

Raised ferritin

Raised IL-6

Raised ACE2

D-dimer >1 μg/mL

Sex-related differences in immune response have been reported, revealing that men had higher plasma innate immune cytokines and chemokines at baseline than women. 31 In contrast, women had notably more robust T cell activation than men, and among male participants T cell activation declined with age, which was sustained among female patients. These findings suggest that adaptive immune response may be important in defining the clinical outcome as older age and male sex is associated with increased risk of severe disease and mortality.

Increased levels of pro-inflammatory cytokines correlate with severe pneumonia and increased ground glass opacities within the lungs. 30 32 In people with severe illness, increased plasma concentrations of inflammatory cytokines and biomarkers were observed compared with people with non-severe illness. 30 33 34

Emerging evidence suggests a correlation between viral dynamics, the severity of illness, and disease outcome. 7 Longitudinal characteristics of immune response show a correlation between the severity of illness, viral load, and IFN- α, IFN-γ, and TNF-α response. 34 In the same study many interferons, cytokines, and chemokines were elevated early in disease for patients who had severe disease and higher viral loads. This emphasises that viral load may drive these cytokines and the possible pathological roles associated with the host defence factors. This is in keeping with the pathogenesis of influenza, SARS, and MERS whereby prolonged viral shedding was also associated with severity of illness. 7 35

Given the substantial role of the immune response in determining clinical outcomes, several immunosuppressive therapies aimed at limiting immune-mediated damage are currently in various phases of development ( table 1 ).

Therapeutics currently under investigation

• View inline

Immune response to the virus and its role in protection

Covid-19 leads to an antibody response to a range of viral proteins, but the spike (S) protein and nucleocapsid are those most often used in serological diagnosis. Few antibodies are detectable in the first four days of illness, but patients progressively develop them, with most achieving a detectable response after four weeks. 36 A wide range of virus-neutralising antibodies have been reported, and emerging evidence suggests that these may correlate with severity but wane over time. 37 The duration and protectivity of antibody and T cell responses remain to be defined through studies with longer follow-up. CD-4 T cell responses to endemic human coronaviruses appear to manifest cross-reactivity with SARS-CoV-2, but their role in protection remains unclear. 38

Unanswered questions

Further understanding of the pathogenesis for SARS-CoV-2 will be vital in developing therapeutics, vaccines, and supportive care modalities in the treatment of covid-19. More data are needed to understand the determinants of healthy versus dysfunctional response and immune markers for protection and the severity of disease. Neutralising antibodies are potential correlates of protection, but other protective antibody mechanisms may exist. Similarly, the protective role of T cell immunity and duration of both antibody and T cell responses and the correlates of protection need to be defined. In addition, we need optimal testing systems and technologies to support and inform early detection and clinical management of infection. Greater understanding is needed regarding the long term consequences following acute illness and multisystem inflammatory disease, especially in children.

Education into practice

How would you describe SARS-CoV-2 transmission routes and ways to prevent infection?

How would you describe to a patient why cough, anosmia, and fever occur in covid-19?

Questions for future research

What is the role of the cytokine storm and how could it inform the development of therapeutics, vaccines, and supportive care modalities?

What is the window period when patients are most infectious?

Why do some patients develop severe disease while others, especially children, remain mildly symptomatic or do not develop symptoms?

What are the determinants of healthy versus dysfunctional response, and the biomarkers to define immune correlates of protection and disease severity for the effective triage of patients?

What is the protective role of T cell immunity and duration of both antibody and T cell responses, and how would you define the correlates of protection?

How patients were involved in the creation of this article

No patients were directly involved in the creation of this article.

How this article was created

We searched PubMed from 2000 to 18 September 2020, limited to publications in English. Our search strategy used a combination of key words including “COVID-19,” “SARS-CoV-2,” “SARS”, “MERS,” “Coronavirus,” “Novel Coronavirus,” “Pathogenesis,” “Transmission,” “Cytokine Release,” “immune response,” “antibody response.” These sources were supplemented with systematic reviews. We also reviewed technical documents produced by the Centers for Disease Control and Prevention and World Health Organization technical documents.

Author contributions: MC, KK, JK, MP drafted the first and subsequent versions of the manuscript and all authors provided critical feedback and contributed to the manuscript.

Competing interests The BMJ has judged that there are no disqualifying financial ties to commercial companies. The authors declare the following other interests: none.

Further details of The BMJ policy on financial interests are here: https://www.bmj.com/about-bmj/resources-authors/forms-policies-and-checklists/declaration-competing-interests

Provenance and peer review: commissioned; externally peer reviewed.

This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

• Bamford CGG ,
• Fischer WM ,
• Gnanakaran S ,
• Sheffield COVID-19 Genomics Group
• Corbett KS ,
• Corman VM ,
• Guggemos W ,
• Cheung MC ,
• Perera RAPM ,
• Cheng H-Y ,
• Taiwan COVID-19 Outbreak Investigation Team
• Meyerowitz E ,
• Richterman A ,
• Nergiz AI ,
• Maraolo AE ,
• Buitrago-Garcia D ,
• Egli-Gany D ,
• Counotte MJ ,
• ↵ European Centre for Disease Prevention and Control. Surveillance definitions for COVID-19. 2020. https://www.ecdc.europa.eu/en/covid-19/surveillance/surveillance-definitions .
• Klompas M ,
• ↵ Centers for Disease Control and Prevention. How COVID-19 spreads. 2020. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html
• ↵ Centers for Disease Control and Prevention. Scientific brief: SARS-CoV-2 and potential airborne transmission. 2020. https://www.cdc.gov/coronavirus/2019-ncov/more/scientific-brief-sars-cov-2.html
• Perera MRA ,
• van Doremalen N ,
• Bushmaker T ,
• Morris DH ,
• Monteil V ,
• Flammer AJ ,
• Steiger P ,
• Mangalmurti N ,
• Blanco-Melo D ,
• Nilsson-Payant BE ,
• Carsana L ,
• Sonzogni A ,
• ↵ Takahashi T, Ellingson MK, Wong P, et al. Sex differences in immune responses that underlie COVID-19 disease outcomes. Nature 2020. https://pubmed.ncbi.nlm.nih.gov/32846427/
• Yale IMPACT Team
• CAP-China Network
• Perera RA ,
• Merrick B ,
• Grifoni A ,
• Weiskopf D ,
• Ramirez SI ,

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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.

Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

PubMed Disclaimer

• 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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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

• View Article
• Google Scholar
• PubMed/NCBI
• 8. Brown A, Edwin E, Fagg J. Evaluate Pharma 2021 Preview; 2020. https://www.evaluate.com/thought-leadership/vantage/evaluate-vantage-2021-preview .
• 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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• NATURE PODCAST
• 17 December 2020

Coronapod: The big COVID research papers of 2020

• Benjamin Thompson ,
• Noah Baker &
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Benjamin Thompson, Noah Baker and Traci Watson discuss some of 2020's most significant coronavirus research papers.

In the final Coronapod of 2020, we dive into the scientific literature to reflect on the COVID-19 pandemic. Researchers have discovered so much about SARS-CoV-2 – information that has been vital for public health responses and the rapid development of effective vaccines. But we also look forward to 2021, and the critical questions that remain to be answered about the pandemic.

Papers discussed

A Novel Coronavirus from Patients with Pneumonia in China, 2019 - New England Journal of Medicine, 24 January

Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China - The Lancet , 24 January

A pneumonia outbreak associated with a new coronavirus of probable bat origin - Nature , 3 February

A new coronavirus associated with human respiratory disease in China - Nature , 3 February

Temporal dynamics in viral shedding and transmissibility of COVID-19 - Nature Medicine , 15 April

Spread of SARS-CoV-2 in the Icelandic Population - New England Journal of Medicine , 11 June

High SARS-CoV-2 Attack Rate Following Exposure at a Choir Practice — Skagit County, Washington, March 2020 - Morbidity & Mortality Weekly Report , 15 August

Respiratory virus shedding in exhaled breath and efficacy of face masks - Nature Medicine , 3 April

Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1 - New England Journal of Medicine , 13 April

Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period - Science , 22 May

Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe - Nature, 8 June

The effect of large-scale anti-contagion policies on the COVID-19 pandemic - Nature , 8 June

Retraction—Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis - The Lancet, 20 June

A Randomized Trial of Hydroxychloroquine as Postexposure Prophylaxis for Covid-19 - New England Journal of Medicine , 3 June

Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19 - JAMA , 2 September

Immunological memory to SARS-CoV-2 assessed for greater than six months after infection - bioRxiv, 16 November

Coronavirus Disease 2019 (COVID-19) Re-infection by a Phylogenetically Distinct Severe Acute Respiratory Syndrome Coronavirus 2 Strain Confirmed by Whole Genome Sequencing - Clinical Infectious Diseases , 25 August

Nature’s COVID research updates – summarising key coronavirus papers as they appear

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doi: https://doi.org/10.1038/d41586-020-03609-2

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eTable 1. Summary of covariate definitions

eTable 2. Summary of cohort characteristics

eTable 3. Medical history of people in the pre-vaccine availability, vaccinated and unvaccinated cohorts

eTable 4. Mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, by prior history of the outcome

eTable 5. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts

eTable 6. Maximally adjusted hazard ratios and 95% CIs for mental illness events following hospitalised COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts

eTable 7. Maximally adjusted hazard ratios and 95% Cis for mental illness events following non-hospitalised COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts

eTable 8. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, in people with no prior history of the outcome

eTable 9. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, in people with prior history of the outcome, more than six months ago

eTable 10. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, in people with prior history of the outcome, within six months

eTable 11. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, in people with history of COVID-19

eTable 12. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for age group 18-39

eTable 13. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for age group 40-59

eTable 14. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for age group 60-79

eTable 15. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for age group 80-110

eTable 16. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for women

eTable 17. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for men

eTable 18. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for White ethnicity

eTable 19. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for South Asian ethnicity

eTable 20. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for Black ethnicity

eTable 21. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for Other ethnicity

eTable 22. Maximally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts for Mixed ethnicity

eTable 23. Excess events per 100,000 people at 28 weeks post-COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts

eFigure 1. COVID-19 cases over time

eFigure 2. Diagram showing cohort construction

eFigure 3. Venn Diagram showing cohort overlap

eFigure 4. Maximally and minimally adjusted hazard ratios and 95% CIs for mental illness events following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts. Events on the day of COVID-19 diagnosis (day 0) were excluded

eFigure 5. Maximally adjusted hazard ratios and 95% CIs for depression and serious mental illness following diagnosis of COVID-19 in the vaccinated and unvaccinated cohorts, by history of COVID-19. Events on the day of COVID-19 diagnosis (day 0) were excluded

eFigure 6. Maximally adjusted hazard ratios and 95% CIs for depression and serious mental illness following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, by age group. Events on the day of COVID-19 diagnosis (day 0) were excluded

eFigure 7. Maximally adjusted hazard ratios and 95% CIs for depression and serious mental illness following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, by sex. Events on the day of COVID-19 diagnosis (day 0) were excluded

eFigure 8. Maximally adjusted hazard ratios and 95% CIs for depression and serious mental illness following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts, by ethnicity. Events on the day of COVID-19 diagnosis (day 0) were excluded

eFigure 9. Absolute excess risk up to 28 weeks for depression and serious mental illness following diagnosis of COVID-19 in the pre-vaccine availability, vaccinated and unvaccinated cohorts

Members of the Longitudinal Health and Wellbeing COVID-19 National Core Study

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Walker VM , Patalay P , Cuitun Coronado JI, et al. COVID-19 and Mental Illnesses in Vaccinated and Unvaccinated People. JAMA Psychiatry. Published online August 21, 2024. doi:10.1001/jamapsychiatry.2024.2339

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COVID-19 and Mental Illnesses in Vaccinated and Unvaccinated People

• 1 Population Health Sciences, University of Bristol, Bristol, United Kingdom
• 2 Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
• 3 Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia
• 4 Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, United Kingdom
• 5 Centre for Longitudinal Studies, University College London, London, United Kingdom
• 6 National Institute for Health and Care Research, Bristol Biomedical Research Centre, Bristol, United Kingdom
• 7 Health Data Research UK South-West, Bristol, United Kingdom
• 8 Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
• 9 The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
• 10 Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
• 11 School of Psychology, University of Sussex, Falmer, United Kingdom
• 12 British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
• 13 Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
• 14 Centre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, United Kingdom
• 15 The National Institute for Health and Care Research Applied Research Collaboration West at University Hospitals Bristol and Weston, United Kingdom
• 16 British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
• 17 National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, United Kingdom
• 18 Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
• 19 Cambridge Centre of Artificial Intelligence in Medicine, Cambridge, United Kingdom
• 20 Swansea University Medical School, University of Swansea, Swansea, United Kingdom

Question   What are the associations between mental illnesses and diagnosed COVID-19 by vaccination status in patients hospitalized for COVID-19 and the general population?

Findings   In this cohort study, depression, serious mental illness, general anxiety, posttraumatic stress disorder, eating disorders, addiction, self-harm, and suicide were elevated during weeks 1 through 4 after COVID-19 diagnosis compared with before or without COVID-19. Incidence was lower in people who were vaccinated when they had COVID-19 and incidence was higher, and persisted longer, after hospitalization for COVID-19.

Meaning   The findings support recommendation of COVID-19 vaccination in the general population and particularly among those with mental illness, who may be at higher risk of both SARS-CoV-2 infection and adverse outcomes following COVID-19.

Importance   Associations have been found between COVID-19 and subsequent mental illness in both hospital- and population-based studies. However, evidence regarding which mental illnesses are associated with COVID-19 by vaccination status in these populations is limited.

Objective   To determine which mental illnesses are associated with diagnosed COVID-19 by vaccination status in both hospitalized patients and the general population.

Design, Setting, and Participants   This study was conducted in 3 cohorts, 1 before vaccine availability followed during the wild-type/Alpha variant eras (January 2020-June 2021) and 2 (vaccinated and unvaccinated) during the Delta variant era (June-December 2021). With National Health Service England approval, OpenSAFELY-TPP was used to access linked data from 24 million people registered with general practices in England using TPP SystmOne. People registered with a GP in England for at least 6 months and alive with known age between 18 and 110 years, sex, deprivation index information, and region at baseline were included. People were excluded if they had COVID-19 before baseline. Data were analyzed from July 2022 to June 2024.

Exposure   Confirmed COVID-19 diagnosis recorded in primary care secondary care, testing data, or the death registry.

Main Outcomes and Measures   Adjusted hazard ratios (aHRs) comparing the incidence of mental illnesses after diagnosis of COVID-19 with the incidence before or without COVID-19 for depression, serious mental illness, general anxiety, posttraumatic stress disorder, eating disorders, addiction, self-harm, and suicide.

Results   The largest cohort, the pre–vaccine availability cohort, included 18 648 606 people (9 363 710 [50.2%] female and 9 284 896 [49.8%] male) with a median (IQR) age of 49 (34-64) years. The vaccinated cohort included 14 035 286 individuals (7 308 556 [52.1%] female and 6 726 730 [47.9%] male) with a median (IQR) age of 53 (38-67) years. The unvaccinated cohort included 3 242 215 individuals (1 363 401 [42.1%] female and 1 878 814 [57.9%] male) with a median (IQR) age of 35 (27-46) years. Incidence of most outcomes was elevated during weeks 1 through 4 after COVID-19 diagnosis, compared with before or without COVID-19, in each cohort. Incidence of mental illnesses was lower in the vaccinated cohort compared with the pre–vaccine availability and unvaccinated cohorts: aHRs for depression and serious mental illness during weeks 1 through 4 after COVID-19 were 1.93 (95% CI, 1.88-1.98) and 1.49 (95% CI, 1.41-1.57) in the pre–vaccine availability cohort and 1.79 (95% CI, 1.68-1.90) and 1.45 (95% CI, 1.27-1.65) in the unvaccinated cohort compared with 1.16 (95% CI, 1.12-1.20) and 0.91 (95% CI, 0.85-0.98) in the vaccinated cohort. Elevation in incidence was higher and persisted longer after hospitalization for COVID-19.

Conclusions and Relevance   In this study, incidence of mental illnesses was elevated for up to a year following severe COVID-19 in unvaccinated people. These findings suggest that vaccination may mitigate the adverse effects of COVID-19 on mental health.

SARS-CoV-2 infection, and consequent COVID-19, are associated with subsequent mental illnesses in both hospital- and population-based studies, 1 , 2 including both common mental health difficulties, such as anxiety and depressive symptoms, 3 and serious mental illness, including psychotic disorders. 4 Potential mechanisms include physiological pathways, such as inflammation and microvascular changes, and psychosocial effects, such as anxiety about the potential outcomes of COVID-19, including post–COVID-19 condition. Previous studies have identified associations of COVID-19 with mental illnesses in both hospitalized patients 5 , 6 and the general population. 1 , 7 , 8 Differentiating between hospitalized patients and the general population may provide insights into the implications of COVID-19 severity for subsequent mental illnesses. 9

Rapid rollout of COVID-19 vaccination was a crucial component of the public health response. Although the impacts of vaccination in preventing and reducing the severity of COVID-19 are well established, 10 , 11 there is limited evidence regarding the implications of vaccination for other adverse outcomes of COVID-19, including mental illnesses. We did not identify any studies investigating differences in mental illnesses following COVID-19 by vaccination status. Furthermore, rates of SARS-CoV-2 infection, vaccination, and disease severity may differ by sociodemographic and health factors, 12 - 16 so mental health outcomes may also vary between subgroups.

Using electronic health record data from more than 18 million people, we examined associations of diagnosed COVID-19 with subsequent mental illnesses prior to vaccine availability and for unvaccinated and vaccinated people after vaccination became available. We compared rates of mental illnesses after COVID-19 with rates before or without COVID-19. We also examined associations in subgroups defined by COVID-19 severity, age, sex, ethnicity, prior mental illness, and prior COVID-19. Ethnicity data were reported because disparities in COVID-19 outcomes by ethnic group have been reported. 17 Follow-up of those diagnosed with COVID-19 during the first year of the pandemic was for up to 2 years postdiagnosis.

Our study used OpenSAFELY-TPP, which provides secure, privacy-protecting access to linked data from 24 million people registered with general practices (GPs) in England using TPP SystmOne software. These data include primary care data linked via pseudonymized National Health Service number to the Secondary Uses Service secondary care data, the Office of National Statistics Death Registry, the Second Generation Surveillance System COVID-19 testing data, and the Index of Multiple Deprivation. COVID-19 vaccination records (National Immunisation Management System) are available within TPP primary care data. In March 2020, the Secretary of State for Health and Social Care used powers under the UK Health Service (Control of Patient Information) Regulations 2002 to require organizations to process confidential patient information for the purposes of protecting public health, providing health care services to the public, and monitoring and managing the COVID-19 outbreak and incidents of exposure; this sets aside the requirement for patient consent. Ethics approval for this study was obtained from the Health Research Authority and the University of Bristol’s Faculty of Health Sciences Ethics Committee. This study is reported in line with the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We present main findings for depression and serious mental illness (composite of schizophrenia, schizoaffective disorder, bipolar disorder, and psychotic depression). We also examined general anxiety disorders, posttraumatic stress disorder, eating disorders, addiction, self-harm, and suicide, which are presented in eTables 5-7 and eFigure 4 in Supplement 1 . Each outcome was defined using the earliest of a Systematized Nomenclature of Medicine–Clinical Terms code (a structured clinical vocabulary to record clinical events) in primary care; start of a hospital admission with an International Statistical Classification of Diseases and Related Health Problems , Tenth Revision ( ICD-10 ) code in any position; or death with an ICD-10 code as the primary or underlying cause. We considered the first record only as individuals were removed from the cohort censored at this point. Codes indicating a relevant mental illness prior to the study period were captured by history covariates. Individuals with multiple mental illnesses were included in all relevant analyses with other mental illnesses captured by history covariates.

Date of COVID-19 was defined as the first of confirmed diagnosis recorded in primary care, positive SARS-COV-2 polymerase chain reaction or antigen test recorded in the Second Generation Surveillance System, start of a hospital admission with a confirmed diagnosis in any position, or death with SARS-COV-2 infection listed as the primary or underlying cause. People with a hospital admission record including a confirmed diagnosis in the primary position within 28 days of first COVID-19 were defined as having had hospitalization for COVID-19. All other COVID-19 diagnoses were defined as not hospitalized for COVID-19. The term confirmed diagnosis refers to diagnoses where the virus was identified by laboratory testing, irrespective of clinical symptom severity. We defined the date of confirmed COVID-19 diagnosis using the first record only, although additional secondary care information was used to classify COVID-19 as either leading to hospitalization or not. We did not consider multiple COVID-19 diagnoses within the study period as there is no agreed-on definition for COVID-19 reinfection using routinely collected data.

Covariates identified as potential confounders included age, sex, ethnicity, deprivation (using the Index of Multiple Deprivation—the official measure of relative deprivation for small areas in England defined by 7 domains, including income and barriers to housing and services), smoking status, care home residence, health care work, GP-patient interactions in 2019, and binary indicators for comorbidities (eTable 1 in Supplement 1 ).

Three cohorts were defined (eTable 2, eFigure 1 in Supplement 1 ). The pre–vaccine availability cohort was followed up from January 1, 2020 (baseline), until the earliest of December 14, 2021, 18 date of outcome, date of deregistration, or date of death. Exposure was defined as recorded COVID-19 between baseline and the earliest date of eligibility for COVID-19 vaccination, date of first vaccination, and June 18, 2021 (when all adults became eligible for vaccination). Follow-up in the vaccinated cohort started at the later of June 1, 2021 (baseline), or 2 weeks after a second COVID-19 vaccination and ended at the earliest of December 14, 2021, date of outcome, date of deregistration, or date of death. The unvaccinated cohort had not received a COVID-19 vaccine by 12 weeks after they became eligible for vaccination. Follow-up started at the later of June 1, 2021 (baseline), or 12 weeks after vaccination eligibility and ended at the earliest of December 14, 2021, date of outcome, date of deregistration, or date of death. Eligibility criteria for each cohort are provided in the eMethods in Supplement 1 . Individuals could potentially be followed up in all 3 cohorts but most often they were in the pre–vaccine availability cohort and either the vaccinated or unvaccinated cohort. The cohort definitions imply that diagnoses of mental illnesses after eligibility for vaccination in people who were not diagnosed with COVID-19 could be included in the comparison incidence rate calculations in both the pre–vaccine availability cohort and at most 1 of the vaccinated and unvaccinated cohorts. However, each COVID-19 diagnosis could be recorded in only 1 of the 3 cohorts, and therefore, each post–COVID-19 mental illness outcome could be included in only 1 of the 3 cohorts. Therefore, findings from each cohort are close to being statistically independent.

For each cohort, baseline characteristics were described, and numbers of outcome events, person-years of follow-up, and incidence rates (per 100 000 person-years) before and after all COVID-19 diagnoses, those leading to hospitalization, and those not leading to hospitalization were tabulated. Time to first event was analyzed for each outcome. Cox models were fitted with calendar time scale using the cohort-specific baseline as the origin. Hazard ratios (HRs) for follow-up after vs before or without COVID-19 were estimated, splitting follow-up into the day of COVID-19 diagnosis (day 0), the remainder of 1 to 4 weeks, and 5 to 28 weeks after COVID-19 for all cohorts and additionally 29 to 52 and 53 to 102 weeks after COVID-19 for the pre–vaccine availability cohort. For computational efficiency, we used sampling for analyses containing more than 4 000 000 people; we included all people with the outcome event, all people with the exposure (COVID-19 diagnosis), and a 10% random sample (for general anxiety, depression, and serious mental illness) or 20% random sample (for all other outcomes) of people who were not diagnosed with COVID-19 and in whom the outcome event was not recorded. We used inverse probability weights to adjust for the sampling and derived confidence intervals using robust standard errors. For each outcome and cohort, we estimated age- and sex-adjusted and maximally adjusted HRs including all covariates. Restricted cubic splines were used to account for age unless otherwise specified. All models were stratified by region to construct risk sets within region, accounting for between-region variation in the baseline hazard.

Subgroup analyses according to history of the outcome, age group, sex, ethnicity, and COVID-19 history were conducted for depression and serious mental illness. We calculated absolute excess risk 28 weeks after COVID-19, including outcomes recorded on the day of COVID-19 diagnosis (day 0) and weighted by the proportion of people in age and sex strata in the pre–vaccine availability cohort (eMethods in Supplement 1 ).

The study was conducted in Python version 3.8.10 (Python Software Foundation), R version 4.0.2 (R Foundation), and Stata/MP version 16.1 (StataCorp) according to a prespecified protocol. Our protocol, analysis code, and code lists are available. 19 All outputs were subjected to OpenSAFELY disclosure controls, including rounding where appropriate. 20 Data were analyzed from July 2022 to June 2024.

The pre–vaccine availability cohort included 18 648 606 people (9 363 710 [50.2%] female and 9 284 896 [49.8%] male; median [IQR] age, 49 [34-64] years) 1 012 335 of whom had COVID-19 ( Table 1 ; eFigure 2 in the Supplement ). The cohort included 1 191 793 (6.4%) Black individuals; 217 132 (1.2%) South Asian individuals; 14 865 866 (79.7%) White individuals; 423 111 individuals (2.3%) of mixed ethnicity; and 400 437 of other ethnicities (2.1%), consolidated for disclosure control; and 1 550 267 individuals (8.3%) for whom ethnicity data were missing. The vaccinated cohort included 14 035 286 individuals (median [IQR] age, 53 [38-67] years; 7 308 556 [52.1%] female and 6 726 730 [47.9%] male; 789 476 [5.6%] Black, 128 514 [0.9%] South Asian, 11 752 297 [83.7%] White, 237 383 [1.7%] mixed, 789 476 [5.6%] other, and 910 299 [6.5%] missing), 866 469 of whom had COVID-19. The unvaccinated cohort included 3 242 215 people (median [IQR] age, 35 [27-46] years; 1 363 401 [42.1%] female and 1 878 814 [57.9%] male; 325 199 [10%] Black, 81 017 [2.5%] South Asian, 2 025 492 [62.5%] White, 190 874 [5.9%] mixed, 173 014 [5.3%] other, 446 619 [13.8%] missing), 149 745 of whom had COVID-19. Differences in demographic characteristics between these cohorts reflect factors associated with COVID-19 vaccine uptake. 21 eTable 3 in Supplement 1 summarizes participants’ medical history by cohort. Rates of COVID-19 differed by cohort ( Table 1 ). Higher rates were observed in cohorts followed up when the Delta variant was dominant (vaccinated: 915 per 100 000 person years and unvaccinated: 1274 per 100 000 person years) than when the wild-type or Alpha variants were dominant (pre–vaccine availability: 308 per 100 000 person years). Of the 18 648 606 individuals in the pre–vaccine availability cohort, 12 969 492 (69.5%) were subsequently followed up in the vaccinated cohort and 2 843 514 (15.2%) were subsequently followed up in the unvaccinated cohort (eFigure 3 in Supplement 1 ). Of 17 121 348 individuals included in either the unvaccinated or vaccinated cohort, 156 144 (0.91%) were included in both.

The incidence of mental illnesses was higher after COVID-19 than before or without COVID-19 ( Table 2 ). Between-cohort differences in the incidence of mental illnesses in the absence of COVID-19 likely reflect both demographic differences and changes in diagnostic practices and access to health care during the pandemic. The highest incidence rates were after hospitalization for COVID-19. Depression was the most common outcome with 1 329 270, 352 944, and 57 810 diagnoses in the pre–vaccine availability, vaccinated, and unvaccinated cohorts, respectively. The corresponding diagnoses of serious mental illness were 397 368, 88 500, and 18 726. Separating individuals by their history of the outcome, incidence of mental illnesses after COVID-19 was greater in those with than without history (eTable 4 in Supplement 1 ).

Maximally adjusted HRs (aHRs) comparing the incidence of each outcome after COVID-19 with the incidence before or without COVID-19 did not differ substantially from the age- and sex-adjusted HRs (eFigure 4 in Supplement 1 ). The incidence of all outcomes was extremely high on day 0 (eTable 5 in Supplement 1 ). The incidence of most outcomes was elevated during the remainder of 1 to 4 weeks after COVID-19, compared with before or without COVID-19, in each cohort. For all figures, aHRs are plotted against the median time from date of COVID-19 diagnosis to date of outcome in each cohort.

Incidence of depression was elevated during weeks 1 through 4 after COVID-19, compared with before or without COVID-19, in the pre–vaccine availability and unvaccinated cohorts (aHR, 1.93; 95% CI, 1.88-1.98 and aHR, 1.79; 95% CI, 1.68-1.90, respectively) and, to a lesser extent, the vaccinated cohort (aHR, 1.16; 95% CI, 1.12-1.20) ( Figure 1 ; eTable 5 in Supplement 1 ). Incidence remained elevated during weeks 5 through 28 in the vaccinated and unvaccinated cohorts (aHR, 1.11; 95% CI, 1.08-1.14 and aHR, 1.28; 95% CI, 1.21-1.36, respectively) and up to weeks 53 through 102 in the pre–vaccine availability cohort (aHR, 1.17; 95% CI, 1.14-1.21). aHRs during weeks 1 through 4 were considerably higher after COVID-19 with hospitalization (pre–vaccine availability: 16.3; 95% CI, 15.6-17.0; vaccinated: 12.9; 95% CI, 12.0-14.0; unvaccinated: 15.6; 95% CI, 13.9-17.4) than without hospitalization (pre–vaccine availability: 1.22; 95% CI, 1.18-1.27; vaccinated: 0.92; 95% CI, 0.88-0.95; unvaccinated: 1.11; 95% CI, 1.02-1.20) ( Figure 1 ; eTables 6 and 7 in Supplement 1 ). In the pre–vaccine availability cohort, aHRs remained higher after COVID-19 with hospitalization than without hospitalization throughout follow-up.

Incidence of serious mental illness was elevated during weeks 1 through 4 after COVID-19, compared with before or without COVID-19, in the pre–vaccinated and unvaccinated cohorts (aHR, 1.49; 95% CI, 1.41-1.57 and aHR, 1.45; 95% CI, 1.27-1.65, respectively) ( Figure 1 ; eTable 5 in Supplement 1 ). However, incidence was lower during weeks 1 through 4 in the vaccinated cohort (aHR, 0.91; 95% CI, 0.85-0.98). Incidence remained slightly elevated during weeks 5 through 28 in the vaccinated and unvaccinated cohorts (aHR, 1.05; 95% CI, 1.00-1.11 and aHR, 1.14; 95% CI, 1.02-1.27) and up to weeks 53 through 102 in the pre–vaccine availability cohort (aHR, 1.14; 95% CI, 1.08-1.21). Incidence of serious mental illness during weeks 1 through 4 was considerably higher after COVID-19 with hospitalization (pre–vaccine availability: aHR, 9.71; 95% CI, 8.80-10.7; vaccinated: aHR, 6.52; 95% CI, 5.36-7.93; unvaccinated: aHR, 8.75; 95% CI, 7.01-10.9) than after COVID-19 without hospitalization (pre–vaccine availability: aHR, 1.05; 95% CI, 0.98-1.12; vaccinated: aHR, 0.79; 95% CI, 0.73-0.86; unvaccinated: aHR, 1.00; 95% CI, 0.85-1.17) ( Figure 1 ; eTables 6 and 7 in Supplement 1 ).

Incidence of depression was highest during weeks 1 through 4 after COVID-19, vs before or without COVID-19, for people with history of the outcome more than 6 months ago ( Figure 2 ; eTables 8-10 in Supplement 1 ). However, incidence of serious mental illness was highest during weeks 1 through 4 after COVID-19 for people with history of the outcome within 6 months for the pre–vaccine availability (aHR, 1.90; 95% CI, 1.60-2.27) and vaccinated cohorts (aHR, 1.03; 95% CI, 0.84-1.27). Incidences of depression and serious mental illness after COVID-19, vs before or without COVID-19, were similar in people with and without a history of COVID-19 (eFigure 5 and eTable 11 in Supplement 1 ). Incidence of depression during weeks 1 through 4 and 5 through 28 and for serious mental illness across all time periods were greater in the age groups 60 years and older than in the age groups 60 years and younger (eFigure 6 and eTables 12-15 in Supplement 1 ). Incidences of depression and serious mental illness were marginally higher for men than women during weeks 1 through 4 after COVID-19 (eFigure 7 and eTables 16 and 17 in Supplement 1 ). Incidences of depression and serious mental illness after COVID-19 were generally comparable between ethnic groups, when they could be estimated (eFigure 8, eTables 18-22 in Supplement 1 ).

Incidences of other mental illnesses were broadly similar to those of depression and serious mental illness, both overall ( Figure 3 , eTable 5 in Supplement 1 ) and for COVID-19 with and without hospitalization (eTables 6 and 7 in Supplement 1 ). An exception was that incidence of posttraumatic stress disorder after COVID-19 with hospitalization, vs before or without COVID-19, was higher during weeks 1 through 4 in the vaccinated cohort than the other cohorts (pre–vaccine availability: aHR, 20.1; 95% CI, 15.8-25.6; vaccinated: aHR, 27.3; 95% CI, 20.3-36.6; unvaccinated: aHR, 13.3; 95% CI, 8.00-22.2). This pattern was not present for COVID-19 without hospitalization or overall.

Estimated excess risks of depression 28 weeks after COVID-19, standardized to the age and sex distribution of the pre–vaccine availability cohort, were 1033, 451, and 1008 per 100 000 people in the pre–vaccine availability, vaccinated, and unvaccinated cohorts, respectively (eFigure 9 and eTable 23 in Supplement 1 ). The equivalent estimated excess risks of serious mental illness were 235, 53, and 209 per 100 000 people. Many of the estimated excess events occurred on the day of COVID-19 diagnosis (day 0).

In this cohort study of more than 18 million people with up to 2 years of follow-up, rates of most mental illnesses were markedly elevated during weeks 1 through 4 after COVID-19 compared with before or without COVID-19. This elevation was less marked in people who were vaccinated before COVID-19. In people with COVID-19 before vaccination was available, incidence of mental illnesses remained elevated more than 28 weeks after diagnosis, particularly in people who were hospitalized. In subgroup analyses according to history of the outcome, associations 1 through 4 weeks after COVID-19 were greater in those with than without history. Subgroup analyses also suggested stronger associations in older age groups and in men. The association of COVID-19 with mental illnesses did not differ markedly between ethnic groups.

Attenuation of adverse effects of COVID-19 on mental illnesses in the vaccinated may be explained by reduced disease severity due to vaccination. 22 Potential mechanisms include reduced systemic inflammation and psychological benefits of vaccination, such as reduced concern about COVID-19 and increased social engagement. 23 A previous study 3 found that associations varied by COVID-19 severity, with poorer mental illness outcomes only found among those who were bedridden with COVID-19.

Rates of mental illness outcomes declined with increasing time since COVID-19, although incidence remained elevated up to a year after COVID-19 with hospitalization in the pre–vaccine availability era. Previous findings have been mixed, with a review reporting no clear long-term associations between COVID-19 and mental illness, 24 while a multicohort study found little evidence of attenuation over time. 1 Persisting associations of COVID-19 with mental illnesses could partly reflect ongoing impacts of post–COVID-19 condition. 25 , 26

Consistent with previous research, 1 we found stronger associations between COVID-19 and mental illnesses among older age groups. This is likely to reflect their increased risk of severe COVID-19 and resulting increased anxiety about its outcomes. The association between COVID-19 and mental illnesses was slightly stronger among men, who have been found to be at greater risk of severe mental illness outcomes than women. 27 These patterns contrast with the wider impacts of the pandemic on mental health, which have been found to be greatest in adults aged 25 to 44 years, women, and those with higher educational degrees. 28 This indicates that mechanisms linking COVID-19 and mental health may differ from those underpinning wider pandemic effects.

Our findings highlight the wider public health benefits of vaccination. Prior mental illness may influence vaccine uptake, highlighting the importance of actively encouraging vaccination of people with mental health difficulties. 21 , 29 , 30 Our analyses suggested that the adverse associations of COVID-19 with mental illnesses were greater prior to the availability of vaccination. This may reflect greater uncertainty and public concern around outcomes of COVID-19 and treatment effectiveness at the beginning of the pandemic.

Strengths of this study include the large sample size, the detailed linked electronic health record data, the relatively long duration of follow-up, and the opportunity to account for vaccination. We also note several limitations. First, electronic health records are routinely collected data for health care provision and so only capture conditions diagnosed and recorded by the health care professional rather than true incidence in the population. Unvaccinated people may have been less likely to contact health services and to test for SARS-CoV-2 infection, leading to underestimated effects. People with recorded COVID-19, particularly COVID-19 with hospitalization, may be more likely to have mental illnesses recorded due to greater contact with health services. This may underpin the particularly high HRs observed initially, especially in those hospitalized, and the rapid fall as service contact is likely highest early after diagnosis. However, this is unlikely to fully explain adverse effects, given the persistent elevation of incidence of mental illnesses following COVID-19 with hospitalization and the variation across mental illnesses. Also, people with prior recorded mental health diagnoses may not have them coded at every visit, even if their mental health had deteriorated due to COVID-19. Additionally, data on mental health are generally incomplete, as they do not include mental health services data or National Health Service Talking Therapies (formerly Improving Access to Psychological Therapies), to which patients can self-refer. This is relevant to the present study as those with more serious COVID-19 are more likely to be in contact with health services and therefore may be more likely to report symptoms, while those not in contact may not seek help or may use other routes that are not captured. Again, this relates to the particularly high HRs observed initially, which may reflect the recording of both COVID-19 and mental illnesses during the same consultation. Furthermore, we could only assess COVID-19 severity according to hospitalization and did not consider the potential role of repeated infections. We cannot exclude the possibility of unmeasured confounding, although we controlled for a wide range of demographic characteristics and prior morbidities. More sophisticated methods for potential confounding exist and may have been more appropriate here, although these more computationally intensive methods were not feasible given the size of dataset analyzed. 31 A previous study 2 found that the mental health impacts of COVID-19 were less apparent when using a negative control group (a group of individuals whose SARS-CoV-2 polymerase chain reaction or antigen test result had a negative result), suggesting that observed associations may have been, at least in part, due to unmeasured confounding related to testing behavior. We were not able to include a negative control group to explore this as the available test data did not include negative results. Additionally, other viruses may have implications for mental illnesses. Our findings may therefore reflect a phenomenon that occurs after many viruses rather than specifically SARS-CoV-2.

The findings in this study add to a growing body of evidence highlighting the increased risk of mental illnesses following COVID-19 diagnosis, with stronger associations found in relation to nonvaccination and more severe COVID-19 disease and longer-term associations relating mainly to new-onset mental illnesses. This has important implications for public health and mental health service provision, as serious mental illnesses are associated with more intensive health care needs and longer-term health and other adverse effects. Our results highlight the importance COVID-19 vaccination in the general population and particularly among those with mental illnesses, who may be at higher risk of both SARS-CoV-2 infection and adverse outcomes following COVID-19.

Accepted for Publication: May 21, 2024.

Published Online: August 21, 2024. doi:10.1001/jamapsychiatry.2024.2339

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Walker VM et al. JAMA Psychiatry .

Corresponding Author: Jonathan Sterne, PhD, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom ( [email protected] ).

Author Contributions: Dr Walker had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Walker, Patalay, and Cuitun Coronado contributed equally. Drs Chaturvedi, Macleod, John, and Sterne contributed equally.

Concept and design : V. Walker, Patalay, Cuitun Coronado, Denholm, Forbes, Moltrecht, Palmer, Thompson, Taylor, Cezard, Wood, Chaturvedi, Macleod, John, Sterne.

Acquisition, analysis, or interpretation of data : V. Walker, Patalay, Cuitun Coronado, Stafford, Moltrecht, Palmer, A. Walker, Cezard, Horne, Wei, Al Arab, Knight, Fisher, Massey, Davy, Mehrkar, Bacon, Goldacre, Wood, Chaturvedi, Macleod, John, Sterne.

Drafting of the manuscript : V. Walker, Patalay, Cuitun Coronado, Stafford, John, Sterne.

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

Statistical analysis : V. Walker, Cuitun Coronado, Denholm, Palmer, Taylor, Cezard, Horne, Wei, Knight, Wood, Sterne.

Obtained funding : Patalay, Goldacre, Chaturvedi, Sterne.

Administrative, technical, or material support : V. Walker, Cuitun Coronado, Denholm, Stafford, A. Walker, Thompson, Wei, Al Arab, Massey, Davy, Mehrkar, Bacon, Goldacre, Sterne.

Supervision : V. Walker, Patalay, Cuitun Coronado, Denholm, Palmer, Fisher, Chaturvedi, Macleod, John, Sterne.

Conflict of Interest Disclosures: Dr A. Walker reported grants from the National Institute for Health and Care Research (NIHR) during the conduct of the study. Dr Mehrkar reported grants obtained from the Bennett Foundation, Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, Mohn-Westlake Foundation, and National Health Service (NHS) England during the conduct of the study and consultancy fees from Induction Healthcare and is a senior clinical researcher at the University of Oxford in the Bennett Institute and from the Royal College of General Practitioners (RCGP)/British Medical Association (member of the RCGP health informatics group) and the NHS Digital General Practice Extraction Service (advisory group that advises on access to general practice data for pandemic planning and research outside the submitted work. Dr Chaturvedi reported grants from UK Research and Innovation (UKRI) during the conduct of the study and personal fees from AstraZeneca (data monitoring and safety committee member) outside the submitted work. Dr Sterne reported grants from the UK National Institute for Health and Care Research, UKRI Medical Research Council, and Health Data Research UK during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was supported by the COVID-19 Longitudinal Health and Wellbeing National Core Study, which is funded by the Medical Research Council (MRC) (MC_PC_20059) and the National Institute for Health and Care Research (NIHR) (COV-LT-0009). Dr V. Walker is also supported by the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MC_UU_00032/03). Dr Wei was supported by a UK Research and Innovation (UKRI) MRC (MC/W021358/1) and received funding from UKRI Engineering and Physical Sciences Research Council impact acceleration account (EP/X525789/1). Dr Mehrkar received funding from the Bennett Foundation, Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, and the Mohn-Westlake Foundation. The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z) and the MRC (MR/V015737/1, MC_PC_20059, and MR/W016729/1). In addition, development of OpenSAFELY has been funded by the Longitudinal Health and Wellbeing strand of the National Core Studies programme (MC_PC_20030: MC_PC_20059), the NIHR-funded CONVALESCENCE programme (COV-LT-0009), the NIHR (NIHR135559 and COV-LT2-0073), and the Data and Connectivity National Core Study funded by UK Research and Innovation (MC_PC_20058) and Health Data Research UK (HDRUK2021.000).

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.

Group Information: A complete list of the members of the Longitudinal Health and Wellbeing COVID-19 National Core Study appears in Supplement 2 .

Data Sharing Statement: See Supplement 3 .

Additional Contributions: We are very grateful for all the support received from the TPP Technical Operations team throughout this work and for generous assistance from the information governance and database teams at the National Health Service (NHS) England and the NHS England Transformation Directorate. We thank the CONVALESCENCE Study Long COVID PPIE group for their input and for sharing their experiences and expertise throughout the duration of the project.

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The impact of COVID-19 on research

a Department of Pediatric Urology and Pediatric Surgery, Hopital Pellegrin-Enfants, CHU Bordeaux, France

b Service de chirurgie et urologie pédiatrique, hôpital Lapeyronie, CHU de Montpellier et Université de Montpellier, France

G.M.A. Beckers

c Department of Urology, Section of Pediatric Urology, AmsterdamUMC, Location VUmc, Amsterdam, the Netherlands

d Indiana University, 702 Barnhill Drive, Suite 4230, Indianapolis, IN, USA

A.J. Nieuwhof-Leppink

e Department of Medical Psychology and Social Work, Urology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, PO box 85090, 3508 AB, Utrecht, the Netherlands

Magdalena Fossum

f Department of Pediatric Surgery, Copenhagen University Hospital Rigshospitalet, DK-2100, Denmark

g Department of Women's and Children's Health, Bioclinicum, Floor 10, Karolinska Institutet, SE-171 76, Stockholm, Sweden

K.W. Herbst

h Division of Urology, Department of Research, Connecticut Children's Medical Center, Hartford, CT, USA

i Hospital for Sick Chidlren, Univeristy of Toronto, Canada

Coronavirus disease 2019 (COVID-19) has swept across the globe causing hundreds of thousands of deaths, shutting down economies, closing borders and wreaking havoc on an unprecedented scale. It has strained healthcare services and personnel to the brink in many regions and will certainly deeply mark medical research both in the short and long-term.

Prior to the COVID pandemic, virology research (including influenza) represented less than 2% of all biomedical research. However, the number of laboratories and investigators that have pivoted to address COVID related research questions is astonishing, likely comprising 10–20% of current biomedical investigation, showing the incredible adaptability of the research community [ 1 ]. The multinational support rapidly infused for COVID-19 research is in the billions of euros [ 2 ]. The sharing of research findings and research data has never been as rapid and efficient [ 3 ]. The crisis has also brought disease, health, and healthcare back to the forefront of societal issues, and will have a lasting impact on public spending. However, with all this optimism and focus, there is a downside.

To begin, the COVID-19 crisis has led to a massive influx of publications. Not only are specialty journals being flooded with submissions by authors being unwittingly granted much needed writing time, but publications on COVID have literally inundated us. More than 20,000 papers have been published since December 2019, many in prestigious journals. There are also an increasing number of studies being uploaded to preprint servers, such as BioRxiv, for rapid dissemination prior to any peer review. However, we cannot assume that the time and quality available for peer review is able to keep pace with the explosion of publication. There is need for increased caution in the wake of this massive influx of submissions, especially since we are increasingly seeing these results being picked up by the media and diffused to a less attuned audience. In recent weeks, several prestigious journals, including the Lancet and the New England Journal of Medicine, have published retractions of earlier and potentially major COVID-related findings [ 4 , 5 ]. On June 15, 2020, The New York Times highlighted potential lapses in the peer review process affecting major scientific journals [ 6 ].

We must strive to improve scientific quality always. The current debate over the use of hydroxychloroquine further illustrates the undermining of the scientific process when faced with global desperation for ready-made truths and solutions [ 4 , 7 , 8 ]. Science needs time, and good science needs a lot of it for data to grow and knowledge to evolve, but this process is ill-prepared to handle the rush for solutions to the COVID crises.

Moreover, just as COVID-19 has shown social, racial, and economic health disparities, the pandemic seems also to have accentuated existing gender inequalities within the field of research [ 9 ]. Indeed, early analyses suggest that female academics are publishing less and starting fewer research projects than their male peers. This might be an effect of the lockdown and the fact that more women than are men are juggling caring for families and children despite both “working” from home [ 10 , 11 ].

Travel, social, and funding restrictions will also take a serious toll on scientific research worldwide. Research staff and resources have been purposely and purposefully prioritized to COVID-19 activities above all else. Distancing and transmission issues have caused most non-COVID clinical research to be suspended, causing a reduction in recruitment of research subjects and a delay in data entry into clinical trial databases [ 12 ]. Research-related hiring has been suspended because of travel restrictions and young researchers might soon find themselves out of a job if their subject is not the pandemic. Indeed, though government-funded medical research bodies worldwide say they are committed to maintaining the continuity and breadth of biomedical research, how the economic downfall will influence government spending remains to be seen. Furthermore, research funding that relies on public fundraising is expected to drop substantially and many researchers will see a significant decrease in funding opportunities [ 13 ]. The global impact the crisis will have on the economy makes it hard to imagine that future research funding will not be substantially affected.

During this crisis, many resources were understandably redirected toward preparing for and caring for COVID-19 patients, but the collateral damage to so many patients with non-COVID-19 medical conditions that did not receive, or failed to seek, treatment will surely emerge [ 14 ]. Finally, children have also paid a high price for the redirecting of medical resources, with delays in their medical and surgical management, as well as vaccinations [ 15 , 16 ]. This may be especially problematic when many aspects of pediatric care is based on their developmental clock, which even the pandemic cannot stop. Whether this was the best option will certainly be analyzed in retrospect. Congenital anomalies alone account for over 400,000 deaths worldwide every year, and inflict a considerable burden both on children, families, and healthcare systems [ 17 ]. Thus, it is essential that funding for medical research does not follow the same pattern with a disproportionate decrease in funding for non-COVID research including pediatric and developmental urology.

COVID-19 has already changed the world, not only because of the disease itself, but because of the long-term effects of the world's reaction to the pandemic. While the pandemic may have brought with it some silver linings, it is crucial that the scientific community conduct current and future research broadly and openly, lest future pandemic preparedness in research repeat the hard-fought lessons of today.