example of literature review about covid 19

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• 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.

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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

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

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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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Current evidence for COVID-19 therapies: a systematic literature review

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Effective therapeutic interventions for the treatment and prevention of coronavirus disease 2019 (COVID-19) are urgently needed. A systematic review was conducted to identify clinical trials of pharmacological interventions for COVID-19 published between 1 December 2019 and 14 October 2020. Data regarding efficacy of interventions, in terms of mortality, hospitalisation and need for ventilation, were extracted from identified studies and synthesised qualitatively.

In total, 42 clinical trials were included. Interventions assessed included antiviral, mucolytic, antimalarial, anti-inflammatory and immunomodulatory therapies. Some reductions in mortality, hospitalisation and need for ventilation were seen with interferons and remdesivir, particularly when administered early, and with the mucolytic drug, bromhexine. Most studies of lopinavir/ritonavir and hydroxychloroquine did not show significant efficacy over standard care/placebo. Dexamethasone significantly reduced mortality, hospitalisation and need for ventilation versus standard care, particularly in patients with severe disease. Evidence for other classes of interventions was limited. Many trials had a moderate-to-high risk of bias, particularly in terms of blinding; most were short-term and some included low patient numbers.

This review highlights the need for well-designed clinical trials of therapeutic interventions for COVID-19 to increase the quality of available evidence. It also emphasises the importance of tailoring interventions to disease stage and severity for maximum efficacy.

This review shows the need for well-designed trials of treatments for COVID-19 to improve the quality of available evidence and emphasises the importance of tailoring interventions to disease stage and severity for maximum efficacy https://bit.ly/3u72MHa

• Introduction

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly discovered coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 1 ]. The global pandemic caused by COVID-19 is ongoing, with a projected death toll of almost 3.5 million by 1 May 2021 [ 2 ].

Although COVID-19 presents primarily as a respiratory tract infection, increasing data have shown the potential for systemic involvement, including cardiovascular, neurological and dermatological manifestations, in patients who present with the disease [ 3 ]. The pathophysiological course of COVID-19 has been proposed to comprise three distinct phases [ 4 ]. In the early infection phase, the SARS-CoV-2 virus enters epithelial cells in the nasal cavity and multiplies in the upper respiratory tract, with or without pulmonary involvement [ 1 , 4 , 5 ]. The second phase is characterised by localised pulmonary inflammation and the development of viral pneumonia, with or without hypoxia. In a minority of patients, the disease enters a third “host response” phase, which manifests as an extrapulmonary systemic hyperinflammation syndrome, characterised by high levels of pro-inflammatory cytokines and potentially leading to thrombotic complications, viral sepsis and multi-organ failure [ 1 , 4 , 6 ].

Increasing understanding of the disease pathways involved in COVID-19 has highlighted the importance of selecting and implementing treatments appropriate for the disease stage that patients present with [ 4 , 6 ]. Since the start of the outbreak, global efforts to validate effective therapeutic interventions for COVID-19 have resulted in the identification of many potential candidates and the initiation of thousands of clinical trials of therapies with diverse mechanisms of action [ 7 – 9 ].

As of February 2021, several therapies have received regulatory approval on the basis of promising results ( figure 1a ) [ 10 , 11 ], these include: the antiviral remdesivir; remdesivir in combination with baricitinib (a Janus kinase inhibitor); dexamethasone (a corticosteroid); convalescent plasma; bamlanivimab (a monoclonal antibody therapy); and casirivimab and imdevimab (a cocktail of two monoclonal antibodies) [ 10 – 13 ]. Additionally, several vaccines against SARS-CoV-2 have been approved by the US Food and Drug Administration (FDA) and/or European Medicines Agency [ 10 , 11 ] and other vaccines are in late-phase clinical trials. Despite these developments, treatment options for COVID-19 remain limited. The precise proportion of patients requiring hospitalisation is challenging to determine, given the uncertain prevalence of infection [ 14 ]. However, it is estimated that up to 20% of patients with COVID-19 have an illness severe enough to warrant hospitalisation, which may require intensive care admission and need for respiratory support [ 15 – 18 ]. Consequently, the COVID-19 pandemic has put substantial pressure on healthcare systems worldwide [ 19 – 23 ], and there remains an urgent need for effective agents for the prevention and treatment of COVID-19.

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a) Current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved interventions for coronavirus disease 2019 (COVID-19). b) Effective therapies in the included trials showing interventions that showed statistically significant differences in outcomes versus the comparator. SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; ECMO: extracorporeal membrane oxygenation; IFN: interferon; SC: standard care; TFF2: trefoil factor 2; SOF: sofosbuvir; DCV: daclatasvir; FAV: favipiravir; LPV/r: lopinavir/ritonavir; RBV: ribavirin; FNC: azvudine; RDV: remdesivir; CHQ: chloroquine diphosphate; BHC: bromhexine; DEX: dexamethasone; MP: methylprednisolone; COL: colchicine; ENOX: enoxaparin; LMW: low molecular weight; rhG-CSF: recombinant human granulocyte colony-stimulating factor. # : includes enoxaparin (anticoagulant), rhG-CSF and auxora (calcium release-activated calcium channel inhibitor).

The large quantity of clinical data being generated, wide spectrum of disease presentations and rapidly changing clinical landscape present a critical need for comprehensive evidence summaries and treatment comparisons. The overall aim of this systematic literature review was to assess available evidence regarding efficacy and safety of potential pharmacological interventions for COVID-19. Evidence retrieved was considered in the context of the evolving understanding of the pathophysiology of COVID-19, the burden of COVID-19 in terms of mortality and healthcare resource utilisation, and current knowledge gaps. Thus, the aim of the analysis presented here was to synthesise evidence for mortality, hospitalisation and need for ventilation with current therapies.

This systematic literature review (registered with the Research Registry, unique identifying number: reviewregistry1019) evaluated studies of pharmacological options for the treatment and prevention of COVID-19. The review was conducted according to the principles embodied in the Cochrane Handbook for Systematic Reviews of Interventions [ 24 ] and guidance published by the Centre for Reviews and Dissemination [ 25 ].

Search strategy

The systematic literature search was conducted from 1 December 2019 to 14 July 2020 and updated on 14 October 2020, using the electronic databases Embase, MEDLINE via the PubMed platform and the Cochrane Library. Details of the search strings used for each database are presented in supplementary file 1 . Searches were supplemented by reviews of reports of pharmacological interventions for COVID-19 included in a systematic and living map of COVID-19 evidence [ 26 ]. These articles were assessed according to the same eligibility criteria as for the systematic searches. As part of the update on 14 October 2020, an additional search of PubMed Central was conducted following retrieval of one article from the systematic and living map that was available in PubMed Central but not in the other databases searched.

Eligibility criteria

Key inclusion criteria were clinical trials of any pharmacological preventive or treatment approach for COVID-19 of any stage, conducted in human subjects of any age.

Exclusion criteria included: studies of non-pharmacological interventions; traditional or herbal medicines; studies that reported on in vitro or in silico investigations; guidelines; clinical trial protocols or projection studies; or observational studies, such as prospective and retrospective cohort studies, case–control studies, cross-sectional studies, case reports and case series. Articles that were not written in English, reviews, comments, editorials, congress abstracts, and articles that had not undergone peer review were also excluded.

Study selection

After removal of duplicates, records identified in the electronic database searches were manually screened for eligibility on the basis of titles and abstracts by a single reviewer, with a second reviewer screening ≥10% of records selected at random. Any disagreements or uncertainties were discussed with a third reviewer to achieve a consensus. Subsequently, full texts of potentially relevant studies were obtained and reviewed for eligibility as for the first-pass screening. Studies containing duplicate information or not meeting the inclusion criteria upon further review were excluded.

Data extraction

Details of study design and population, interventions, comparators, follow-up duration and safety and efficacy outcomes were extracted from identified articles using a pilot-tested data extraction spreadsheet constructed in Microsoft Excel. If available, information on statistical comparisons between interventions was recorded; any studies reporting insufficient data meeting the inclusion criteria were excluded at the data extraction stage. All extracted data were cross-checked by an independent reviewer.

Data synthesis and quality assessment

Included studies were reviewed and assessed for comparability in terms of study design and outcomes reported. Randomised studies were included in the qualitative synthesis if they reported ≥1 of the selected outcomes: mortality, hospitalisation (any reported outcome, including duration, proportion of patients discharged and incidence) and need for ventilation (use of oxygen, noninvasive ventilation and intensive mechanical ventilation). Non-randomised trials reporting these outcomes were only included if no randomised trial evidence was available for a particular intervention.

Studies were grouped according to intervention and the disease phase targeted by the intervention; details of the categorisation used are presented in supplementary table 1 . Studies reporting similar outcome measures were summarised descriptively according to the type of intervention. Studies with outcomes that were defined differently to other studies ( e.g. event-free survival rather than percentage mortality, or outcomes reported for the overall population rather than according to treatment with (or allocation to) specific interventions) could not be grouped, and these were assessed separately.

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Summary of hospitalisation outcomes in trials included in the qualitative synthesis

Each study was assessed in terms of methodological quality based on criteria consistent with those in the Cochrane Handbook for Systematic Reviews of Interventions [ 24 ] and the Cochrane risk-of-bias tool [ 24 , 27 ].

Summary of included studies

The literature searches identified 33 674 unique citations, and from these, 436 full-text articles were assessed for eligibility ( supplementary figure 1 ). A total of 375 articles were excluded as they did not meet eligibility criteria for reasons such as reporting no outcomes of interest, presenting duplicate data from other studies or having a non-interventional study design. A total of 61 articles were retained for inclusion in the systematic literature review and, of these, 43 articles, reporting on 42 trials [ 28 – 70 ], were selected for qualitative synthesis, based on whether they reported ≥1 of the outcomes of interest.

Details of the 18 articles that were not retained for qualitative synthesis are summarised in supplementary table 2 . An overview of the 43 articles included in the qualitative synthesis is presented in supplementary figure 2 , and detailed characteristics of the individual trials are presented in supplementary table 3 . The majority of trials were conducted in patients hospitalised with COVID-19, but three trials included non-hospitalised patients [ 35 , 40 , 42 ] and one was conducted in asymptomatic adults with occupational or household exposure to COVID-19 [ 30 ] ( supplementary figure 2 ). Most trials used either standard care, which differed between trials, or placebo as a comparator.

Summary of mortality outcomes in trials of a) antivirals, b) antimalarial and mucolytic drugs and c) other therapies included in the qualitative synthesis. Other therapies include anti-inflammatory drugs, anticoagulants, kinase inhibitors, calcium release-activated calcium channel inhibitors, anticoagulants, immunomodulatory therapies and repair therapies. Results from one study are not presented graphically. D eftereos et al. [ 36 ] reported event-free survival as a primary outcome, which was defined as survival without meeting the primary clinical end-point (deterioration by 2 points on a 7-grade clinical status scale, ranging from able to resume normal activities to death). IFN: interferon; SC: standard care; TFF2: trefoil factor 2; SOF: sofosbuvir; DCV: daclatasvir; RBV: ribavirin; HCQ: hydroxychloroquine; LPV/r: lopinavir/ritonavir; FAV: favipiravir; TZV: triazavirin; PBO: placebo; DRV: darunavir; COBI: cobicistat; ARB: arbidol; RDV: remdesivir; F/U: follow-up; FNC: azvudine; LEF: leflunomide; CHQ: chloroquine diphosphate; AZ: azithromycin; BHC: bromhexine; HC: hydrocortisone; DEX: dexamethasone; IMV: invasive mechanical ventilation; MP: methylprednisolone; RUX: ruxolitinib; ENOX: enoxaparin; LMW: low molecular weight; CP: convalescent plasma; rhG-CSF: recombinant human granulocyte colony-stimulating factor; NAC: N-acetylcysteine. # : treatment administered in addition to SC, as defined by the investigators in each trial; ¶ : no between-group comparison; + : treatment on day 1 of study participation; § : treatment on day 6 of study participation; ƒ : outcome was disease progression or death; ## : p-value for comparison of 7-level ordinal outcome (including death) at 14 days; ¶¶ : treatment ≤10 days of symptom onset; ++ : treatment >10 days of symptom onset; §§ : re-analysis of data from W ang et al. [ 44 ] using different criteria; ƒƒ : outcome was incidence of hospitalisation or death; ### : p-value for comparisons of the 7-level ordinal outcome (including death) at 15 days; : participants could be randomly assigned to other interventions within other therapeutic domains; +++ : median (95% CI) adjusted odds ratios versus the no-hydrocortisone group were 1.03 (0.53–1.95) and 1.10 (0.58–2.11) for the fixed-dose and shock-dependent dosing hydrocortisone groups, respectively. These yielded 54% and 62% Bayesian posterior probabilities of superiority. *: statistically significant p-value.

Significant findings across all outcomes assessed are summarised in figure 1b , with a detailed description of all findings in the following sections.

In total, 39 randomised and one non-randomised trial reported on mortality, either as the number of deaths that occurred during the study or as a pre-specified study end-point, with or without a statistical comparison between groups [ 28 – 34 , 36 – 54 , 56 – 68 , 70 ]. One study included [ 69 ] was a re-analysis of data from another trial (W ang et al. [ 44 ]) included in the qualitative synthesis. The outcomes for individual trials are shown in figure 2 .

Among trials of antivirals ( figure 2a ), four trials (n=48–66) reported trends towards decreased mortality with interferons (IFNs), sofosbuvir+daclatasvir, and triazavirin, compared with standard care or placebo in patients with COVID-19 of varying severity [ 59 , 60 , 67 , 68 ]. Another trial (n=81) reported a significant reduction in 28-day mortality with IFN-β-1a plus standard care compared with standard care alone (19.0% versus 43.6%; p=0.015) in patients with severe COVID-19 [ 64 ]. The analysis also showed that administration of IFN β-1a early in the disease significantly reduced mortality (OR 13.5 (95% CI) 1.5–118) whereas late administration did not [ 64 ].

Of four trials that assessed lopinavir/ritonavir, three trials of patients with COVID-19 of varying severity (n=86–127) reported no deaths in either treatment group [ 37 , 39 , 62 ]. One trial (n=199) reported a numerical but nonsignificant reduction in 28-day mortality with lopinavir/ritonavir versus standard care in patients with severe COVID-19 [ 31 ]. Of four trials that investigated remdesivir in patients with moderate or severe COVID-19, two trials (n=1062 and n=236) and the re-analysis of W ang et al. [ 44 ] by S hih et al. [ 69 ] showed no significant mortality benefit of remdesivir compared with placebo [ 28 , 44 ], although one study showed a trend towards reduced mortality in patients who received treatment earlier in their disease course (within 10 days of symptom onset) [ 44 ]. The other two trials (n=397 and n=596) reported comparable mortality with 5- and 10-day remdesivir treatment [ 61 , 70 ].

Antimalarial and mucolytic drugs

Among eight trials of hydroxychloroquine or its derivatives ( figure 2b ), one trial (n=81) reported significantly greater lethality with high doses of chloroquine diphosphate compared with low doses (log-rank: −2.183; p=0.03) in patients with severe COVID-19 [ 29 ]. Six trials (n=150–821) reported similar mortality with hydroxychloroquine, with or without azithromycin, compared with standard care or placebo in hospitalised [ 33 , 45 , 63 ] or non-hospitalised [ 30 , 40 , 42 ] patients with mild, mild-to-moderate or severe COVID-19. Another trial (n=447) reported no mortality benefit of adding azithromycin to hydroxychloroquine compared with hydroxychloroquine alone in patients with severe COVID-19 [ 53 ]. In a trial of the mucolytic drug, bromhexine (n=78), there was a significant reduction in mortality with bromhexine plus standard care versus standard care alone (0% versus 12.8%; p=0.027) in patients with COVID-19 of unspecified severity [ 47 ].

Anti-inflammatory drugs

Among trials of corticosteroids conducted in patients with severe COVID-19, three trials (n=149–403) showed numerical but nonsignificant trends towards reduced mortality with hydrocortisone or methylprednisolone compared with placebo or standard care ( figure 2c ) [ 46 , 50 , 66 ]. One trial (n=62) reported significantly reduced mortality (5.9% versus 42.9%; p <0.001) with methylprednisolone plus standard care versus standard care alone [ 52 ] ( figure 2c ). In a large trial (n=6425) of patients with COVID-19 of unspecified severity, 28-day mortality was significantly decreased with dexamethasone plus standard care versus standard care alone, both overall (22.9% versus 25.7%; p <0.001) and in patients receiving oxygen (23.3% versus 26.2%) or mechanical ventilation (29.3% versus 41.4%) at randomisation ( figure 2c ) [ 43 ]. The mortality benefit was greatest in patients with a longer duration of symptoms (>7 days versus ≤7 days; 12.3 by Chi-squared test for trend) [ 43 ]. Another trial (n=105), also conducted in patients with COVID-19 of unspecified severity, showed significantly increased event-free survival with the anti-inflammatory drug colchicine in combination with standard care versus standard care alone (97% versus 83% of patients after 10 days; p=0.03; data not shown graphically) [ 36 ].

Other therapies

Trials (n=20–135) investigating the kinase inhibitor, ruxolitinib [ 32 ], the calcium release-activated calcium channel inhibitor, auxora [ 56 ], the anticoagulant, enoxaparin [ 54 ], and N-acetylcysteine, a mucolytic drug with anti-oxidant properties [ 49 ], in patients with severe COVID-19 reported no significant difference in mortality versus the comparator groups ( figure 2c ). One trial (n=200) reported a reduced 21-day mortality with recombinant human granulocyte colony-stimulating factor (rhG-CSF) added to standard care versus standard care alone (HR 0.19 (95% CI 0.04–0.88)) in patients with severe COVID-19 [ 48 ].

Two studies reported on immunomodulatory therapies in patients with moderate and/or severe COVID-19 ( figure 2c ): one single-arm trial (n=46) reported a mortality of 6.5% with hyperimmune plasma [ 41 ], and another trial (n=103) reported numerical but nonsignificant trends towards decreased mortality with convalescent plasma versus standard care [ 38 ].

Hospitalisation

In total, 36 randomised trials and one non-randomised trial reported on hospitalisation, either as a pre-specified study end-point or the number of patients experiencing a particular outcome during the study, with or without a statistical comparison between groups [ 28 , 30 – 33 , 35 – 38 , 40 – 55 , 57 – 62 , 64 – 68 , 70 ]. Additionally, one study included [ 69 ] was a re-analysis of data from another trial (W ang et al. [ 44 ]) included in the qualitative synthesis. Outcomes reported across studies included median duration of hospitalisation ( figure 3 ), proportion of patients discharged during the study ( table 1 ), incidence of intensive care unit (ICU) admittance ( table 1 ) and incidence of hospitalisation ( supplementary figure 3 ).

Duration of hospitalisation in trials of a) antivirals, antimalarial and mucolytic drugs and b) other therapies included in the qualitative synthesis. Data are presented as median (IQR) unless indicated otherwise. Other therapies include anti-inflammatory drugs, anticoagulants, immunomodulatory therapies and repair therapies. IFN: interferon; SC: standard care; TFF2: trefoil factor 2; SOF: sofosbuvir; DCV: daclatasvir; RBV: ribavirin; HCQ: hydroxychloroquine; LPV/r: lopinavir/ritonavir; FAV: favipiravir; RDV: remdesivir; PBO: placebo; FNC: azvudine; LEF: leflunomide; AZ: azithromycin; DEX: dexamethasone; MP: methylprednisolone; COL: colchicine; ENOX: enoxaparin; LMW: low molecular weight; CP: convalescent plasma; rhG-CSF: recombinant human granulocyte colony-stimulating factor; NAC: N-acetylcysteine. # : mean± sd ; ¶ : treatment administered in addition to SC, as defined by the investigators in each trial; + : mean (95% CI); § : post-hoc analysis; ƒ : treatment on day 1 of study participation; ## : treatment on day 6 of study participation; ¶¶ : treatment <7 days from symptom onset; ++ : treatment ≥7 days from symptom onset; §§ : no between-group comparison; ƒƒ : outcome only assessed in survivors; ### : no IQR reported; : per hospital protocol, all patients meeting acute respiratory distress syndrome criteria were given pre-emptively intravenous ceftriaxone (1 g twice for 7 days) plus azithromycin (500 mg once for 5 days) or clarithromycin (500 mg twice for 7 days), starting on day 1; +++ : upper IQR limit could not be determined; §§§ : median value could not be determined, HR 1.90 (95% CI 0.45–8.04); p=0.38. *: statistically significant p-value.

Three trials of IFNs (IFN-β-1a, IFN-β-1b or IFN-κ+trefoil factor 2) (n=66–81) added to standard care in patients with moderate or severe COVID-19 reported improvements in hospitalisation outcomes, including reduced hospitalisation duration [ 65 , 67 ] ( figure 3a ), a greater proportion of patients discharged [ 64 , 67 ] ( table 1 ) and reduced incidence of ICU admittance [ 64 , 67 ] ( table 1 ) versus standard care alone. Other trials (n=66 and n=88) reported reduced hospitalisation duration with sofosbuvir+daclatasvir+standard care (6 versus 8 days; p=0.029) and early versus late administration of favipiravir (14.5 versus 20 days; HR 1.963 (95% CI 1.331–2.894)) in patients with moderate or severe COVID-19 [ 68 ] or asymptomatic to mild COVID-19 [ 51 ], respectively ( figure 3a ). Patients treated with sofosbuvir+daclatasvir+standard care also had a significantly higher probability of hospital discharge by day 14 (p=0.041) versus standard care alone ( table 1 ).

Another trial (n=127), conducted in patients with COVID-19 of unspecified severity, reported a significant reduction in median hospitalisation duration with lopinavir/ritonavir+ribavirin+IFN-β-1b versus lopinavir/ritonavir alone [ 37 ]. When patients were stratified according to the timing of treatment administration, median hospitalisation was significantly reduced in patients who received treatment within 7 days of symptom onset, but not in those who received treatment later than this [ 37 ].

Among trials of remdesivir and the re-analysis of W ang et al. [ 44 ], one trial (n=1062) reported a reduced initial length of hospital stay with remdesivir versus placebo in patients with severe COVID-19 (median 12 versus 17 days) [ 28 ] ( figure 3a ). There were trends towards more patients discharged with remdesivir versus placebo/standard care, as well as with earlier remdesivir treatment in the remaining trials [ 44 , 61 , 69 , 70 ] ( table 1 ), but between-group differences were either not significant or not tested. A small pilot study (n=20) also reported a reduced mean duration of hospitalisation (7 versus 13 days; p=0.02) with the antiretroviral azvudine plus standard care versus standard care alone in patients with mild COVID-19 [ 57 ].

Among six trials assessing hydroxychloroquine (n=194–821) [ 30 , 33 , 40 , 42 , 45 , 53 ], with or without azithromycin, no benefit was seen relative to the comparator groups in terms of hospitalisation duration ( figure 3a ), incidence of ICU admittance ( table 1 ) or incidence of hospitalisation ( supplementary figure 3 ). In a trial conducted in patients with COVID-19 of unspecified severity (n=78), ICU admittance was significantly reduced with bromhexine plus standard care versus standard care alone (5.1% versus 28.2%; p=0.006) [ 47 ] ( table 1 ).

One trial (n=6425) reported a numerically shorter median duration of hospitalisation (12 versus 13 days) and a greater probability of discharge alive within 28 days with dexamethasone plus standard care versus standard care alone (rate ratio 1.10 (95% CI 1.03–1.17)) in patients with COVID-19 of unspecified severity [ 43 ] ( figure 3b and table 1 ). In another trial of patients with severe COVID-19 (n=403), treatment with a 7-day fixed-dose course or shock-dependent dosing of hydrocortisone were associated with reduced hazard ratios for length of hospital and ICU stay; however, neither treatment strategy met pre-specified criteria for statistical superiority [ 46 ]. A further trial (n=62) reported a significantly reduced time to the composite outcome of hospital discharge or death with methylprednisolone plus standard care versus standard care alone in patients with severe COVID-19 (median 11.6 versus 17.6 days; p=0.006) [ 52 ] ( table 1 ). Other trials of anti-inflammatory agents (n=54–416) reported no differences in hospitalisation outcomes versus comparators ( figure 3b , table 1 and supplementary figure 3 ) [ 35 , 36 , 50 , 66 ].

Among trials of other therapies, one trial (n=103) reported numerical but nonsignificant trends towards reduced hospitalisation duration and increased numbers of patients discharged with convalescent plasma versus standard care [ 38 ] ( figure 3b and table 1 ). Trials of rhG-CSF (n=200), N-acetylcysteine (n=135), enoxaparin (n=20) and ruxolitinib (n=43) reported no significant impact of these interventions on hospitalisation outcomes versus standard care or placebo [ 32 , 48 , 49 , 54 ] ( figure 3b and table 1 ).

Need for ventilation

In total, 30 randomised and one non-randomised trial, and one re-analysis of the study by W ang et al. [ 44 ], reported outcomes relating to the need for oxygen, noninvasive ventilation or intensive mechanical ventilation [ 28 , 31 – 34 , 36 , 37 , 39 – 41 , 43 – 50 , 52 – 56 , 59 – 61 , 64 , 66 – 70 ]. These end-points were reported either as pre-specified study end-points or the number of patients receiving a particular intervention during the study, with or without a statistical comparison between groups. The outcomes for individual trials are shown in figure 4 and supplementary table 4 .

Need for ventilation in trials of a) antivirals and b) other therapies included in the qualitative synthesis. Other therapies include antimalarial drugs, mucolytic drugs, anti-inflammatory drugs, anticoagulants, immunomodulatory therapies and repair therapies. Results from two studies are not presented graphically. L i et al. [ 39 ] reported need for intensive mechanical ventilation in two (15.4%) out of 13 severe patients; D eftereos et al. [ 36 ] reported need for ventilation among seven patients who met the primary clinical end-point: in the control group, one (14.3%) out of seven patients needed noninvasive mechanical ventilation and five (71.4%) were intubated and ventilated mechanically. The patient in the colchicine group who met the end-point needed invasive mechanical ventilation (MV). NIV: noninvasive ventilation; IFN: interferon; SC: standard care; SOF: sofosbuvir; DCV: daclatasvir; RBV: ribavirin; HCQ: hydroxychloroquine; LPV/r: lopinavir/ritonavir; TZV: triazavirin; PBO: placebo; DRV: darunavir; COBI: cobicistat; RDV: remdesivir; AZ: azithromycin; BHC: bromhexine; DEX: dexamethasone; HC: hydrocortisone; MP: methylprednisolone; RUX: ruxolitinib; rhG-CSF: recombinant human granulocyte colony-stimulating factor; NAC: N-acetylcysteine. # : treatment administered in addition to SC, as defined by the investigators in each trial; ¶ : no comparison between groups; + : p-value for clinical status at day 14 (composite of death and ventilation requirement outcomes); § : difference in clinical status distribution versus standard care, OR 1.65 (95% CI 1.09–2.48); ƒ : outcomes reported in patients not receiving ventilation at baseline; between-group differences (95% CI): oxygen −8 (−24– –8); NIV/high-flow oxygen −7 (−14– −1); MV/ECMO, −10 (−15– −4); ##: re-analysis of data from W ang et al . [ 44 ] using different criteria; ¶¶ : effect estimates versus SC: need for NIV 1.10 (95% CI 0.60–2.03); need for MV 1.77 (95% CI 0.81–3.87); ++ : effect estimates versus SC: need for NIV 1.19 (95% CI 0.65–2.21); need for MV 1.15 (95% CI 0.49–2.70); §§ : risk ratio: 0.77 (95% CI 0.62–0.95); ƒƒ : HR 2.6 (95% CI −8.6–13.6); ### : values are in comparison to baseline after 3 days after treatment. After discharge/death, the proportion of patients requiring supplementary oxygen was significantly decreased compared to baseline in both groups; : absolute risk reduction: 32% (95% CI −0.07–0.71). +++ : OR (95% CI) 1.21 (0.53–2.72). *: significant p-value or other comparison.

Most trials of antivirals did not report a significant impact of the interventions assessed on the number of patients requiring ventilation, or on related outcomes including duration of respiratory support ( figure 4a and supplementary table 4 ). However, there were trends towards decreased use of ventilation with IFN therapies, sofosbuvir+daclatasvir and triazavirin ( figure 4a ) [ 59 , 60 , 64 , 68 ]. Additionally, one trial (n=81) of patients with severe COVID-19 reported an increased number of patients extubated following treatment with IFN-β-1a plus standard care than with standard care alone (53.5% versus 11.8%; p=0.019) [ 64 ] ( supplementary table 4 ). Another trial (n=1062), also conducted in patients with severe COVID-19, reported fewer patients requiring new use of oxygen (36% versus 44%), noninvasive ventilation (17% versus 24%) or intensive mechanical ventilation (13% versus 23%) with remdesivir versus placebo ( figure 4a ) [ 28 ].

Four trials (n=194–665) reported no significant impact of hydroxychloroquine, with or without azithromycin, on reducing the need for ventilation or improving other respiratory outcomes compared with standard care, in patients with COVID-19 of a range of severities [ 33 , 40 , 45 , 53 ] ( figure 4b and supplementary table 4 ). Among two trials (n=78 and n=18) of bromhexine plus standard versus standard care alone, one reported significantly reduced numbers of patients with COVID-19 of unspecified severity requiring intensive mechanical ventilation with bromhexine (2.6% versus 23.1%; p=0.007) [ 47 ] ( figure 4b ). The other trial showed a numerical but nonsignificant trend towards reduced need for oxygen therapy with bromhexine (16.7% versus 33.3%; p=0.11) versus standard care, in patients with mild or moderate COVID-19 [ 55 ] ( figure 4b ).

Among studies of anti-inflammatory drugs ( figure 4b and supplementary table 4 ), one randomised trial (n=6425) reported a statistically significantly decreased need for invasive mechanical ventilation with dexamethasone plus standard care versus standard care alone in patients with COVID-19 of unspecified severity [ 43 ] ( figure 4b ). The risk of progression to invasive mechanical ventilation was also significantly lower with dexamethasone than with standard care (risk ratio 0.77 (95% CI 0.62–0.95)) [ 43 ]. Another trial (n=62) of patients with severe COVID-19 reported a significant reduction in the proportion of patients receiving oxygen after 3 days of treatment with methylprednisolone, compared with before treatment (82.4% versus 100%; p=0.025) [ 52 ] ( figure 4b ).

In a small trial (n=30), fewer patients with severe COVID-19 required invasive mechanical ventilation; and the composite end-point of death or invasive mechanical ventilation occurred significantly less frequently in patients receiving auxora than in those receiving standard care (HR 0.23 (95% CI 0.05–0.96); p<0.05) ( figure 4b and supplementary table 4 ) [ 56 ]. In another small trial (n=20), administration of enoxaparin significantly reduced the median number of ventilator-free days compared with low molecular weight/unfractionated heparin (0 versus 15 days; p=0.028) and resulted in a higher ratio of successful liberation from mechanical ventilation after respiratory failure (HR 4.0 (95% CI 1.035–15.053); p=0.031) in patients with severe COVID-19 ( supplementary table 4 ) [ 54 ]. Trials of ruxolitinib, N-acetylcysteine and rhG-CSF (n=43–200) showed no significant efficacy in reducing need for ventilation in patients with severe COVID-19 [ 32 , 48 , 49 ] ( figure 4b and supplementary table 4 ).

Quality of evidence

There was variation among trials in terms of the risk of bias in the various domains assessed ( supplementary figure 4 ). Most trials were deemed at low risk of bias in terms of complete reporting of patient and outcomes data. However, most trials were also judged to be at high risk of bias in terms of blinding of participants and researchers to the intervention received. Only four randomised double-blind trials were considered to be at low risk of bias for all domains assessed [ 42 , 44 , 50 , 66 ].

This systematic literature review assessed evidence, up to 14 October 2020, for the efficacy of pharmacological interventions for COVID-19 in terms of mortality, hospitalisation and need for ventilation. These outcomes were selected because COVID-19 has caused significant mortality worldwide and continues to impart a substantial burden on healthcare systems [ 19 – 22 , 71 ]. Although observational studies have yielded important and useful data, only clinical trials were included in this study to allow a synthesis of the highest quality evidence available for each of the interventions assessed.

Of 42 included trials, all but one assessed a treatment or treatment combination for patients with established COVID-19. One trial assessed the use of hydroxychloroquine as post-exposure prophylaxis in patients (n=821) with high-risk or moderate exposure to COVID-19 versus placebo. There was no difference in mortality or incidence of hospitalisation between groups, but the numbers of patients experiencing these events were very low (no deaths occurred and only one hospitalisation was reported in each group).

Among the trials assessing treatments in patients with COVID-19, interventions from several treatment classes showed significant effects in improving one or more of mortality, hospitalisation or need for ventilation outcomes. The most consistent effect across all outcomes assessed was reported for the corticosteroid, dexamethasone, in a preliminary report from the Randomised Evaluation of COVid-19 thERapY (RECOVERY) trial ( clinicaltrials.gov identifier NCT04381936 ). The study investigators reported a significant reduction in mortality and a greater probability of discharge alive within 28 days with dexamethasone plus standard care versus standard care alone in patients who were receiving either invasive mechanical ventilation or oxygen at randomisation. Duration of hospitalisation was numerically shorter and the need for invasive mechanical ventilation in patients who were not already receiving this at randomisation was significantly reduced with the addition of dexamethasone to standard care [ 43 ].

Other trials of anti-inflammatory drugs included in the present review presented some evidence for efficacy in improving one or more of the outcomes assessed [ 36 , 46 , 50 , 52 , 66 ]. However, issues reported in these studies included small patient numbers [ 52 , 66 ], potentially being underpowered [ 50 ], and late administration of the study treatment in some patients [ 66 ]. Since our systematic searches were conducted, further randomised trial evidence has emerged to support the efficacy of colchicine in reducing the composite end-point of death or hospitalisation in outpatients with COVID-19 [ 72 ]. Additionally, in an initial report from the international Randomised, Embedded, Multifactorial Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) trial ( clinicaltrials.gov identifier NCT02735707 ), the interleukin-6 receptor antagonists tocilizumab and sarilumab improved clinical outcomes, including survival, compared with standard care in critically ill patients with COVID-19 [ 73 ]. These findings are promising and, together with further well-designed and adequately powered studies, will increase the body of evidence for anti-inflammatory drugs as treatment for COVID-19.

In this review, findings from trials assessing drugs acting in the early infection or early pulmonary disease phase varied depending on the type of agent evaluated, the disease stage of patients enrolled in the study and the timing of drug administration. Some efficacy in reducing mortality, hospitalisation duration and need for ventilation was seen with IFN therapies [ 64 , 65 , 67 ]; conversely, most trials of lopinavir/ritonavir reported no significant benefit above standard care [ 31 , 39 , 62 ]. Similarly, and in line with conclusions from published meta-analyses [ 74 , 75 ] and a large randomised trial published after the cut-off dates of our systematic searches [ 76 ], trials that assessed hydroxychloroquine, an antimalarial drug, or its derivatives reported no impact of these interventions on any of the outcomes of interest. Moreover, high doses of chloroquine diphosphate were associated with increased lethality compared with low doses in one trial [ 29 ].

Trials that assessed remdesivir compared with placebo or standard care also failed to show a clear benefit in terms of reducing mortality, although one trial showed a reduction in median hospitalisation duration and need for ventilation with remdesivir compared with placebo [ 28 ]. Despite the lack of strong evidence of efficacy with remdesivir, it is notable that this drug has received endorsement from regulatory bodies for treating COVID-19 on the basis of data showing improved time to recovery and fewer adverse events in remdesivir-treated patients versus placebo in two clinical trials included in the present review, particularly in patients with less severe disease [ 28 , 44 ]. Consistent with these findings, a re-analysis of data from one trial [ 44 ] suggested that remdesivir induced good responses in patients with moderately severe, rather than critical, disease at enrolment [ 69 ]. Taken together, the data suggest that remdesivir may improve recovery time if administered early enough in the disease course; appropriate selection of patients with early-stage disease in future trials of remdesivir may help to confirm this hypothesis. Additionally, the combination of remdesivir with other agents may be more efficacious than remdesivir alone, as demonstrated in a randomised clinical trial of remdesivir plus baricitinib, published after the cut-off date for the systematic searches in this review [ 77 ].

Collectively, the findings from trials of drugs acting early in the pathophysiological course of COVID-19 summarised in the present review are broadly in line with those from the SOLIDARITY trial. This international, randomised trial of COVID-19 treatments concluded that all four treatments evaluated (remdesivir, hydroxychloroquine, lopinavir/ritonavir and IFNs) had little or no effect on overall mortality, initiation of ventilation and hospitalisation duration in patients hospitalised with COVID-19 [ 78 ]. A notable difference is that some studies in the present review did show some efficacy of IFN therapies in reducing mortality and improving hospitalisation outcomes [ 64 , 65 , 67 ], suggesting that these therapies may warrant further investigation. Indeed, findings from a phase 2 pilot study published after the cut-off date for the systematic searches in this review reported that SNG001, an inhaled INF-β-1a formulation, was associated with greater odds of improvement and more rapid recovery versus placebo in patients hospitalised with COVID-19 [ 79 ]. A global phase 3 trial investigating SNG001 was initiated in January 2021 (EudraCT number: 2020-004743-83).

A key finding from this review was the relationship between the disease phase of patients with COVID-19 and the efficacy of interventions. Several studies that investigated agents targeting processes early in the disease course of COVID-19, such as viral replication, showed improved efficacy in patients who received early treatment compared with late treatment [ 31 , 37 , 44 , 67 ]. Similarly, in a report from the RECOVERY trial, the mortality benefit of dexamethasone was greatest in patients with either the most severe disease or with the longest duration of symptoms [ 43 ], which is consistent with it acting during the inflammatory phase of the disease. These findings highlight the need for COVID-19 therapies to be tailored to patients with disease stage and severity appropriate to the mechanism of action of the intervention, as has been reported previously [ 80 ], in order for maximum efficacy to be attained.

A small number of trials included in the present review reported on other types of therapies, including kinase or calcium release-activated calcium channel inhibitor, anticoagulants, convalescent plasma and other immunomodulatory or repair therapies [ 32 , 38 , 41 , 48 , 49 , 54 , 56 ]. Some studies showed either a significant efficacy effect or a trend towards improved efficacy [ 32 , 38 , 48 , 49 , 54 , 56 ]; however, in general the studies were of low quality and further clinical trials are required for a more conclusive demonstration of efficacy. In particular, although convalescent plasma has received emergency use authorisation from the US FDA, the decision was controversial, owing to a lack of robust supporting data [ 13 ]. Only one study of convalescent plasma (n=103) was included in the current review, and did not show significant improvements in any of the outcomes assessed compared with standard care [ 38 ]. Notably, a study published after the systematic searches for this systematic literature review were conducted reported a benefit of high-titre convalescent plasma in reducing progression to severe illness when administered to adults aged ≥75 years within 72 h of mild symptoms [ 81 ]. These findings support the premise that the timing of treatment and patient selection may be important determinants of efficacy with COVID-19 therapies. Ongoing randomised studies, including the UK's RECOVERY trial, will be important in providing further evidence to clarify whether convalescent plasma offers any benefit in treating COVID-19.

It was also notable that only one small clinical trial (n=20) [ 54 ] reported on an anticoagulant, enoxaparin, despite many institutions having adopted anticoagulant therapy as standard care for hospitalised patients with COVID-19. The data presented were promising and showed a reduced need for ventilation compared with patients receiving prophylactic anticoagulation. These findings support those from a recently published large observational study, which reported an association between anticoagulant use and lower in-hospital mortality and intubation rates in patients hospitalised with COVID-19 [ 82 ]. Upcoming randomised trials will provide valuable data regarding the optimal type, duration and dose of anticoagulants for different patients.

Strengths of this systematic literature review include the comprehensive search strategy, rigorous screening methodology and quality assessment of included studies. Although several other reviews of COVID-19 therapies have been published [ 7 , 83 , 84 ], we believe that our overview of the current state of the field makes an important contribution to the existing body of evidence, particularly by considering the efficacy of current treatments in the context of the disease phase in which they are used.

Limitations include the exclusion of non-English articles from searches, which may have resulted in some relevant trials not being identified. Additionally, the searches were conducted in three major databases but did not include smaller or country-specific databases. Congress abstracts were excluded on the basis of their data being preliminary and not peer reviewed. Owing to time constraints and the fast-moving nature of the field, a review of citations in the reference lists of published systematic literature reviews and meta-analysis was not conducted; and, similarly, the review will not have captured relevant studies published after the date of the final search update.

Our ability to analyse and compare the data collected was limited by the significant variation between the trials identified, owing to differences in study design, blinding of participants, severity of illness of participants, drug doses and timing, comparators, follow-up times and outcome definitions. Most trials administered interventions in addition to standard care, which varies between countries and has changed during the pandemic as the body of evidence for the efficacy and safety of different therapies has accumulated. Similarly, the overall lack of high-quality evidence for all interventions makes it difficult to make comparisons between interventions.

Most of the trials included in this systematic literature review were assessed as having a high risk of bias in one or more of the domains evaluated, meaning that the results of the analysis of all outcomes should be interpreted with caution. A variety of factors contributed to this: some trials were pilot [ 34 , 55 , 57 , 59 ] or proof-of-concept [ 41 ] trials, including small patient numbers and might not have been optimally designed to show an improvement in the study outcomes. Similarly, some trials were under-powered to show significant differences between interventions [ 32 , 38 , 40 , 44 , 54 , 62 , 63 ]. Some trials did not include a randomised placebo control group and many trials were not double-blinded. Moreover, the follow-up period in most trials was between 14 and 28 days, and most studies did not report any longer term data. This might have limited the ability to assess mortality and hospital discharge, particularly in patients with severe COVID-19 who often have a prolonged duration of illness.

These findings are in agreement with those reported in a living systematic review and meta-analysis of drug treatments for COVID-19, last updated in September 2020 [ 84 ]. The authors concluded that the effectiveness of most of the interventions that they assessed was uncertain, owing to the small numbers of patients enrolled in randomised controlled trials at the time and important limitations of the study designs [ 84 ]. A European Medicines Agency statement noted that small studies and compassionate programmes are unlikely to generate the required level of evidence to define the best treatment options for COVID-19 and stressed the need for multi-arm randomised controlled trials of interventions [ 85 ]. However, it should be acknowledged that the COVID-19 global public health emergency has presented an urgent need for clinical data on available interventions for the disease, with the US FDA granting some substances emergency use authorisation ( table 1 ). Thus, the available data are valuable for the purpose of practical clinical decision making.

Although no relevant data from vaccine, antibody and other novel COVID-19-specific interventions were available at the time of conducting this review, interim data from several phase 3 clinical trials of vaccines have shown high efficacy in preventing symptomatic COVID-19 infection and protection against severe disease [ 86 – 88 ]. Further findings from ongoing clinical trials are awaited with interest.

Conclusions

This systematic literature review summarises evidence regarding efficacy of pharmacological interventions in terms of mortality, hospitalisation and need for ventilation in patients with COVID-19, and highlights the need for adequately powered, well-designed clinical trials to increase the quality of available evidence. The summary of findings also suggests the need to use interventions appropriate for the disease stage of COVID-19, to maximise treatment efficacy.

• Supplementary material

Supplementary Material

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Supplementary material ERR-0384-2020.Supplementary_material

• Acknowledgements

Editorial support was provided by Rachael Cazaly (Core Medica, London, UK), supported by AstraZeneca according to Good Publication Practice guidelines. The sponsor was involved in the study design, analysis and interpretation of data. However, ultimate responsibility for opinions, conclusions and data interpretation lies with the authors.

This article has supplementary material available from err.ersjournals.com

Provenance: Submitted article, peer reviewed

Conflict of interest: T. Welte reports personal fees, fees for lectures and consultancy fees from AstraZeneca, Basilea, Biotest, Bayer, Boehringer, GlaxoSmithKline, Janssens, Merck Sharp & Dohme, Novartis, Pfizer, Roche and Sanofi Aventis, outside the submitted work.

Conflict of interest: L.J. Ambrose reports other funding from AstraZeneca, during the conduct of the study.

Conflict of interest: G.C. Sibbring reports other funding from AstraZeneca, during the conduct of the study.

Conflict of interest: S. Sheikh was an employee and shareholder of AstraZeneca at the time of manuscript preparation.

Conflict of interest: H. Müllerová is an employee and shareholder of AstraZeneca.

Conflict of interest: I. Sabir is an employee and shareholder of AstraZeneca.

• Received December 4, 2020.
• Accepted February 10, 2021.
• Copyright ©The authors 2021.

This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions{at}ersnet.org

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Related Articles

• Open access
• Published: 05 June 2021

Covid-19 and non-communicable diseases: evidence from a systematic literature review

• Zlatko Nikoloski 1 ,
• Ada Mohammed Alqunaibet 2 ,
• Rasha Abdulrahman Alfawaz 2 ,
• Sami Saeed Almudarra 3 ,
• Christopher H. Herbst 4 ,
• Sameh El-Saharty 5 ,
• Reem Alsukait 4 &
• Abdullah Algwizani 2  

BMC Public Health volume  21 , Article number:  1068 ( 2021 ) Cite this article

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Since early 2020, the Covid-19 pandemic has engulfed the world. Amidst the growing number of infections and deaths, there has been an emphasis of patients with non-communicable diseases as they are particularly susceptible to the virus. The objective of this literature review is to systematize the available evidence on the link between non-communicable diseases and Covid-19.

We have conducted a systematic review of the literature on Covid-19 and non-communicable diseases from December, 2019 until 15th of November, 2020. The search was done in PubMed and in doing so we used a variety of searching terms in order to isolate the final set of papers. At the end of the selection process, 45 papers were selected for inclusion in the literature review.

The results from the review indicate that patients with certain chronic illnesses such as diabetes, hypertension (and other cardiovascular diseases), chronic respiratory illnesses, chronic kidney and liver conditions are more likely to be affected by Covid-19. More importantly, once they do get infected by the virus, patients with chronic illnesses have a much higher likelihood of having worse clinical outcomes (developing a more severe form of the disease or dying) than an average patient. There are two hypothesized channels that explain this strong link between the chronic illnesses enumerated above and Covid 19: (i) increased ACE2 (angiotensin-converting enzyme 2) receptor expressions, which facilitates the entry of the virus into the host body; and (ii) hyperinflammatory response, referred to as “cytokine storm”. Finally, the literature review does not find any evidence that diabetes or hypertension related medications exacerbate the overall Covid-19 condition in chronic illness patients.

Conclusions

Thus, the evidence points out to ‘business as usual’ disease management model, although with greater supervision. However, given the ongoing Covid-19 vulnerabilities among people with NCDs, prioritizing them for the vaccination process should also figure high on the agenda on health authorities.

Peer Review reports

Introduction

The novel Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) caused a cluster of pneumonia cases in China at the end of 2019. After a few months, it led to a pandemic that has spread throughout most countries of the world. SARS-CoV-2 disease (Covid-19) primarily manifests as a lung infection and its clinical course is characterized by respiratory symptoms ranging from a mild respiratory infection (including fever, cough and fatigue) to pneumonia, acute respiratory distress syndrome (ARDS), shock, and death. While Covid-19 had been initially considered as a respiratory infection, causing harm primarily through inflammatory and immunological processes in the respiratory tract, emerging evidence points out that patients with non-communicable diseases (NCDs) are at higher risk of contracting Covid-19 and suffering worse consequences; moreover, emerging evidence points to a strong feedback mechanism between Covid-19 and existing chronic illnesses (e.g. diabetes) thus further contributing to organ damage and fatal consequences.

The interplay between Covid-19 and NCD shows a set of different effects, both direct and indirect. Direct effects relate primarily to the fact that there is a significant number of preliminary reports connecting certain pre-existing conditions, such as cardiac failure, coronary heart disease, hypertension and diabetes, to a more severe course of Covid-19. Thus, comorbidities may play an important role both in increased susceptibility for infection with SARS-CoV-2 as well as increase the risk of a more severe course of the disease. For now, it seems that an important mechanism is inflammation in the small vessels, particularly in the heart and lungs, but potentially also in other organs, e.g. digestive tract.

Indirect effects are more difficult to measure as they may range from the avoidance of using health services due to the fear of infection. This may lead to: (a) delays in the diagnosis of more acute conditions, such as acute myocardial infarction (AMI) or stroke (CVI); (b) skipping screening appointments or their cancellation due to the running epidemic; (c) lengthening of the waiting lists for diagnostic and therapeutic procedures.

An important feature of the Covid-19 pandemic is also the fact that the knowledge about it is only being gained and it is still unfolding. We are faced with an interesting challenge, where it has become easy to publish quickly about the different findings. But this goes against the scientific rigor in some cases. It is essential to exert an above average level of caution when interpreting results.

Against this background, we conduct this literature review with the main purpose of shedding more light on: (i) the prevalence of Covid-19 (and hence susceptibility) among patients with chronic illnesses; (ii) the analytical link between NCDs, disease progression and disease outcome among patients with selected NCDs; (iii) the pathways through which Covid-19 impacts upon patients with various NCDs from a clinical perspective and (iv) to provide a more definitive answer on the link between medications used to manage various NCDs and Covid-19 progression and outcome.

Methodology

We conducted literature review on published papers from 31st of December, 2019 until the 15th of November, 2020. The search was done in PubMed and in doing so we used a variety of searching terms in order to isolate the final set of papers. The template below provides a snapshot of the search mechanisms that we used:

(“covid 19” or “COVID 19” or “sars Cov 2” or “coronavirus” or “corona virus”) and (“non communicable diseases” or “non communicable disease” or “NCD” or “NCDs” or “chronic illnesses” or “chronic diseases”)

(“covid 19” or “COVID 19” or “sars Cov 2” or “coronavirus” or “corona virus”) and (“diabetes” or “cardiovascular diseases” or “hypertension” or “cancer” or “kidney disease”).

The studies that were extracted were carefully examined. The inclusion criteria included, inter alia, prevalence of the NCDs in the search criteria. Moreover, we also included studies that reported clinical outcomes in form of disease severity as well as death. The studies were limited to adult humans and had to be written in English in order to be considered. Duplicate studies, letters, case reports, abstracts, studies written in languages other than English were excluded.

No ethical approval was needed, given that this was a literature review of published studies.

Figure  1 above provides a snapshot of the selection criteria for the papers in this literature review. Initially, 2732 records were identified and after screening for titles, abstracts and full papers, 45 records were retained which were used in this literature review and which we elaborate on throughout this paper.

Flow chart of the publication selection process. Source: Authors

Covid-19 and diabetes

Most of the studies included in this literature review study the link between Covid-19 and diabetes (Table  1 ). A strand of this literature has focused on studying the prevalence of diabetes in Covid-19 patients, albeit descriptively. According to the existing studies, the prevalence of diabetes in Covid-19 patients varies, but it is almost always in double digit levels. More specifically, the prevalence of diabetes among Covid-19 patients ranges from 14% [ 1 ], 17% [ 2 ], 22% [ 3 ] to 44% [ 4 ] (Additional file 1 : Table A1).

A special strand of the literature has also focused on a more analytical link between diabetes and Covid-19, focusing on a few specific outcomes, such as mortality or severity of the disease. The common thread in this strand of the literature is that patients with diabetes show a consistently lower likelihood of survival or recovery and are much more likely to have a severe disease progression, compared to the non-diabetic patients.

In a retrospective case series, Yan et al. [ 5 ] for example, report that the survival rate was lower among the diabetic patients compared to the non-diabetic ones. More specifically, in their study, the HR was 1.5 (95% CI 1.0–2.3) after adjustment for demographic factors. Similarly, Yan et [ 6 ] find the patients with diabetes had consistently and independently poorer outcomes with a relative risk of dying at 3.0 (95% CI 1.3–6.8). In the context of Mexico, a study also found that diabetes is associated with hospitalization and worse outcomes among patients with Covid-19. These findings are echoed in some existing literature reviews [ 7 ]. For example, in a study by Du et al. [ 8 ], the risk of severe cases was higher in Covid-19 patients with diabetes (RR = 2.1, 95%CI 1.8–2.6) and the risk of death was also higher in Covid-19 patients with diabetes (RR = 3.2, 95%CI 2.6–3.8). Furthermore, and going beyond mortality as an outcome, Praveen et al. [ 9 ] find that diabetes was lower in the survivors (OR: 0.6; 95%CI: 0.4–0.9) and non-severe patients (OR: 1.7; 95%CI 1.2–2.3). Finally, Noor et al. [ 10 ] found a significant association between Covid-19 and mortality among diabetes patients (RR 1.9, 95% CI 1.2–2.8).

A few of the literature reviews that we include have conducted meta-analyses in order to further unearth the link between diabetes and Covid-19 mortality. In most of the cases, the authors find statistically significant link between diabetes and dying from Covid-19 with odds ratios higher than one. In a meta-analysis by Wu et al. [ 11 ], the authors find a close link between diabetes and mortality with OR of 1.75. In another literature review conducted by Ssentonoga et al. [ 12 ], diabetes was associated with a significantly greater risk of mortality from Covid-19 (OR 1.5, 95% CI 1.0–2.2). In the rest of the studies, the odds ratios capturing the link between diabetes and Covid-19 mortality teeter around 2.5 [ 13 , 14 , 15 , 16 , 17 ]. In only one of the studies the odds ratios of death due to Covid-19 among diabetics were higher than 3. More specifically, Lu et al. [ 18 ] find that diabetes comorbidity was one of the key mortality risk factors (OR = 3.7, 95% CI 2.4–5.9). Furthermore, and taking a more comprehensive approach Awortwe et al. [ 19 ] suggest that cardio-metabolic syndrome (mainly characterized by insulin resistance, impaired glucose tolerance, dyslipidemia, hypertension, and central adiposity) is associated with negative clinical outcomes including mortality (risk difference RD 0.1, 95%-CI 0.1–0.2), admission to ICU (RD 0.1, 95%-CI 0.04–0.2) and severe infection (RD 0.1, 95%-CI 0.01–0.09) in Covid-19 patients (Additional file  1 : Table A1).

Covid-19 and hypertension/cardiovascular diseases

The second most studied set of chronic illnesses are cardiovascular illnesses, including hypertension (Table 1 ). Similarly, to the case of diabetes, one strand of the literature has focused descriptively on the prevalence of hypertension and cardiovascular diseases among Covid-19 patients. In a literature review, Wolff et al. [ 20 ], establish that, along with diabetes, hypertension and other cardiovascular diseases are the most prevalent chronic illnesses among Covid-19 patients. The prevalence of hypertension in Covid-19 patients vary, from 15.6% [ 4 ], 16.4% [ 21 ], 22% [ 1 ], 27.4% [ 2 ] to 38.6% [ 3 ]. Similarly, the prevalence of other cardiovascular diseases is in double-digits varying form 4.7% [ 4 ], 8.9% [ 2 ], 12.1% [ 21 ], 13% [ 1 ] to 17.5% [ 3 ]. (Additional file 1 : Table A2).

In the second strand of the literature, the authors establish a more analytical link between hypertension (and the rest of the cardiovascular diseases) and negative clinical outcomes (e.g. death or severity of illness) among Covid-19 patients. In that respect, the link between hypertension and Covid-19 outcomes is particularly studied. A very comprehensive literature review by Pranata et al. [ 22 ] suggests that hypertension was associated with increased composite poor outcome (risk ratio (RR) 2.1 95% CI 1.9–2.4) and its sub-group, including mortality (RR 2.2 95% CI 1.7–2.8), severe Covid-19 (RR 2.0, CI 1.7–2.5), ARDS (RR 1.6, 95% CI: 1.1–2.4), ICU care (RR 2.1, 95% CI: 1.3–3.3), and disease progression (RR 3.0, 95% CI: 1.5–6.0). Similarly, in a literature review by Parveen et al. [ 9 ], hypertension was positively associated with death (OR: 0.5; 95% CI: 0.3–0.7), ICU care (OR: 0.4; 95%CI: 0.2–0.8) and severity (OR: 2.7; 95% CI 1.3–5.7) (Additional file 1 : Table A2).

While establishing a link between hypertension and Covid-19 mortality, most of the literature reviews also conduct meta-analyses, which find significantly higher odds of Covid-19 mortality among hypertensive patients, with odds ratios usually ranging from 2.5 to 3 [ 10 , 12 , 13 , 14 , 23 , 24 , 25 ]. Finally, only in one of the meta-analyses were the odds ratios of dying from Covid-19 among hypertensive patients was higher than 3. More specifically, a literature review by Liu et al. [ 17 ] confirms the finding that hypertension (OR 3.4, 95% CI 2.5–4.7) was one of the key mortality risk factors.

Similarly, to hypertension, a strand of this literature has focused exclusively on the clinical outcomes of Covid-19 patients with other cardio-vascular comorbidities. In a study by Gu et al. [ 26 ], the estimated mortality risk in patients with pre-existing coronary heart disease (CHD) was three times that of those without CHD. The estimated 30-day survival probability for a profile patient with pre-existing CHD (65-year-old woman with no other comorbidities) was 0.5 (95% CI 0.3–0.8). Furthermore, a study in Oman [ 27 ] found that patients with cardiac injury had higher mortality than those without cardiac injury (53.3% vs 7.1%). The literature review by de Almeida-Pittito [ 13 ] mentioned above also suggested that cardiovascular disease was strongly associated with both severity and mortality, respectively (OR 4.0 95% CI 2.8–5.9 and OR 6.3 95% CI 3.7–10.8) also reflecting the previous findings [ 10 ]. In addition, the literature review by Matshushita et al. [ 23 ] suggested that acute myocardial injury, determined by elevated high-sensitivity troponin levels, is commonly observed in severe cases, and is strongly associated with mortality. Moreover, a comprehensive review by Ssentonoga et al. [ 12 ] found that cardiovascular disease (risk ratio (RR) 2.3, 95% CI 1.6–3.2) and congestive heart failure (RR 2.03 95% CI 1.3–3.2) were associated with a significantly greater risk of mortality from Covid-19. A review by Khan et al. [ 16 ] found that higher likelihood of dying was found among Covid-19 patients who had pre-existing cardiovascular diseases (odds ratio 3.4 95% CI 2.9–4.1), reflecting the findings by Liu et al. [ 17 ]. Finally, a literature review by Hessami et al. [ 28 ] indicated that acute cardiac injury, (OR: 13.3, 95% CI 7.4–24.0), heart failure (OR: 6.7, 95% CI 3.3–13.5), arrhythmia (OR: 2.8, 95% CI 1.4–5.3), coronary artery disease (OR: 3.8, 95% CI 2.4–5.9), and cardiovascular disease (OR: 2.6, 95% CI 1.9–3.6) were significantly associated with Covid-19 mortality (Additional file 1 : Table A2).

Covid-19 and COPD

The third most prevalent chronic illness associated with negative outcomes due to Covid-19 is COPD (chronic obstructive pulmonary disease) as well as other underlying chronic illnesses. As in the rest of the literature, here as well, there are two strands that emerge: one of them is focused on the link between Covid-19 and chronic respiratory illnesses from a descriptive point of view, while the second strand is more analytical. In their literature review, Mahmud et al. [ 1 ] and Bajgain et al. [ 2 ] find that most prevalent chronic comorbid conditions were, inter alia, respiratory diseases (5%).

The second strand of the literature is more analytical and tries to establish a more robust link between pre-existing chronic lung illnesses and Covid-19 disease outcomes. Nachtigall et al. [ 29 ] for example argue that pre-existing lung disease was one of the main predictors of death (HR 1.6; 95%CI 1.2–2.2). Similarly, Lu et al. [ 18 ] in a literature review suggest that chronic lung disease (OR 3.4, 95% CI 1.8–6.5) was one of the key mortality risk factors, which is further echoed in the other studies included in this literature review [ 14 , 16 , 17 ] (Additional file 1 : Table A3).

Finally, a special strand of the literature has focused on COPD as the most dominant pulmonary chronic illness and its link with Covid-19. Graziani et al. [ 30 ] find that compared with COPD-free individuals, COPD patients with Covid-19 showed significantly poorer disease prognosis, as evaluated by hospitalizations (31.1% vs. 39.8%: OR 1.6; 95% CI 1.1–1.2) and mortality (3.4% vs. 9.3%: OR 2.9; 95% CI 2.3–3.8). In their literature review, Awortwe et al. [ 19 ], indicated that chronic obstructive pulmonary disease, inter alia, worsen the clinical outcomes including mortality (risk difference RD 0.1, 95%-CI 0.05–0.2), admission to ICU (RD 0.1, 95%-CI 0.04–0.2) and severe infection (RD 0.05, 95%-CI 0.01–0.09) in Covid-19 patients (Additional file 1 : Table A3).

Covid-19 and chronic kidney disease

The fourth most common chronic illnesses associated with negative outcomes related to Covid-19 are chronic kidney diseases. In a literature review by Bajgain et al. [ 2 ], around 2.6% of patients with chronic kidney disease also had Covid-19.

A literature review by Awowrtwe et al. [ 19 ] found a significantly higher likelihood of poor Covid-19 outcomes among patients with chronic kidney disease. Results suggested that chronic kidney disease, inter alia, was associated with worse clinical outcomes including mortality (risk difference RD 0.1, 95%-CI 0.1–0.12), admission to ICU (RD 0.1, 95%-CI 0.04–0.2) and severe infection (RD 0.05, 95%-CI 0.01–0.09) in Covid-19 patients. A literature review by Sepandi et al. [ 14 ] found that some chronic diseases such as kidney disorder (OR 2.6 95% CI 1.2–5.6) can increase the risk of Covid-19 mortality, which is similar to the rest of the studied included in this review [ 12 , 16 , 31 ] (Additional file 1 : Table A4).

Covid-19 and cancer

As in the rest of the chronic illnesses the literature on the link between Covid-19 and cancers could be divided into two strands. In the first one, authors are mainly concerned with finding the prevalence of cancer among Covid-19 patients. The literature reviews that we cover suggest prevalence of cancer among Covid-19 patients ranging from 1.2% [ 4 ] to 3.5% [ 2 ] and 8% [ 1 ] (Table A 5 ).

The second strand of the literature has analytically established a link between cancer and Covid-19 outcomes. In a literature review by Noor et al. [ 10 ] a significant association were found between mortality among Covid-19 infected patients and cancer (RR 2.3, 95% CI 1.8–3.0). Similarly, a literature review by Ssentonoga et al. [ 12 ] found that cancer (1.5 95% CI 1.01 to 2.2) was associated with a significantly greater risk of mortality from Covid-19. In their own review, Khan et al. [ 16 ] suggest higher likelihood of deaths was found among Covid-19 patients who had any types of cancers (OR 2.2, 95% CI 1.6–3.0). Finally, and specifically focusing on cancer patients, Zhang et al. [ 32 ] find a significantly higher mortality rate, particularly if the anti-tumour treatment was within the last 14 days. There is a nuance however. While existing evidence finds that the fatality rate in the lung cancer patients with Covid-19 was 32.9% (95% CI 27.9 to 38.0%) and the fatality rate in haematological cancer patients was 34.2% (95% CI 23.1 to 46.2%), in other types of solid cancer excluding lung, the overall case fatality and severe event rates were 17.2% (95% CI 12.3 to 22.7%) [ 33 ]. Similarly, another study finds that patients with solid versus hematologic cancers exhibit different clinical outcomes, with patients with hematologic cancers having a significantly higher mortality relative to patients with solid cancers after accounting for confounders [ 34 ].

Covid-19 and liver disease

The literature on the link between Covid-19 and chronic liver disease is less sanguine. In three of the four studies that we had identified, there is a clear link between existing liver chronic illness and higher likelihood of mortality. Oyelade et al. [ 35 ] found that in patients with Covid-19 and underlying liver diseases, 57.3% (43/75) of cases were severe, with 17.65% mortality. Khan et al. [ 16 ] found that there was a higher likelihood of deaths was found among Covid-19 patients who had pre-existing liver diseases (OR = 2.4, 95% CI 1.5–3.7), echoing previously established notion [ 10 ]. However, in another literature review, Wang et al. [ 31 ] found no relationship between Covid-19 mortality and pre-existing liver disease (Table A 6 ).

Covid-19 and asthma

As in the rest of the cases, a strand of the literature has focused on estimating the prevalence of asthma among Covid-19 patients. In a study in South Korea, the prevalence of asthma among Covid-19 patients was 2.9% [ 36 ], somewhat similar to another review which finds that asthma is a pre-morbid condition in about 1.6% of the Covid-19 patients [ 37 ]. Another systematic review of the link between asthma and Covid-19 finds a somewhat higher prevalence of asthma among Covid-19 patients (7.46%) [ 38 ] echoing the heterogeneity of prevalence across different countries and regions as reported in another systematic review [ 39 ].

The second strand of the literature has focused on studying the clinical outcomes of asthma patients with Covid-19. A systematic literature review finds that there was no significant difference in the combined risk of requiring admission to ICU and/or receiving mechanical ventilation for people with asthma (RR 0.87, 95% CI 0.94–1.37) and risk of death from Covid-19 (RR 0.87; 95% CI 0.68–1.10) [ 38 ].These findings are similar to the ones conducted in another meta-analysis [ 40 , 41 ]. Overall, the literature suggests that asthma is not an independent risk factors for the clinical outcomes of Covid-19 [ 36 ]. In a study by Chibba et al. [ 42 ], asthma was not associated with an increased risk of hospitalization (relative risk, 0.96; 95% CI, 0.8–1.2) after adjusting for age, sex, and comorbidities. Similarly, a literature review by Morais-Almeida et al. [ 43 ] found that there is no strong evidence supporting that patients with asthma have a higher risk of becoming seriously ill from coronavirus disease 2019 (Table A 7 ).

There are a few findings that stem from this review on the link between Covid-19 and non-communicable diseases. First, as evidenced by this review, studies have observed a high prevalence of certain chronic illnesses (diabetes, hypertension) among Covid-19 patients. Second, and going beyond descriptive observation, majority of the studies find that Covid-19 patients have higher likelihood of worse clinical outcomes (e.g. higher mortality) compared to patients without chronic illnesses. This is particularly the case for diabetes, hypertension, COPD and chronic kidney disease. Third, while our findings are similar for the rest of the chronic illnesses featured in this review, they are less sanguine in the case of chronic liver disease. Finally, the result of the literature review suggests no link between asthma and Covid-19.

While the research on the interplay between diabetes and Covid-19 is still ongoing, there are a few preliminary findings/research hypotheses that have been put forth as to why diabetic patients are associated with more pronounced Covid-19 complications.

First, the existing knowledge suggests that patients with chronic illnesses (diabetes, hypertension, other cardio-vascular diseases, chronic kidney disease) have increased ACE2 (angiotensin-converting enzyme 2) receptor expressions, which facilitates the entry of the virus into the host body [ 44 ]. Moreover, as the study by Erener et al. [ 45 ] suggests, ACE2 is expressed in various tissues including the lung, heart, kidney tubules, the luminal surface of the small intestine, blood vessels, endocrine and exocrine pancreas [ 45 ]. Similar explanations, specific to cardio-vascular diseases have been put forth by Pranata et al. [ 22 ]. In the case of asthma, it has been argued that respiratory epithelial cells in patients with asthma have decreased gene expression for ACE2 receptors and therefore may be protective against Covid-19 infection [ 46 ].

Another potential reason for the increased risk of severe Covid-19 disease in patients with chronic illnesses might be attributed to the hyperinflammatory response, referred to as “cytokine storm” [ 47 , 44 ]. Patients with certain chronic illnesses (e.g. diabetes, hypertension) suffer from a continuous low-grade inflammation facilitating the emergence of a cytokine storm, which in turn appears to be directly related to the severity of Covid-19 pneumonia cases and to subsequent death [ 47 ]. More specifically, patients with diabetes appear to have an impaired adaptive immune response characterized by an initial delay of Th1 cell-mediated immunity and a late hyperinflammatory response. In the absence of an immunostimulant, diabetes is associated with an increased pro-inflammatory cytokine response marked by increased secretion of IL-1, IL-6, IL-8 and TNF-α, which in turn play a more deleterious role in Covid-19 infection [ 44 , 45 ]. When specifically focusing on cancer, ACE2 and TMPRSS2 expression is found higher in cancer patients, and coagulopathy is a potential risk observed in a number of cancer patients [ 48 ]. Different hypotheses have been put forth regarding the differences in clinical outcomes between Covid-19 patients with solid vs. hematologic cancers. A study has found that the principal cause of elevated mortality risk from Covid-19 in solid cancer patients is cancer progression [ 49 ]. In contrast, the same study suggests that in haematological cancer patients, there was a particularly striking expression of exhaustion markers by CD8+ T cells. Exhausted T cells, in turn, may compromise virus clearance [ 49 ].

These links between Covid-19 and some of the chronic illnesses mentioned above have implications on the impact of current medical treatments for certain chronic illnesses on the probability of developing severe Covid-19. However, the results presented in studies covered in this literature review reveal that there is no evidence to support this hypothesis currently. In view of lack of robust evidence for either benefit or harm, it is reasonable for patients to continue using ACE inhibitors and ARB, as recommended by European Society of Cardiology Council on Hypertension, European Society of Hypertension and American Heart Association [ 44 ]. Moreover, there are several studies about the protective effect of statins in pneumonia [ 44 ]. Statins are known to increase ACE-2 levels and may protect against viral entry of SARS CoV-2. However, this increase in ACE-2 could be counterintuitive in the current context. Nevertheless, statins are known to inhibit Nuclear factor kappa B (NFkB) activation and might help in blunting the cytokine storm [ 44 ].

Similarly, to the case of diabetes, the literature review on the link between medications prescribed for managing hypertension and severity of Covid-19 finds no conclusive evidence. In a review by Hessami et al. [ 28 ] in 9 studies that were included, with a total of 10.900 Covid-19 cases, the random-effects analysis showed a combined OR for severity 0.76 (95% CI 0.39–1.49). Moreover, in their study there was a high heterogeneity indicating that there was no association between use of ACEI/ARB and Covid-19 severity. Continuation of ongoing treatment, coupled with self-management and remote interventions has also been suggested in the context of other chronic illnesses such as asthma [ 50 ].

Conclusions and policy implications

There are a few conclusions that stem from this comprehensive literature review on the link between NCDs and Covid-19. First, patients with certain chronic illnesses such as diabetes, hypertension (and other cardiovascular diseases), chronic respiratory illnesses, chronic kidney and liver conditions are more likely to be affected by Covid-19. This is further attested by the high prevalence of some of these chronic illnesses (such as diabetes and hypertension) among Covid-19 patients. More importantly, once they do get infected by the virus, patients with chronic illnesses have a much higher likelihood to either develop a more severe illness than an average patient; moreover they are more likely to die relative to patients who do not have chronic illnesses. Our literature review presents evidence on this obtained both from case- controlled studies as well as from other literature reviews that we distilled. Third, while the research on the reasons behind the high susceptibility of NCD patients to Covid-19 is still ongoing, researchers have hypothesised two main channels: (i) increased ACE2 (angiotensin-converting enzyme 2) receptor expressions, which facilitates the entry of the virus into the host body; and (ii) hyperinflammatory response, referred to as “cytokine storm”. Our literature review points out that these transmission mechanisms are at play when it comes to all Covid-19/NCD linkages. Finally, the literature review does not find any evidence that diabetes or hypertension related medications exacerbate the overall Covid-19 condition in chronic illness patients. Based on this there are a few implications/policy recommendations that stem from this research. First, it is recommended that patients continued with existing treatment for chronic illnesses, especially as there is no evidence that certain medications (e.g. for managing diabetes or hypertension) are associated with worse Covid-19 clinical outcomes. Second, there should be a greater emphasis on telemedicine and virtual visits. With physical distances no longer a factor, virtualizing the care provided by diabetes educators, dieticians, and specialized mental health professionals could improve access further than what was previously possible with in-person encounters [ 51 ]. Third, through the virtual visits there should also be an improvement in patient education and self-management [ 52 ]. Fourth, given the ongoing Covid-19 vulnerabilities among people with NCDs, prioritizing them for the vaccination process should also figure high on the agenda on health authorities. Finally, all of the steps above have to be done in resource-constrained setting, with resources being diverted to Covid-19 needs. Thus and going beyond just covering the immediate needs to patients, health system strengthening, while putting particular emphasis on primary healthcare, could go long way in providing effective and safe management of chronic illnesses [ 53 ].

Availability of data and materials

Not applicable.

Abbreviations

Acute respiratory distress syndrome

Noncommunicable disease

Acute myocardial infarction

Confidence interval

Odds ratios

Intensive care unit

Coronary heart disease

Chronic obstructive pulmonary disease

Angiotensin-converting enzyme 2

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Acknowledgements

The paper was produced by the Saudi Public Health Authority, in collaboration with technical support from the World Bank. The authors are grateful for the overall support provided by Rekha Menon, World Bank Practice Manager, Health Nutrition and Population, Middle East and North Africa region, and Issam Abousleiman, World Bank Country Director for GCC countries.

This paper was funded under the Reimbursable Advisory Services Program on Health, Nutrition and Population (P172148) between the World Bank and the Ministry of Finance, Saudi Arabia.

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Additional file 1: table a1..

Covid-19 and diabetes: overview of the papers included in this literature review. Table A2. Covid-19, hypertension and cardiovascular diseases: overview of the papers included in this literature review. Table A3. Covid-19, COPD and other chronic respiratory illnesses: overview of the papers included in this literature review. Table A4. Covid-19 and chronic kidney disease: overview of the papers included in this literature review. Table A5. Covid-19 and cancer: overview of the papers included in this literature review. Table A6. Covid-19 and chronic liver disease: overview of the papers included in this literature review. Table A7. Covid-19 and asthma: overview of the papers included in this literature review.

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Nikoloski, Z., Alqunaibet, A.M., Alfawaz, R.A. et al. Covid-19 and non-communicable diseases: evidence from a systematic literature review. BMC Public Health 21 , 1068 (2021). https://doi.org/10.1186/s12889-021-11116-w

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A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic

• Bastian A. Betthäuser   ORCID: orcid.org/0000-0002-4544-4073 1 , 2 , 3 ,
• Anders M. Bach-Mortensen   ORCID: orcid.org/0000-0001-7804-7958 2 &
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To what extent has the learning progress of school-aged children slowed down during the COVID-19 pandemic? A growing number of studies address this question, but findings vary depending on context. Here we conduct a pre-registered systematic review, quality appraisal and meta-analysis of 42 studies across 15 countries to assess the magnitude of learning deficits during the pandemic. We find a substantial overall learning deficit (Cohen’s d  = −0.14, 95% confidence interval −0.17 to −0.10), which arose early in the pandemic and persists over time. Learning deficits are particularly large among children from low socio-economic backgrounds. They are also larger in maths than in reading and in middle-income countries relative to high-income countries. There is a lack of evidence on learning progress during the pandemic in low-income countries. Future research should address this evidence gap and avoid the common risks of bias that we identify.

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Measuring and forecasting progress in education: what about early childhood?

The coronavirus disease 2019 (COVID-19) pandemic has led to one of the largest disruptions to learning in history. To a large extent, this is due to school closures, which are estimated to have affected 95% of the world’s student population 1 . But even when face-to-face teaching resumed, instruction has often been compromised by hybrid teaching, and by children or teachers having to quarantine and miss classes. The effect of limited face-to-face instruction is compounded by the pandemic’s consequences for children’s out-of-school learning environment, as well as their mental and physical health. Lockdowns have restricted children’s movement and their ability to play, meet other children and engage in extra-curricular activities. Children’s wellbeing and family relationships have also suffered due to economic uncertainties and conflicting demands of work, care and learning. These negative consequences can be expected to be most pronounced for children from low socio-economic family backgrounds, exacerbating pre-existing educational inequalities.

It is critical to understand the extent to which learning progress has changed since the onset of the COVID-19 pandemic. We use the term ‘learning deficit’ to encompass both a delay in expected learning progress, as well as a loss of skills and knowledge already gained. The COVID-19 learning deficit is likely to affect children’s life chances through their education and labour market prospects. At the societal level, it can have important implications for growth, prosperity and social cohesion. As policy-makers across the world are seeking to limit further learning deficits and to devise policies to recover learning deficits that have already been incurred, assessing the current state of learning is crucial. A careful assessment of the COVID-19 learning deficit is also necessary to weigh the true costs and benefits of school closures.

A number of narrative reviews have sought to summarize the emerging research on COVID-19 and learning, mostly focusing on learning progress relatively early in the pandemic 2 , 3 , 4 , 5 , 6 . Moreover, two reviews harmonized and synthesized existing estimates of learning deficits during the pandemic 7 , 8 . In line with the narrative reviews, these two reviews find a substantial reduction in learning progress during the pandemic. However, this finding is based on a relatively small number of studies (18 and 10 studies, respectively). The limited evidence that was available at the time these reviews were conducted also precluded them from meta-analysing variation in the magnitude of learning deficits over time and across subjects, different groups of students or country contexts.

In this Article, we conduct a systematic review and meta-analysis of the evidence on COVID-19 learning deficits 2.5 years into the pandemic. Our primary pre-registered research question was ‘What is the effect of the COVID-19 pandemic on learning progress amongst school-age children?’, and we address this question using evidence from studies examining changes in learning outcomes during the pandemic. Our second pre-registered research aim was ‘To examine whether the effect of the COVID-19 pandemic on learning differs across different social background groups, age groups, boys and girls, learning areas or subjects, national contexts’.

We contribute to the existing research in two ways. First, we describe and appraise the up-to-date body of evidence, including its geographic reach and quality. More specifically, we ask the following questions: (1) what is the state of the evidence, in terms of the available peer-reviewed research and grey literature, on learning progress of school-aged children during the COVID-19 pandemic?, (2) which countries are represented in the available evidence? and (3) what is the quality of the existing evidence?

Our second contribution is to harmonize, synthesize and meta-analyse the existing evidence, with special attention to variation across different subpopulations and country contexts. On the basis of the identified studies, we ask (4) to what extent has the learning progress of school-aged children changed since the onset of the pandemic?, (5) how has the magnitude of the learning deficit (if any) evolved since the beginning of the pandemic?, (6) to what extent has the pandemic reinforced inequalities between children from different socio-economic backgrounds?, (7) are there differences in the magnitude of learning deficits between subject domains (maths and reading) and between age groups (primary and secondary students)? and (8) to what extent does the magnitude of learning deficits vary across national contexts?

Below, we report our answers to each of these questions in turn. The questions correspond to the analysis plan set out in our pre-registered protocol ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249944 ), but we have adjusted the order and wording to aid readability. We had planned to examine gender differences in learning progress during the pandemic, but found there to be insufficient evidence to conduct this subgroup analysis, as the large majority of the identified studies do not provide evidence on learning deficits separately by gender. We also planned to examine how the magnitude of learning deficits differs across groups of students with varying exposures to school closures. This was not possible as the available data on school closures lack sufficient depth with respect to variation of school closures within countries, across grade levels and with respect to different modes of instruction, to meaningfully examine this association.

The state of the evidence

Our systematic review identified 42 studies on learning progress during the COVID-19 pandemic that met our inclusion criteria. To be included in our systematic review and meta-analysis, studies had to use a measure of learning that can be standardized (using Cohen’s d ) and base their estimates on empirical data collected since the onset of the COVID-19 pandemic (rather than making projections based on pre-COVID-19 data). As shown in Fig. 1 , the initial literature search resulted in 5,153 hits after removal of duplicates. All studies were double screened by the first two authors. The formal database search process identified 15 eligible studies. We also hand searched relevant preprint repositories and policy databases. Further, to ensure that our study selection was as up to date as possible, we conducted two full forward and backward citation searches of all included studies on 15 February 2022, and on 8 August 2022. The citation and preprint hand searches allowed us to identify 27 additional eligible studies, resulting in a total of 42 studies. Most of these studies were published after the initial database search, which illustrates that the body of evidence continues to expand. Most studies provide multiple estimates of COVID-19 learning deficits, separately for maths and reading and for different school grades. The number of estimates ( n  = 291) is therefore larger than the number of included studies ( n  = 42).

Flow diagram of the study identification and selection process, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

The geographic reach of evidence is limited

Table 1 presents all included studies and estimates of COVID-19 learning deficits (in brackets), grouped by the 15 countries represented: Australia, Belgium, Brazil, Colombia, Denmark, Germany, Italy, Mexico, the Netherlands, South Africa, Spain, Sweden, Switzerland, the UK and the United States. About half of the estimates ( n  = 149) are from the United States, 58 are from the UK, a further 70 are from other European countries and the remaining 14 estimates are from Australia, Brazil, Colombia, Mexico and South Africa. As this list shows, there is a strong over-representation of studies from high-income countries, a dearth of studies from middle-income countries and no studies from low-income countries. This skewed representation should be kept in mind when interpreting our synthesis of the existing evidence on COVID-19 learning deficits.

The quality of evidence is mixed

We assessed the quality of the evidence using an adapted version of the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool 9 . More specifically, we analysed the risk of bias of each estimate from confounding, sample selection, classification of treatments, missing data, the measurement of outcomes and the selection of reported results. A.M.B.-M. and B.A.B. performed the risk-of-bias assessments, which were independently checked by the respective other author. We then assigned each study an overall risk-of-bias rating (low, moderate, serious or critical) based on the estimate and domain with the highest risk of bias.

Figure 2a shows the distribution of all studies of COVID-19 learning deficits according to their risk-of-bias rating separately for each domain (top six rows), as well as the distribution of studies according to their overall risk of bias rating (bottom row). The overall risk of bias was considered ‘low’ for 15% of studies, ‘moderate’ for 30% of studies, ‘serious’ for 25% of studies and ‘critical’ for 30% of studies.

a , Domain-specific and overall distribution of studies of COVID-19 learning deficits by risk of bias rating using ROBINS-I, including studies rated to be at critical risk of bias ( n  = 19 out of a total of n  = 61 studies shown in this figure). In line with ROBINS-I guidance, studies rated to be at critical risk of bias were excluded from all analyses and other figures in this article and in the Supplementary Information (including b ). b , z curve: distribution of the z scores of all estimates included in the meta-analysis ( n  = 291) to test for publication bias. The dotted line indicates z  = 1.96 ( P  = 0.050), the conventional threshold for statistical significance. The overlaid curve shows a normal distribution. The absence of a spike in the distribution of the z scores just above the threshold for statistical significance and the absence of a slump just below it indicate the absence of evidence for publication bias.

In line with ROBINS-I guidance, we excluded studies rated to be at critical risk of bias ( n  = 19) from all of our analyses and figures, except for Fig. 2a , which visualizes the distribution of studies according to their risk of bias 9 . These are thus not part of the 42 studies included in our meta-analysis. Supplementary Table 2 provides an overview of these studies as well as the main potential sources of risk of bias. Moreover, in Supplementary Figs. 3 – 6 , we replicate all our results excluding studies deemed to be at serious risk of bias.

As shown in Fig. 2a , common sources of potential bias were confounding, sample selection and missing data. Studies rated at risk of confounding typically compared only two timepoints, without accounting for longer time trends in learning progress. The main causes of selection bias were the use of convenience samples and insufficient consideration of self-selection by schools or students. Several studies found evidence of selection bias, often with students from a low socio-economic background or schools in deprived areas being under-represented after (as compared with before) the pandemic, but this was not always adjusted for. Some studies also reported a higher amount of missing data post-pandemic, again generally without adjustment, and several studies did not report any information on missing data. For an overview of the risk-of-bias ratings for each domain of each study, see Supplementary Fig. 1 and Supplementary Tables 1 and 2 .

No evidence of publication bias

Publication bias can occur if authors self-censor to conform to theoretical expectations, or if journals favour statistically significant results. To mitigate this concern, we include not only published papers, but also preprints, working papers and policy reports.

Moreover, Fig. 2b tests for publication bias by showing the distribution of z -statistics for the effect size estimates of all identified studies. The dotted line indicates z  = 1.96 ( P  = 0.050), the conventional threshold for statistical significance. The overlaid curve shows a normal distribution. If there was publication bias, we would expect a spike just above the threshold, and a slump just below it. There is no indication of this. Moreover, we do not find a left-skewed distribution of P values (see P curve in Supplementary Fig. 2a ), or an association between estimates of learning deficits and their standard errors (see funnel plot in Supplementary Fig. 2b ) that would suggest publication bias. Publication bias thus does not appear to be a major concern.

Having assessed the quality of the existing evidence, we now present the substantive results of our meta-analysis, focusing on the magnitude of COVID-19 learning deficits and on the variation in learning deficits over time, across different groups of students, and across country contexts.

Learning progress slowed substantially during the pandemic

Figure 3 shows the effect sizes that we extracted from each study (averaged across grades and learning subject) as well as the pooled effect size (red diamond). Effects are expressed in standard deviations, using Cohen’s d . Estimates are pooled using inverse variance weights. The pooled effect size across all studies is d  = −0.14, t (41) = −7.30, two-tailed P  = 0.000, 95% confidence interval (CI) −0.17 to −0.10. Under normal circumstances, students generally improve their performance by around 0.4 standard deviations per school year 10 , 11 , 12 . Thus, the overall effect of d  = −0.14 suggests that students lost out on 0.14/0.4, or about 35%, of a school year’s worth of learning. On average, the learning progress of school-aged children has slowed substantially during the pandemic.

Effect sizes are expressed in standard deviations, using Cohen’s d , with 95% CI, and are sorted by magnitude.

Learning deficits arose early in the pandemic and persist

One may expect that children were able to recover learning that was lost early in the pandemic, after teachers and families had time to adjust to the new learning conditions and after structures for online learning and for recovering early learning deficits were set up. However, existing research on teacher strikes in Belgium 13 and Argentina 14 , shortened school years in Germany 15 and disruptions to education during World War II 16 suggests that learning deficits are difficult to compensate and tend to persist in the long run.

Figure 4 plots the magnitude of estimated learning deficits (on the vertical axis) by the date of measurement (on the horizontal axis). The colour of the circles reflects the relevant country, the size of the circles indicates the sample size for a given estimate and the line displays a linear trend. The figure suggests that learning deficits opened up early in the pandemic and have neither closed nor substantially widened since then. We find no evidence that the slope coefficient is different from zero ( β months  = −0.00, t (41) = −7.30, two-tailed P  = 0.097, 95% CI −0.01 to 0.00). This implies that efforts by children, parents, teachers and policy-makers to adjust to the changed circumstance have been successful in preventing further learning deficits but so far have been unable to reverse them. As shown in Supplementary Fig. 8 , the pattern of persistent learning deficits also emerges within each of the three countries for which we have a relatively large number of estimates at different timepoints: the United States, the UK and the Netherlands. However, it is important to note that estimates of learning deficits are based on distinct samples of students. Future research should continue to follow the learning progress of cohorts of students in different countries to reveal how learning deficits of these cohorts have developed and continue to develop since the onset of the pandemic.

The horizontal axis displays the date on which learning progress was measured. The vertical axis displays estimated learning deficits, expressed in standard deviation (s.d.) using Cohen’s d . The colour of the circles reflects the respective country, the size of the circles indicates the sample size for a given estimate and the line displays a linear trend with a 95% CI. The trend line is estimated as a linear regression using ordinary least squares, with standard errors clustered at the study level ( n  = 42 clusters). β months  = −0.00, t (41) = −7.30, two-tailed P  = 0.097, 95% CI −0.01 to 0.00.

Socio-economic inequality in education increased

Existing research on the development of learning gaps during summer vacations 17 , 18 , disruptions to schooling during the Ebola outbreak in Sierra Leone and Guinea 19 , and the 2005 earthquake in Pakistan 20 shows that the suspension of face-to-face teaching can increase educational inequality between children from different socio-economic backgrounds. Learning deficits during the COVID-19 pandemic are likely to have been particularly pronounced for children from low socio-economic backgrounds. These children have been more affected by school closures than children from more advantaged backgrounds 21 . Moreover, they are likely to be disadvantaged with respect to their access and ability to use digital learning technology, the quality of their home learning environment, the learning support they receive from teachers and parents, and their ability to study autonomously 22 , 23 , 24 .

Most studies we identify examine changes in socio-economic inequality during the pandemic, attesting to the importance of the issue. As studies use different measures of socio-economic background (for example, parental income, parental education, free school meal eligibility or neighbourhood disadvantage), pooling the estimates is not possible. Instead, we code all estimates according to whether they indicate a reduction, no change or an increase in learning inequality during the pandemic. Figure 5 displays this information. Estimates that indicate an increase in inequality are shown on the right, those that indicate a decrease on the left and those that suggest no change in the middle. Squares represent estimates of changes in inequality during the pandemic in reading performance, and circles represent estimates of changes in inequality in maths performance. The shading represents when in the pandemic educational inequality was measured, differentiating between the first, second and third year of the pandemic. Estimates are also arranged horizontally by grade level. A large majority of estimates indicate an increase in educational inequality between children from different socio-economic backgrounds. This holds for both maths and reading, across primary and secondary education, at each stage of the pandemic, and independently of how socio-economic background is measured.

Each circle/square refers to one estimate of over-time change in inequality in maths/reading performance ( n  = 211). Estimates that find a decrease/no change/increase in inequality are grouped on the left/middle/right. Within these categories, estimates are ordered horizontally by school grade. The shading indicates when in the pandemic a given measure was taken.

Learning deficits are larger in maths than in reading

Available research on summer learning deficits 17 , 25 , student absenteeism 26 , 27 and extreme weather events 28 suggests that learning progress in mathematics is more dependent on formal instruction than in reading. This might be due to parents being better equipped to help their children with reading, and children advancing their reading skills (but not their maths skills) when reading for enjoyment outside of school. Figure 6a shows that, similarly to earlier disruptions to learning, the estimated learning deficits during the COVID-19 pandemic are larger for maths than for reading (mean difference δ  = −0.07, t (41) = −4.02, two-tailed P  = 0.000, 95% CI −0.11 to −0.04). This difference is statistically significant and robust to dropping estimates from individual countries (Supplementary Fig. 9 ).

Each plot shows the distribution of COVID-19 learning deficit estimates for the respective subgroup, with the box marking the interquartile range and the white circle denoting the median. Whiskers mark upper and lower adjacent values: the furthest observation within 1.5 interquartile range of either side of the box. a , Learning subject (reading versus maths). Median: reading −0.09, maths −0.18. Interquartile range: reading −0.15 to −0.02, maths −0.23 to −0.09. b , Level of education (primary versus secondary). Median: primary −0.12, secondary −0.12. Interquartile range: primary −0.19 to −0.05, secondary −0.21 to −0.06. c , Country income level (high versus middle). Median: high −0.12, middle −0.37. Interquartile range: high −0.20 to −0.05, middle −0.65 to −0.30.

No evidence of variation across grade levels

One may expect learning deficits to be smaller for older than for younger children, as older children may be more autonomous in their learning and better able to cope with a sudden change in their learning environment. However, older students were subject to longer school closures in some countries, such as Denmark 29 , based partly on the assumption that they would be better able to learn from home. This may have offset any advantage that older children would otherwise have had in learning remotely.

Figure 6b shows the distribution of estimates of learning deficits for students at the primary and secondary level, respectively. Our analysis yields no evidence of variation in learning deficits across grade levels (mean difference δ  = −0.01, t (41) = −0.59, two-tailed P  = 0.556, 95% CI −0.06 to 0.03). Due to the limited number of available estimates of learning deficits, we cannot be certain about whether learning deficits differ between primary and secondary students or not.

Learning deficits are larger in poorer countries

Low- and middle-income countries were already struggling with a learning crisis before the pandemic. Despite large expansions of the proportion of children in school, children in low- and middle-income countries still perform poorly by international standards, and inequality in learning remains high 30 , 31 , 32 . The pandemic is likely to deepen this learning crisis and to undo past progress. Schools in low- and middle-income countries have not only been closed for longer, but have also had fewer resources to facilitate remote learning 33 , 34 . Moreover, the economic resources, availability of digital learning equipment and ability of children, parents, teachers and governments to support learning from home are likely to be lower in low- and middle-income countries 35 .

As discussed above, most evidence on COVID-19 learning deficits comes from high-income countries. We found no studies on low-income countries that met our inclusion criteria, and evidence from middle-income countries is limited to Brazil, Colombia, Mexico and South Africa. Figure 6c groups the estimates of COVID-19 learning deficits in these four middle-income countries together (on the right) and compares them with estimates from high-income countries (on the left). The learning deficit is appreciably larger in middle-income countries than in high-income countries (mean difference δ  = −0.29, t (41) = −2.78, two-tailed P  = 0.008, 95% CI −0.50 to −0.08). In fact, the three largest estimates of learning deficits in our sample are from middle-income countries (Fig. 3 ) 36 , 37 , 38 .

Two years since the COVID-19 pandemic, there is a growing number of studies examining the learning progress of school-aged children during the pandemic. This paper first systematically reviews the existing literature on learning progress of school-aged children during the pandemic and appraises its geographic reach and quality. Second, it harmonizes, synthesizes and meta-analyses the existing evidence to examine the extent to which learning progress has changed since the onset of the pandemic, and how it varies across different groups of students and across country contexts.

Our meta-analysis suggests that learning progress has slowed substantially during the COVID-19 pandemic. The pooled effect size of d  = −0.14, implies that students lost out on about 35% of a normal school year’s worth of learning. This confirms initial concerns that substantial learning deficits would arise during the pandemic 10 , 39 , 40 . But our results also suggest that fears of an accumulation of learning deficits as the pandemic continues have not materialized 41 , 42 . On average, learning deficits emerged early in the pandemic and have neither closed nor widened substantially. Future research should continue to follow the learning progress of cohorts of students in different countries to reveal how learning deficits of these cohorts have developed and continue to develop since the onset of the pandemic.

Most studies that we identify find that learning deficits have been largest for children from disadvantaged socio-economic backgrounds. This holds across different timepoints during the pandemic, countries, grade levels and learning subjects, and independently of how socio-economic background is measured. It suggests that the pandemic has exacerbated educational inequalities between children from different socio-economic backgrounds, which were already large before the pandemic 43 , 44 . Policy initiatives to compensate learning deficits need to prioritize support for children from low socio-economic backgrounds in order to allow them to recover the learning they lost during the pandemic.

There is a need for future research to assess how the COVID-19 pandemic has affected gender inequality in education. So far, there is very little evidence on this issue. The large majority of the studies that we identify do not examine learning deficits separately by gender.

Comparing estimates of learning deficits across subjects, we find that learning deficits tend to be larger in maths than in reading. As noted above, this may be due to the fact that parents and children have been in a better position to compensate school-based learning in reading by reading at home. Accordingly, there are grounds for policy initiatives to prioritize the compensation of learning deficits in maths and other science subjects.

A limitation of this study and the existing body of evidence on learning progress during the COVID-19 pandemic is that the existing studies primarily focus on high-income countries, while there is a dearth of evidence from low- and middle-income countries. This is particularly concerning because the small number of existing studies from middle-income countries suggest that learning deficits have been particularly severe in these countries. Learning deficits are likely to be even larger in low-income countries, considering that these countries already faced a learning crisis before the pandemic, generally implemented longer school closures, and were under-resourced and ill-equipped to facilitate remote learning 32 , 33 , 34 , 35 , 45 . It is critical that this evidence gap on low- and middle-income countries is addressed swiftly, and that the infrastructure to collect and share data on educational performance in middle- and low-income countries is strengthened. Collecting and making available these data is a key prerequisite for fully understanding how learning progress and related outcomes have changed since the onset of the pandemic 46 .

A further limitation is that about half of the studies that we identify are rated as having a serious or critical risk of bias. We seek to limit the risk of bias in our results by excluding all studies rated to be at critical risk of bias from all of our analyses. Moreover, in Supplementary Figs. 3 – 6 , we show that our results are robust to further excluding studies deemed to be at serious risk of bias. Future studies should minimize risk of bias in estimating learning deficits by employing research designs that appropriately account for common sources of bias. These include a lack of accounting for secular time trends, non-representative samples and imbalances between treatment and comparison groups.

The persistence of learning deficits two and a half years into the pandemic highlights the need for well-designed, well-resourced and decisive policy initiatives to recover learning deficits. Policy-makers, schools and families will need to identify and realize opportunities to complement and expand on regular school-based learning. Experimental evidence from low- and middle-income countries suggests that even relatively low-tech and low-cost learning interventions can have substantial, positive effects on students’ learning progress in the context of remote learning. For example, sending SMS messages with numeracy problems accompanied by short phone calls was found to lead to substantial learning gains in numeracy in Botswana 47 . Sending motivational text messages successfully limited learning losses in maths and Portuguese in Brazil 48 .

More evidence is needed to assess the effectiveness of other interventions for limiting or recovering learning deficits. Potential avenues include the use of the often extensive summer holidays to offer summer schools and learning camps, extending school days and school weeks, and organizing and scaling up tutoring programmes. Further potential lies in developing, advertising and providing access to learning apps, online learning platforms or educational TV programmes that are free at the point of use. Many countries have already begun investing substantial resources to capitalize on some of these opportunities. If these interventions prove effective, and if the momentum of existing policy efforts is maintained and expanded, the disruptions to learning during the pandemic may be a window of opportunity to improve the education afforded to children.

Eligibility criteria

We consider all types of primary research, including peer-reviewed publications, preprints, working papers and reports, for inclusion. To be eligible for inclusion, studies have to measure learning progress using test scores that can be standardized across studies using Cohen’s d . Moreover, studies have to be in English, Danish, Dutch, French, German, Norwegian, Spanish or Swedish.

Search strategy and study identification

We identified relevant studies using the following steps. First, we developed a Boolean search string defining the population (school-aged children), exposure (the COVID-19 pandemic) and outcomes of interest (learning progress). The full search string can be found in Section 1.1 of Supplementary Information . Second, we used this string to search the following academic databases: Coronavirus Research Database, the Education Resources Information Centre, International Bibliography of the Social Sciences, Politics Collection (PAIS index, policy file index, political science database and worldwide political science abstracts), Social Science Database, Sociology Collection (applied social science index and abstracts, sociological abstracts and sociology database), Cumulative Index to Nursing and Allied Health Literature, and Web of Science. Second, we hand-searched multiple preprint and working paper repositories (Social Science Research Network, Munich Personal RePEc Archive, IZA, National Bureau of Economic Research, OSF Preprints, PsyArXiv, SocArXiv and EdArXiv) and relevant policy websites, including the websites of the Organization for Economic Co-operation and Development, the United Nations, the World Bank and the Education Endowment Foundation. Third, we periodically posted our protocol via Twitter in order to crowdsource additional relevant studies not identified through the search. All titles and abstracts identified in our search were double-screened using the Rayyan online application 49 . Our initial search was conducted on 27 April 2021, and we conducted two forward and backward citation searches of all eligible studies identified in the above steps, on 14 February 2022, and on 8 August 2022, to ensure that our analysis includes recent relevant research.

Data extraction

From the studies that meet our inclusion criteria we extracted all estimates of learning deficits during the pandemic, separately for maths and reading and for different school grades. We also extracted the corresponding sample size, standard error, date(s) of measurement, author name(s) and country. Last, we recorded whether studies differentiate between children’s socio-economic background, which measure is used to this end and whether studies find an increase, decrease or no change in learning inequality. We contacted study authors if any of the above information was missing in the study. Data extraction was performed by B.A.B. and validated independently by A.M.B.-M., with discrepancies resolved through discussion and by conferring with P.E.

Measurement and standardizationr

We standardize all estimates of learning deficits during the pandemic using Cohen’s d , which expresses effect sizes in terms of standard deviations. Cohen’s d is calculated as the difference in the mean learning gain in a given subject (maths or reading) over two comparable periods before and after the onset of the pandemic, divided by the pooled standard deviation of learning progress in this subject:

Effect sizes expressed as β coefficients are converted to Cohen’s d :

We use a binary indicator for whether the study outcome is maths or reading. One study does not differentiate the outcome but includes a composite of maths and reading scores 50 .

Level of education

We distinguish between primary and secondary education. We first consulted the original studies for this information. Where this was not stated in a given study, students’ age was used in conjunction with information about education systems from external sources to determine the level of education 51 .

Country income level

We follow the World Bank’s classification of countries into four income groups: low, lower-middle, upper-middle and high income. Four countries in our sample are in the upper-middle-income group: Brazil, Colombia, Mexico and South Africa. All other countries are in the high-income group.

Data synthesis

We synthesize our data using three synthesis techniques. First, we generate a forest plot, based on all available estimates of learning progress during the pandemic. We pool estimates using a random-effects restricted maximum likelihood model and inverse variance weights to calculate an overall effect size (Fig. 3 ) 52 . Second, we code all estimates of changes in educational inequality between children from different socio-economic backgrounds during the pandemic, according to whether they indicate an increase, a decrease or no change in educational inequality. We visualize the resulting distribution using a harvest plot (Fig. 5 ) 53 . Third, given that the limited amount of available evidence precludes multivariate or causal analyses, we examine the bivariate association between COVID-19 learning deficits and the months in which learning was measured using a scatter plot (Fig. 4 ), and the bivariate association between COVID-19 learning deficits and subject, grade level and countries’ income level, using a series of violin plots (Fig. 6 ). The reported estimates, CIs and statistical significance tests of these bivariate associations are based on common-effects models with standard errors clustered by study, and two-sided tests. With respect to statistical tests reported, the data distribution was assumed to be normal, but this was not formally tested. The distribution of estimates of learning deficits is shown separately for the different moderator categories in Fig. 6 .

Pre-registration

We prospectively registered a protocol of our systematic review and meta-analysis in the International Prospective Register of Systematic Reviews (CRD42021249944) on 19 April 2021 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021249944 ).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data used in the analyses for this manuscript were compiled by the authors based on the studies identified in the systematic review. The data are available on the Open Science Framework repository ( https://doi.org/10.17605/osf.io/u8gaz ). For our systematic review, we searched the following databases: Coronavirus Research Database ( https://proquest.libguides.com/covid19 ), Education Resources Information Centre database ( https://eric.ed.gov ), International Bibliography of the Social Sciences ( https://about.proquest.com/en/products-services/ibss-set-c/ ), Politics Collection ( https://about.proquest.com/en/products-services/ProQuest-Politics-Collection/ ), Social Science Database ( https://about.proquest.com/en/products-services/pq_social_science/ ), Sociology Collection ( https://about.proquest.com/en/products-services/ProQuest-Sociology-Collection/ ), Cumulative Index to Nursing and Allied Health Literature ( https://www.ebsco.com/products/research-databases/cinahl-database ) and Web of Science ( https://clarivate.com/webofsciencegroup/solutions/web-of-science/ ). We also searched the following preprint and working paper repositories: Social Science Research Network ( https://papers.ssrn.com/sol3/DisplayJournalBrowse.cfm ), Munich Personal RePEc Archive ( https://mpra.ub.uni-muenchen.de ), IZA ( https://www.iza.org/content/publications ), National Bureau of Economic Research ( https://www.nber.org/papers?page=1&perPage=50&sortBy=public_date ), OSF Preprints ( https://osf.io/preprints/ ), PsyArXiv ( https://psyarxiv.com ), SocArXiv ( https://osf.io/preprints/socarxiv ) and EdArXiv ( https://edarxiv.org ).

Code availability

All code needed to replicate our findings is available on the Open Science Framework repository ( https://doi.org/10.17605/osf.io/u8gaz ).

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Acknowledgements

Carlsberg Foundation grant CF19-0102 (A.M.B.-M.); Leverhulme Trust Large Centre Grant (P.E.), the Swedish Research Council for Health, Working Life and Welfare (FORTE) grant 2016-07099 (P.E.); the French National Research Agency (ANR) as part of the ‘Investissements d’Avenir’ programme LIEPP (ANR-11-LABX-0091 and ANR-11-IDEX-0005-02) and the Université Paris Cité IdEx (ANR-18-IDEX-0001) (P.E.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Betthäuser, B.A., Bach-Mortensen, A.M. & Engzell, P. A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat Hum Behav 7 , 375–385 (2023). https://doi.org/10.1038/s41562-022-01506-4

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This paper is in the following e-collection/theme issue:

Published on 29.7.2024 in Vol 10 (2024)

This is a member publication of University College London (Jisc)

Preferences for COVID-19 Vaccines: Systematic Literature Review of Discrete Choice Experiments

Authors of this article:

• Yiting Huang 1, 2 * , MPH   ; 
• Shuaixin Feng 3 * , MPH   ; 
• Yuyan Zhao 1 * , BMed   ; 
• Haode Wang 4 , PhD   ; 
• Hongbo Jiang 1, 5 , PhD  

1 Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China

2 Department of Medical Statistics, School of Basic Medicine and Public Health, Jinan University, Guangzhou, China

3 Outpatient department of Baogang, the First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China

4 School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom

5 Institute for Global Health, University College London, London, United Kingdom

*these authors contributed equally

Corresponding Author:

Hongbo Jiang, PhD

Department of Epidemiology and Biostatistics, School of Public Health

Guangdong Pharmaceutical University

Department of Epidemiology and Biostatistics, School of Public Health Guangdong Pharmaceutical University

No. 283 Jianghai Road, Haizhu District

Guangzhou, 510310

Phone: 86 0 203 405 5355

Fax:86 0 203 405 5355

Email: [email protected]

Background: Vaccination can be viewed as comprising the most important defensive barriers to protect susceptible groups from infection. However, vaccine hesitancy for COVID-19 is widespread worldwide.

Objective: We aimed to systematically review studies eliciting the COVID-19 vaccine preference using discrete choice experiments.

Methods: A literature search was conducted in PubMed, Embase, Web of Science, Scopus, and CINAHL Plus platforms in April 2023. Search terms included discrete choice experiments , COVID-19 , and vaccines and related synonyms. Descriptive statistics were used to summarize the study characteristics. Subgroup analyses were performed by factors such as high-income countries and low- and middle-income countries and study period (before, during, and after the pandemic wave). Quality appraisal was performed using the 5-item Purpose, Respondents, Explanation, Findings, and Significance checklist.

Results: The search yield a total of 623 records, and 47 studies with 53 data points were finally included. Attributes were grouped into 4 categories: outcome, process, cost, and others. The vaccine effectiveness (21/53, 40%) and safety (7/53, 13%) were the most frequently reported and important attributes. Subgroup analyses showed that vaccine effectiveness was the most important attribute, although the preference varied by subgroups. Compared to high-income countries (3/29, 10%), a higher proportion of low- and middle-income countries (4/24, 17%) prioritized safety. As the pandemic progressed, the duration of protection (2/24, 8%) during the pandemic wave and COVID-19 mortality risk (5/25, 20%) after the pandemic wave emerged as 2 of the most important attributes.

Conclusions: Our review revealed the critical role of vaccine effectiveness and safety in COVID-19 vaccine preference. However, it should be noticed that preference heterogeneity was observed across subpopulations and may change over time.

Trial Registration: PROSPERO CRD42023422720; https://tinyurl.com/2etf7ny7

Introduction

Although the World Health Organization has declared the end of COVID-19 as a public health emergency [ 1 ], the persistence of this disease as a global threat should not be overlooked or underestimated [ 2 ]. Vaccination has been regarded as one of the most effective strategies against COVID-19 and reduced global COVID-19 mortality, severe disease, symptomatic cases, and COVID-19 infections [ 2 , 3 ]. Furthermore, studies have shown that COVID-19 vaccine also had a preventive effect against post–COVID-19 condition [ 4 - 6 ].

Despite significant progress made with vaccination efforts, achieving high vaccination coverage remains a challenge due to disparities in vaccine distribution and vaccine hesitancy [ 7 - 9 ]. Disparities in vaccine distribution have been observed between different countries, with vaccination rates varying markedly between high- and low-income countries [ 10 ]. In addition, COVID-19 vaccine hesitancy has been reported across countries [ 11 ], and booster hesitancy has also become a growing concern for public health officials [ 12 ]. Vaccine hesitancy can change over time and in response to different circumstances. Notably, vaccine hesitancy tends to increase when population-level side-effect studies are released after emergency approvals [ 13 ]. These challenges underline the need for well-designed vaccination programs to ensure equitable access and high uptake.

Designing a successful vaccination program, including vaccine selection, rollout, and accessibility, is crucial [ 14 , 15 ]. A thorough understanding of individual needs and preferences will allow us to better tailor vaccination programs, which will facilitate the appeal and uptake of COVID-19 vaccines [ 16 , 17 ]. One approach increasingly used to elicit preferences for vaccines and vaccination programs is the discrete choice experiment (DCE) [ 18 , 19 ]. DCEs are scientific research methods that assess preferences by presenting respondents with a series of hypothetical scenarios. In these scenarios, individuals choose among different alternatives which are characterized by specific attributes. By analyzing these choices, researchers can identify the relative importance of each attribute and estimate utility functions [ 20 , 21 ]. DCEs provide valuable insights into decision-making processes and allow for objective evaluation of attribute-based benefits [ 22 - 24 ]. Published studies have been conducted to identify and review choice-based experiments that assess vaccine preferences [ 18 , 19 ]. However, it is important to note that the nature of various vaccines is different, and the preference for vaccines of COVID-19 was not specifically included in these studies.

The COVID-19 vaccines were developed under emergency conditions where there were no peer-reviewed systematic reviews of DCEs on COVID-19 vaccine preference data to inform global decision-making. The diversity in COVID-19 vaccine preferences may be attributed to disparities in vaccine development and production, vaccination scheduling and management, public trust and uptake, as well as vaccine prioritization strategies across various countries and regions [ 25 ]. Moreover, new mutant variants are more likely to infect new individuals, highlighting the need for more effective booster vaccines [ 26 , 27 ]. This study provides empirical evidence on the development, implementation, and follow-up of the COVID-19 vaccine and provides references for vaccine decision-making of other infectious diseases.

We conducted our review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ( Multimedia Appendix 1 ) [ 28 ]. This study was registered in the international prospective register of systematic reviews (PROSPERO CRD42023422720).

Search Strategy

A literature search was conducted in PubMed, Embase, Web of Science, Scopus, and CINAHL Plus platforms in April 2023. Search terms included discrete choice experiments , COVID-19 , and vaccines and related synonyms. Further details are provided in Multimedia Appendix 2 .

Eligibility Criteria

The inclusion and exclusion criteria are detailed in Textbox 1 .

Inclusion criteria

• Study focus: Focused on preferences for COVID-19 vaccine (product, service and distribution, policy intervention, etc)
• Article or study type: First-hand discrete choice experiment (DCE) data analysis research

Exclusion criteria

• Study focus: No preferences for COVID-19 vaccine reported
• Article or study type: Not DCE research; nonoriginal research (including secondary reports, systematic reviews, conference abstracts and presentations, correspondence, editorials, and commentaries); theoretical articles; protocols; book chapters; and duplicates

Data Screening and Extraction

Two reviewers (YH and SF) independently performed a 2-stage screening process to identify eligible studies. In the first stage, titles and abstracts were screened to exclude irrelevant studies using the web-based tool Rayyan (Rayyan Systems, Inc [ 29 ]). In the second stage, full-text versions of selected papers were assessed to ensure that the inclusion criteria were met. Both reviewers compared the selected papers at each stage to ensure agreement. Any discrepancy or uncertainty between the reviewers was addressed through discussion until a consensus was reached. If not, a third (senior) reviewer (HJ) was consulted to resolve the disagreement.

The extracted data were recorded and managed in Microsoft Excel (Microsoft Corp) software. Full texts were extracted and reviewed independently by 2 authors (YH and YZ), and any disagreements were resolved by a third reviewer (HJ). Data extraction was performed for 3 specific aspects, focusing on their relevance and importance for the analysis of the DCE: (1) study information (author, publication year, study period, country, population, and sample size); (2) information on the DCE methodology (survey administration, attribute and level selection, pilot-tested, experimental study design, choice sets per respondent, options per choice set, inclusion of an opt-out option, and statistical models); and (3) information on the DCE results (number of attributes, included attributes classified into 4 categories [outcome, process, cost, and other], and the most important attribute).

Choice-based experiments use different definitions for similar attributes [ 19 ]. To address this issue, the attributes were initially grouped into 4 main categories: outcomes, process, cost, and other. The outcomes category encompassed the outcomes or consequences of vaccine administration, such as safety and effectiveness. The process category included activities related to the delivery and administration of vaccines, such as service delivery, dosing, and visits. The cost category focused on the financial aspects of vaccines. Any attributes that did not fit into these 3 categories were classified as other , such as disease risk, incentives or penalties for vaccination, vaccine advice or support, and so on. The classification of outcome, process, cost, and other attributes depended on the aim and design of the studies. It should be noted that vaccine effectiveness and safety were phrased differently in different studies. To facilitate a comparison between studies, efficacy [ 11 , 30 - 41 ], protection rate [ 42 , 43 ], and decreased deaths [ 44 ] were summarized as vaccine effectiveness, whereas side effects [ 11 , 26 , 31 , 35 , 37 , 40 , 41 , 43 , 45 - 61 ], rare but serious risks [ 62 ], and the likelihood of having a flare [ 62 ] were summarized as vaccine safety ( Multimedia Appendix 3 [ 11 , 26 , 30 - 74 ]).

High-income countries (HICs) and low- and middle-income countries (LMICs) were classified according to the World Bank [ 75 ]. LMICs encompass low-income, lower-middle–income, and upper-middle–income countries. On the basis of previous literatures [ 63 , 76 , 77 ], we hypothesized that individuals’ preferences for vaccines may vary depending on the status of the pandemic. Therefore, we sought to explore how COVID-19 vaccine preferences differed during different study periods. To do this, we used data from the surveillance website [ 78 ] to define the pandemic periods based on daily COVID-19 cases. The first group, before the pandemic wave , referred to the period before the outbreak of the pandemic, when the number of incident cases was low. The second group, during the pandemic wave , represented the peak of the pandemic or was characterized by a rapid increase in the number of incident cases. The third group, after the pandemic wave , was when the number of incident cases decreased and remained low ( Multimedia Appendix 4 [ 11 , 26 , 30 - 74 ]).

Quality Appraisal

The 5-item Purpose, Respondents, Explanation, Findings, and Significance (PREFS) checklist, developed by Joy et al [ 79 ], is widely accepted and used to assess the reporting quality of preference studies [ 18 , 80 - 84 ]. It evaluates studies based on criteria such as the study’s purpose, respondent sampling, explanation of assessment methods, inclusion of complete response sets in the findings, and use of significance testing.

Data Synthesis and Analysis

This review used a combination of text and summary tables to effectively convey information about the characteristics and results of the included studies. Descriptive statistics were used to summarize the study characteristics. The findings were synthesized in a narrative format, providing an overview of the included studies, highlighting the key features of the study designs, and presenting the main findings of the COVID-19 vaccine preference studies. Subgroup analyses were performed by independent factors such as HICs or LMICs and study period (before, during, and after the pandemic wave).

Study Selection

The search yielded a total of 623 records. After title and abstract screening, 513 (82.3%) records were excluded. An additional 63 (10.1%) studies were excluded after full-text assessment. Finally, 47 (7.5%) studies met the eligibility criteria and were included in the review ( Figure 1 ).

Study and Sample Characteristics

We included 47 studies from 29 countries. Among them, 5 (11%) studies were conducted in multiple countries, with 4 studies conducted in both HICs and LMICs and 1 study conducted in >1 HICs. In addition, 22 (47%) studies were conducted in HICs, while 21 (45%) studies were conducted in LMICs. China stood out with the highest number of preference-based DCEs for COVID-19 vaccines, with 19 (40%) studies. The United States followed closely with 9 (19%) studies, followed by France (n=5, 11%), the United Kingdom (n=4, 9%), Germany (n=4, 9%), and Spain (n=3, 6%). Australia, Canada, India, Italy, Japan, the Netherlands, and South Africa had 2 (4%) studies each. All other countries had only 1 (2%) study ( Figure 2 ). The studies were published between the years 2020 and 2023, with sample sizes ranging from 194 to 13,128 participants. The median number of participants per study was 1456 (IQR 872-2109).

Most participants were adults, although the specific focus varied. Most studies (36/47, 77%) involved general population samples, whereas some studies (11/47, 23%) included specific groups of participants. These included 5 studies conducted in universities using web-based tools, including 3 studies with university students and 2 studies with both students and staff. In addition, 3 studies involved health care workers (Chinese intensive care unit clinicians, health care workers, and health care and welfare workers); 2 studies involved parents with children aged <18 years, and 1 study involved people with chronic immune-mediated inflammatory diseases ( Table 1 ).

a ICU: intensive care unit.

b NR: not reported.

The Implementation of DCEs

Among these 47 studies, researchers commonly used a multifaceted approach to identify and select attributes and levels. Among the studies reviewed, 23 (49%) studies reported a literature review with qualitative assessments such as expert interviews and public surveys. A total of 25 (53%) studies reported a pilot DCE survey. In terms of survey administration, most studies (40/47, 85%) reported that the DCE was conducted through web-based surveys ( Table 2 ).

a NR: not reported.

b CDC: Center for disease control and prevention.

c UKZN: the University of KwaZulu-Natal.

Attributes in DCE Studies

Of the 286 attributes identified in the 47 studies, 126 (44.1%) were categorized as outcome attributes, followed by 82 (28.7%) as process attributes, and 22 (7.7%) as cost attributes. The remaining 55 (19.2%) attributes were categorized as other attributes ( Table 3 and Multimedia Appendix 3 ).

a Attribute is significant ( P <.05).

b Not available.

c The corresponding coefficients and P values are not provided.

The Most Important Attribute Reported in DCE Studies

In total, 2 of the 5 multicountry studies did not report preferences for each country and were therefore excluded from the synthesis of the most important attribute. A total of 53 data points on COVID-19 vaccine preferences were collected from the study population of the corresponding country. In the outcome category, among the 30 attributes examined, effectiveness emerged as the most prominent, accounting for 40% (21/53) of the studies [ 31 , 35 , 36 , 38 - 42 , 48 , 50 - 52 , 57 , 58 , 60 - 62 , 64 - 67 ]. Safety was addressed in 13% (7/53) of the studies [ 33 , 43 , 47 , 56 , 59 , 68 , 69 ], while protection duration was mentioned in 4% (2/53) [ 11 , 50 ]. In the process category, 13 attributes were identified. Brand (1/53, 2%) [ 32 ], region of vaccine manufacturer (1/53, 2%) [ 34 ], and halal content (1/53, 2%) [ 53 ] were associated with vaccine production. In addition, waiting time for COVID-19 vaccination (1/53, 2%) [ 70 ] and vaccine frequency (1/53, 2%) [ 71 ] were considered. Furthermore, 3 (6%) studies on vaccine distribution prioritized vaccination for the medical risk group (1/53, 2%) [ 72 ], those who had a higher COVID-19 mortality risk (6/53, 11%) [ 63 ], and those who had the potential capacity to spread the virus (1/53, 2%) [ 72 ]. In the cost category, personal vaccination cost accounted for 6% (3/53) [ 31 , 37 , 41 ]. Among the other attributes (7/53, 13%), disease risk threat was of particular importance, including possible trends of the epidemic (1/53, 2%) [ 30 ] and COVID-19 mortality rate (1/53, 2%) [ 55 ]. In addition, incentives and penalties for vaccination were identified, including quarantine-free travel (1/53, 2%) [ 33 ] and mandatory testing at own expense if not vaccinated (1/53, 2%) [ 44 ]. Vaccine advice or support included vaccination invitation sender (1/53, 2%) [ 73 ] and recommenders (1/53, 2%) [ 46 ]. The proportion of friends and family members who had received the vaccine (1/53, 2%) [ 26 ] was also among the other attributes influencing decision-making ( Table 2 ).

Although effectiveness remained the most important attribute, it is worth noting that variations in preferences were also observed among different subgroups. A higher proportion of studies conducted in LMICs (4/24, 17%) than in HICs (3/29, 10%) prioritized on safety ( Multimedia Appendix 5 ). In addition, COVID-19 mortality risk was the second most important attribute (6/29, 21%) after effectiveness in HICs. Cost was considered to be another most important attribute (3/24, 13%) in LMICs. Interestingly, many other attributes also became more important as the pandemic progressed. Protection duration (2/24, 8%) emerged as one of the most important attributes during the pandemic wave. COVID-19 mortality risk (5/25, 20%) and cost (3/25, 12%) were considered as the most important attributes after the pandemic wave ( Multimedia Appendix 6 ).

Study Quality

The overall reporting quality was deemed acceptable but there is room for improvement. The PREFS scores of the 47 studies ranged from 2 to 4, with a mean of 3.23 (SD 0.52). No study scored 5. Most studies scored 3 (32/47, 68%) or 4 (13/47, 28%), while 2 studies (2/47, 4%) scored 2 ( Multimedia Appendix 7 [ 11 , 26 , 30 - 74 ]).

Principal Findings

This systematic review synthesizes existing data on preference for COVID-19 vaccine using DCE, with the aim of informing improvements in vaccine coverage and vaccine policy development. We identified 47 studies conducted in 29 countries, including 21 HICs and 8 LMICs. HICs had an adequate supply of vaccine since the early emergency availability of COVID-19 vaccine, and HICs had 1.5 times more doses of COVID-19 vaccinations than LMICs by September 2023 [ 85 ]. In total, 19 (40%) studies were conducted in China and 9 (19%) in the United States, demonstrating their significant contribution to the research and their leadership in vaccine research and development. Vaccine effectiveness and safety were the most important attributes in DCEs, although preferences differed among subgroups.

Recent years have seen new trends in the design, implementation, and validation of the DCE. For example, most studies (40/47, 85%) reported that the DCE was administered through web-based surveys, which have become a quick and cost-effective way to collect DCE data [ 66 ]. Almost half of the studies (25/47, 53%) did not report a pilot test. However, piloting in multiple stages throughout the development of a DCE is conducive to identifying appropriate and understandable attributes, considering whether participants can effectively evaluate the full profiles, and producing an efficient design [ 21 , 86 , 87 ].

Overall, vaccine effectiveness and safety have emerged as the most commonly investigated attributes in the outcome category. Despite heterogeneity in preferences across subpopulations, effectiveness remains the primary driver for COVID-19 vaccination across the studies [ 31 , 35 , 36 , 38 - 42 , 48 , 50 , 51 , 57 , 58 , 60 - 62 , 64 - 67 ], similar to the previous findings [ 18 ]. A study conducted in India and Europe found that respondents’ preference for the COVID-19 vaccine increased with effectiveness and peaked at 95% effectiveness [ 45 ]. Another study conducted among university staff and students in South Africa found that vaccine effectiveness not only was a concern but also significantly influenced vaccine choice behavior [ 64 ]. Interestingly, a nationwide stated choice survey in the United States found a strong interaction between effectiveness and other attributes [ 58 ]. These findings support the ongoing efforts to maximize vaccine effectiveness while emphasizing the importance of communicating information on vaccine effectiveness to the target population for promotion [ 62 ].

Safety has also been identified as a crucial factor influencing the acceptance of COVID-19 vaccine [ 33 , 43 , 47 , 56 , 59 , 68 , 69 ]. One study indicated that the likelihood of the general public choosing vaccines with low or moderate side effects increased by 75% and 63%, respectively, compared with vaccines with high side effects. While the likelihood changed within a 30% range when most attributes other than effectiveness and safety were changed [ 69 ]. In addition, respondents in Australia expressed a willingness to wait an additional 0.04 and 1.2 months to reduce the incidence of mild and severe adverse events by 1/10,000, respectively [ 56 ].

Similar to the results of previous systematic reviews of DCEs for various vaccines [ 18 , 19 ], the most common predictors of COVID-19 vaccine acceptance are effectiveness and safety, particularly during the rapid development and rollout of COVID-19 vaccines, which essentially boils down to trust in the vaccine [ 31 ]. Respondents expressed the importance of having a safe and effective COVID-19 vaccine available as soon as possible, but the majority preferred to wait a few months to observe the experience of others rather than be the first in line [ 43 ]. Therefore, collaborating to enhance vaccine effectiveness while reducing the risk of severe side effects could be a highly effective strategy to address vaccine hesitancy and augment vaccine desirability. Dissemination of this important vaccine-related information by governments and health care institutions, along with effective communication by health care professionals, can help build public trust and ultimately increase vaccination rates [ 69 ]. However, these inherent vaccine attributes are typically beyond the control of a vaccination program, and given the ongoing mutations of SARS-CoV-2, it is challenging to predict the effectiveness of the vaccines currently in development [ 66 ]. Global collaboration between scientists and pharmaceutical companies is therefore essential to improve vaccine effectiveness and minimize side effects [ 41 ].

Vaccine production, including its origin, brand, vaccine frequency, and content, are key considerations in the process category. Vaccine brand also has a significant impact on vaccine choice [ 32 ], independent of effectiveness and safety, due to factors such as reputation, country of origin, technological advances, and reported side effects associated with the brands [ 35 ]. For vaccine origin, some studies found that participants preferred domestic vaccines to imported vaccines, which may depend on the availability or the approval of vaccines in different countries [ 31 , 41 , 50 ] or the incidence of side effects among different types of COVID-19 vaccines [ 37 ]. However, some studies found that imported vaccines were more likely to be accepted than domestically produced vaccines, which may be attributed to less trust in domestically produced vaccines [ 57 , 66 ]. A study on vaccine preferences among the Malaysian population found that the composition and production process of the COVID-19 vaccine, which complied with Islamic dietary requirements (ie, halal content) was an important factor for many Malaysians when deciding whether to be vaccinated. This underscores the substantial influence of religion on vaccine choice [ 53 ].

Vaccine frequency was emphasized to play an important role in the choice of COVID-19 vaccine among the US public, while the 90% efficacy with low side effect rate of the COVID-19 vaccine was set. The prospect of vaccinating once to get lifelong immunity was very attractive, reflecting the fact that people were effort minimizers [ 71 ]. This is similar to the nature of the 2 studies referenced in the outcome attribute, where the protection duration is prioritized. Given the threat of COVID-19, people expect the protection duration to be as long as possible [ 11 , 50 ].

When vaccine supply is limited, people tend to prioritize vaccination for those who are more susceptible to the disease, have higher mortality rates from infectious diseases, or have greater potential to spread the virus. A study in Iran found that individuals tend to prioritize vaccination for those in the community with higher potential for virus transmission [ 57 ]. In addition, results from a study in 6 European countries revealed unanimous agreement among respondents that candidates with higher mortality and infection risks should be prioritized for vaccination [ 63 ]. While another study conducted among Belgians also found that respondents would prioritize populations at higher medical risk [ 72 ].

Cost was another important factor influencing COVID-19 vaccine preferences, mostly related to out-of-pocket costs [ 31 , 37 , 41 ]. In 2 studies comparing public preferences for COVID-19 vaccines in China and the United States, vaccine efficacy emerged as the most important driver for the American public, whereas the cost of vaccination had the greatest impact on the Chinese public. This difference was likely due to the relatively stable pandemic situation in China at the time and the lower perceived risk of COVID-19. As a result, the Chinese population was more price sensitive and reluctant to pay for vaccination [ 31 , 37 , 41 ].

For the other category, several different attributes were highlighted, depending on the specific population or situation. When people perceive the threat of a disease, their desire to be vaccinated becomes more urgent. In a study among health care workers in China, participants’ expectations about the future development of COVID-19 had a greater impact on their decision to be vaccinated than their perceived risk of infection or actual case rates, which may have been influenced by their previous experience with seasonal influenza vaccination [ 30 ]. The mortality rate of COVID-19 was considered the most influential factor in the uptake of COVID-19 booster shots in Vietnam. This study was conducted during a pandemic wave in Vietnam, which may have led to an increased perception of public health risks and a greater inclination toward COVID-19 vaccination [ 55 ]. To achieve herd immunity, government authorities can implement policies of incentives and penalties for vaccination to encourage population-wide uptake. A study conducted in the Netherlands revealed that respondents particularly disliked policies that penalized those who were not vaccinated, such as mandatory testing at their own expense if they were not vaccinated [ 44 ]. Instead, they favored policies that rewarded vaccination, such as giving vaccinated individuals additional privileges through a vaccination passport. This finding is consistent with a study in Hong Kong, which found that quarantine-free travel was considered the most important motivator among university students and staff, given their frequent engagement in international travel [ 33 ].

The source of vaccine information also influences vaccine decision-making [ 30 ]. Variation in the sender of vaccination appointment invitation via SMS text messaging and recommenders may potentially influence the public’s willingness to vaccinate against a disease [ 30 , 46 , 73 ]. Furthermore, the acceptance of vaccines was observed to change as the firsthand information about vaccine side effects and effectiveness was provided by friends and family in India [ 26 ].

In HICs, COVID-19 mortality risk was the second most important attribute after effectiveness, as respondents in all 6 high-income European countries from a study of public preferences for COVID-19 vaccine distribution prioritized candidates with higher mortality risks [ 63 ]. However, individuals from LMICs appeared to be more concerned about vaccine safety than those from HICs. This may be related to greater confidence in vaccine safety in HICs due to the earlier initiation and higher rates of COVID-19 vaccination [ 85 ]. In contrast, in some LMICs, vaccine safety was reported as the main reason influencing the willingness to vaccinate due to the rapid development of the COVID-19 vaccines [ 26 , 43 , 47 , 59 , 68 , 69 , 74 , 88 ].

Interestingly, the preference for COVID-19 vaccines may also have changed as the pandemic progressed [ 63 ]. Similarly, effectiveness remained the most important attribute in all periods, possibly due to the continuing severity of the pandemic and the fear of the possible emergence of new coronavirus strains [ 43 ]. Before the pandemic wave, the information on vaccine effectiveness was limited [ 26 ], but people still considered vaccine effectiveness to be the most important driver of vaccination. However, during the pandemic, the public’s perception of the health risk increased. As vaccines were introduced and used, people seemed to become more concerned about the duration of vaccine protection and preferred a longer vaccine protection [ 11 , 50 ]. After the pandemic wave, as the pandemic situation gradually stabilized, cost, combined with their perception of the risk of susceptibility, became more important in their preferences. However, despite this shift, most of the public still believed that people who are at higher risk of infection or death should be vaccinated first [ 63 ].

Limitations

Our study had several limitations. First, not all studies used the same attributes and levels, which limited our ability to perform a quantitative synthesis and directly compare the estimates of model parameters. Instead, we qualitatively synthesized and summarized the range of attributes that may be useful in the formative stage of attribute selection in future DCE surveys investigating the preference for COVID-19 vaccine. Second, although DCEs have been shown to be a valid method for eliciting preferences, the experiment may not represent real market choices but rather hypothetical scenarios with plausible and realistic attributes. However, it offers opportunities to evaluate vaccines that are not yet available in the market or to specific population [ 68 ]. Third, the commonly used classification of outcome, cost, and process was used in order to better explain the public’s preference for vaccine attributes. However, several attributes could not be properly classified, and a fourth category (ie, other attributes) had to be added [ 19 ]. Meanwhile, the variety of attributes included may make it difficult to appropriately name and interpret this category as a whole. Fifth, the PREFS checklist is limited to 5 questions and fails to elicit several criteria that should be reported in DCE studies. Also, it does not provide sufficient tools to assess the biases in a DCE, such as selection bias and nonresponse bias [ 79 , 89 ]. Finally, although there was no specific theoretical framework to structure our qualitative analysis from the 4 identified categories, our classification was based on previous studies [ 18 , 19 , 82 , 90 , 91 ] and our own findings. This synthesis led us to categorize attributes into 4 main classes, providing a clear structure for analyzing and presenting participants’ vaccine preferences and making it easier to compare their preferences across different studies.

Conclusions

In conclusion, this systematic review synthesized the global evidence on preferences for COVID-19 vaccines using the DCE methodology. Vaccine effectiveness and safety were found to be the main drivers for COVID-19 vaccination, highlighting the importance of global collaboration to improve vaccine effectiveness and minimize side effects, as well as the importance of communicating this vaccine-related information to the public to maximize the uptake of COVID-19 vaccines. The subgroup analyses emphasized the importance of differences in vaccine preference of specific populations and time periods in optimizing the acceptance of COVID-19 vaccines. These findings may serve as valuable insights for government agencies involved in the social mobilization process for COVID-19 vaccination. However, the response to the pandemic is a continuous learning process [ 92 ]. It is crucial for policy makers to consider preference evidence when designing policies to promote vaccination.

Acknowledgments

The authors have not received a specific grant for this research from any funding agency in the public, commercial, or not-for-profit sectors.

Data Availability

All data relevant to the study are included in the article or uploaded as supplemental information. Data sets of this study are available upon reasonable request to the corresponding author.

Authors' Contributions

YH, SF, and YZ are joint first authors. HJ conceived the study and its methodology. YH, SF, and YZ designed, refined, and implemented the search strategy; screened articles for inclusion; and extracted and curated the data. All authors contributed to the interpretation of the results. YH, SF, and YZ wrote the initial draft of the manuscript. HJ and HW critically reviewed the manuscript. HJ supervised the study design and provided overall guidance. All authors approved the final draft of the manuscript. HJ had full access to all the data used in this study, and all authors had final responsibility for the decision to submit for publication.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist.

Search strategies.

Attributes included in each category.

The detailed distribution of the study period across countries.

Preference for COVID-19 vaccines among high-income countries and low- and middle-income countries (n=53).

Preference for COVID-19 vaccines in the different study periods (n=53).

Assessment of 47 included studies quality using the Purpose, Respondents, Explanation, Findings, and Significance checklist.

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Abbreviations

Edited by A Mavragani; submitted 19.01.24; peer-reviewed by T Ricks, I Saha; comments to author 11.04.24; revised version received 01.05.24; accepted 26.05.24; published 29.07.24.

©Yiting Huang, Shuaixin Feng, Yuyan Zhao, Haode Wang, Hongbo Jiang. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 29.07.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

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Psychological Distress and COVID-19

Evidence-based interventions for frontline health care workers—a literature review.

Delassalle, Nancy BSN, RN, CCRN; Cavaciuti, Mary MPH, BSN, RN, CCRN

Nancy Delassalle, BSN, RN, CCRN , has more than 35 years of experience as a critical care nurse at Long Island Jewish Medical Center, New Hyde Park, New York. She has been on the Rapid Response/Code Blue team for the past 8 years.

Mary Cavaciuti, MPH, BSN, RN, CCRN , has more than 31 years of experience in critical care at Long Island Jewish Medical Center, New Hyde Park, New York.

Nancy Delassalle and Mary Cavaciuti are cochairs of the Research and Evidence-Based council.

Nancy Delassalle and Mary Cavaciuti are co–primary authors.

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

Address correspondence and reprint requests to: Nancy Delassalle BSN, RN, CCRN ( [email protected] ).

Objectives 

The COVID-19 pandemic has had a serious impact on the psychological well-being of frontline health care workers. A variety of interventions have been offered to health care workers in their workplace that has them questioning which intervention would be most beneficial. The purpose of this review is to determine what evidence-based interventions would have an impact on alleviating COVID-19-related psychological distress.

Methods 

A search was conducted from multiple databases, including Pubmed, CINAHL, Joanna Briggs, and Cochrane, using the PRISMA framework. The search included COVID-19 as well as previous pandemics. Critical appraisal and synthesis of the 16 relevant sources of evidence were completed.

Results 

Based on the current evidence, one cannot conclude that any specific intervention is effective for pandemic-relate distress.

Conclusion 

The development, implementation, and scientific evaluation of evidence-based interventions to address the immediate, as well as the long-term, psychological effects of COVID-19 on the mental well-being of health care workers, are needed.

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The reports to the Vaccine Adverse Event Reporting System met the case definition of myocarditis (reported cases). Among individuals older than 40 years of age, there were no more than 8 reports of myocarditis for any individual age after receiving either vaccine. For the BNT162b2 vaccine, there were 114 246 837 first vaccination doses and 95 532 396 second vaccination doses; and for the mRNA-1273 vaccine, there were 78 158 611 and 66 163 001, respectively. The y-axis range differs between panels A and B.

The reports to the Vaccine Adverse Event Reporting System met the case definition of myocarditis (reported cases). Among recipients of either vaccine, there were only 13 reports or less of myocarditis beyond 10 days for any individual time from vaccination to symptom onset. The y-axis range differs between panels A and B.

A, For the BNT162b2 vaccine, there were 138 reported cases of myocarditis with known date for symptom onset and dose after 114 246 837 first vaccination doses and 888 reported cases after 95 532 396 second vaccination doses.

B, For the mRNA-1273 vaccine, there were 116 reported cases of myocarditis with known date for symptom onset and dose after 78 158 611 first vaccination doses and 311 reported cases after 66 163 001 second vaccination doses.

eMethods. Medical Dictionary for Regulatory Activities Preferred Terms, Definitions of Myocarditis and Pericarditis, Myocarditis medical review form

eFigure. Flow diagram of cases of myocarditis and pericarditis reported to Vaccine Adverse Event Reporting System (VAERS) after receiving mRNA-based COVID-19 vaccine, United States, December 14, 2020-August 31, 2021.

eTable 1. Characteristics of all myocarditis cases reported to Vaccine Adverse Event Reporting System (VAERS) after mRNA-based COVID-19 vaccination, United States, December 14, 2020–August 31, 2021.

eTable 2. Characteristics of all pericarditis cases reported to Vaccine Adverse Event Reporting System (VAERS) after mRNA-based COVID-19 vaccination, United States, December 14, 2020–August 31, 2021.

eTable 3. Characteristics of myocarditis cases reported to Vaccine Adverse Event Reporting System after mRNA-based COVID-19 vaccination by case definition status.

• Myocarditis and Pericarditis After Vaccination for COVID-19 JAMA Research Letter September 28, 2021 This study investigates the incidence of myocarditis and pericarditis emergency department or inpatient hospital encounters before COVID-19 vaccine availability (January 2019–January 2021) and during a COVID-19 vaccination period (February-May 2021) in a large US health care system. George A. Diaz, MD; Guilford T. Parsons, MD, MS; Sara K. Gering, BS, BSN; Audrey R. Meier, MPH; Ian V. Hutchinson, PhD, DSc; Ari Robicsek, MD
• Myocarditis Following a Third BNT162b2 Vaccination Dose in Military Recruits in Israel JAMA Research Letter April 26, 2022 This study assessed whether a third vaccine dose was associated with the risk of myocarditis among military personnel in Israel. Limor Friedensohn, MD; Dan Levin, MD; Maggie Fadlon-Derai, MHA; Liron Gershovitz, MD; Noam Fink, MD; Elon Glassberg, MD; Barak Gordon, MD
• Myocarditis Cases After mRNA-Based COVID-19 Vaccination in the US—Reply JAMA Comment & Response May 24, 2022 Matthew E. Oster, MD, MPH; David K. Shay, MD, MPH; Tom T. Shimabukuro, MD, MPH, MBA
• Myocarditis Cases After mRNA-Based COVID-19 Vaccination in the US JAMA Comment & Response May 24, 2022 Sheila R. Weiss, PhD
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• Patient Information: Acute Myocarditis JAMA JAMA Patient Page August 8, 2023 This JAMA Patient Page describes acute myocarditis and its symptoms, causes, diagnosis, and treatment. Kristin Walter, MD, MS
• Myocarditis Following Immunization With mRNA COVID-19 Vaccines in Members of the US Military JAMA Cardiology Brief Report October 1, 2021 This case series describes myocarditis presenting after COVID-19 vaccination within the Military Health System. Jay Montgomery, MD; Margaret Ryan, MD, MPH; Renata Engler, MD; Donna Hoffman, MSN; Bruce McClenathan, MD; Limone Collins, MD; David Loran, DNP; David Hrncir, MD; Kelsie Herring, MD; Michael Platzer, MD; Nehkonti Adams, MD; Aliye Sanou, MD; Leslie T. Cooper Jr, MD
• Patients With Acute Myocarditis Following mRNA COVID-19 Vaccination JAMA Cardiology Brief Report October 1, 2021 This study describes 4 patients who presented with acute myocarditis after mRNA COVID-19 vaccination. Han W. Kim, MD; Elizabeth R. Jenista, PhD; David C. Wendell, PhD; Clerio F. Azevedo, MD; Michael J. Campbell, MD; Stephen N. Darty, BS; Michele A. Parker, MS; Raymond J. Kim, MD
• Association of Myocarditis With BNT162b2 Vaccination in Children JAMA Cardiology Brief Report December 1, 2021 This case series reviews comprehensive cardiac imaging in children with myocarditis after COVID-19 vaccine. Audrey Dionne, MD; Francesca Sperotto, MD; Stephanie Chamberlain; Annette L. Baker, MSN, CPNP; Andrew J. Powell, MD; Ashwin Prakash, MD; Daniel A. Castellanos, MD; Susan F. Saleeb, MD; Sarah D. de Ferranti, MD, MPH; Jane W. Newburger, MD, MPH; Kevin G. Friedman, MD

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Oster ME , Shay DK , Su JR, et al. Myocarditis Cases Reported After mRNA-Based COVID-19 Vaccination in the US From December 2020 to August 2021. JAMA. 2022;327(4):331–340. doi:10.1001/jama.2021.24110

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Myocarditis Cases Reported After mRNA-Based COVID-19 Vaccination in the US From December 2020 to August 2021

• 1 US Centers for Disease Control and Prevention, Atlanta, Georgia
• 2 School of Medicine, Emory University, Atlanta, Georgia
• 3 Children’s Healthcare of Atlanta, Atlanta, Georgia
• 4 Vanderbilt University Medical Center, Nashville, Tennessee
• 5 Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
• 6 Boston Medical Center, Boston, Massachusetts
• 7 Duke University, Durham, North Carolina
• 8 US Food and Drug Administration, Silver Spring, Maryland
• Research Letter Myocarditis and Pericarditis After Vaccination for COVID-19 George A. Diaz, MD; Guilford T. Parsons, MD, MS; Sara K. Gering, BS, BSN; Audrey R. Meier, MPH; Ian V. Hutchinson, PhD, DSc; Ari Robicsek, MD JAMA
• Research Letter Myocarditis Following a Third BNT162b2 Vaccination Dose in Military Recruits in Israel Limor Friedensohn, MD; Dan Levin, MD; Maggie Fadlon-Derai, MHA; Liron Gershovitz, MD; Noam Fink, MD; Elon Glassberg, MD; Barak Gordon, MD JAMA
• Comment & Response Myocarditis Cases After mRNA-Based COVID-19 Vaccination in the US—Reply Matthew E. Oster, MD, MPH; David K. Shay, MD, MPH; Tom T. Shimabukuro, MD, MPH, MBA JAMA
• Comment & Response Myocarditis Cases After mRNA-Based COVID-19 Vaccination in the US Sheila R. Weiss, PhD JAMA
• Medical News & Perspectives JAMA Network Articles of the Year 2022 Melissa Suran, PhD, MSJ JAMA
• Review Diagnosis and Treatment of Acute Myocarditis—A Review Enrico Ammirati, MD, PhD; Javid J. Moslehi, MD JAMA
• JAMA Patient Page Patient Information: Acute Myocarditis Kristin Walter, MD, MS JAMA
• Brief Report Myocarditis Following Immunization With mRNA COVID-19 Vaccines in Members of the US Military Jay Montgomery, MD; Margaret Ryan, MD, MPH; Renata Engler, MD; Donna Hoffman, MSN; Bruce McClenathan, MD; Limone Collins, MD; David Loran, DNP; David Hrncir, MD; Kelsie Herring, MD; Michael Platzer, MD; Nehkonti Adams, MD; Aliye Sanou, MD; Leslie T. Cooper Jr, MD JAMA Cardiology
• Brief Report Patients With Acute Myocarditis Following mRNA COVID-19 Vaccination Han W. Kim, MD; Elizabeth R. Jenista, PhD; David C. Wendell, PhD; Clerio F. Azevedo, MD; Michael J. Campbell, MD; Stephen N. Darty, BS; Michele A. Parker, MS; Raymond J. Kim, MD JAMA Cardiology
• Brief Report Association of Myocarditis With BNT162b2 Vaccination in Children Audrey Dionne, MD; Francesca Sperotto, MD; Stephanie Chamberlain; Annette L. Baker, MSN, CPNP; Andrew J. Powell, MD; Ashwin Prakash, MD; Daniel A. Castellanos, MD; Susan F. Saleeb, MD; Sarah D. de Ferranti, MD, MPH; Jane W. Newburger, MD, MPH; Kevin G. Friedman, MD JAMA Cardiology

Question   What is the risk of myocarditis after mRNA-based COVID-19 vaccination in the US?

Findings   In this descriptive study of 1626 cases of myocarditis in a national passive reporting system, the crude reporting rates within 7 days after vaccination exceeded the expected rates across multiple age and sex strata. The rates of myocarditis cases were highest after the second vaccination dose in adolescent males aged 12 to 15 years (70.7 per million doses of the BNT162b2 vaccine), in adolescent males aged 16 to 17 years (105.9 per million doses of the BNT162b2 vaccine), and in young men aged 18 to 24 years (52.4 and 56.3 per million doses of the BNT162b2 vaccine and the mRNA-1273 vaccine, respectively).

Meaning   Based on passive surveillance reporting in the US, the risk of myocarditis after receiving mRNA-based COVID-19 vaccines was increased across multiple age and sex strata and was highest after the second vaccination dose in adolescent males and young men.

Importance   Vaccination against COVID-19 provides clear public health benefits, but vaccination also carries potential risks. The risks and outcomes of myocarditis after COVID-19 vaccination are unclear.

Objective   To describe reports of myocarditis and the reporting rates after mRNA-based COVID-19 vaccination in the US.

Design, Setting, and Participants   Descriptive study of reports of myocarditis to the Vaccine Adverse Event Reporting System (VAERS) that occurred after mRNA-based COVID-19 vaccine administration between December 2020 and August 2021 in 192 405 448 individuals older than 12 years of age in the US; data were processed by VAERS as of September 30, 2021.

Exposures   Vaccination with BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna).

Main Outcomes and Measures   Reports of myocarditis to VAERS were adjudicated and summarized for all age groups. Crude reporting rates were calculated across age and sex strata. Expected rates of myocarditis by age and sex were calculated using 2017-2019 claims data. For persons younger than 30 years of age, medical record reviews and clinician interviews were conducted to describe clinical presentation, diagnostic test results, treatment, and early outcomes.

Results   Among 192 405 448 persons receiving a total of 354 100 845 mRNA-based COVID-19 vaccines during the study period, there were 1991 reports of myocarditis to VAERS and 1626 of these reports met the case definition of myocarditis. Of those with myocarditis, the median age was 21 years (IQR, 16-31 years) and the median time to symptom onset was 2 days (IQR, 1-3 days). Males comprised 82% of the myocarditis cases for whom sex was reported. The crude reporting rates for cases of myocarditis within 7 days after COVID-19 vaccination exceeded the expected rates of myocarditis across multiple age and sex strata. The rates of myocarditis were highest after the second vaccination dose in adolescent males aged 12 to 15 years (70.7 per million doses of the BNT162b2 vaccine), in adolescent males aged 16 to 17 years (105.9 per million doses of the BNT162b2 vaccine), and in young men aged 18 to 24 years (52.4 and 56.3 per million doses of the BNT162b2 vaccine and the mRNA-1273 vaccine, respectively). There were 826 cases of myocarditis among those younger than 30 years of age who had detailed clinical information available; of these cases, 792 of 809 (98%) had elevated troponin levels, 569 of 794 (72%) had abnormal electrocardiogram results, and 223 of 312 (72%) had abnormal cardiac magnetic resonance imaging results. Approximately 96% of persons (784/813) were hospitalized and 87% (577/661) of these had resolution of presenting symptoms by hospital discharge. The most common treatment was nonsteroidal anti-inflammatory drugs (589/676; 87%).

Conclusions and Relevance   Based on passive surveillance reporting in the US, the risk of myocarditis after receiving mRNA-based COVID-19 vaccines was increased across multiple age and sex strata and was highest after the second vaccination dose in adolescent males and young men. This risk should be considered in the context of the benefits of COVID-19 vaccination.

Myocarditis is an inflammatory condition of the heart muscle that has a bimodal peak incidence during infancy and adolescence or young adulthood. 1 - 4 The clinical presentation and course of myocarditis is variable, with some patients not requiring treatment and others experiencing severe heart failure that requires subsequent heart transplantation or leads to death. 5 Onset of myocarditis typically follows an inciting process, often a viral illness; however, no antecedent cause is identified in many cases. 6 It has been hypothesized that vaccination can serve as a trigger for myocarditis; however, only the smallpox vaccine has previously been causally associated with myocarditis based on reports among US military personnel, with cases typically occurring 7 to 12 days after vaccination. 7

With the implementation of a large-scale, national COVID-19 vaccination program starting in December 2020, the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration began monitoring for a number of adverse events of special interest, including myocarditis and pericarditis, in the Vaccine Adverse Event Reporting System (VAERS), a long-standing national spontaneous reporting (passive surveillance) system. 8 As the reports of myocarditis after COVID-19 vaccination were reported to VAERS, the Clinical Immunization Safety Assessment Project, 9 a collaboration between the CDC and medical research centers, which includes physicians treating infectious diseases and other specialists (eg, cardiologists), consulted on several of the cases. In addition, reports from several countries raised concerns that mRNA-based COVID-19 vaccines may be associated with acute myocarditis. 10 - 15

Given this concern, the aims were to describe reports and confirmed cases of myocarditis initially reported to VAERS after mRNA-based COVID-19 vaccination and to provide estimates of the risk of myocarditis after mRNA-based COVID-19 vaccination based on age, sex, and vaccine type.

VAERS is a US spontaneous reporting (passive surveillance) system that functions as an early warning system for potential vaccine adverse events. 8 Co-administered by the CDC and the US Food and Drug Administration, VAERS accepts reports of all adverse events after vaccination from patients, parents, clinicians, vaccine manufacturers, and others regardless of whether the events could plausibly be associated with receipt of the vaccine. Reports to VAERS include information about the vaccinated person, the vaccine or vaccines administered, and the adverse events experienced by the vaccinated person. The reports to VAERS are then reviewed by third-party professional coders who have been trained in the assignment of Medical Dictionary for Regulatory Activities preferred terms. 16 The coders then assign appropriate terms based on the information available in the reports.

This activity was reviewed by the CDC and was conducted to be consistent with applicable federal law and CDC policy. The activities herein were confirmed to be nonresearch under the Common Rule in accordance with institutional procedures and therefore were not subject to institutional review board requirements. Informed consent was not obtained for this secondary use of existing information; see 45 CFR part 46.102(l)(2), 21 CFR part 56, 42 USC §241(d), 5 USC §552a, and 44 USC §3501 et seq.

The exposure of concern was vaccination with one of the mRNA-based COVID-19 vaccines: the BNT162b2 vaccine (Pfizer-BioNTech) or the mRNA-1273 vaccine (Moderna). During the analytic period, persons aged 12 years or older were eligible for the BNT162b2 vaccine and persons aged 18 years or older were eligible for the mRNA-1273 vaccine. The number of COVID-19 vaccine doses administered during the analytic period was obtained through the CDC’s COVID-19 Data Tracker. 17

The primary outcome was the occurrence of myocarditis and the secondary outcome was pericarditis. Reports to VAERS with these outcomes were initially characterized using the Medical Dictionary for Regulatory Activities preferred terms of myocarditis or pericarditis (specific terms are listed in the eMethods in the Supplement ). After initial review of reports of myocarditis to VAERS and review of the patient’s medical records (when available), the reports were further reviewed by CDC physicians and public health professionals to verify that they met the CDC’s case definition for probable or confirmed myocarditis (descriptions previously published and included in the eMethods in the Supplement ). 18 The CDC’s case definition of probable myocarditis requires the presence of new concerning symptoms, abnormal cardiac test results, and no other identifiable cause of the symptoms and findings. Confirmed cases of myocarditis further require histopathological confirmation of myocarditis or cardiac magnetic resonance imaging (MRI) findings consistent with myocarditis.

Deaths were included only if the individual had met the case definition for confirmed myocarditis and there was no other identifiable cause of death. Individual cases not involving death were included only if the person had met the case definition for probable myocarditis or confirmed myocarditis.

We characterized reports of myocarditis or pericarditis after COVID-19 vaccination that met the CDC’s case definition and were received by VAERS between December 14, 2020 (when COVID-19 vaccines were first publicly available in the US), and August 31, 2021, by age, sex, race, ethnicity, and vaccine type; data were processed by VAERS as of September 30, 2021. Race and ethnicity were optional fixed categories available by self-identification at the time of vaccination or by the individual filing a VAERS report. Race and ethnicity were included to provide the most complete baseline description possible for individual reports; however, further analyses were not stratified by race and ethnicity due to the high percentage of missing data. Reports of pericarditis with evidence of potential myocardial involvement were included in the review of reports of myocarditis. The eFigure in the Supplement outlines the categorization of the reports of myocarditis and pericarditis reviewed.

Further analyses were conducted only for myocarditis because of the preponderance of those reports to VAERS, in Clinical Immunization Safety Assessment Project consultations, and in published articles. 10 - 12 , 19 - 21 Crude reporting rates for myocarditis during a 7-day risk interval were calculated using the number of reports of myocarditis to VAERS per million doses of COVID-19 vaccine administered during the analytic period and stratified by age, sex, vaccination dose (first, second, or unknown), and vaccine type. Expected rates of myocarditis by age and sex were calculated using 2017-2019 data from the IBM MarketScan Commercial Research Database. This database contains individual-level, deidentified, inpatient and outpatient medical and prescription drug claims, and enrollment information submitted to IBM Watson Health by large employers and health plans. The data were accessed using version 4.0 of the IBM MarketScan Treatment Pathways analytic platform. Age- and sex-specific rates were calculated by determining the number of individuals with myocarditis ( International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] codes B33.20, B33.22, B33.24, I40.0, I40.1, I40.8, I40.9, or I51.4) 22 identified during an inpatient encounter in 2017-2019 relative to the number of individuals of similar age and sex who were continually enrolled during the year in which the myocarditis-related hospitalization occurred; individuals with any diagnosis of myocarditis prior to that year were excluded. Given the limitations of the IBM MarketScan Commercial Research Database to capture enrollees aged 65 years or older, an expected rate for myocarditis was not calculated for this population. A 95% CI was calculated using Poisson distribution in SAS version 9.4 (SAS Institute Inc) for each expected rate of myocarditis and for each observed rate in a strata with at least 1 case.

In cases of probable or confirmed myocarditis among those younger than 30 years of age, their clinical course was then summarized to the extent possible based on medical review and clinician interviews. This clinical course included presenting symptoms, diagnostic test results, treatment, and early outcomes (abstraction form appears in the eMethods in the Supplement ). 23

When applicable, missing data were delineated in the results or the numbers with complete data were listed. No assumptions or imputations were made regarding missing data. Any percentages that were calculated included only those cases of myocarditis with adequate data to calculate the percentages.

Between December 14, 2020, and August 31, 2021, 192 405 448 individuals older than 12 years of age received a total of 354 100 845 mRNA-based COVID-19 vaccines. VAERS received 1991 reports of myocarditis (391 of which also included pericarditis) after receipt of at least 1 dose of mRNA-based COVID-19 vaccine (eTable 1 in the Supplement ) and 684 reports of pericarditis without the presence of myocarditis (eTable 2 in the Supplement ).

Of the 1991 reports of myocarditis, 1626 met the CDC’s case definition for probable or confirmed myocarditis ( Table 1 ). There were 208 reports that did not meet the CDC’s case definition for myocarditis and 157 reports that required more information to perform adjudication (eTable 3 in the Supplement ). Of the 1626 reports that met the CDC’s case definition for myocarditis, 1195 (73%) were younger than 30 years of age, 543 (33%) were younger than 18 years of age, and the median age was 21 years (IQR, 16-31 years) ( Figure 1 ). Of the reports of myocarditis with dose information, 82% (1265/1538) occurred after the second vaccination dose. Of those with a reported dose and time to symptom onset, the median time from vaccination to symptom onset was 3 days (IQR, 1-8 days) after the first vaccination dose and 74% (187/254) of myocarditis events occurred within 7 days. After the second vaccination dose, the median time to symptom onset was 2 days (IQR, 1-3 days) and 90% (1081/1199) of myocarditis events occurred within 7 days ( Figure 2 ).

Males comprised 82% (1334/1625) of the cases of myocarditis for whom sex was reported. The largest proportions of cases of myocarditis were among White persons (non-Hispanic or ethnicity not reported; 69% [914/1330]) and Hispanic persons (of all races; 17% [228/1330]). Among persons younger than 30 years of age, there were no confirmed cases of myocarditis in those who died after mRNA-based COVID-19 vaccination without another identifiable cause and there was 1 probable case of myocarditis but there was insufficient information available for a thorough investigation. At the time of data review, there were 2 reports of death in persons younger than 30 years of age with potential myocarditis that remain under investigation and are not included in the case counts.

Symptom onset of myocarditis was within 7 days after vaccination for 947 reports of individuals who received the BNT162b2 vaccine and for 382 reports of individuals who received the mRNA-1273 vaccine. The rates of myocarditis varied by vaccine type, sex, age, and first or second vaccination dose ( Table 2 ). The reporting rates of myocarditis were highest after the second vaccination dose in adolescent males aged 12 to 15 years (70.73 [95% CI, 61.68-81.11] per million doses of the BNT162b2 vaccine), in adolescent males aged 16 to 17 years (105.86 [95% CI, 91.65-122.27] per million doses of the BNT162b2 vaccine), and in young men aged 18 to 24 years (52.43 [95% CI, 45.56-60.33] per million doses of the BNT162b2 vaccine and 56.31 [95% CI, 47.08-67.34] per million doses of the mRNA-1273 vaccine). The lower estimate of the 95% CI for reporting rates of myocarditis in adolescent males and young men exceeded the upper bound of the expected rates after the first vaccination dose with the BNT162b2 vaccine in those aged 12 to 24 years, after the second vaccination dose with the BNT162b2 vaccine in those aged 12 to 49 years, after the first vaccination dose with the mRNA-1273 vaccine in those aged 18 to 39 years, and after the second vaccination dose with the mRNA-1273 vaccine in those aged 18 to 49 years.

The reporting rates of myocarditis in females were lower than those in males across all age strata younger than 50 years of age. The reporting rates of myocarditis were highest after the second vaccination dose in adolescent females aged 12 to 15 years (6.35 [95% CI, 4.05-9.96] per million doses of the BNT162b2 vaccine), in adolescent females aged 16 to 17 years (10.98 [95% CI, 7.16-16.84] per million doses of the BNT162b2 vaccine), in young women aged 18 to 24 years (6.87 [95% CI, 4.27-11.05] per million doses of the mRNA-1273 vaccine), and in women aged 25 to 29 years (8.22 [95% CI, 5.03-13.41] per million doses of the mRNA-1273 vaccine). The lower estimate of the 95% CI for reporting rates of myocarditis in females exceeded the upper bound of the expected rates after the second vaccination dose with the BNT162b2 vaccine in those aged 12 to 29 years and after the second vaccination dose with the mRNA-1273 vaccine in those aged 18 to 29 years.

Among the 1372 reports of myocarditis in persons younger than 30 years of age, 1305 were able to be adjudicated, with 92% (1195/1305) meeting the CDC’s case definition. Of these, chart abstractions or medical interviews were completed for 69% (826/1195) ( Table 3 ). The symptoms commonly reported in the verified cases of myocarditis in persons younger than 30 years of age included chest pain, pressure, or discomfort (727/817; 89%) and dyspnea or shortness of breath (242/817; 30%). Troponin levels were elevated in 98% (792/809) of the cases of myocarditis. The electrocardiogram result was abnormal in 72% (569/794) of cases of myocarditis. Of the patients who had received a cardiac MRI, 72% (223/312) had abnormal findings consistent with myocarditis. The echocardiogram results were available for 721 cases of myocarditis; of these, 84 (12%) demonstrated a notable decreased left ventricular ejection fraction (<50%). Among the 676 cases for whom treatment data were available, 589 (87%) received nonsteroidal anti-inflammatory drugs. Intravenous immunoglobulin and glucocorticoids were each used in 12% of the cases of myocarditis (78/676 and 81/676, respectively). Intensive therapies such as vasoactive medications (12 cases of myocarditis) and intubation or mechanical ventilation (2 cases) were rare. There were no verified cases of myocarditis requiring a heart transplant, extracorporeal membrane oxygenation, or a ventricular assist device. Of the 96% (784/813) of cases of myocarditis who were hospitalized, 98% (747/762) were discharged from the hospital at time of review. In 87% (577/661) of discharged cases of myocarditis, there was resolution of the presenting symptoms by hospital discharge.

In this review of reports to VAERS between December 2020 and August 2021, myocarditis was identified as a rare but serious adverse event that can occur after mRNA-based COVID-19 vaccination, particularly in adolescent males and young men. However, this increased risk must be weighed against the benefits of COVID-19 vaccination. 18

Compared with cases of non–vaccine-associated myocarditis, the reports of myocarditis to VAERS after mRNA-based COVID-19 vaccination were similar in demographic characteristics but different in their acute clinical course. First, the greater frequency noted among vaccine recipients aged 12 to 29 years vs those aged 30 years or older was similar to the age distribution seen in typical cases of myocarditis. 2 , 4 This pattern may explain why cases of myocarditis were not discovered until months after initial Emergency Use Authorization of the vaccines in the US (ie, until the vaccines were widely available to younger persons). Second, the sex distribution in cases of myocarditis after COVID-19 vaccination was similar to that seen in typical cases of myocarditis; there is a strong male predominance for both conditions. 2 , 4

However, the onset of myocarditis symptoms after exposure to a potential immunological trigger was shorter for COVID-19 vaccine–associated cases of myocarditis than is typical for myocarditis cases diagnosed after a viral illness. 24 - 26 Cases of myocarditis reported after COVID-19 vaccination were typically diagnosed within days of vaccination, whereas cases of typical viral myocarditis can often have indolent courses with symptoms sometimes present for weeks to months after a trigger if the cause is ever identified. 1 The major presenting symptoms appeared to resolve faster in cases of myocarditis after COVID-19 vaccination than in typical viral cases of myocarditis. Even though almost all individuals with cases of myocarditis were hospitalized and clinically monitored, they typically experienced symptomatic recovery after receiving only pain management. In contrast, typical viral cases of myocarditis can have a more variable clinical course. For example, up to 6% of typical viral myocarditis cases in adolescents require a heart transplant or result in mortality. 27

In the current study, the initial evaluation and treatment of COVID-19 vaccine–associated myocarditis cases was similar to that of typical myocarditis cases. 28 - 31 Initial evaluation usually included measurement of troponin level, electrocardiography, and echocardiography. 1 Cardiac MRI was often used for diagnostic purposes and also for possible prognostic purposes. 32 , 33 Supportive care was a mainstay of treatment, with specific cardiac or intensive care therapies as indicated by the patient’s clinical status.

Long-term outcome data are not yet available for COVID-19 vaccine–associated myocarditis cases. The CDC has started active follow-up surveillance in adolescents and young adults to assess the health and functional status and cardiac outcomes at 3 to 6 months in probable and confirmed cases of myocarditis reported to VAERS after COVID-19 vaccination. 34 For patients with myocarditis, the American Heart Association and the American College of Cardiology guidelines advise that patients should be instructed to refrain from competitive sports for 3 to 6 months, and that documentation of a normal electrocardiogram result, ambulatory rhythm monitoring, and an exercise test should be obtained prior to resumption of sports. 35 The use of cardiac MRI is unclear, but it may be useful in evaluating the progression or resolution of myocarditis in those with abnormalities on the baseline cardiac MRI. 36 Further doses of mRNA-based COVID-19 vaccines should be deferred, but may be considered in select circumstances. 37

This study has several limitations. First, although clinicians are required to report serious adverse events after COVID-19 vaccination, including all events leading to hospitalization, VAERS is a passive reporting system. As such, the reports of myocarditis to VAERS may be incomplete, and the quality of the information reported is variable. Missing data for sex, vaccination dose number, and race and ethnicity were not uncommon in the reports received; history of prior SARS-CoV-2 infection also was not known. Furthermore, as a passive system, VAERS data are subject to reporting biases in that both underreporting and overreporting are possible. 38 Given the high verification rate of reports of myocarditis to VAERS after mRNA-based COVID-19 vaccination, underreporting is more likely. Therefore, the actual rates of myocarditis per million doses of vaccine are likely higher than estimated.

Second, efforts by CDC investigators to obtain medical records or interview physicians were not always successful despite the special allowance for sharing information with the CDC under the Health Insurance Portability and Accountability Act of 1996. 39 This challenge limited the ability to perform case adjudication and complete investigations for some reports of myocarditis, although efforts are still ongoing when feasible.

Third, the data from vaccination administration were limited to what is reported to the CDC and thus may be incomplete, particularly with regard to demographics.

Fourth, calculation of expected rates from the IBM MarketScan Commercial Research Database relied on administrative data via the use of ICD-10 codes and there was no opportunity for clinical review. Furthermore, these data had limited information regarding the Medicare population; thus expected rates for those older than 65 years of age were not calculated. However, it is expected that the rates in those older than 65 years of age would not be higher than the rates in those aged 50 to 64 years. 4

Based on passive surveillance reporting in the US, the risk of myocarditis after receiving mRNA-based COVID-19 vaccines was increased across multiple age and sex strata and was highest after the second vaccination dose in adolescent males and young men. This risk should be considered in the context of the benefits of COVID-19 vaccination.

Corresponding Author: Matthew E. Oster, MD, MPH, US Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333 ( [email protected] ).

Correction: This article was corrected March 21, 2022, to change “pericarditis” to “myocarditis” in the first row, first column of eTable 1 in the Supplement.

Accepted for Publication: December 16, 2021.

Author Contributions: Drs Oster and Su had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Oster, Shay, Su, Creech, Edwards, Dendy, Schlaudecker, Woo, Shimabukuro.

Acquisition, analysis, or interpretation of data: Oster, Shay, Su, Gee, Creech, Broder, Edwards, Soslow, Schlaudecker, Lang, Barnett, Ruberg, Smith, Campbell, Lopes, Sperling, Baumblatt, Thompson, Marquez, Strid, Woo, Pugsley, Reagan-Steiner, DeStefano, Shimabukuro.

Drafting of the manuscript: Oster, Shay, Su, Gee, Creech, Marquez, Strid, Woo, Shimabukuro.

Critical revision of the manuscript for important intellectual content: Oster, Shay, Su, Creech, Broder, Edwards, Soslow, Dendy, Schlaudecker, Lang, Barnett, Ruberg, Smith, Campbell, Lopes, Sperling, Baumblatt, Thompson, Pugsley, Reagan-Steiner, DeStefano, Shimabukuro.

Statistical analysis: Oster, Su, Marquez, Strid, Woo, Shimabukuro.

Obtained funding: Edwards, DeStefano.

Administrative, technical, or material support: Oster, Gee, Creech, Broder, Edwards, Soslow, Schlaudecker, Smith, Baumblatt, Thompson, Reagan-Steiner, DeStefano.

Supervision: Su, Edwards, Soslow, Dendy, Schlaudecker, Campbell, Sperling, DeStefano, Shimabukuro.

Conflict of Interest Disclosures: Dr Creech reported receiving grants from the National Institutes of Health for the Moderna and Janssen clinical trials and receiving personal fees from Astellas and Horizon. Dr Edwards reported receiving grants from the National Institutes of Health; receiving personal fees from BioNet, IBM, X-4 Pharma, Seqirus, Roche, Pfizer, Merck, Moderna, and Sanofi; and receiving compensation for being the associate editor of Clinical Infectious Diseases . Dr Soslow reported receiving personal fees from Esperare. Dr Schlaudecker reported receiving grants from Pfizer and receiving personal fees from Sanofi Pasteur. Drs Barnett, Ruberg, and Smith reported receiving grants from Pfizer. Dr Lopes reported receiving personal fees from Bayer, Boehringer Ingleheim, Bristol Myers Squibb, Daiichi Sankyo, GlaxoSmithKline, Medtronic, Merck, Pfizer, Portola, and Sanofi and receiving grants from Bristol Myers Squibb, GlaxoSmithKline, Medtronic, Pfizer, and Sanofi. No other disclosures were reported.

Funding/Support: This work was supported by contracts 200-2012-53709 (Boston Medical Center), 200-2012-53661 (Cincinnati Children’s Hospital Medical Center), 200-2012-53663 (Duke University), and 200-2012-50430 (Vanderbilt University Medical Center) with the US Centers for Disease Control and Prevention (CDC) Clinical Immunization Safety Assessment Project.

Role of the Funder/Sponsor: The CDC provided funding via the Clinical Immunization Safety Assessment Project to Drs Creech, Edwards, Soslow, Dendy, Schlaudecker, Lang, Barnett, Ruberg, Smith, Campbell, and Lopes. The authors affiliated with the CDC along with the other coauthors conducted the investigations; performed collection, management, analysis, and interpretation of the data; were involved in the preparation, review, and approval of the manuscript; and made the decision to submit the manuscript for publication.

Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the CDC or the US Food and Drug Administration. Mention of a product or company name is for identification purposes only and does not constitute endorsement by the CDC or the US Food and Drug Administration.

Additional Contributions: We thank the following CDC staff who contributed to this article without compensation outside their normal salaries (in alphabetical order and contribution specified in parenthesis at end of each list of names): Nickolas Agathis, MD, MPH, Stephen R. Benoit, MD, MPH, Beau B. Bruce, MD, PhD, Abigail L. Carlson, MD, MPH, Meredith G. Dixon, MD, Jonathan Duffy, MD, MPH, Charles Duke, MD, MPH, Charles Edge, MSN, MS, Robyn Neblett Fanfair, MD, MPH, Nathan W. Furukawa, MD, MPH, Gavin Grant, MD, MPH, Grace Marx, MD, MPH, Maureen J. Miller, MD, MPH, Pedro Moro, MD, MPH, Meredith Oakley, DVM, MPH, Kia Padgett, MPH, BSN, RN, Janice Perez-Padilla, MPH, BSN, RN, Robert Perry, MD, MPH, Nimia Reyes, MD, MPH, Ernest E. Smith, MD, MPH&TM, David Sniadack, MD, MPH, Pamela Tucker, MD, Edward C. Weiss, MD, MPH, Erin Whitehouse, PhD, MPH, RN, Pascale M. Wortley, MD, MPH, and Rachael Zacks, MD (for clinical investigations and interviews); Amelia Jazwa, MSPH, Tara Johnson, MPH, MS, and Jamila Shields, MPH (for project coordination); Charles Licata, PhD, and Bicheng Zhang, MS (for data acquisition and organization); Charles E. Rose, PhD (for statistical consultation); and Scott D. Grosse, PhD (for calculation of expected rates of myocarditis). We also thank the clinical staff who cared for these patients and reported the adverse events to the Vaccine Adverse Event Reporting System.

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The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid)

• Paul Levay National Institute for Health and Care Excellence https://orcid.org/0000-0003-1784-3314
• Amy Finnegan National Institute for Health and Care Excellence https://orcid.org/0000-0002-7632-8956

Objective : In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision.

Methods : We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register.

Results : In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records.

Conclusion : We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature.

Author Biographies

Paul levay, national institute for health and care excellence.

Senior Information Specialist, National Institute for Health and Care Excellence, Manchester, United Kingdom

Amy Finnegan, National Institute for Health and Care Excellence

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Copyright (c) 2023 Paul Levay, Amy Finnegan

This work is licensed under a Creative Commons Attribution 4.0 International License .

Current Issue

ISSN 1558-9439 (Online)

• Open access
• Published: 30 July 2024

The heart versus the brain, are they also different when it comes to post-vaccination complications, insights from a systematic review of post-COVID-19 vaccines ADEM cases

• Antoine AbdelMassih   ORCID: orcid.org/0000-0001-8876-3229 1 , 2 ,
• Aya Kamel 3 ,
• Ameera Barakat 4 ,
• Lana Mohammad 4 ,
• Hanya Gaber 5 ,
• Yasmine Hisham Mousa 5 ,
• Hana Hassanein 6 ,
• Robert Saleb 5 &
• Noha Khalil 7  

Bulletin of the National Research Centre volume  48 , Article number:  74 ( 2024 ) Cite this article

21 Accesses

Metrics details

COVID-19 vaccines have been a game changer in the pandemic, their extensive use was favorable compared to the burden of COVID-19 complications. Despite the low incidence of complications, it was important to analyze them carefully to understand the underlying mechanisms and predisposing factors. For instance, myopericarditis especially from mRNA vaccines, and its relatively higher prevalence in young adults and adolescents has raised a public concern about the use of this vaccine in this group. We aimed through this review to compare the age likelihood of ADEM from COVID-19 vaccines, with that reported in myopericarditis cases; secondary outcome parameters included the gender and number of doses needed to induce COVID-19 vaccines related to ADEM.

Methodology

A literature search has been conducted on relevant databases to retrieve all case reports/series and systematic reviews describing ADEM with possible linkage to COVID-19. Exclusion criteria included any report not including the desired outcome parameters. Our results were then qualitatively compared with a similar systematic review reporting myopericarditis from COVID-19 vaccines.

In 38 cases with ADEM, mean age was 49 ± 16 compared to 25 ± 14 in myopericarditis, females were more likely to be affected, and while most of myopericarditis cases develop after the second dose, most of ADEM cases develop after the first dose (76%). Moreover, age > 56 years was more predictive of negative outcome after ADEM in the form of death or permanent vegetative state.

Short conclusion

The discrepancy in age, gender and number of doses needed to induce complications between ADEM and myopericarditis, signify that the tissue affected is the major orchestrator of the age, gender, and dose characteristics, and not the type of vaccines. A leakier blood brain barrier with aging, might allow easier passage of autoantibodies and cytokines into the brain while lack of inhibitory immune checkpoints in the myocardium in young age might explain the higher prevalence of those cases in young adults and adolescents.

There has been a dilemma in the diversified sequelae of post-COVID-19 (coronavirus disease 2019) mRNA (messenger Ribonucleic acid) vaccinations. Although most of the outcomes are satisfactory, and vaccination benefits outweighs the risk, there have been some case reports that intrigued further analysis. A study conducted by  Minghui Li et al. assessed the incidence rate of myocarditis and pericarditis following COVID-19 vaccination in the USA in perspective to age group and vaccine type (Li et al. 2021 ). It was found that the rates of myocardial affection are more prevalent in adolescents and young adults than in older age groups while using the mRNA vaccines. As the reporting odds ratio (ROR) of BNT162b2 (Pfizer-Biontech) and mRNA-1273 (Moderna) vaccine subtypes were higher than the ROR of viral vector vaccines of Ad26.COV2. S (Janssen), 5.3, 2.91 and 1.39, respectively. Another study supports the outcomes of mRNA vaccination post-COVID-19 in youths. A retrospective study was implemented by  Dongngan T. Truong et al., and the results of the collected data on patients < 21 years old following mRNA vaccination were significant.(Truong et al. 2022 ) The incidence of suspected myocarditis in younger patients was noticeable.

The affection of young age by postvaccine myocarditis has not only be observed with mRNA vaccines; as myocardial and pericardial complications can also occur in young age groups following the smallpox vaccine, not only after the COVID-19 mRNA vaccine. This can be supported by an observational cohort study conducted by Engler et al., where the outcomes of smallpox vaccines were elaborated (Engler et al. 2015 ). Three hundred and forty-eight individuals out of over 5000 case reports that showed side effects post smallpox vaccination, manifested with cardiological adversities such as myocarditis and pericarditis: 276 and 72 cases, respectively. The median age of the myopericarditis cases was 24 years old, emphasizing the prevalence of myocarditis post-vaccination in the younger segment of the age spectrum, irrespective to vaccination subtype.

This young age trend for postvaccine myocarditis, regardless of the type of vaccine, is poorly understood.

In contrast, postvaccine acute disseminated encephalomyelitis (ADEM), tend to occur in a relatively older age. A report by Huynh and colleagues illustrated a case of 61-year-old male with ADEM following influenza vaccine, while Nakamura et al. documented two adult cases aged 62 and 70 with post-influenza vaccine ADEM (Huynh et al. 2008 ; NAKAMURA et al. 2003 ). The rest of systematic reviews were mainly focused on postvaccine neurologic sequelae overall and not specifically targeting the specific age of ADEM following different types of vaccines. In addition most of the studies assessing postvaccine ADEM, cannot be reliably cited as the involved vaccines are exclusive childhood compulsory vaccines, thus they cannot reflect the true age trend of postvaccine ADEM. (Sejvar 2005 ; Williams et al. 2011 ).

It seems, from the above that age likelihood of postvaccine tissue affection, might be related to the tissue characteristics rather than to the vaccine type. For this purpose, we dedicate this systematic review, to study the age likelihood of acute disseminated encephalomyelitis (ADEM), post-COVID-19 vaccination specifically post-mRNA COVID-19 vaccines. We hypothesize that we might find a discrepancy between the mean age of ADEM cases reported after COVID-19 vaccines compared to myocarditis seen after the same vaccines. The latter finding might consolidate our initial impression that tissue characteristics might be closely tied to the age predilection of post-vaccination immune sequelae in the respective tissue.

Inclusion and exclusion criteria for literature search

A literature search was implemented on PubMed, Scopus, Google scholar and Web of science to identify studies using the following key words ADEM and COVID-19 vaccination. The bibliography of any identified study was clearly inspected to find any report that could have been missed during the initial computer run.

Inclusion criteria included any age, developing ADEM after COVID-19 vaccination, accepted type of studies were systematic reviews, case reports, and case series.

Studies not fulfilling the outcome parameters targeted by the study were excluded.

Outcome parameters

Three of the authors examined each study for the following outcome parameters: age, gender, type, and dose of the vaccine incriminated, the time interval between the vaccination and the development of ADEM, the main neurologic presentation, the treatment lines used, and the outcome of the case.

Statistical analysis

Data collected were analyzed using Excel and MedCalc statistical software. Numerical data were represented using mean and standard deviation when normally distributed and using median, minimum, and maximum when non-normally distributed. Non-recovery was defined as persistence of neurologic abnormalities, vegetative state or death in our collected cases, this categorical division was essential to perform a Receiver Operating Characteristic analysis (ROC) to determine the cut-off age predicting non-recovery from ADEM developing from COVID-19 vaccines. The latter was represented using an interactive dot diagram.

A systematic review has been identified (Nabizadeh et al. 2023 ) including 20 studies, out of which 19 were eligible to be included in our review: (Ahmad, Timmermans, and Dakakni 2022 ; Al-Quliti et al. 2022 ; Ancau et al. 2022 ; Ballout et al. 2022 ; Cao and Ren 2022 ; Kania et al. 2021 ; Lazaro et al. 2022 ; Maramattom et al. 2022 ; Miyamoto et al. 2022 ; Mumoli et al. 2022 ; Nagaratnam et al. 2022 ; Netravathi et al. 2022 ; Ozgen Kenangil et al. 2021 ; Permezel et al. 2022 ; Rinaldi et al. 2022 ; Shimizu et al. 2021 ; Simone et al. 2021 ; Vogrig et al. 2021 ; Yazdanpanah et al. 2022 ) (Fig.  1 ).

PRISMA flow chart

In addition to the 19 studies included, our literature search has identified nine other studies:

(Bastide et al. 2022 ; Garg et al. 2023 ; Gustavsen et al. 2023 ; Lohmann et al. 2022 ; Mousa et al. 2022 ; Nimkar et al. 2022 ; Raknuzzaman 2021 ; Sazgarnejad and Kordipour 2022 ). Thus, a total of 28 studies were analyzed comprising a total of 38 cases. (Table  1 ).

Most of the cases were attributed to the adenoviral vector vaccines (63%) ADEM occurred following the first dose of vaccination (76%), with a median interval of 14 after vaccination. (Table  2 ).

The oldest age seen in ADEM cases following vaccination was seen in patients receiving mRNA vaccines (57 ± 19) compared to a mean of 48 ± 14 following adenoviral vector vaccines.

Complete recovery was observed in 37% of patients, while non-recovery (defined as residual motor deficit or the development of vegetative state) or death was observed in a total of 45% of cases. (Table  2 ).

Receiver operating characteristic analysis illustrated as an interactive dot diagram showed that an age > 56, predicts non-recovery in ADEM cases following post-COVID-19 vaccines. (Fig.  2 ).

The diagnostic accuracy of age in predicting non-recovery after ADEM post COVID-19 Vaccines. ADEM acute disseminated encephalomyelitis, COVID-19 Coronavirus Disease 2019

Treatment received was mainly steroids (oral and pulse intravenous), intravenous immunoglobulins, and plasma exchange. While only three cases received rituximab (8%), and one received eculizumab and another one received cyclophosphamide (3%) (Table  1 ). Table 1 illustrates the details of each case in the included cases and case reports.

Our review describes the demographic, and clinical characteristics of a rare complication of COVID-19 vaccines. It is the second systematic review of reported cases in this context, after Nabizadeh et al. study(Nabizadeh et al. 2023 ), however with a different aim. The aim of our systematic review was mainly to study the differences of age predisposition, type of vaccine and number of doses between myocarditis and ADEM following COVID-19 vaccines. Several major differences were observed, notably the number of literature reports, which points to a relatively higher incidence of myocarditis as our group could only find 28 reports with a total of 38 cases compared to thousands of cases of myopericarditis in the literature; this can make the comparison between the two complications flawful, as the scarcity of ADEM reports is not very helpful to draw solid conclusions.

However, we still decided to compare the aforementioned outcome parameters across the two complications. We took Goyal et al. study as a reference for myopericarditis cases as it shares the different outcome parameters intended in our study, and it is not a VAERS based study; thus, its results can be qualitatively compared to the results of our research. (Goyal et al. 2023 ).

Age of clustered ADEM cases was 49 ± 16 compared to 25 ± 14 in myopericarditis cases. The young age of myopericarditis especially from mRNA vaccines, lead to fears among parents, planning to vaccinate their children using these vaccines, and led to an overall impression that mRNA vaccines might be associated with increased complications’ rate at the young age. Our study contradicts this false belief, as it clearly shows that ADEM occurring from mRNA vaccines is likely to occur in older age groups compared to myocarditis and to ADEM cases from other COVID-19 vaccines. Nevertheless, older age was also predictive of worst outcomes in our collected cases (Fig.  2 ), patients older than 56 were more prone to develop residual neurologic deficit, vegetative state, or death. This might also mean that age likelihood and other demographic characteristics of any vaccine complication are related to the type of the complication and tissue involved rather than the vaccine type.

One of the theories that can explain the young age of myocarditis from COVID-19 and other vaccines (such as vaccinia virus vaccine used for smallpox) is the mechanism of immune inhibition inside the myocardium. The heart muscle harbors a strict system for immune surveillance, that can prevent any immune-mediated damage, this immune surveillance is particularly important in the heart as the regenerative capacity of myocardial cells is absent.

Two main mechanisms of peripheral tolerance protect myocytes from T cell damage namely cytotoxic T-lymphocyte-associated protein-4 (CTLA4), and Programmed cell protein death-1 (PD1). CTLA4 and PD1 block T cell activation by binding to CD-28 receptors on the surface of T cells, thereby preventing any viral antigen from its activation. (Grabie et al. 2019 ) The myocardial protection from autoimmunity, offered by inhibitory immune checkpoints, such as PD1, is upregulated by aging, which might mean that the susceptibility of myocardium to immune-mediated inflammation, should decrease with aging. (Platt et al. 2017 ) On another note, antibodies implicated in CNS autoimmune inflammation, must gain access to the CNS via the blood brain barrier, olfactory route, or blood-cerebrospinal fluid barrier, or sometimes produced locally witing the CNS itself. The latter mechanism has been particularly of focus in multiple sclerosis, as Quintana and colleagues proved the present of myelin reactive antibodies that are locally produced in the brain. There also hypotheses that post-infectious ADEM, which is intriguingly common in the pediatric age group, involves the local production of antibodies in the CNS against viral antigen entering the CNS through the olfactory route. But, for antibodies, to gain access to the CNS, this implies a leakier BBB Blood brain barrier) or BCSFB (Blood cerebrospinal fluid barrier), this can be understandable in post-infectious ADEM, where implicated micro-organisms weaken the tight junctions of the BBB, and this allows access of cross-reactive antibodies to the brain (Spindler and Hsu 2012 ); but this cannot be the case in post-vaccination ADEM, as no offending organism is present to play this synergistic getaway role. An explanation for antibody access to the CNS in post-vaccination ADEM, is aging. If aging protects the myocardium against autoimmunity, it plays an inverse role in the CNS by rendering the BBB and BCSFB more permeable to antibodies and to external antigens. (Adesse et al. 2022 ).

Back to the findings, of our study, which also showed that ADEM mainly occurs after the first dose of vaccination, this is different than the immune-priming pattern seen in myocarditis from COVID-19 vaccines, as they need two doses usually to produce this complication.

This pattern might be consistent with a cytokine rather than immune-mediated damage. Wu et al. described a subtype of ADEM known as acute necrotizing encephalitis which involves a personal susceptibility to CNS damage due to hypercytokinemia. (Wu et al. 2015 ).

Finally, yet importantly female patients were more likely to develop ADEM following COVID-19 vaccination compared to male patients. A study by Falahi et al., examined COVID-19 outcomes across both genders, and showed that female patients have a higher susceptibility to cytokine storm, and suggested that estrogen upregulates pro-inflammatory molecules, leading to an augmented inflammatory response in females. This might consolidate the impression taken from dose pattern of post-COVID-19 vaccination ADEM, that it is mainly mediated via hypercytokinemia rather than auto-antibodies. (Falahi and Kenarkoohi 2021 ).

It is worth highlighting again as mentioned in the first paragraph of the discussion, that one of the most important limitations of this study that can hinder the solidity of our results; is the discrepancy in the number of cases across the two complications (ADEM and myopericarditis); with only 38 ADEM cases compared to hundreds of myopericarditis patients.

Figure  3 summarizes the differences outlined above between ADEM and myopericarditis developing following COVID-19 vaccines in view of our findings compared to the findings of Goyal et study ( 2023 ).

Summary figure for comparison of age, sex, and dose characteristics of post COVID-19 myopericarditis vs. ADEM. ADEM: acute disseminated encephalomyelitis, COVID-19: Coronavirus Disease 2019

Conclusions

This review compares the demographic, vaccine types and dose characteristics of post-COVID-19 vaccines ADEM and Post-COVID-19 vaccines myopericarditis. Older age, predominance of female gender, and first dose implication all characterize ADEM compared to myopericarditis. And despite the rarity of these complications, they open new horizons in understanding post-vaccination complications and their underlying mechanisms. They might signify that aging can be protective against autoimmunity in the myocardium, but the same aging can jeopardize the BBB, rendering it more susceptible to delivery of antibodies and cytokines to the CNS. More studies at the molecular level, should be implemented to confirm these findings and to prove that vaccine complications are not only determined by the vaccine type but also by the type of the target tissue. The findings highlighted by our study, can wipe out the public-based impression that mRNA vaccines are linked to higher complications in younger individuals, and can help in combating vaccine hesitancy especially toward a vaccine mechanism that might be very promising in the future for other infectious and non-infectious disorders. Risk of Bias assessment has been performed and illustrated in Fig.  4 :

Risk of bias assessment for our systematic review

Availability of data and materials

Not applicable.

Abbreviations

Janssen vaccine

Acute Disseminated Encephalomyelitis

Blood brain Barrier

Blood cerebrospinal fluid barrier

Pfizer Biontech vaccine

Cluster of differentiation

Is a chimpanzee (Ch) adenovirus-vectored vaccine (Ad), whose development was led by the University of Oxford (Ox).

Central nervous system

Coronavirus vaccine initiative

Coronavirus Disease 2019

Cytotoxic T-lymphocyte-associated protein-4

Disturbed Conscious level

Gam: Gamaleya, COVID-Sputnik Vaccine

Glasgow Coma Scale

Intravenous

Intravenous immunoglobulins

Methylprednisolone

Messenger Ribonucleic acid

Moderna Spikevax vaccine

Not reported

Programmed Death ligand 1

Plasmapheresis

Vaccine adverse events Reporting system

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As the primary author, I aimed to dedicate this piece of work to individuals whose perseverance remains unwavering in challenging circumstances and who value enduring friendships. The capacity to persist and endure adversity is a trait that can supersede any innate talent.

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Osteonecrosis as a manifestation of Long-COVID Syndrome: a systematic review

• Published: 01 August 2024

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• P. Za 1 , 2 ,
• G. F. Papalia   ORCID: orcid.org/0000-0002-4140-738X 1 , 2 ,
• P. Gregori 1 , 2 ,
• S. Vasta 1 , 2 &
• R. Papalia 1 , 2  

Purpose SARS-CoV-2 is an RNA virus responsible for COVID-19 pandemic. Some authors described the set of persistent symptoms COVID-related as “Long-COVID Syndrome.” Several cases of post-COVID-19 osteonecrosis (ON) are described. Our primary aim was to study the hypothetical correlation between SARS-CoV-2 infection and ON; our secondary aim was to understand if ON can be considered part of Long-COVID. Materials and methods We performed a systematic review following the Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) guidelines. Because COVID-19 is a recently described disease, we included all levels of evidence studies. We excluded studies lacking specification regarding the use of corticosteroids (CCS) and studies not related to COVID-19. The variables extracted were age, sex, risk factors, affected joints, signs and symptoms, magnetic resonance imaging (MRI) and X-ray features, histology, treatment of COVID-19, dose and duration of treatment with CCS, treatment of ON, follow-up, and treatment outcome. Results A total of 13 studies were included, involving 95 patients and 159 joints. Time between the diagnosis of COVID-19 and the onset of symptoms related to ON was 16 weeks on average. Time between the onset of symptoms and the MRI was 6 weeks. An average of 926.4 mg of prednisolone equivalent per patient were administered. On average, CCS were administered for 20.6 days. Conclusions Patients with a history of COVID-19 infection developed osteonecrosis prematurely and with a lower dose of CCS than usually reported in the literature. Symptoms of osteonecrosis occur within the interval of the period described as Long-COVID. Surgeons should not underestimate the persistence of arthralgia when a history of SARS-CoV-2 infection and use of CCS is reported.

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Za, P., Papalia, G.F., Gregori, P. et al. Osteonecrosis as a manifestation of Long-COVID Syndrome: a systematic review. Musculoskelet Surg (2024). https://doi.org/10.1007/s12306-024-00854-w

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A literature review of the economics of COVID-19

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• 1 Department of Economics University of Ottawa Ottawa Ontario Canada.
• PMID: 34230772
• PMCID: PMC8250825
• DOI: 10.1111/joes.12423

The goal of this piece is to survey the developing and rapidly growing literature on the economic consequences of COVID-19 and the governmental responses, and to synthetize the insights emerging from a very large number of studies. This survey: (i) provides an overview of the data sets and the techniques employed to measure social distancing and COVID-19 cases and deaths; (ii) reviews the literature on the determinants of compliance with and the effectiveness of social distancing; (iii) mentions the macroeconomic and financial impacts including the modelling of plausible mechanisms; (iv) summarizes the literature on the socioeconomic consequences of COVID-19, focusing on those aspects related to labor, health, gender, discrimination, and the environment; and (v) summarizes the literature on public policy responses.

Keywords: COVID‐19; coronavirus; economic impact; lockdowns; social impact.

© 2021 John Wiley & Sons Ltd.

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COVID‐19 publications in 2020 in…

COVID‐19 publications in 2020 in the NBER working paper series. [Color figure can…

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Systematic Review of the Literature About the Effects of the COVID-19 Pandemic on the Lives of School Children

Background: The year 2020 has been marked by the emergence of coronavirus disease 2019 (COVID-19). This virus has reached many countries and has paralyzed the lives of many people who have been forced to stay at home in confinement. There have been many studies that have sought to analyze the impact of this pandemic from different perspectives; however, this study will pay attention to how it has affected and how it may affect children between 0 and 12 years in the future after the closure of schools for months.

Objective: The objective of this article is to learn about the research carried out on the child population in times of confinement, especially those dealing with the psychological and motor aspects of minors.

Methods: To carry out this systematic review, the PRISMA statement has been followed to achieve an adequate and organized structure of the manuscript. The bibliography has been searched in the Web of Science (WOS), Scopus, and Dialnet databases, using as keywords: “COVID-19” and “Children.” The criteria that were established for the selection of the articles were (1) articles focusing on an age of up to 12 years, (2) papers relating COVID-19 to children, and (3) studies analyzing the psychological and motor characteristics of children during confinement.

Results: A total of nine manuscripts related to the psychological and motor factors in children under 12 have been found. The table presenting the results includes the authors, title, place of publication, and key ideas of the selected manuscripts.

Conclusion: After concluding the systematic review, it has been detected that there are few studies that have focused their attention on the psychological, motor, or academic problems that can occur to minors after a situation of these characteristics. Similarly, a small number of studies have been found that promote actions at the family and school level to reverse this situation when life returns to normal. These results may be useful for future studies that seek to expand the information according to the evolution of the pandemic.

Introduction

When news of an epidemic began to spread in a Chinese city in early 2020, no one anticipated the scope of the epidemic for the entire world in a very short period. From Wuhan (China) to New York (USA) through Africa, South America, Asia, and Europe, the new coronavirus, coronavirus disease 2019 (COVID-19) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has paralyzed, to a greater or lesser extent, the life in many countries, causing thousands of deaths and about 6 million infections. For these reasons, the scientific community is on the alert by conducting studies on the virus, the disease it produces, the situation it creates, and the population it attacks, from different perspectives, including systematic reviews of the literature, such as the one presented in this paper.

However, researchers on this topic are not only biologists or physicians. It is worth noting the contribution of Maestre Maestre ( 2020 ), President of the Society for Latin Studies, in an article on the virus that has caused the pandemic, in which, playing with different related terms, he explains that the neutral noun “virus” means “poison” in Latin, so most current research is trying to find a medicine that will kill the virus. Likewise, the Greek term ϕάρμακoν (in Latin pharmacum) also means poison. The relationship between the two terms is that pharmacies are looking for poisons that will kill the “poisons” that undermine people's health or their desire to be safe. Remember the symbol of the pharmacies, the “Bowl of Hygieia” with the snake that pours a “poison” into it that stops being a poison to become an antidote. The name “coronavirus” is given to it because, through the microscope, the “virus-poison” is shaped like a “crown” that makes it king of poisons.

However, in addition to scientists who study the pandemic, biologists, doctors, and humanists, educators are obliged to care for the psychological and emotional health, as well as cultivate the minds, of children. The consequences of the containment measures of COVID-19 are being detrimental to the mental health of people around the world. It is logical that the most vulnerable are children who do not understand what is happening and who, along with the concern and frustration of their elders, may present risk factors, such as anxiety and affective and post-traumatic stress disorders (Giallonardo et al., 2020 ). However, not only minors are affected. According to Roy et al. ( 2020 ), more than 80% of people over 18 have shown the need for attention to their mental health as a result of the anxiety and stress experienced during the pandemic. Forte et al. ( 2020 ) agree with this idea, stating that the pandemic has caused stress, psychological discomfort, sleep disorders, and instability, among others, in a large part of the population.

In this sense, many questionnaires have been applied to obtain information in the educational context or related to it from research groups at different universities, including the one from the IDIBAPS research group at the Hospital Universitario de Barcelona, concerning behaviors to reduce emotional distress during the pandemic and confinement by COVID-19, https://enquesta.clinic.cat/index.php/268395?lang=es ; Universidad de las Palmas de Gran Canaria on family relationships during confinement: Study of the effect of COVID-19 in the family context, https://forms.gle/2xpmqRtQ8mtBMAz77 ; Universidad de Oviedo, as a longitudinal study on how isolation and the practice of physical activity (PA) during confinement is affecting to offer effective strategies that it called “pills”: EDAFIDES Questionnaire COVID-19, https://docs.google.com/forms/d/e/1FAIpQLSfyID6X7YgUejwXNv2YyOQ1YU2LrFsPkkvHzux_TD_BjPIGNw/viewform?usp=sf_link ; Euskal Herriko Unibertsitatea, to find out about the situation of university students in confinement and to propose improvements: https://forms.gle/jDkFgW7xeKfSFNHB6 ; Universidad da Coruña y Universidad de Jaén, on the activities of children in Spanish homes in times of confinement. This last questionnaire was applied in Spain and in South America: https://docs.google.com/forms/d/e/1FAIpQLSeyBBkMEmPxj-AoPQG98QorsaLyNex9wlI2FJ2Ku2q8nbsdNQ/viewform .

Based on the above-mentioned questionnaires, there is a concern to analyze how confinement has affected children under 12 at the motor and psychological levels. This literature review is carried out and explained in detail in the procedure and search strategy of the methodology. The impact of the pandemic is such that many national and international journals are offering special issues on COVID-19, including Frontiers, which, being digital, contains 229 articles signed by many authors from various countries, which look at the subject from different perspectives: there are eight that refer to age and especially to children in some way, including: who cares about the elderly (Fischer et al., 2020 ), physical inactivity (Ricci et al., 2020 ), age distribution (Cortis, 2020 ), and newborns (Ovali, 2020 ), but none discusses parents' views on the period of confinement from the psychological, educational, academic, physical, and emotional points of view of their children. Neither do they inquire into the opinion of the children themselves, understanding by these those who are in infant and primary education, that is, up to the age of 12.

Education must seek to provide the child with a comprehensive education, trying to help his or her physical, emotional, intellectual, family, social, and moral development. Active methods are crucial for early childhood education, and teachers are needed to apply them in schools (Salvador, 2008 ), now in the homes of their students, which they access through the Internet. The role of parents is also to educate, but from different perspectives, complementing those of teachers in the acquisition of children's learning. For these reasons, many families say that they do not know how to undertake these activities with their children for so long.

Likewise, the lack of other family members, such as grandparents, who had been playing a role in accompanying, especially with children in preschool, complicates the state of confinement and the lack of school attendance that is taking place, initially planned for 6 months in a row. The study by Clemente-González ( 2016 ) of the University of Murcia highlights the relevance of grandparent–grandchild relationships and the role of the former in the social and emotional development of the child, which gives great significance to their grandparents for the appreciation observed in them, recognizing their importance in the family structure. At this point, it is also necessary to point out the lack of relationships between equals, which is so important for the correct emotional development of children.

Another important aspect that has been affected by the coronavirus pandemic is the practice of PA. Many schoolchildren practice physical exercise based solely on the subject of Physical Education. This subject is not only based on motor skills but is a practice that affects schoolchildren in a global way, influences many aspects of their daily lives, and helps teachers to better understand students in their different dimensions (Founaud and González-Audicana, 2020 ). Lack of PA is associated with obesity, as indicated by different studies that relate the regular practice of physical exercise with the reduction of health problems (Castañeda-Vázquez et al., 2020 ).

The opinion article written by the Spanish secondary school teacher, Fandino-Pérez ( 2020 ), is significant in which he reflects on the virtuality of education and his position regarding personalized education, so demanded in times of normality, where teachers and students know each other, interact, and socialize, precisely the attitude that has taken away the virus. Fandino-Pérez says that the pandemic has put us in front of the mirror to see a distorted and absurd image of the work of teachers as producers of programming and good results, which turns them and their students into a kind of machine. We have forgotten the main thing: to be human beings capable of creating a better world and of overcoming ignorance, fear, and demagogy.

As a background to this study, we refer to March 11, 2020 when the World Health Organization (World Health Organization, 2020a ) declared this disease produced by the coronavirus (COVID-19) to be a pandemic. It was first reported in Wuhan (China) on December 31, 2019. According to World Health Organization ( 2009 ), the global public health community recognized the need for standardized research and data collection after the 2009 flu epidemics, so the WHO Expert Working Group on Special Research and Studies has developed several standard protocols for pandemic flu. This has led World Health Organization ( 2019a , 2020b ) to develop similar protocols for the Middle East respiratory syndrome coronavirus (MERS-CoV) and, with the support of expert advisors, has adapted the protocols for influenza and MERS-CoV to help better understand the clinical, epidemiological, and virological characteristics of COVID-19.

Some months have passed, and most of the inhabitants of planet Earth, more or less surprised, have been confined to their homes for about 60 days, where they have carried out their work online and have had to attend to their younger children, also confined without attending school and without being able to go out into the street or use the recreational facilities that some residential areas have.

When we find ourselves at the moment of reincorporation into the daily life known before the appearance of the pandemic (May 2020), other illnesses arise as a consequence of the involuntary confinement to which the population has been subjected; this is the cave syndrome or agoraphobia (fear of open spaces), and it is possible that with the passage of time, other psychological and affective disorders will arise in the adults who will be those who have suffered this confinement and this disaster as children.

The disease mainly attacks people over 70 years old and only 0.3% of children in countries where there have been more deaths (for example, Spain). According to the Instituto de Salud Carlos, this may be the reason why medical research does not deal with children, but these subjects have special psychological, academic, and emotional characteristics at a stage of their lives when they are in full development, so from the educational point of view, it is necessary to find out how children have developed in their homes, what their parents think, and what future expectations experts, teachers, and psychologists have for them.

For all these reasons, the aim of this work is to find out about the research carried out on the child population in times of confinement, especially those that deal with the psychological and motor aspects of minors.

Considering this objective and following the Population, Intervention, Comparison, and Outcome (PICO) strategy, the following research question arises: what do the studies already published determine about how confinement has affected children under the age of 12 on a psychological and motor level?

Methodology

For the elaboration of this systematic review, we have followed the items to publish systematic reviews and meta-analyses of the PRISMA statement (Sotos-Prieto et al., 2014 ; Hutton et al., 2015 ), in order to achieve an adequate and organized structure of the manuscript. The guidelines of Cochrane Training (Higgins and Green, 2011 ) have also been used.

Procedure and Search Strategy

The literature review took place during the last weeks of May 2020 and focused mainly on the Web of Science (WOS) database, using Scopus and Dialnet as support. The topic considered for the selection of articles was the one related to the global pandemic caused by COVID-19 and how it has affected psychologically and motorically children up to 12 years old. The following keywords were used: “COVID-19” and “children” and the Boolean operator “and.” After this first search and taking into account only the works published in 2020 (since that is when the pandemic occurred), 837 scientific documents were obtained. By restricting the search to only journal articles, the documents were reduced to 576 articles, after which the language filter was applied, selecting only those papers published in English and Spanish, leaving a total of 537. Since the pandemic started in China, the initial search was also done in that language, not finding any related articles. The articles signed by researchers of Chinese nationality are written in English. Finally, the following areas of research were chosen: “Psychology,” “Sociology,” and “Education Educational Research,” finally limiting the search to 48 scientific articles, which make up the sample of this study.

Inclusion and Exclusion Criteria

The criteria that were established for the selection of the articles were (1) articles focusing on an age of up to 12 years, (2) papers relating COVID-19 to children, and (3) studies analyzing the psychological and motor characteristics of children during confinement.

In order to apply these criteria, a first preliminary reading of the title and summary of each article was carried out, which made it possible to rule out papers that did not meet the above-mentioned criteria. A more exhaustive reading of the selected articles was then carried out, leaving a final sample of nine scientific papers ( Figure 1 ).

PRISMA flowchart.

Article Coding

To extract the data from the articles, the following coding process was followed: (1) author/authors and year of publication, (2) title of the research, (3) place/country of publication, and (4) key ideas of the research.

The research included in this systematic review was coded by four of the authors, in order to check the reliability of the coding and the degree of agreement among the researchers in relation to the selection and extraction of the data (González-Valero et al., 2019 ). The degree of agreement on the rating of the articles was 93%. This was obtained by dividing the number of coincidences by the total number of categories defined for each study and multiplying it by 100.

In order to establish the methodological quality of the present study, reliability was determined according to the detection and selection of the Fleiss' Kappa (Fk) statistical index for more than two evaluators (Fleiss, 1971 ). A value of Fk = 0.780 was obtained for data extraction and selection, which indicates that there is substantial agreement (0.61–0.80).

Table 1 presents the main results of different studies following the codification indicated in the previous section: (1) author/authors and year of publication, (2) title of the research, (3) place/country of publication, and (4) key ideas of the research.

Basis of the study.

Of the nine articles analyzed because they met the characteristics of the search, three have been published in The Lancet , which began as an independent international weekly medical journal, founded in 1823 by Thomas Wakley. Since its first issue, it has strived to make science widely available so that medicine can serve, transform society, and positively impact people's lives. It has evolved into a family of journals including The Lancet Child & Adolescent Health , in which one of the three articles cited appears. These three articles, and most of those analyzed, relate to the classical medicine that should serve society to help improve life.

Most of the references in this article (84.22%) are from the year 2020, a sign of the interest in the subject and the dedication of scientists and teachers. Only three are earlier, the one by Hutton et al. ( 2015 ) that deals with a more technical content, the extension of PRISMA for network meta-analysis, and the ones by Salvador ( 2008 ) and Clemente-González ( 2016 ) that highlight the role of grandparents in children's lives.

Of the two articles by Spanish teachers, the one by Álvarez-Zarzuelo ( 2020 ) is a personal opinion of a social educator who is ahead of other research. It only provides the experts' ideas on the possible repercussions of confinement. For his part, Gómez-Gerdel ( 2020 ) writes an opinion article that, exceptionally, is being published by the International Journal of Education for Social Justice in its special issue 9(e) on “Consequences of the Closure of Schools by COVID-19 on Educational Inequalities.” The author, from the perspective of the departments of Educational Guidance that deal with inclusive education, raises the chaos that it has meant for the Spanish Educational System to apply teaching only on line, which means for the most vulnerable families: difficulties in accessing technologies and delays in education. On the other hand, it raises what could be a return to the family whose members had been living together for a long time, something absolutely necessary for the correct development of the minors who spend too much time away from home.

The teaching–learning system, which should seek the comprehensive training of the child, in which parents and teachers should participate, has been drastically modified, trying not to abandon the active methods used in schools (Salvador, 2008 ), with the difficulties that this entails for families, which in many cases have no training in this area.

Of the three articles by Chinese authors, Liu et al. ( 2020 ) analyze the situation of children whose parents have been infected with the virus or have died; Zhang et al. ( 2020 ) observe the behavior of children with attention-deficit/hyperactive disorder (ADHD) during this period; and finally, Guan et al. ( 2020 ) deal with the practice of childhood PA during confinement. Therefore, only one of them studies a type of activity in this period, the one dealing with PA coinciding with what is written by the Italians Ricci et al. ( 2020 ); in the same line, we find the Turks Yarimkaya and Esentürk ( 2020 ) who deal with the importance of PA in confinement for children with autism spectrum disorder (ASD). It is important to remember that World Health Organization ( 2010 , 2019b ) recommends a minimum of 1 h/day of moderate–vigorous PA in children, but that only one-third of children exceed these recommendations (Salas-Sánchez et al., 2020 ).

The American and British authors analyze the role of parents in the confinement of their children and provide some advice on this subject. They also look at the future psychological problems that may arise as a result of over-information, change of routines, and manifestation of feelings of distress and guilt, as well as the need to see peers and other carers (teachers, grandparents). They coincide with Clemente-González ( 2016 ) project based on the grandparent–grandchild relationship and the promotion of identity, which seemed to be a premonition of what would happen with the arrival of the COVID-19 pandemic that would force the disappearance of these relationships for a long time.

It is important to note that, according to the review carried out, there are authors who analyze the pandemic from different perspectives with which we agree: cultural aspects (Maestre Maestre, 2020 ); actions of biologists and doctors, more distant from our intentions; humanists (Fandino-Pérez, 2020 ), and especially for this study, of educators who are aware that the essence of being in the classroom and the immediate feedback that students offer in this situation has been lost. To this must be added the role of the WHO, overwhelmed by the health events that have occurred so quickly, as described in these lines.

We believe that the application of many questionnaires during the confinement and currently post-COVID-19 pandemic has saturated the patience of the respondents, although most have helped scientists and educators to obtain information that will facilitate a smooth exit from this disaster.

Conclusions

The above leads us to the general conclusion that there are very few studies on how confinement has affected children under 12 years old psychologically and motorly. These articles agree on the consequences that confinement can have on minors and on the importance of psychological support from the family, and the establishment of routines can be effective. The manuscripts that deal with PA remind us of the importance of it and indicate that the rates of sedentarism have increased during these months.

It is necessary to insist on the search for and analysis of other activities, as well as the behavior of parents and children in these circumstances, in order to prevent possible psychological and academic problems and because if the online teaching situation is prolonged, it is very important to know how to act from the educational and family environment.

The main limitation the authors have faced has been the small number of scientific articles related to the area of study. This scarcity of published works makes it necessary to continue researching this. This is the reason why our study can serve as a starting point or theoretical foundation for further studies.

Author Contributions

JC-Z, MS-Z, DS-M, GG-V, AL-S, and MZ-S contributed to the conception and design of the revision. All authors wrote some part of the manuscript and all reviewed the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

* References marked with an asterisk are those articles analyzed in the systematic review.

Funding. This article has been financed by the Ministry of Science, Innovation and Universities through two grants for university teacher training (FPU) with references FPU17/00803 and FPU18/02567. This article has counted with the collaboration of the group HUM-653 of the University of Jaén.

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