• Research article
  • Open access
  • Published: 04 June 2021

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

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

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

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

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

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

Methodology

Research question.

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

Study design

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

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

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

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

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

Eligibility criteria

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

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

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

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

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

Information sources

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

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

Study selection

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

Data collection process

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

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

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

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

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

Quality assessment in individual reviews

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

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

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

Synthesis of results

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

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

Managing overlapping systematic reviews

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

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

figure 1

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 ).

figure 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%).

figure 3

A meta-analysis of the prevalence of fever

figure 4

A meta-analysis of the prevalence of cough

figure 5

A meta-analysis of the prevalence of dyspnea

figure 6

A meta-analysis of the prevalence of fatigue or myalgia

figure 7

A meta-analysis of the prevalence of headache

figure 8

A meta-analysis of the prevalence of gastrointestinal disorders

figure 9

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.

This research received no external funding.

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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

Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany

Thilo Caspar von Groote

Department of Sport and Health Science, Technische Universität München, Munich, Germany

Hebatullah Mohamed Abdulazeem

School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia

Ishanka Weerasekara

Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka

Cochrane Croatia, University of Split, School of Medicine, Split, Croatia

Ana Marusic, Irena Zakarija-Grkovic & Tina Poklepovic Pericic

Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia

Livia Puljak

Cochrane Brazil, Evidence-Based Health Program, Universidade Federal de São Paulo, São Paulo, Brazil

Vinicius Tassoni Civile & Alvaro Nagib Atallah

Yorkville University, Fredericton, New Brunswick, Canada

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Contributions

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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Correspondence to Livia Puljak .

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

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

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

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

Additional file 5: Appendix 5.

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

Additional file 6: Appendix 6.

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

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

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

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Our COVID-19 Research Summary - 2021         

Related articles.

The published literature on COVID now exceeds 211,000 papers, books, and documents, which include: 22,866 observational studies, 19,591 reviews, 1496 meta-analyses and 781 randomized control trials. These publications comprise the backdrop for our research and writing. The project began in the spring of 2020 based on a limited source of cumulative COVID-19 data and has broadened considerably. Here is what we have learned.

conclusion for covid 19 research

Our objectives remain to

  • Describe trends and the geographic extent of the pandemic, including associated predictors
  • Evaluate the effectiveness of vaccinations and exposure limitations.
  • Provide public health perspectives.

“We know that people’s behavior, the mode of transmission, and the virus‘s characteristics all play a role but we don’t have a clear quantitative understanding of how all of these forces interact. With COVID, the biggest wild card has been human behavior.”

Dr. R. Rosenfeld , Head, Department of Machine Learning, Carnegie Mellon

Our findings along with other COVID studies are observational , focusing on numbers rather than people. Since the annual COVID mortality rate is about is only about 1 in 700, achieving reliable statistics by state would require a cohort approaching a million subjects, each of which would have to be tracked over time in order to estimate exposures. Observational studies are thus the only practical option and uncertainties about causality vs. association are inevitable.

COVID-19 trends continue to defy analysis in large part because of unpredictable variants, the latest of which is still unfolding. However, some aspects remain and will continue to dominate:

  • Daily cases, deaths, and incidence of long-haul COVID can be reduced more than 10-fold by vaccination, notwithstanding deterioration over time that requires boosting. 
  • Acceptance of vaccination remains a personal choice - a choice that may be associated with personal characteristics including income, education, and political perspective.
  • COVID-19 comprises a major cause of death in the United States and may continue to do so.

Findings In 2021 .

January . We showed that COVID-19 cases increased 10-fold, by 30% per month, during the 2020-21 winter; deaths generally followed suit, while case fatality rates (CFRs) decreased up to 10-fold. The Northeast region had shifted from worst to best, so that urban predictor variables like population density were no longer important. Regional rates coalesced in January.

February . The regional trend analyses showed declining cases but not deaths, with steady CFRs. We reported that “No plausible hypotheses have been advanced for the order-of-magnitude increases in cases and deaths since September”, now referred to as the “winter surge”. We compared urban and rural rates and noted the shift towards higher cases, deaths, and CFRs in rural areas.

March . We found that cases and deaths declined while CFRs increased 5-fold. Regional trends, which had ranged 4-fold for cases and deaths now coalesced. We analyzed short-term deaths and found a strong day-of-week effect, probably due to reporting error, but no evidence of important holiday surges.

April . We tried explaining the cyclical behavior of cases in terms of “susceptibles;” predicting an underlying trend of 5000 new cases per million per month. By contrast, the average case rate is now about 8000 cases per million per month - 53 million cases in total. However, after vaccinations got underway and prior to the Delta variant, new cases dropped to levels similar to those at the beginning of the pandemic. CFR’s ranged from about 0.015 in northern regions to 0.05 in the Southwest by April. We showed that Caucasian and mixed-race subjects had far lower COVID death rates than persons of color, and that COVID death rates increased with age at the same rate as non-COVID deaths.

May . COVID rates remained low in May. Comparing states, we reported significant relationships between COVID rates and political preference along with situational factors like household crowding. An increase in Republican voters of 60 percentage points, used as a marker of political perspective, was associated with a doubling of cumulative cases.

June . We revisited our previous consideration of airborne virus transmission, which had been espoused by CDC and the epidemiological community. We estimated ventilation rates and concluded that exposures in a small apartment were likely worse than in subways or aircraft. We also revisited urban-rural differences in more detail and showed that regional COVID rates had continued to coalesce.

July . We did a detailed analysis of vaccination rates and benefits. Daily vaccination rates peaked in April, at about 50% higher in the Northeast than elsewhere. We showed a strong significant decrease in daily state-level cases associated with full vaccination. We estimated unvaccinated case rates to be hundred-folds higher than with full vaccinations. We compared vaccination effects with education, and air pollution concluding that such personal characteristics could also be important. We also showed a negative state-level relationship between voting Republican and cumulative vaccination rates. Interestingly, vaccination rates correlated with COVID rates in 2020  before  mass vaccinations began, and vaccinations at this time, also apparently, reduced mortality not associated with COVID.  “ Could the decision to vaccinate have been more critical than the vaccination itself? ”

August . We reported that COVID case rates showed a sharp upturn, followed by death, likely due to the arrival of the Delta variant. Death rates and cases had decreased steadily until July to about 30 per million or 10,000 per day – a level the CDC considered as a “tolerable” endemic. We have not had these low levels since then. CFRs peaked in July, growing six-fold with substantial geographic variability.

September . We examined cyclical variations in daily infection rates and found substantial heterogeneity. State-level vaccination rates predicted both cases and death; and complete vaccination decreased case and death rates about 100-fold, even in the presence of the Delta variant.

October . We compared October’s COVID rates with those of the 2018-19 influenza to obtain a public health perspective. Total COVID-19 and influenza cases were similar at about 30 million and both were controlled by vaccination.  Compare to influenza, COVID hospitalizations were 4-fold higher and deaths were 20-fold higher - COVID is clearly the more serious disease. We concluded that 178,000 lives may have been saved by COVID-19 vaccination.

November . We continued examining vaccine effects and found no difference in the real-world effectiveness of Pfizer or Moderna vaccines. We found that COVID vaccinations were associated with reduced non-COVID deaths by 3-fold. We built an empirical mathematical model of the temporal variation of cases that fit the existing data very well but grossly underestimated the current situation. We predicted that full vaccinations for the U.S. might reach 72% in the next year, but with a range of 50-90% among states.

December . Cases began a sharp upward trend at years end, with deaths lagging behind. Regional gradients shifted, with Northwest highest and Northeast lowest. Vaccination rates continued to increase slowly, led by the Northeast. Previous beneficial effects of vaccination had been overshadowed by the severity of the Delta variant. We reported that vaccine effectiveness appears to decrease substantially over time. Long-haul COVID, neglected by the epidemiology community, was inversely associated with  vaccination rates and the socioeconomic factors underlying vaccine reluctance or refusal. We estimated trends and the contributions of immunity acquired from previous infection, which we found to be statistically modest.

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Covid-19: emergence, spread, possible treatments, and global burden.

\nRaghuvir Keni

  • 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India
  • 2 Department of Health Sciences, School of Education and Health, Cape Breton University, Sydney, NS, Canada

The Coronavirus (CoV) is a large family of viruses known to cause illnesses ranging from the common cold to acute respiratory tract infection. The severity of the infection may be visible as pneumonia, acute respiratory syndrome, and even death. Until the outbreak of SARS, this group of viruses was greatly overlooked. However, since the SARS and MERS outbreaks, these viruses have been studied in greater detail, propelling the vaccine research. On December 31, 2019, mysterious cases of pneumonia were detected in the city of Wuhan in China's Hubei Province. On January 7, 2020, the causative agent was identified as a new coronavirus (2019-nCoV), and the disease was later named as COVID-19 by the WHO. The virus spread extensively in the Wuhan region of China and has gained entry to over 210 countries and territories. Though experts suspected that the virus is transmitted from animals to humans, there are mixed reports on the origin of the virus. There are no treatment options available for the virus as such, limited to the use of anti-HIV drugs and/or other antivirals such as Remdesivir and Galidesivir. For the containment of the virus, it is recommended to quarantine the infected and to follow good hygiene practices. The virus has had a significant socio-economic impact globally. Economically, China is likely to experience a greater setback than other countries from the pandemic due to added trade war pressure, which have been discussed in this paper.

Introduction

Coronaviridae is a family of viruses with a positive-sense RNA that possess an outer viral coat. When looked at with the help of an electron microscope, there appears to be a unique corona around it. This family of viruses mainly cause respiratory diseases in humans, in the forms of common cold or pneumonia as well as respiratory infections. These viruses can infect animals as well ( 1 , 2 ). Up until the year 2003, coronavirus (CoV) had attracted limited interest from researchers. However, after the SARS (severe acute respiratory syndrome) outbreak caused by the SARS-CoV, the coronavirus was looked at with renewed interest ( 3 , 4 ). This also happened to be the first epidemic of the 21st century originating in the Guangdong province of China. Almost 10 years later, there was a MERS (Middle East respiratory syndrome) outbreak in 2012, which was caused by the MERS-CoV ( 5 , 6 ). Both SARS and MERS have a zoonotic origin and originated from bats. A unique feature of these viruses is the ability to mutate rapidly and adapt to a new host. The zoonotic origin of these viruses allows them to jump from host to host. Coronaviruses are known to use the angiotensin-converting enzyme-2 (ACE-2) receptor or the dipeptidyl peptidase IV (DPP-4) protein to gain entry into cells for replication ( 7 – 10 ).

In December 2019, almost seven years after the MERS 2012 outbreak, a novel Coronavirus (2019-nCoV) surfaced in Wuhan in the Hubei region of China. The outbreak rapidly grew and spread to neighboring countries. However, rapid communication of information and the increasing scale of events led to quick quarantine and screening of travelers, thus containing the spread of the infection. The major part of the infection was restricted to China, and a second cluster was found on a cruise ship called the Diamond Princess docked in Japan ( 11 , 12 ).

The new virus was identified to be a novel Coronavirus and was thus initially named 2019-nCoV; later, it was renamed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ( 13 ), and the disease it causes is now referred to as Coronavirus Disease-2019 (COVID-19) by the WHO. The virus was suspected to have begun its spread in the Huanan seafood wholesale market in the Wuhan region. It is possible that an animal that was carrying the virus was brought into or sold in the market, causing the spread of the virus in the crowded marketplace. One of the first claims made was in an article published in the Journal of Medical Virology ( 14 ), which identified snakes as the possible host. A second possibility was that pangolins could be the wild host of SARS-CoV-2 ( 15 ), though the most likely possibility is that the virus originated from bats ( 13 , 16 – 19 ). Increasing evidence and experts are now collectively concluding the virus had a natural origin in bats, as with previous such respiratory viruses ( 2 , 20 – 24 ).

Similarly, SARS and MERS were also suspected to originate from bats. In the case of MERS, the dromedary camel is an intermediate host ( 5 , 10 ). Bats have been known to harbor coronaviruses for quite some time now. Just as in the case of avian flu, SARS, MERS, and possibly even HIV, with increasing selection and ecological pressure due to human activities, the virus made the jump from animal to man. Humans have been encroaching increasingly into forests, and this is true over much of China, as in Africa. Combined with additional ecological pressure due to climate change, such zoonotic spillovers are now more common than ever. It is likely that the next disease X will also have such an origin ( 25 ). We have learned the importance of identification of the source organism due to the Ebola virus pandemic. Viruses are unstable organisms genetically, constantly mutating by genetic shift or drift. It is not possible to predict when a cross-species jump may occur and when a seemingly harmless variant form of the virus may turn into a deadly strain. Such an incident occurred in Reston, USA, with the Reston virus ( 26 ), an alarming reminder of this possibility. The identification of the original host helps us to contain future spreads as well as to learn about the mechanism of transmission of viruses. Until the virus is isolated from a wild animal host, in this case, mostly bats, the zoonotic origin will remain hypothetical, though likely. It should further be noted that the virus has acquired several mutations, as noted by a group in China, indicating that there are more than two strains of the virus, which may have had an impact on its pathogenicity. However, this claim remains unproven, and many experts have argued otherwise; data proving this are not yet available ( 27 ). A similar finding was reported from Italy and India independently, where they found two strains ( 28 , 29 ). These findings need to be further cross-verified by similar analyses globally. If true, this finding could effectively explain why some nations are more affected than others.

Transmission

When the spread of COVID-19 began ( Figure 1 ), the virus appeared to be contained within China and the cruise ship “Diamond Princess,” which formed the major clusters of the virus. However, as of April 2020, over 210 countries and territories are affected by the virus, with Europe, the USA, and Iran forming the new cluster of the virus. The USA ( Figure 2 ) has the highest number of confirmed COVID-19 cases, whereas India and China, despite being among the most population-dense countries in the world, have managed to constrain the infection rate by the implementation of a complete lockdown with arrangements in place to manage the confirmed cases. Similarly, the UK has also managed to maintain a low curve of the graph by implementing similar measures, though it was not strictly enforced. Reports have indicated that the presence of different strains or strands of the virus may have had an effect on the management of the infection rate of the virus ( 27 – 29 ). The disease is spread by droplet transmission. As of April 2020, the total number of infected individuals stands at around 3 million, with ~200,000 deaths and more than 1 million recoveries globally ( 30 , 34 ). The virus thus has a fatality rate of around 2% and an R 0 of 3 based on current data. However, a more recent report from the CDC, Atlanta, USA, claims that the R 0 could be as high as 5.7 ( 35 ). It has also been observed from data available from China and India that individuals likely to be infected by the virus from both these countries belong to the age groups of 20–50 years ( 36 , 37 ). In both of these countries, the working class mostly belongs to this age group, making exposure more likely. Germany and Singapore are great examples of countries with a high number of cases but low fatalities as compared to their immediate neighbors. Singapore is one of the few countries that had developed a detailed plan of action after the previous SARS outbreak to deal with a similar situation in the future, and this worked in their favor during this outbreak. Both countries took swift action after the outbreak began, with Singapore banning Chinese travelers and implementing screening and quarantine measures at a time when the WHO recommended none. They ordered the elderly and the vulnerable to strictly stay at home, and they ensured that lifesaving equipment and large-scale testing facilities were available immediately ( 38 , 39 ). Germany took similar measures by ramping up testing capacity quite early and by ensuring that all individuals had equal opportunity to get tested. This meant that young, old, and at-risk people all got tested, thus ensuring positive results early during disease progression and that most cases were mild like in Singapore, thus maintaining a lower death percentage ( 40 ). It allowed infected individuals to be identified and quarantined before they even had symptoms. Testing was carried out at multiple labs, reducing the load and providing massive scale, something which countries such as the USA did quite late and India restricted to select government and private labs. The German government also banned large gatherings and advocated social distancing to further reduce the spread, though unlike India and the USA, this was done quite late. South Korea is another example of how a nation has managed to contain the spread and transmission of the infection. South Korea and the USA both reported their first COVID-19 cases on the same day; however, the US administration downplayed the risks of the disease, unlike South Korean officials, who constantly informed their citizens about the developments of the disease using the media and a centralized messaging system. They also employed the Trace, Test, and Treat protocol to identify and isolate patients fast, whereas the USA restricted this to patients with severe infection and only later broadened this criterion, like many European countries as well as India. Unlike the USA, South Korea also has universal healthcare, ensuring free diagnostic testing.

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Figure 1 . Timeline of COVID-19 progression ( 30 – 32 ).

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Figure 2 . Total confirmed COVID 19 cases as of May 2020 ( 33 ).

The main mode of transmission of 2019-nCoV is human to human. As of now, animal-to-human transfer has not yet been confirmed. Asymptomatic carriers of the virus are at major risk of being superinfectors with this disease, as all those infected may not develop the disease ( 41 ). This is a concern that has been raised by nations globally, with the Indian government raising concerns on how to identify and contain asymptomatic carriers, who could account for 80% of those infected ( 42 ). Since current resources are directed towards understanding the hospitalized individuals showing symptoms, there is still a vast amount of information about asymptomatic individuals that has yet to be studied. For example, some questions that need to be answered include: Do asymptomatic individuals develop the disease at any point in time at all? Do they eventually develop antibodies? How long do they shed the virus for? Can any tissue of these individuals store the virus in a dormant state? Asymptomatic transmission is a gray area that encompasses major unknowns in COVID-19.

The main route of human-to-human transmission is by droplets, which are generated during coughing, talking, or sneezing and are then inhaled by a healthy individual. They can also be indirectly transmitted to a person when they land on surfaces that are touched by a healthy individual who may then touch their nose, mouth, or eyes, allowing the virus entry into the body. Fomites are also a common issue in such diseases ( 43 ).

Aerosol-based transmission of the virus has not yet been confirmed ( 43 ). Stool-based transmission via the fecal-oral route may also be possible since the SARS-CoV-2 has been found in patient feces ( 44 , 45 ). Some patients with COVID-19 tend to develop diarrhea, which can become a major route of transmission if proper sanitation and personal hygiene needs are not met. There is no evidence currently available to suggest intrauterine vertical transmission of the disease in pregnant women ( 46 ).

More investigation is necessary of whether climate has played any role in the containment of the infection in countries such as India, Singapore, China, and Israel, as these are significantly warmer countries as compared with the UK, the USA, and Canada ( Figure 2 ). Ideally, a warm climate should prevent the virus from surviving for longer periods of time on surfaces, reducing transmissibility.

Pathophysiology

On gaining entry via any of the mucus membranes, the single-stranded RNA-based virus enters the host cell using type 2 transmembrane serine protease (TMPRSS2) and ACE2 receptor protein, leading to fusion and endocytosis with the host cell ( 47 – 49 ). The uncoated RNA is then translated, and viral proteins are synthesized. With the help of RNA-dependant RNA polymerase, new RNA is produced for the new virions. The cell then undergoes lysis, releasing a load of new virions into the patients' body. The resultant infection causes a massive release of pro-inflammatory cytokines that causes a cytokine storm.

Clinical Presentation

The clinical presentation of the disease resembles beta coronavirus infections. The virus has an incubation time of 2–14 days, which is the reason why most patients suspected to have the illness or contact with an individual having the illness remain in quarantine for the said amount of time. Infection with SARS-CoV-2 causes severe pneumonia, intermittent fever, and cough ( 50 , 51 ). Symptoms of rhinorrhoea, pharyngitis, and sneezing have been less commonly seen. Patients often develop acute respiratory distress syndrome within 2 days of hospital admission, requiring ventilatory support. It has been observed that during this phase, the mortality tends to be high. Chest CT will show indicators of pneumonia and ground-glass opacity, a feature that has helped to improve the preliminary diagnosis ( 51 ). The primary method of diagnosis for SARS-CoV-2 is with the help of PCR. For the PCR testing, the US CDC recommends testing for the N gene, whereas the Chinese CDC recommends the use of ORF lab and N gene of the viral genome for testing. Some also rely on the radiological findings for preliminary screening ( 52 ). Additionally, immunodiagnostic tests based on the presence of antibodies can also play a role in testing. While the WHO recommends the use of these tests for research use, many countries have pre-emptively deployed the use of these tests in the hope of ramping up the rate and speed of testing ( 52 – 54 ). Later, they noticed variations among the results, causing them to stop the use of such kits; there was also debate among the experts about the sensitivity and specificity of the tests. For immunological tests, it is beneficial to test for antibodies against the virus produced by the body rather than to test for the presence of the viral proteins, since the antibodies can be present in larger titers for a longer span of time. However, the cross-reactivity of these tests with other coronavirus antibodies is something that needs verification. Biochemical parameters such as D-dimer, C-reactive protein, and variations in neutrophil and lymphocyte counts are some other parameters that can be used to make a preliminary diagnosis; however, these parameters vary in a number of diseases and thus cannot be relied upon conclusively ( 51 ). Patients with pre-existing diseases such as asthma or similar lung disorder are at higher risk, requiring life support, as are those with other diseases such as diabetes, hypertension, or obesity. Those above the age of 60 have displayed the highest mortality rate in China, a finding that is mirrored in other nations as well ( Figure 3 ) ( 55 ). If we cross-verify these findings with the population share that is above the age of 70, we find that Italy, the United Kingdom, Canada, and the USA have one of the highest elderly populations as compared to countries such as India and China ( Figure 4 ), and this also reflects the case fatality rates accordingly ( Figure 5 ) ( 33 ). This is a clear indicator that aside from comorbidities, age is also an independent risk factor for death in those infected by COVID-19. Also, in the US, it was seen that the rates of African American deaths were higher. This is probably due to the fact that the prevalence of hypertension and obesity in this community is higher than in Caucasians ( 56 , 57 ). In late April 2020, there are also claims in the US media that young patients in the US with COVID-19 may be at increased risk of stroke; however, this is yet to be proven. We know that coagulopathy is a feature of COVID-19, and thus stroke is likely in this condition ( 58 , 59 ). The main cause of death in COVID-19 patients was acute respiratory distress due to the inflammation in the linings of the lungs caused by the cytokine storm, which is seen in all non-survival cases and in respiratory failure. The resultant inflammation in the lungs, served as an entry point of further infection, associated with coagulopathy end-organ failure, septic shock, and secondary infections leading to death ( 60 – 63 ).

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Figure 3 . Case fatality rate by age in selected countries as of April 2020 ( 33 ).

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Figure 4 . Case fatality rate in selected countries ( 33 ).

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Figure 5 . Population share above 70 years of age ( 33 ).

For COVID-19, there is no specific treatment available. The WHO announced the organization of a trial dubbed the “Solidarity” clinical trial for COVID-19 treatments ( 64 ). This is an international collaborative study that investigates the use of a few prime candidate drugs for use against COVID-19, which are discussed below. The study is designed to reduce the time taken for an RCT by over 80%. There are over 1087 studies ( Supplementary Data 1 ) for COVID-19 registered at clinicaltrials.gov , of which 657 are interventional studies ( Supplementary Data 2 ) ( 65 ). The primary focus of the interventional studies for COVID-19 has been on antimalarial drugs and antiviral agents ( Table 1 ), while over 200 studies deal with the use of different forms of oxygen therapy. Most trials focus on improvement of clinical status, reduction of viral load, time to improvement, and reduction of mortality rates. These studies cover both severe and mild cases.

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Table 1 . List of therapeutic drugs under study for COVID-19 as per clinical trials registered under clinicaltrials.gov .

Use of Antimalarial Drugs Against SARS-CoV-2

The use of chloroquine for the treatment of corona virus-based infection has shown some benefit in the prevention of viral replication in the cases of SARS and MERS. However, it was not validated on a large scale in the form of a randomized control trial ( 50 , 66 – 68 ). The drugs of choice among antimalarials are Chloroquine (CQ) and Hydroxychloroquine (HCQ). The use of CQ for COVID-19 was brought to light by the Chinese, especially by the publication of a letter to the editor of Bioscience Trends by Gao et al. ( 69 ). The letter claimed that several studies found CQ to be effective against COVID-19; however, the letter did not provide many details. Immediately, over a short span of time, interest in these two agents grew globally. Early in vitro data have revealed that chloroquine can inhibit the viral replication ( 70 , 71 ).

HCQ and CQ work by raising the pH of the lysosome, the cellular organelle that is responsible for phagocytic degradation. Its function is to combine with cell contents that have been phagocytosed and break them down eventually, in some immune cells, as a downstream process to display some of the broken proteins as antigens, thus further enhancing the immune recruitment against an antigen/pathogen. The drug was to be administered alone or with azithromycin. The use of azithromycin may be advocated by the fact that it has been seen previously to have some immunomodulatory role in airway-related disease. It appears to reduce the release of pro-inflammatory cytokines in respiratory illnesses ( 72 ). However, HCQ and azithromycin are known to have a major drug interaction when co-administered, which increases the risk of QT interval prolongation ( 73 ). Quinine-based drugs are known to have adverse effects such as QT prolongation, retinal damage, hypoglycemia, and hemolysis of blood in patients with G-6-PD deficiency ( 66 ). Several preprints, including, a metanalysis now indicate that HCQ may have no benefit for severe or critically ill patients who have COVID-19 where the outcome is need for ventilation or death ( 74 , 75 ). As of April 21, 2020, after having pre-emptively recommended their use for SARS-CoV-2 infection, the US now advocates against the use of these two drugs based on the new data that has become available.

Use of Antiviral Drugs Against SARS-CoV-2

The antiviral agents are mainly those used in the case of HIV/AIDS, these being Lopinavir and Ritonavir. Other agents such as nucleoside analogs like Favipiravir, Ribavirin, Remdesivir, and Galidesivir have been tested for possible activity in the prevention of viral RNA synthesis ( 76 ). Among these drugs, Lopinavir, Ritonavir, and Remdesivir are listed in the Solidarity trial by the WHO.

Remdesivir is a nucleotide analog for adenosine that gets incorporated into the viral RNA, hindering its replication and causing chain termination. This agent was originally developed for Ebola Virus Disease ( 77 ). A study was conducted with rhesus macaques infected with SARS-CoV-2 ( 78 ). In that study, after 12 h of infection, the monkeys were treated with either Remdesivir or vehicle. The drug showed good distribution in the lungs, and the animals treated with the drug showed a better clinical score than the vehicle group. The radiological findings of the study also indicated that the animals treated with Remdesivir have less lung damage. There was a reduction in viral replication but not in virus shedding. Furthermore, there were no mutations found in the RNA polymerase sequences. A randomized clinical control study that became available in late April 2020 ( 79 ), having 158 on the Remdesivir arm and 79 on the placebo arm, found that Remdesivir reduced the time to recovery in the Remdesivir-treated arm to 11 days, while the placebo-arm recovery time was 15 days. Though this was not found to be statistically significant, the agent provided a basis for further studies. The 28-days mortality was found to be similar for both groups. This has now provided us with a basis on which to develop future molecules. The study has been supported by the National Institute of Health, USA. The authors of the study advocated for more clinical trials with Remdesivir with a larger population. Such larger studies are already in progress, and their results are awaited. Remdesivir is currently one of the drugs that hold most promise against COVID-19.

An early trial in China with Lopinavir and Ritonavir showed no benefit compared with standard clinical care ( 80 ). More studies with this drug are currently underway, including one in India ( 81 , 82 ).

Use of Convalescent Patient Plasma

Another possible option would be the use of serum from convalescent individuals, as this is known to contain antibodies that can neutralize the virus and aid in its elimination. This has been tried previously for other coronavirus infections ( 83 ). Early emerging case reports in this aspect look promising compared to other therapies that have been tried ( 84 – 87 ). A report from China indicates that five patients treated with plasma recovered and were eventually weaned off ventilators ( 84 ). They exhibited reductions in fever and viral load and improved oxygenation. The virus was not detected in the patients after 12 days of plasma transfusion. The US FDA has provided detailed recommendations for investigational COVID-19 Convalescent Plasma use ( 88 ). One of the benefits of this approach is that it can also be used for post-exposure prophylaxis. This approach is now beginning to be increasingly adopted in other countries, with over 95 trials registered on clinicaltrials.gov alone, of which at least 75 are interventional ( 89 ). The use of convalescent patient plasma, though mostly for research purposes, appears to be the best and, so far, the only successful option for treatment available.

From a future perspective, the use of monoclonal antibodies for the inhibition of the attachment of the virus to the ACE-2 receptor may be the best bet. Aside from this, ACE-2-like molecules could also be utilized to attach and inactivate the viral proteins, since inhibition of the ACE-2 receptor would not be advisable due to its negative repercussions physiologically. In the absence of drug regimens and a vaccine, the treatment is symptomatic and involves the use of non-invasive ventilation or intubation where necessary for respiratory failure patients. Patients that may go into septic shock should be managed as per existing guidelines with hemodynamic support as well as antibiotics where necessary.

The WHO has recommended that simple personal hygiene practices can be sufficient for the prevention of spread and containment of the disease ( 90 ). Practices such as frequent washing of soiled hands or the use of sanitizer for unsoiled hands help reduce transmission. Covering of mouth while sneezing and coughing, and disinfection of surfaces that are frequently touched, such as tabletops, doorknobs, and switches with 70% isopropyl alcohol or other disinfectants are broadly recommended. It is recommended that all individuals afflicted by the disease, as well as those caring for the infected, wear a mask to avoid transmission. Healthcare works are advised to wear a complete set of personal protective equipment as per WHO-provided guidelines. Fumigation of dormitories, quarantine rooms, and washing of clothes and other fomites with detergent and warm water can help get rid of the virus. Parcels and goods are not known to transmit the virus, as per information provided by the WHO, since the virus is not able to survive sufficiently in an open, exposed environment. Quarantine of infected individuals and those who have come into contact with an infected individual is necessary to further prevent transmission of the virus ( 91 ). Quarantine is an age-old archaic practice that continues to hold relevance even today for disease containment. With the quarantine being implemented on such a large scale in some countries, taking the form of a national lockdown, the question arises of its impact on the mental health of all individuals. This topic needs to be addressed, especially in countries such as India and China, where it is still a matter of partial taboo to talk about it openly within the society.

In India, the Ministry of Ayurveda, Yoga, and Naturopathy, Unani, Siddha and Homeopathy (AYUSH), which deals with the alternative forms of medicine, issued a press release that the homeopathic, drug Arsenicum album 30, can be taken on an empty stomach for 3 days to provide protection against the infection ( 92 ). It also provided a list of herbal drugs in the same press release as per Ayurvedic and Unani systems of medicine that can boost the immune system to deal with the virus. However, there is currently no evidence to support the use of these systems of medicine against COVID-19, and they need to be tested.

The prevention of the disease with the use of a vaccine would provide a more viable solution. There are no vaccines available for any of the coronaviruses, which includes SARS and MERS. The development of a vaccine, however, is in progress at a rapid pace, though it could take about a year or two. As of April 2020, no vaccine has completed the development and testing process. A popular approach has been with the use of mRNA-based vaccine ( 93 – 96 ). mRNA vaccines have the advantage over conventional vaccines in terms of production, since they can be manufactured easily and do not have to be cultured, as a virus would need to be. Alternative conventional approaches to making a vaccine against SARS-CoV-2 would include the use of live attenuated virus as well as using the isolated spike proteins of the virus. Both of these approaches are in progress for vaccine development ( 97 ). Governments across the world have poured in resources and made changes in their legislation to ensure rapid development, testing, and deployment of a vaccine.

Barriers to Treatment

Lack of transparency and poor media relations.

The lack of government transparency and poor reporting by the media have hampered the measures that could have been taken by healthcare systems globally to deal with the COVID-19 threat. The CDC, as well as the US administration, downplayed the threat and thus failed to stock up on essential supplies, ventilators, and test kits. An early warning system, if implemented, would have caused borders to be shut and early lockdowns. The WHO also delayed its response in sounding the alarm regarding the severity of the outbreak to allow nations globally to prepare for a pandemic. Singapore is a prime example where, despite the WHO not raising concerns and banning travel to and from China, a country banned travelers and took early measures, thus managing the outbreak quite well. South Korea is another example of how things may have played out had those measures by agencies been taken with transparency. Increased transparency would have allowed the healthcare sector to better prepare and reduced the load of patients they had to deal with, helping flatten the curve. The increased patient load and confusion among citizens arising from not following these practices has proved to be a barrier to providing effective treatments to patients with the disease elsewhere in the world.

Lack of Preparedness and Protocols

Despite the previous SARS outbreak teaching us important lessons and providing us with data on a potential outbreak, many nations did not take the important measures needed for a future outbreak. There was no allocation of sufficient funds for such an event. Many countries experienced severe lack of PPE, and the lockdown precautions hampered the logistics of supply and manufacturing of such essential equipment. Singapore and South Korea had protocols in place and were able to implement them at a moment's notice. The spurt of cases that Korea experienced was managed well, providing evidence to this effect. The lack of preparedness and lack of protocol in other nations has resulted in confusion as to how the treatment may be administered safely to the large volume of patients while dealing with diagnostics. Both of these factors have limited the accessibility to healthcare services due to sheer volume.

Socio-Economic Impact

During the SARS epidemic, China faced an economic setback, and experts were unsure if any recovery would be made. However, the global and domestic situation was then in China's favor, as it had a lower debt, allowing it to make a speedy recovery. This is not the case now. Global experts have a pessimistic outlook on the outcome of this outbreak ( 98 ). The fear of COVID-19 disease, lack of proper understanding of the dangers of the virus, and the misinformation spread on the social media ( 99 ) have caused a breakdown of the economic flow globally ( 100 ). An example of this is Indonesia, where a great amount of fear was expressed in responses to a survey when the nation was still free of COVID-19 ( 101 ). The pandemic has resulted in over 2.6 billion people being put under lockdown. This lockdown and the cancellation of the lunar year celebration has affected business at the local level. Hundreds of flights have been canceled, and tourism globally has been affected. Japan and Indonesia are estimated to lose over 2.44 billion dollars due to this ( 102 , 103 ). Workers are not able to work in factories, transportation in all forms is restricted, and goods are not produced or moved. The transport of finished products and raw materials out of China is low. The Economist has published US stock market details indicating that companies in the US that have Chinese roots fell, on average, 5 points on the stock market as compared to the S&P 500 index ( 104 ). Companies such as Starbucks have had to close over 4,000 outlets due to the outbreak as a precaution. Tech and pharma companies are at higher risk since they rely on China for the supply of raw materials and active pharmaceutical ingredients. Paracetamol, for one, has reported a price increase of over 40% in India ( 104 – 106 ). Mass hysteria in the market has caused selling of shares of these companies, causing a tumble in the Indian stock market. Though long-term investors will not be significantly affected, short-term traders will find themselves in soup. Politically, however, this has further bolstered support for world leaders in countries such as India, Germany, and the UK, who are achieving good approval ratings, with citizens being satisfied with the government's approach. In contrast, the ratings of US President Donald Trump have dropped due to the manner in which the COVID-19 pandemic was handled. These minor impacts may be of temporary significance, and the worst and direct impact will be on China itself ( 107 – 109 ), as the looming trade war with the USA had a negative impact on the Chinese and Asian markets. The longer production of goods continues to remain suspended, the more adversely it will affect the Chinese economy and the global markets dependent on it ( 110 ). If this disease is not contained, more and more lockdowns by multiple nations will severely affect the economy and lead to many social complications.

The appearance of the 2019 Novel Coronavirus has added and will continue to add to our understanding of viruses. The pandemic has once again tested the world's preparedness for dealing with such outbreaks. It has provided an outlook on how a massive-scale biological event can cause a socio-economic disturbance through misinformation and social media. In the coming months and years, we can expect to gain further insights into SARS-CoV-2 and COVID-19.

Author Contributions

KN: conceptualization. RK, AA, JM, and KN: investigation. RK and AA: writing—original draft preparation. KN, PN, and JM: writing—review and editing. KN: supervision.

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.

Acknowledgments

The authors would like to acknowledge the contributions made by Dr. Piya Paul Mudgal, Assistant Professor, Manipal Institute of Virology, Manipal Academy of Higher Education towards inputs provided by her during the drafting of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2020.00216/full#supplementary-material

Supplementary Data 1, 2. List of all studies registered for COVID-19 on clinicaltrials.gov .

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Keywords: 2019-nCoV, COVID-19, SARS-CoV-2, coronavirus, pandemic, SARS

Citation: Keni R, Alexander A, Nayak PG, Mudgal J and Nandakumar K (2020) COVID-19: Emergence, Spread, Possible Treatments, and Global Burden. Front. Public Health 8:216. doi: 10.3389/fpubh.2020.00216

Received: 21 February 2020; Accepted: 11 May 2020; Published: 28 May 2020.

Reviewed by:

Copyright © 2020 Keni, Alexander, Nayak, Mudgal and Nandakumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Krishnadas Nandakumar, mailnandakumar77@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Coronavirus (COVID-19) and society: what matters to people in Scotland?

Findings from an open free text survey taken to understand in greater detail how the pandemic has changed Scotland.

  • This research has captured the diversity and complexity of people’s experiences.
  • People’s experiences of the pandemic and their ability to stay safe has been impacted by a range of factors, including: their geographical environment, their financial situation, profession, their living situation and if they have any physical or mental health conditions.
  • Even though the direct level of threat from COVID-19 has reduced (for some people), there is still concern about the longer term harm and disruption that COVID-19 has caused to people and communities, and worry about the threat of future waves of infection.
  • This report captures a number of specific suggestions for support. For example, support for key workers, creating safer public environments, wide-scale financial support, greater awareness around the experiences of those who are at higher risk to COVID-19 and putting in place robust processes for learning and reflection on the impact of the pandemic.
  • Public engagement in this open and unfiltered format is an essential part of making sense of people’s attitudes and behaviours within the context of their life.

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Experience, Perceptions, and Recommendations Concerning COVID-19-Related Clinical Research Adjustments

Affiliations.

  • 1 1Department of Internal Medicine, Division of Hematology-Oncology.
  • 2 2Harold C. Simmons Comprehensive Cancer Center, and.
  • 3 3Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
  • PMID: 33027755
  • PMCID: PMC8173586
  • DOI: 10.6004/jnccn.2020.7643

Background: During the COVID-19 public health emergency, the FDA and NIH altered clinical trial requirements to protect participants and manage study conduct. Given their detailed knowledge of research protocols and regular contact with patients, clinicians, and sponsors, clinical research professionals offer important perspectives on these changes.

Methods: We developed and distributed an anonymous survey assessing COVID-19-related clinical trial adjustment experiences, perceptions, and recommendations to Clinical Research Office personnel at the Harold C. Simmons Comprehensive Cancer Center. Responses were compared using the Fisher exact test.

Results: A total of 94 of 109 contacted research personnel (87%) responded. Among these individuals, 58% had >5 years' professional experience in clinical research, and 56% had personal experience with a COVID-19-related change. Respondents perceived that these changes had a positive impact on patient safety; treatment efficacy; patient and staff experience; and communication with patients, investigators, and sponsors. More than 90% felt that positive changes should be continued after COVID-19. For remote consent, telehealth, therapy shipment, off-site diagnostics, and remote monitoring, individuals with personal experience with the specific change and individuals with >5 years' professional experience were numerically more likely to recommend continuing the adjustment, and these differences were significant for telehealth (P=.04) and therapy shipment (P=.02).

Conclusions: Clinical research professionals perceive that COVID-19-related clinical trial adjustments positively impact multiple aspects of study conduct. Those with greatest experience-both specific to COVID-19-related changes and more generally-are more likely to recommend that these adjustments continue in the future.

Publication types

  • Research Support, N.I.H., Extramural
  • Biomedical Research / standards*
  • COVID-19 / prevention & control*
  • COVID-19 / virology
  • Delivery of Health Care / standards*
  • Interdisciplinary Communication*
  • Practice Guidelines as Topic / standards*
  • SARS-CoV-2 / isolation & purification*
  • Surveys and Questionnaires
  • Telemedicine / methods*

Grants and funding

  • UL1 TR001105/TR/NCATS NIH HHS/United States
  • UG1 CA233302/CA/NCI NIH HHS/United States
  • P30 CA142543/CA/NCI NIH HHS/United States
  • K24 CA201543/CA/NCI NIH HHS/United States
  • T32 CA124334/CA/NCI NIH HHS/United States
  • Open access
  • Published: 30 November 2020

Family perspectives of COVID-19 research

  • Shelley M. Vanderhout 1 ,
  • Catherine S. Birken 2 ,
  • Peter Wong 3 ,
  • Sarah Kelleher 4 ,
  • Shannon Weir 4 &
  • Jonathon L. Maguire 1 , 5  

Research Involvement and Engagement volume  6 , Article number:  69 ( 2020 ) Cite this article

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The COVID-19 pandemic has uniquely affected children and families by disrupting routines, changing relationships and roles, and altering usual child care, school and recreational activities. Understanding the way families experience these changes from parents’ perspectives may help to guide research on the effects of COVID-19 among children.

As a multidisciplinary team of child health researchers, we assembled a group of nine parents to identify concerns, raise questions, and voice perspectives to inform COVID-19 research for children and families. Parents provided a range of insightful perspectives, ideas for research questions, and reflections on their experiences during the pandemic.

Including parents as partners in early stages of COVID-19 research helped determine priorities, led to more feasible data collection methods, and hopefully has improved the relevance, applicability and value of research findings to parents and children.

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Plain English summary

Understanding the physical, mental, and emotional impacts of the COVID-19 pandemic for children and families will help to guide approaches to support families and children during the pandemic and after. As a team of child health researchers in Toronto, Canada, we assembled a group of parents and clinician researchers during the COVID-19 pandemic to identify concerns, raise questions, and voice perspectives to inform COVID-19 research for children and families. Parents were eager to share their experience of shifting roles, priorities, and routines during the pandemic, and were instrumental in guiding research priorities and methods to understand of the effects of COVID-19 on families. First-hand experience that parents have in navigating the COVID-19 pandemic with their families contributed to collaborative relationships between researchers and research participants, helped orient research about COVID-19 in children around family priorities, and offered valuable perspectives for the development of guidelines for safe return to school and childcare. Partnerships between researchers and families in designing and delivering COVID-19 research may lead to a better understanding of how health research can best support children and their families during the COVID-19 pandemic.

Children and families have been uniquely affected by the COVID-19 pandemic. While children appear to experience milder symptoms from COVID-19 infection than older individuals [ 1 ], sudden changes in routines, resources, and relationships as a result of restrictions on physical interaction have resulted in major impacts on families with young children. In the absence of school, child care, extra-curricular activities and family gatherings, children’s social and support networks have been broadly disrupted. Stress from COVID-19 has been compounded by additional responsibilities for parents as they adapt to their new roles as educators and playmates while balancing full-time caregiving with their own stressful changes to work, financial and social situations. On the contrary, families with greater parental support and perceived control have had less perceived stress during COVID-19 [ 2 ].

The COVID-19 pandemic has rapidly sparked research activity across the globe. Patient and family voices are increasingly considered essential to research agenda and priority setting [ 3 ]. Understanding the physical, mental, and emotional consequences of the COVID-19 pandemic for families will inform approaches to support parents and children during the pandemic and after. In this unusual time, patient and family voices can be valuable in informing health research priorities, study designs, implementation plans and knowledge translation strategies that directly affect them [ 4 ].

As a multidisciplinary team of child health researchers with expertise in general paediatrics, nutrition and mental health, we assembled a group of nine parents to identify concerns, raise questions, and voice perspectives to inform COVID-19 research for children and families. Parents were recruited from the TARGet Kids! primary care research network [ 5 ], which is a collaboration between applied health researchers at the SickKids and St. Michael’s Hospitals, primary care providers from the Departments of Pediatrics and Family and Community Medicine at the University of Toronto, and families. Parents were contacted by email and invited to voluntary meetings on April 7 and 23, 2020 via Zoom [ 6 ] for 3 h. In an unstructured discussion, we asked how parents imagined research about COVID-19 could make an impact on child and family well-being. Parents were encouraged to share their lived experience and perspectives on the anticipated effects of COVID-19 and social distancing policies on their children and families, and opinions to inform how research on child mental and physical health during and after the pandemic could best be conducted. Parents had opportunities to review proposed data collection tools such as smartphone apps and serology testing devices, and provided feedback about the feasibility and meaningfulness of each. Content, frequency and organization of questionnaires were also reviewed by parents to ensure they were appropriate in length and feasible to complete.

Parent perspectives

Parents were optimistic that research would provide an understanding of the effects of COVID-19 on families and deliver solutions to minimize negative effects and bolster positive effects. Parents wondered about several questions which they hoped research would answer including: What will be the effects of physical distancing and disrupted routines for my children? How can I help my children develop healthy coping habits? How can I appropriately talk about the virus with my children? What factors might predict resiliency against negative effects of the pandemic among children and families, and how can these be strengthened?

Parents speculated what risks children might face as a result of schoolwork transitioning to home, educational activities provided online, child care being limited or unavailable, social relationships changing, sports and extra-curricular activities being cancelled, and stress and anxiety increasing at home. Some parents reflected on feeling some relief from not having to coordinate usual extracurricular activities. However, they expressed frustration in finding high quality educational activities and resources to support physical and mental health for their children during physical isolation. Parents voiced a need for a centralized, accessible hub with peer reviewed, high quality resources to keep children entertained and supported while spending more time indoors, away from usual activities and school. They hoped for resources to help families adjust to new routines and roles, as well as answer children’s questions in truthful ways that would not increase anxiety.

Parents were curious about studying the impact of COVID-19 on children and families. How would researchers use information about children who are affected physically, mentally, or socially by the pandemic? What could be the possible implications of testing for COVID-19 on social relationships and parents’ employment? This question generated discussion about difficult positions families of lower socio-economic status, who may need to maintain attendance at work but have a suspected COVID-19 infected household member. Would health and social care for children going forward reflect the unique ways they had been impacted by changes in their daily routines and relationships? How can families return to school and everyday routines with a minimum of disruption? What will be done to prepare children and families for emergency situations in the future? Considering these questions may lead child health researchers to study relevant and contemporary concepts to families during the COVID-19 pandemic.

When presented with options to include more measures on other family members, parents maintained that the focus of our COVID-19 research should be on children. Parents provided essential feedback about the length and frequency of questionnaires, to ensure they were appropriate given the limited time available for completing them. Parent involvement early in the research process helped to direct research priorities, informed data collection strategies and hopefully has increased the relevance of research conducted for children and families. Conducting a follow-up meeting with parents was important to understand shifting concerns and ensure data collection was reflecting current routines, habits and policies affecting families.

Conclusions

As researchers who are seeking to understand the impact of COVID-19 on children and families, we felt it important to involve families in designing and implementing new research. First-hand experience that parents have in navigating the COVID-19 pandemic with their children contributed to co-building between researchers and research participants. Parents were generous with their time and provided insightful, honest suggestions for how researchers could create knowledge that would be directly relevant to them. Next steps will include expanding our dialogue with a more diverse group of parents in terms of gender, as all parents in our meetings were women, and ethnicity to better represent the diversity of Toronto. Other researchers conducting COVID-19 research among children and families may consider engaging parents and caregivers in preliminary stages to identify priorities, understand lived experiences and help guide all stages of the research process. This presents value in focusing research on the most important priorities for families and developing data collection methods which are feasible in the context of the COVID-19 pandemic. As the nature of the COVID-19 pandemic is dynamic, ongoing communication between researchers and parents to understand changing perspectives and concerns is important to respond to family needs. We hope that ongoing partnerships between parents and researchers will promote leadership among parents as co-investigators in COVID-19 research, and result in research which addresses the needs of parents and children during the COVID-19 pandemic. Ideally, engaging with families in COVID-19 research will result in findings that will be valuable to families, assist them in developing collective resilience, and provide a foundation for family-oriented research throughout the COVD-19 pandemic and beyond.

Availability of data and materials

Not applicable.

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Acknowledgements

We thank the TARGet Kids! Parent And Clinician Team for their generous contribution of time and participation in discussions about COVID-19 in children and families.

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Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada

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Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, 686 Bay Street, 11th floor, Toronto, Ontario, M5G 0A4, Canada

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Shelley Vanderhout, Catherine Birken, Peter Wong, Shannon Weir, Sarah Kelleher and Jonathon Maguire participated in the concept and design, drafting and revising of the manuscript. All authors approved the manuscript as submitted and agree to be accountable for all aspects of the work.

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Vanderhout, S.M., Birken, C.S., Wong, P. et al. Family perspectives of COVID-19 research. Res Involv Engagem 6 , 69 (2020). https://doi.org/10.1186/s40900-020-00242-1

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Received : 18 August 2020

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DOI : https://doi.org/10.1186/s40900-020-00242-1

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Research Involvement and Engagement

ISSN: 2056-7529

conclusion for covid 19 research

conclusion for covid 19 research

Beneficial Cardiovascular Outcomes Linked to COVID-19 Vaccination: A Study Overview

R ecent findings indicate that the advantages of COVID-19 vaccination may extend beyond just preventing viral infection. The vaccines are also reportedly associated with a lowered incidence of heart failure and blood clots that could result from the SARS-CoV-2 virus.

An international cohort of researchers compiled a study analyzing the health records of approximately 10.17 million vaccinated individuals alongside 10.39 million unvaccinated individuals from the UK, Spain, and Estonia.

Adjusting for variables such as age, gender, and underlying health conditions, the vaccinated cohort appeared to have a substantially decreased risk of cardiac and thrombotic complications following a COVID-19 infection, with the protective effects lasting up to a year post-infection.

Data scientist Núria Mercadé-Besora from the University of Oxford suggests, “These results could encourage COVID-19 vaccination among hesitant people who are worried about the potential risk of vaccine side effects.”

Vaccinated individuals experienced a 78 percent lower risk of venous blood clots, a 47 percent lower risk of arterial blood clots, and a 55 percent lower risk of heart failure within the first 30 days post-COVID-19 infection compared to their unvaccinated counterparts.

Although the degree of risk reduction decreased over time, the numbers were still significant, with 50 percent, 38 percent, and 48 percent lower risks for the respective conditions between 181-365 days post-infection. This study aligns with earlier research but is one of the largest and most extended analyses to date.

Considering that blood clots and heart failure are significantly more prevalent post-COVID-19 infection, the study implies that vaccination might also mitigate these serious aftereffects.

While causation isn’t definitively established in the study, the trend observed supports the conclusion that COVID-19 vaccines, which thus far have shown significant safety and efficacy, also bring about positive secondary health outcomes. The researchers advocate for further investigations into the vaccines’ protective properties, particularly regarding booster shots across diverse populations.

Mercadé-Besora adds, “The protective effects of vaccination are consistent with known reductions in disease severity, but we need to do more research to understand the effects of a booster vaccination in different populations.”

This study can be found detailed in the journal Heart .

FAQ Section

What are the primary benefits of covid-19 vaccination according to this study.

Aside from preventing viral infection, COVID-19 vaccination is associated with a significantly lowered risk of heart failure and venous or arterial blood clots post COVID-19 infection.

How long do these beneficial cardiovascular effects last?

The study indicated that the beneficial cardiovascular effects of the COVID-19 vaccination could last up to a year post-infection, although the degree of risk reduction may decline slightly over time.

Does the research show a cause and effect relationship between vaccination and reduced cardiovascular complications?

While the study presents a correlation between vaccination and fewer complications, it does not establish a direct cause and effect. More research is needed to understand the mechanisms involved fully.

Are COVID-19 vaccines safe?

Yes, current data and research indicate that COVID-19 vaccines are mostly safe and effective, with the benefits greatly outweighing any potential risks.

What further research do the study’s authors suggest is needed?

The authors of the study suggest that more detailed research is needed to explore the protective effects of COVID-19 vaccines, particularly concerning booster vaccinations in various populations.

The research outlined in this article adds to the growing body of evidence that COVID-19 vaccines offer numerous health benefits beyond preventing COVID-19 itself. As vaccines continue to play a critical role in mitigating the effects of the pandemic, these findings regarding reduced risks of cardiovascular issues could be a compelling argument for vaccination, especially among those who may be hesitant due to concerns about vaccine side effects. With the call for additional studies, including the effects of booster shots across diverse groups, the scientific community remains focused on providing comprehensive insights into the short-term and long-term benefits of COVID-19 vaccination.

CovidVaccine

  • Research article
  • Open access
  • Published: 13 March 2024

Evidence linking COVID-19 and the health/well-being of children and adolescents: an umbrella review

  • Chengchen Duan   ORCID: orcid.org/0009-0008-1380-0417 1   na1 ,
  • Liu Liu   ORCID: orcid.org/0000-0001-8681-3413 1 , 2   na1 ,
  • Tianyi Wang   ORCID: orcid.org/0000-0002-0402-3707 1   na1 ,
  • Guanru Wang 1 , 3 ,
  • Zhishen Jiang 1 ,
  • Honglin Li 1 , 3 ,
  • Gaowei Zhang 1 , 3 ,
  • Li Ye 1 , 3 ,
  • Chunjie Li 1 , 3 , 5 &
  • Yubin Cao   ORCID: orcid.org/0000-0001-8553-1430 1 , 4 , 5  

BMC Medicine volume  22 , Article number:  116 ( 2024 ) Cite this article

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Experiences during childhood and adolescence have enduring impacts on physical and mental well-being, overall quality of life, and socioeconomic status throughout one’s lifetime. This underscores the importance of prioritizing the health of children and adolescents to establish an impactful healthcare system that benefits both individuals and society. It is crucial for healthcare providers and policymakers to examine the relationship between COVID-19 and the health of children and adolescents, as this understanding will guide the creation of interventions and policies for the long-term management of the virus.

In this umbrella review (PROSPERO ID: CRD42023401106), systematic reviews were identified from the Cochrane Database of Systematic Reviews; EMBASE (OvidSP); and MEDLINE (OvidSP) from December 2019 to February 2023. Pairwise and single-arm meta-analyses were extracted from the included systematic reviews. The methodological quality appraisal was completed using the AMSTAR-2 tool. Single-arm meta-analyses were re-presented under six domains associated with COVID-19 condition. Pairwise meta-analyses were classified into five domains according to the evidence classification criteria. Rosenberg’s FSN was calculated for both binary and continuous measures.

We identified 1551 single-arm and 301 pairwise meta-analyses from 124 systematic reviews that met our predefined criteria for inclusion. The focus of the meta-analytical evidence was predominantly on the physical outcomes of COVID-19, encompassing both single-arm and pairwise study designs. However, the quality of evidence and methodological rigor were suboptimal. Based on the evidence gathered from single-arm meta-analyses, we constructed an illustrative representation of the disease severity, clinical manifestations, laboratory and radiological findings, treatments, and outcomes from 2020 to 2022. Additionally, we discovered 17 instances of strong or highly suggestive pairwise meta-analytical evidence concerning long-COVID, pediatric comorbidity, COVID-19 vaccines, mental health, and depression.

Conclusions

The findings of our study advocate for the implementation of surveillance systems to track health consequences associated with COVID-19 and the establishment of multidisciplinary collaborative rehabilitation programs for affected younger populations. In future research endeavors, it is important to prioritize the investigation of non-physical outcomes to bridge the gap between research findings and clinical application in this field.

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The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been spreading globally for more than 3 years [ 1 , 2 ]. As of April 20, 2023, there have been over 765 million confirmed cases and over 6.9 million deaths reported worldwide [ 3 ]. COVID-19 has had varied effects on the health of children and adolescents, both directly and indirectly. COVID-19 infection can cause symptoms and impact the physical health of young people, affecting multiple organ systems directly [ 4 , 5 , 6 ]. Additionally, the policies implemented during the pandemic, as well as the preventive measures aimed at reducing the direct impact of COVID-19, often give rise to indirect consequences for children and adolescents. These indirect effects of COVID-19 have a disruptive impact on routine healthcare services and social interactions, which can further exacerbate mental and cognitive health challenges and worsen existing health disparities among this vulnerable population [ 7 , 8 ]. Child and adolescent health refer to the physical, mental/cognitive, quality of life, and social well-being, of individuals from newborns until the age of 19. Experiences during childhood and adolescence have enduring impacts on physical and mental health, quality of life, and socioeconomic status over the lifespan [ 9 ]. Consequently, exploring the subsequent effects of COVID-19 on the health of children and adolescents has the potential to influence the future provision and design of comprehensive services for those affected by COVID‐19. By gaining insights into an individual’s informational, spiritual, psychological, social, and physical requirements during follow-up phases, personalized services can be developed to enhance the survivor experience. This endeavor plays a vital role in establishing a resilient and prosperous healthcare system that benefits both individuals and society.

Currently, the World Health Organization (WHO) has declared that the global health emergency caused by COVID-19 has ended. This highlights the need to transition from an emergency response to the long-term management of COVID-19 and other infectious diseases [ 10 , 11 ]. Even after the emergency phase concludes, the ongoing transmission and emergence of new COVID-19 variants, as well as the remaining unvaccinated younger individuals and the significant global impact on health inequity, societal consequences, and economic repercussions, collectively emphasize the importance of continually assessing the available evidence on the correlation between COVID-19 and the health of children and adolescents. This assessment will inform stakeholders, including patients, healthcare providers, and policymakers, to mitigate conflicting effects and prioritize resources, interventions, and policies.

During the first year of the pandemic, a study analyzed all 6338 pediatric emergency admissions in England related to COVID-19 and found that adolescents have a higher likelihood of being hospitalized due to COVID-19 compared to younger children [ 12 ]. Surveys conducted among 13,002 American and 11,681 Chinese adolescents showed a similar 1-year prevalence of clinically significant depressive and anxiety symptoms during the COVID-19 pandemic [ 13 , 14 ]. These nationally representative studies, conducted with large sample sizes, are commonly regarded as robust evidence to establish a link between COVID-19 and child and adolescent health across diverse domains and time periods. However, the changing public health policies of COVID-19 and the emergence of new viral strains could introduce complexities and inconsistencies in the overall evidence [ 15 , 16 ]. In addition, many primary studies examining the relationship between COVID-19 and the health of children and adolescents used convenience sampling, including cross-sectional and observational designs that lacked control or comparison groups. The meta-analytical estimates from these studies may not accurately represent the true effects of the disease, as they are prone to biases such as measurement errors, poor control of confounders, biased participant selection, and data publication issues, ultimately weakening the strength of the aggregated scientific evidence [ 17 ]. The emergence of meta-analytical evidence through rapid reviews, compared to formal systematic reviews, further complicates this issue due to inadequate reporting of evidence, limited literature search, and increased publication bias [ 18 ].

The umbrella review, which involves quantifying systematic reviews and meta-analyses, provides a comprehensive assessment that captures the most extensive and high-quality medical evidence available [ 19 ]. By utilizing umbrella reviews, studies have reported evidence on the characteristic features of COVID-19 in children and adolescents during the initial phase of the pandemic [ 20 ], as well as the epidemiological impact and associations with mental health problems among this demographic [ 21 , 22 ]. Although these studies to some extent synthesized evidence on various factors influencing pediatric health outcomes during the COVID-19 pandemic, they fell short of providing a comprehensive perspective on the diverse array of health and well-being outcomes among children and adolescents. Moreover, the conclusions drawn from these umbrella reviews lack an assessment based on evidence grading criteria, neglecting the systematic grading of the evidence obtained from studies. This oversight regarding the overall strength of evidence could potentially limit the credibility of the conclusions presented in umbrella reviews. Therefore, this study aims to conduct an umbrella review to evaluate the strength of meta-analytic estimates and summarize the current evidence linking direct and indirect impacts of COVID-19 to the health/well-being of children and adolescents. Furthermore, we intend to explore the potential impact of future research on the conclusions drawn from existing significant meta-analyses.

Protocol and reporting

The protocol of this umbrella review was prospectively defined and registered on the PROSPERO [ 23 ] website (ID: CRD42023401106) ( https://www.crd.york.ac.uk/PROSPERO/ ). The differences between registered protocol and review were provided in Additional file 1 . This review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) 2020 statement guidelines and a PRISMA checklist is included (Additional file 2 ) [ 24 ].

Study search and selection

We performed a comprehensive systematic literature search without any restrictions on the date or language of publication. Three key electronic databases including the Cochrane Database of Systematic Reviews (CDSR) via The Cochrane Library; EMBASE (OvidSP) and MEDLINE (OvidSP) were searched from December 2019 to February 2023. Moreover, the Google Scholar and WHO database of publications on COVID-19 and reference lists of included studies were also searched manually to identify reports of additional studies. We merged keywords and subject headings appropriately for each database using the following search terms: (COVID-19 [MeSH] OR 2019-nCoV.m.p. OR SARS-CoV-2.m.p. OR novel coronavirus pneumonia.m.p.) AND (pediatrics [MeSH] OR pediatrics.m.p. OR neonate.m.p. OR children.m.p. OR adolescence.m.p. OR teenagers.m.p.) AND (meta-analysis [MeSH] OR systematic review.m.p.) (Additional file 3 ). Two independent authors (C.D. and T.W.) carried out the electronic database search and decided the final inclusion according to the following criteria: (1) systematic reviews with meta-analysis; (2) results from children and adolescents between 0 and 19 years old; (3) observing COVID-19 as the exposure. Full texts were obtained and independently assessed for eligibility if certain studies seemed to have any potentiality for inclusion if they could not be judged completely by titles and abstracts. Any disagreements were settled by consulting a third review author (L.L.). Observational studies, intervention studies, other types of reviews (descriptive, scoping), program evaluations, animal studies, conference abstracts, and letters/comments were excluded from the review.

Data extraction

Two independent reviewers (C.D. and L.L.) screened the titles and abstracts, assigning unique identification numbers to all the included articles. Two authors (C.D. and T.W.) independently extracted the necessary data from each eligible review through a pre-designed extraction table and resolved any disagreements by discussion with a third reviewer (L.L.). Pooled estimates, including prevalence, odds ratio (OR), relative risk (RR), hazard ratio (HR), and standard mean difference (SMD), were extracted from each systematic review for all eligible health and well-being outcomes. The pre-designed extraction table included study identification (authors, year, and origin country), number of studies and participants included in the meta-analysis, outcome domain (physical, psychological/cognitive, quality of life, social, and health system), direct or indirect impact(s), COVID-19 condition(s) being assessed, health and well-being condition(s) of children and adolescents being assessed, methodological quality tool used, effect size and 95% CI, heterogeneity ( I 2 statistic), and publication bias assessment. We defined the “direct effects” as the consequences that directly correlate with COVID-19 infection or transmission, specifically within outcome domains. On the other hand, the “indirect effects” encompassed the broader consequences that arise from the pandemic, as well as the public health or political regulations associated with it. Furthermore, two senior researchers (Z.J. and G.W.), specializing in epidemiology and disease prevention, critically reviewed both the methodology and the coding results.

To address missing data, we initially reached out to the authors of the meta-analytical studies in an attempt to acquire the missing information directly from the original research teams. If the pooled estimates were not provided and no response was received from the authors, the entire row of data was excluded without any further statistical transformations [ 25 ]. In cases where the statistical significance of the combined effect in the meta-analysis was determined using Z -tests but did not include reported P -values, we calculated the corresponding P -values based on the respective Z -value [ 26 ]. All data domains were verified to be free of missing values through the aforementioned processes.

Methodological quality appraisal, evidence grading, and presentation

Methodological quality of the systematic review will be made using the A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2) [ 27 ] tool by two examiners (C.D. and T.W.). In this sense, systematic reviews are categorized as: High (Zero or one non-critical weakness); Moderate (More than one non-critical weakness); Low (One critical flaw with or without non-critical weaknesses); and Critically Low (More than one critical flaw with or without non-critical weaknesses).

For pairwise meta-analytical evidence, statistical quality was assessed by applying the previously published evidence grading protocol [ 28 , 29 ]. Significant associations shown in meta-analyses were categorized into four evidence levels: strong, highly suggestive, suggestive, and weak evidence. Strong evidence was considered if all the following criteria were met: > 1000 cases included in the meta-analysis; a P -value ≤ 10 −6 of statistical significance in valid meta-analysis; heterogeneity ( I 2 ) below 50%; the null value was excluded by the 95% prediction interval; and no evidence of small study effects and excess significance bias. Highly suggestive evidence was set if meta-analyses with > 1000 cases; a random effects P -value ≤ 10 −6 , and the largest study in the meta-analysis was statistically significant. Suggestive evidence was defined if meta-analyses with > 1000 cases, random effects P -value ≤ 10 −3 were categorized. If the latter conditions were not verified, the meta-analysis was classified as weak evidence. The classifications were subgrouped based on health domains, and the results were tabulated accordingly: the main focus of interest for this study encompassed the direct and indirect impacts associated with COVID-19, which included physical, psychological/cognitive, quality of life, and social impacts. The data was compared by considering the evidence grade and subgroups, and various methods such as counting and clustering were employed.

For single-arm meta-analytical evidence, six pre-defined COVID-19 condition domains were created: laboratory-confirmed COVID-19, COVID-19-associated MIS-C, newborns from COVID-19-diagnosed mothers, long-COVID, events caused by the COVID-19 vaccine, and health impacts during the pandemic. The domain of newborns from COVID-19 diagnosed mothers is exclusively limited to infants. The remaining domains, however, encompass children and adolescents aged 0–19 years old. Summarizations were conducted under each domain using all relevant meta-analytical evidence regardless of topic overlap to present and describe the current body of systematic review evidence on impacts of COVID-19 on children and adolescents. The most meta-analytical evidence was centered around laboratory-confirmed COVID-19. It is important to acknowledge that data on patient disease presentations collected during the early stages of the COVID-19 pandemic may differ significantly from those observed in later phases. Nonetheless, the effect summaries and publication years of original meta-analyses may not accurately capture these variations. To examine the changing trends in the occurrence rates of various symptoms during the pandemic, taking into account viral strain evolution and diverse health interventions, we conducted additional reanalysis of primary studies in this domain. Specifically, we screened and extracted relevant information from the primary studies included in each systematic review, including details of the authors, data collection years, outcome indicators, number of events, and sample sizes. Subsequently, we removed duplicated evidence and reanalyzed the primary data reported for at least 2 years within the meta-analytical evidence. To account for heterogeneity among the included studies, a random effects model was used to combine the primary data outcomes if Q  < 0.05 or I 2  > 50% [ 30 ]. Alternatively, a fixed-effects model was applied to pool outcomes if these criteria were not met [ 30 , 31 ].

We adhered to the presentation guidelines outlined in the Cochrane Handbook for Systematic Reviews of Interventions to effectively present our current work [ 25 ]. To present pairwise meta-analytical evidence, we initially employed the “summarizing outcome data” [ 25 ] strategy. This strategy enabled us to systematically summarize the meta-analyzed data, along with the previously mentioned quality assessments, without considering any overlap in study topics. By doing so, we were able to provide a comprehensive overview of the current body of evidence from systematic reviews on the study topic. To present single-arm meta-analytical evidence, we also utilized the “summarizing outcome data” strategy to offer a comprehensive perspective on the available evidence. Additionally, we conducted a “reanalyzing outcome data” [ 25 ] specifically for primary studies of laboratory-confirmed COVID-19. This reanalysis aided in the elimination of duplicated primary studies and standardized the collection years of data. As a result, we achieved a more coherent and consistent presentation of disease severity, clinical manifestations, laboratory and radiological findings, treatments, and outcomes associated with laboratory-confirmed COVID-19 across different years.

Calculations of FSN

The Rosenberg’s FSN is the number of missing studies averaging a z -value of zero that should be added to make the combined effect size statistically insignificant. For statistically significant meta-analytic evidence, Rosenberg’s FSN was calculated using the workbook “Meta-Essentials” [ 32 ] for binary and continuous measures.

Data handling and processing

All the data were collected using MS Office 365. Data processing and statistical analysis were conducted in the R programming environment (version 4.1.0). The “bibliometrix” package (version 4.1.2) was employed to perform bibliometric analysis on the included studies, following its standard analyzing protocol [ 33 ]. The “meta” package (version 6.2.1) was used for statistical transformation and reanalysis of the primary studies from single-arm meta-analytical evidence [ 34 ]. To visualize the reanalyzed data, the “forestploter” package (version 1.1.2) was utilized ( https://github.com/cran/forestploter ). Data table formatting and cleaning were achieved through the utilization of the “tidyverse” (version 1.3.1) and “reshape2” packages (version 1.4.4) [ 35 , 36 ].

Selection and characteristics of the included meta-analyses

We retrieved a total of 1100 records from databases and registers (Fig.  1 ). After removing duplicates ( n  = 87), the title and abstract of 1013 records were screened against including criteria, and 814 records were excluded. Cohen’s kappa coefficient for title and abstract screening was 0.97 (95% CI 0.95–0.99). The full-text analysis was conducted on the remaining 199 records, 76 records were excluded, and 1 additional record was added through citation searching of reference lists. The list of excluded studies with reasons for exclusion is detailed in Additional file 4 . Cohen’s kappa coefficient for full-text screening was 0.93 (95% CI 0.86–0.94), confirming excellent inter-examiner reliability. Ultimately, 124 systematic reviews with meta-analyses were included for data extraction and further analysis (Additional file 5 ) [ 4 , 6 , 20 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 ]. Cohen’s kappa coefficient for data extraction was 0.90 (95% CI 0.89–0.91).

figure 1

The PRISMA flowchart. This flow diagram provides a visual summary of the screening and selection processes, illustrating the number of articles recorded at each different stage

All the included systematic reviews were published in 2020–2023. There was an increased trend of publishing relevant systematic reviews (Fig.  2 a). The top 15 most cited journals in the field of interest are shown in Fig.  2 b, in which 5 journals are multidisciplinary, 7 journals are focusing on pediatrics, and 3 journals are focusing on infectious disease and virology. Based on the number and relationship of publications in each country, a collaborative network was constructed and visualized (Fig.  2 c). China, the USA, Australia, India, and the UK shared most collaborations.

figure 2

Bibliometrics for included systematic reviews. a Number of included publications by years. b Top 15 most cited journals in the field of interest. c The geographical collaborative distribution of included systematic reviews

Most included studies adhered to PRISMA guidelines ( n  = 99, 78.6%), followed by Meta-analyses of Observational Studies in Epidemiology (MOOSE) guidelines ( n  = 12, 9.5%) or a combination of multiple reporting guidelines ( n  = 9, 7.1%). However, a small portion of studies ( n  = 7, 7.9%) did not report following any systematic review reporting guidelines. To assess methodological quality, the Newcastle–Ottawa Scale (NOS) was used in most studies ( n  = 36, 28.6%), followed by the National Institutes of Health (NIH) Quality Assessment Tool ( n  = 14, 11.1%) and Joanna Briggs Institute (JBI) tools ( n  = 13, 10.3%).

In addition to encompassing the field of neonates for COVID19-diagnosed mothers, other domains include children and adolescents aged 0–19 years. We also explicitly indicate the age group sources of the relevant evidence for each subset within these domains. The descriptive characteristics of included meta-analyses under five major outcome domains (physical, psychological/cognitive, quality of life, social, and health system) are summarized in Tables  1 and  2 . For pre-defined COVID-19 condition domains, the six domains were not evenly distributed: most topics focused on laboratory-confirmed infections ( n  = 60; 48.4%), followed by health impacts during the pandemic ( n  = 21; 16.9%), adverse events (AEs) caused by the COVID-19 vaccine ( n  = 20; 16.1%), COVID-19 associated multisystem inflammatory syndrome (MIS-C) ( n  = 15; 12.1%), newborns from diagnosed mothers ( n  = 6; 4.8%), and long-COVID ( n  = 2; 1.6%).

Methodological quality assessment

The Cohen kappa score for the AMSTAR-2 assessments was 0.89 (95% CI 0.83–0.91). No meta-analysis gained high overall confidence for methodological quality, 1 with moderate (0.8%), 18 studies were scored as low quality (14.5%), and 105 studies presented as critically low quality (84.7%) (Additional file 6 ). For critical flaws, most studies did not report a list of excluded studies and justify the exclusions ( n  = 116, 93.5%). In addition, inadequate accounting for the risk of bias (RoB) in primary studies when interpreting/discussing the results ( n  = 92, 74.2%), lacking report review methods as a priori ( n  = 60, 48.4%), and neglecting publication bias analysis during conducting relevant meta-analyses ( n  = 22, 17.7%) were also evident critical flaws. Most included studies have non-critical flaws such as lacking reports on the sources of funding ( n  = 115, 92.7%), inadequate assessment of the impact of individual RoB when performing evidence synthesis ( n  = 105, 84.7%), inadequate discussion of heterogeneity ( n  = 80), and lacking statements of the study designs for inclusion ( n  = 50, 40.3%).

COVID-19-related evidence from single-arm meta-analyses

In total, 1551 meta-analytical comparisons were included in this umbrella review. These single-armed meta-analyses commonly utilized prevalence as effect size ( n  = 1464; 94.4%). Figures  3 and  4 present a comprehensive overview based on the reanalysis of primary data (Additional file 7 ) on the disease severity, clinical manifestations, laboratory and radiological findings, treatments, and outcomes related to laboratory-confirmed COVID-19.

figure 3

Forest plot for disease severity, and clinical manifestation associated with single-arm meta-analytical evidence of laboratory-confirmed COVID-19. Data are presented as effect size (ES) with 95% confidence intervals (CI). NA not available

figure 4

Forest plot for laboratory findings, radiological findings, treatment, and outcomes associated with single-arm meta-analytical evidence of laboratory-confirmed COVID-19. Data are presented as effect size (ES) with 95% confidence intervals (CI). SAA serum amyloid A, PCT procalcitonin, LDH lactate dehydrogenase, LFTs liver function tests, CK-MB creatine kinase-MB, IL-6 interleukin-6, ALT alanine transaminase, BNP brain natriuretic peptide, CRP C-reactive protein, AST aspartate aminotransferase, CT computed tomography, ECMO extracorporeal membrane oxygenation, ICU intensive care unit, NA not available

Disease severity

In the past 3 years, mild symptoms consistently remained the primary manifestation of the disease, with a prevalence of approximately 50%. In contrast, the prevalence of severe and critical symptoms consistently stayed below 15% over the same period, showing a declining trend year by year. Notably, there was an upward trend in the prevalence of asymptomatic cases. In 2020, the percentage was 29.0% (95% CI 24.0–33.0%), which increased to 34.0% (95% CI 28.0–39.0%) in 2021, and further increased to 45.0% (95% CI 21.0–69.0%) in 2022.

Clinical manifestations

Fever consistently emerged as the most frequently reported symptom over the past 3 years, with a prevalence close to 50%. Concurrently, respiratory symptoms, primarily cough, also sustained a prevalence exceeding 30% throughout this period. Noteworthy patterns emerged in 2020, indicating a higher incidence of hematologic symptoms such as anemia, lymphocytosis, lymphocytopenia, neutropenia, thrombocytopenia, and thrombocytosis, with prevalence rates ranging from 22 to 46%. Subsequent observations in 2021 revealed a more frequent occurrence of low oxygen saturation (46%, 95% CI 23.0–70.0%) compared to preceding years. However, conjunctivitis (50.0%, 95% CI 31.0–69.0%) and rhinorrhea (32.0%, 95% CI 0–80.0%) appeared to be more prevalent in 2022. Conversely, cardiovascular and neurologic symptoms exhibited considerably lower combined prevalence rates of 4.0% (95% CI 0–9.0%) and 3.0% (95% CI 1.0–5.0%), respectively.

Laboratory findings

Most laboratory results were reported in studies conducted between 2020 and 2021. In 2020, there seemed to be a more pronounced prevalence of abnormal laboratory markers, with abnormalities observed in fibrinogen, troponin, ferritin, SAA, BNP, ESR, and albumin, each surpassing 40%. Contrastingly, by 2021, the prevalence of albumin, troponin, BNP, and SAA abnormalities did not exceed 6%. In 2022, abnormalities in D-dimer, PCT, and LDH became more prevalent, surpassing the threshold of 40%.

Radiological findings

In 2021 and 2022, normal CT scans and lung chest radiographs exhibited a high prevalence of 65.0% (CI 52.0–77.0%) and 54.0% (CI 35.0–72.0%), respectively, surpassing the prevalence of 38.0% (CI 30.0–47.0%) observed in 2020. Abnormal imaging findings such as bilateral pneumonia lesions, unilateral pneumonia lesions, and multiple lung lobe lesions were notably prevalent in 2020, exceeding 30% prevalence, which markedly decreased in the subsequent 2 years.

COVID-19-related treatments

During the 3-year duration, antibiotics emerged as a common treatment for COVID-19 in youths. However, therapies such as anticoagulation, antiviral medications, glucocorticoids, and intravenous immunoglobulin were sparingly utilized, each with a prevalence not exceeding 37.0%. The use of oxygen therapy exhibited an increasing trend, reaching 39.0% (CI 9.0–70.0%) in 2022. Conversely, mechanical ventilation was predominantly employed in 2020, with a prevalence of 15.0% (95% CI 11.0–19.0%), which notably decreased to 3.0% (95% CI 1.0–5.0%) by 2022.

COVID-19-related outcomes

Reported outcomes and prognoses for COVID-19 patients among youths consistently show relatively high rates of recovery and discharge. Additionally, a declining trend in the prevalence of ICU admissions and mortality has been observed year by year. Merely 3% (CI 0–6%) of cases necessitated ICU admission, and there were no reported mortalities in 2022.

Additional File 8 provides further details on COVID-19-related evidence obtained from single-arm meta-analyses. Additional file 9 [ 40 , 69 , 75 , 97 , 108 , 109 , 117 , 122 , 123 , 132 , 137 ] summarizes single-arm evidence about COVID-19-associated MIS-C. Additional file 10 [ 62 , 156 , 157 ] summarizes single-arm evidence about newborns from COVID-19-diagnosed mothers. Additional file 11 [ 47 , 87 ] summarizes single-arm evidence about long-COVID. Additional file 12 [ 54 , 67 , 77 , 139 , 153 ] summarizes single-arm evidence about events caused by the COVID-19 vaccine. Additional file 13 [ 49 , 50 , 58 , 82 , 99 , 113 , 142 ] summarizes single-arm evidence about health impacts during the pandemic.

COVID-19-related evidence from pairwise meta-analyses

Three hundred one meta-analytic comparisons from 47 pairwise systematic reviews were analyzed. Out of these, only 1.7% ( n  = 5) were considered to have strong meta-analytical evidence, while 4.0% ( n  = 12) and 8.0% ( n  = 24) were categorized as highly suggestive and suggestive evidence respectively (Table  2 ). A stricter P -value threshold revealed that 8.9% ( n  = 27) and 7.3% ( n  = 22) of the meta-analyses had significance at 10 −3 and 10 −6 . The remaining 39.9% ( n  = 120) were statistically significant ( p  < 0.05). In terms of heterogeneity, approximately 49.2% ( n  = 148) of the included meta-analyses had high heterogeneity ( I 2  > 50%), while 29.6% ( n  = 89) presented low heterogeneity ( I 2  ≤ 25%).

A total of 176 meta-analyses (58.4%) explored the direct impact of COVID-19 on children and adolescents. The existing evidence base is largely skewed in favor of a biomedical evaluation of health outcomes in COVID-19-infected individuals, focusing primarily on physical outcomes and suggesting an increased risk of impaired health (Fig.  5 ). Only one had strong meta-analytical evidence: long COVID-19 impact on physical outcomes ( n  = 1), while pediatric comorbidities presented highly suggestive evidence of impacting COVID-19 severity ( n  = 2). In addition, suggestive evidence was found on the effect of long-COVID ( n  = 1) as well as survival and associated complications ( n  = 1) on physical outcomes. Furthermore, transmission and risks for COVID-19 in children present suggestive evidence on both physical ( n  = 1) and social ( n  = 1) outcomes. A total of 84 meta-analyses indicated weak evidence, leaving 85 meta-analyses with no statistically significant results.

figure 5

Evidence grading on direct effects of COVID-19 on physical, psychological/cognitive, quality of life, social, and health system domains. The right side illustrates associations that elevate the risk for the respective health condition (in red), while the left side demonstrates associations that lower the risk (in green). COVID coronavirus disease, MIS-C multisystem inflammatory syndrome in children, ICU intensive care unit

The COVID-19 pandemic’s indirect impacts on children and adolescents were reported in 125 meta-analyses (41.6%). Existing evidence tends to show an increased health risk for children and adolescents, particularly in physical, psychological, and quality of life outcomes. Specifically, among these meta-analyses, two were categorized as having strong evidence (Fig.  6 ), indicating an elevated risk of depression ( n  = 1) and weight gain ( n  = 1). Five meta-analyses presented highly suggestive evidence, associating the increased risk with myopia progression ( n  = 2), depression ( n  = 2), and mental health issues ( n  = 1). The population examined in the meta-analysis, which yielded highly suggestive evidence regarding mental health issues, consisted of children aged 5 to 13 years. In terms of health system outcomes, an additional meta-analysis offered highly suggestive evidence, highlighting an increased risk of asthma-related hospitalization during the COVID-19 pandemic. A further twenty meta-analyses had suggestive evidence, ten of which pertained to associations that already received strong or highly suggestive evidence. The remaining ten meta-analyses showed an increased risk in outcomes, including complicated appendicitis ( n  = 2), neurodevelopmental impairment ( n  = 1), pediatric new-onset type 1 diabetes and diabetic ketoacidosis ( n  = 2), pregnancy and neonatal outcomes ( n  = 1), sleep quality ( n  = 2), and physical activity decline ( n  = 1). A total of 53 meta-analyses were supported by weak evidence, while the remaining 48 meta-analyses did not have nominally statistically significant findings. Additionally, the health system outcomes section notably emphasized evidence concerning the effectiveness and safety of COVID-19 vaccines. Two more meta-analyses focusing on the effectiveness of COVID-19 vaccines ( n  = 2) were categorized as having strong evidence (Fig.  6 ). Four other meta-analyses presented highly suggestive evidence, reporting the effectiveness ( n  = 3) and safety ( n  = 1) of COVID-19 vaccines. The meta-analysis that provided highly suggestive evidence regarding the effectiveness of COVID-19 vaccines focused on a study population comprising children aged 5 to 11 years.

figure 6

Evidence grading on indirect effects of COVID-19 on physical, psychological/cognitive, quality of life, social, and health system domains. The right side illustrates associations that elevate the risk for the respective health condition (in red), while the left side demonstrates associations that lower the risk (in green). COVID coronavirus disease

Number of additional studies needed to change current pairwise meta-analytic evidence

For strong evidence, the median fail-safe number (FSN) was 8 (range 4–25). For highly suggestive and suggestive evidence the median FSN were 13 (range 4–158) and 11 (range 1–163), respectively. The FSN in 73.2% of these studies ( n  = 30) were higher than the number of studies included in the meta-analyses, meaning that adding studies in the future is unlikely to change the robustness of the statistical significance for these metanalytic evidence. For weak evidence, the median FSN was 11 (range 1–2569), and only 37.8% of studies ( n  = 48) had FSN higher than the number of studies included in the meta-analyses.

Main findings from the single-arm meta-analytical evidence

Single-arm meta-analyses have provided extensive evidence on the prevalence and estimations across six domains associated with COVID-19 condition. These domains include laboratory-confirmed COVID-19, COVID-19-associated MIS-C, newborns from COVID-19-diagnosed mothers, long-COVID, events caused by the COVID-19 vaccine, and health impacts during the pandemic.

In this umbrella review, we specifically focus on laboratory-confirmed COVID-19 infections. Through reanalyzing the primary studies from these meta-analyses by removing overlapping data and remapping the actual data collection year, we investigated the distinct clinical characteristics, management, and outcomes of children and adolescents with COVID-19 infections from 2020 to 2022. Clinical manifestations among children exhibited variability over the years, illustrating a diverse range of features. More than half of these manifestations demonstrated a downward trend over time. Our analysis illustrates prevailing patterns in prevalence, indicating an increase in asymptomatic cases and a decrease in other severity levels of cases. In terms of COVID-19-related outcomes, there was a decrease in both admissions to the intensive care unit (ICU) and mortality rates over the years, while the number of discharged and recovered cases remained relatively stable. Interestingly, hospitalization rates rebounded in 2022, potentially attributed to the emergence and spread of novel COVID-19 strains with immune escape mechanisms [ 158 ]. COVID-19-related MIS-C is characterized by recurring high fever, damage to multiple organs, heightened inflammatory indicators, and frequent severe outcomes. Newborns from mothers diagnosed with COVID-19 generally experienced mild symptoms and had a low risk of vertical transmission, although adverse health outcomes were still possible. Furthermore, our findings suggest that children and adolescents affected by long-COVID commonly report symptoms such as fatigue, dyspnea, sore throat, mood changes, and sleep disorders. For events caused by COVID-19 vaccines, we observed that AEs were more frequently reported following booster doses compared to earlier doses. Solicited local and systemic AEs were also found to be common across all doses. Lastly, regarding the domain of pandemic lockdown, our findings reveal a significant correlation between social isolation and adverse effects on the mental health, sleep habits, and physical activity of children and adolescents.

Main findings from the pairwise meta-analytical evidence

Among the pairwise meta-analyses, we observed strong evidence for five effects and highly suggestive evidence for 12 effects. These results were supported by highly significant findings. Based on the available evidence, we have classified the strong and highly suggestive evidence into three primary categories: (i) the direct effects of COVID-19 infection on children’s health, (ii) the indirect impacts of the COVID-19 pandemic on children’s well-being, and (iii) the efficacy and safety of COVID-19 vaccines. The direct effect is demonstrated by a higher risk of severe COVID-19 in children with comorbidities and persistent negative health challenges resulting from long-COVID. Several factors can explain the manifestations linked with long-COVID, including persistent acute organ damage, the presence of the virus in the body, and the activation of autoimmune mechanisms that target both the COVID-19 virus and host tissues [ 159 ]. Moreover, the indirect effects of COVID-19 had strong correlations with significant economic disruptions, increased social isolation, mental health challenges, and a shift towards remote work and online activities for children and adolescents [ 160 ]. Our study revealed that these indirect impacts are manifested in increased anxiety and depression levels, accelerated myopia progression, as well as significant increases in body weight and BMI during the COVID-19 pandemic home quarantine. The effectiveness and safety of COVID-19 vaccines have been confirmed to a certain extent, although several potential AEs have been reported.

Similarities and disparities of the previous and current studies

In our study, we investigated and presented evidence of the negative physical correlations observed in individuals with long-COVID. Previous umbrella reviews, which primarily focused on adults, have examined the long-term consequences experienced by COVID-19 survivors beyond the acute phase. However, these evaluations were limited by the absence of graded evidence [ 161 , 162 ]. In contrast, our study pooled single-arm meta-analyses specifically on long-COVID in children and adolescents, demonstrating similar outcomes. Nonetheless, it is important to note that these findings did not achieve high-level evidence ranking like those obtained through pairwise meta-analysis. This limitation can be attributed to the constraints imposed by the study design and sample size, which were influenced by limited time, resources, and evolving understanding of long-term consequences associated with acute COVID-19 [ 17 ]. The management of individuals with post-COVID conditions presents significant challenges due to the diverse range of symptoms, unpredictable duration, and absence of definitive risk factors. Furthermore, the symptoms of long-COVID can manifest in various combinations from patient to patient, with fluctuations in both frequency and severity. This dynamic nature adds an extra layer of complexity to the issue of long-COVID.

This umbrella review offered a comprehensive analysis of the correlation between the COVID-19 pandemic and the mental health of children and adolescents. Similar to previous umbrella reviews, we observed differing impacts regarding various mental health issues among this demographic. However, the methodological approaches of these umbrella reviews varied considerably. Due to significant heterogeneity in the methods and outcomes of the reviews included, some lead to a narrative synthesis presentation and an omission in evidence grading [ 21 , 163 ]. One umbrella review [ 164 ] conducted a reanalysis of systematic reviews to re-examine data from preliminary studies captured within each systematic review, aiming to reduce potential biases that could have previously impacted the assessment of mental health during the pandemic. Despite the absence of evidence grading, the insights provided by this evidence remain significant. It is also important to acknowledge that disentangling the direct impact of the pandemic on the mental health of children and adolescents remains challenging due to the complexity of mental health disorders. Additionally, the implementation of bundled mitigation strategies at national or subnational levels complicates the identification of individual strategies that may have contributed to exacerbated mental health effects. To sum up, the integration of our current findings with previous studies has the potential to substantially enhance policymaking and practice in the field of mental and child health, thereby guiding future research endeavors to strengthen the global knowledge base.

Mechanisms and implications from current evidence

The gradual reduction of health risk of covid-19 in children and adolescents.

The health risk of COVID-19 in children and adolescents appears to decrease gradually over time. Our research also indicates a rise in the number of asymptomatic cases among children over the years, alongside a decline in ICU admissions and mortality rates. These trends may be attributed to a globally prevalent variant strain during the period when the data included in the study was collected. With the ongoing pandemic, the Omicron variant (B.1.1.529) emerged as the dominant strain during that time, surpassing Delta in its transmission rate [ 165 ]. Additionally, the Omicron variant has demonstrated a lower rate of hospitalization, ICU admission, invasive mechanical ventilation (IMV), and in-hospital deaths, along with a higher prevalence of asymptomatic cases compared to the Delta variant [ 166 , 167 ]. These observations suggest that the Omicron variant may have reduced pathogenicity and milder symptoms than previous variants. Our findings indicate that the most frequently reported symptoms are fever and cough, which aligns with the previous umbrella reviews not limited by age [ 20 ]. However, our study discovered a higher incidence of conjunctivitis in 2022, with a rate of 48.4%. This symptom is considered rare, as its prevalence among positive cases typically ranges from 0.8 to 31.6% [ 168 ]. Our findings indicate that the most frequently reported symptoms are fever and cough, which aligns with the previous umbrella reviews not limited by age [ 20 ]. However, our study discovered a higher incidence of conjunctivitis in 2022, with a rate of 48.4%. This symptom is considered rare, as its prevalence among positive cases typically ranges from 0.8 to 31.6% [ 168 ]. It has been established that frequency of hand-eye contact presents as an independent risk factor for COVID-19-related conjunctivitis [ 169 ].

Another key finding in our study is the lower health risk of COVID-19 for children and adolescents when compared to adults. The current umbrella review provides ample evidence to support the notion that most children and adolescents infected with COVID-19 exhibit mild or even asymptomatic symptoms. During the Omicron epidemic, the proportion of asymptomatic cases across all age groups was 33.72% in 2022 and 23.57% in 2021, both of which indicate a lower proportion of asymptomatic cases among adults compared to our reported findings [ 170 ]. Umbrella reviews without age limitations often overlook the critical role of age in influencing both COVID-19 susceptibility and disease severity [ 171 ]. The relative susceptibility among children and adolescents aged 0–19 years was also notably lower, ranging from 6 to 16% compared to adult groups, with the rate of critical illness in adults being 4.95 times higher than that in children [ 172 ]. However, the reasons behind this phenomenon remain incompletely understood, despite available data suggesting similar viral loads in both children and adults at the time of presentation [ 173 ]. Several hypotheses have been proposed to explain the disparity in COVID-19 severity between younger and older individuals, including more efficient local tissue responses [ 174 ], better thymic function [ 175 ], and cross-reactive immunity [ 176 ]. Currently, the prevailing viewpoint suggests that the lower incidence and severity of COVID-19 disease in infants can be explained by maternal immune transfer [ 177 ], an immature immune system [ 178 ], and reduced expression of COVID-19 attachment receptors such as ACE-2 [ 179 ]. However, there is also evidence suggesting that maternal COVID-19 may impact the neonatal immune system, potentially leading to an exacerbated inflammatory response and immune activation [ 180 ]. Consequently, the programming of the neonatal immune system by the maternal inflammatory milieu induced by COVID-19 remains uncertain.

From the perspective of childhood growth evolution, recent evidence suggests that the body’s energy allocation often avoids costly systemic inflammatory responses [ 181 ]. By prioritizing disease tolerance rather than maximal resistance, children are more likely to experience mild or even asymptomatic illness. However, this approach may result in a lower efficiency in clearing viruses, which could lead to some degree of viral persistence and the subsequent manifestation of other diseases associated with such persistence. Therefore, personalized treatments tailored to the severity of the disease should be implemented for pediatric patients, along with thorough long-term monitoring and follow-up care. Furthermore, our findings indicate that dysautonomia often presents symptoms commonly associated with long-COVID. However, whether dysautonomia is a direct consequence of COVID-19 infection or an immune-mediated process remains unclear [ 182 ]. Although current evidence suggests that long-COVID has a relatively milder health impact on pediatric patients compared to adults, the life course implications, including psychological, social, and economic impacts, are not fully revealed by current evidence and require more extensive long-term follow-up studies [ 183 ]. Insufficient lifecycle observations limit the evidence of the impact of long-COVID on pediatric patients, requiring caution when interpreting the relatively lower impact on this group. Nevertheless, school closure and social distancing measures may further compromise the social well-being of children with long-COVID symptoms [ 184 ]. It is crucial to implement social policies that promote the return to school and participation in extracurricular activities among young individuals to address specific risk factors like obesity associated with long-COVID. Additionally, this can help mitigate the negative cultural impact and psychological consequences caused by remote learning [ 184 , 185 ].

The immune microenvironment, physiological and psychological factors, and social activities contribute to disparities in disease susceptibility among children and adolescents at different developmental stages [ 186 , 187 ]. Previous literature has reported a higher susceptibility to severe COVID-19 and increased hospital admissions among pediatric patients aged ≤ 4 years or 12–17 years, indicating a bimodal age distribution [ 188 ]. Our research shows that serious consequences such as dyspnea, ICU admission, and death have also been observed and cannot be ignored, although a lower health risk of COVID-19 for children and adolescents due to uncommon vertical transmission during neonatal admission from infected mothers was observed.

However, at the umbrella review level, the current available primary and meta-analytical evidence has limited the evaluation process of COVID-19’s impact on age-specific subgroups of children and adolescents. Most of the included evidence has focused on the 0–19 age group as a whole [ 4 , 6 , 20 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 ], with only a few studies examining specific age ranges. For example, Yasuhara J’s study included children and adolescents aged 12–19 years [ 152 ], while Watanabe A’s study included children aged 5–11 years [ 153 ], without direct comparison to other age-specific subgroups. The lack of age-specific research and comparisons poses challenges for subgroup analyses across various age groups, excluding neonatal groups that are typically distinguished in the literature and technically feasible to evaluate [ 154 , 155 , 156 , 157 ]. Therefore, our main analysis includes the 0–19 age group, with a separate domain for newborns from COVID-19-diagnosed mothers, to demonstrate the impact of COVID-19 on children and adolescents at different developmental stages.

The mental health risks of COVID-19-related social isolation on children and adolescents

Over the past 3 years, significant efforts have been made to control the spread of COVID-19 through various interventions, such as social distancing, mobility restrictions, and school closures. Nevertheless, these measures may have unintended and detrimental effects on the mental health of children and adolescents [ 189 , 190 ]. The present umbrella review provides evidence supporting the idea that the pandemic has resulted in an increased burden of mental health concerns among this population, including conditions like depression and sleep disorders. These findings are consistent with previous research studies [ 21 , 22 ]. However, their report indicated a pooled prevalence rate of 32% for depression (95% CI 27–38%) and 32% for anxiety (95% CI 27–37%) among children and adolescents worldwide following COVID-19 mitigation measures, which was lower than our findings. This suggests that the deterioration of mental health in the younger population during the pandemic may not solely be attributed to indirect impacts during the gradual relaxation of mandatory control measures in many countries. The underlying causes of this phenomenon may vary and encompass financial stressors [ 191 ], social isolation [ 192 ], physical health concerns [ 193 ], and heightened anxiety and fear stemming from the uncertainties of COVID-19 [ 194 ]. Therefore, it is crucial to monitor the negative impact on the mental health of children and adolescents in the future, with dedicated efforts aimed at enhancing their well-being. Policymakers and healthcare professionals should adopt a holistic approach that addresses these multifaceted issues to effectively mitigate the detrimental effects on mental health.

The safety and efficacy of vaccines for children

The present study indicates that COVID-19 vaccines effectively prevent severe illness and reduce transmission. Evidence from pooled studies in healthy children and adolescents suggests that the occurrence of AEs, including local and systemic AEs, is similar between the vaccine and placebo groups. Furthermore, serious AEs are mostly unrelated to vaccination [ 195 ]. Recent studies have also confirmed the favorable and safe response to COVID-19 vaccination among pediatric patients with inflammatory rheumatic diseases [ 196 ], endocrinological disorders [ 197 ], or inflammatory bowel disease [ 198 ], addressing concerns about potential AEs in vulnerable populations with inadequate or overactive immune responses. However, it is important to note that the evidence supporting these findings is currently limited to cross-sectional studies. Additionally, both local and systemic AEs were reported to occur slightly more frequently than in the adult population. Nonetheless, this does not suggest evidence against the vaccine’s safety, as reactogenicity is more common in young individuals than in adults [ 199 ]. Although side effects may vary depending on the person, they typically tend to be mild and temporary, similar to common childhood vaccines [ 200 ]. Based on emerging safety and efficacy data, along with increased vaccine availability, widespread vaccination is recommended, particularly for high-risk children.

COVID-19-related global policies related to children and adolescents

The impact of COVID-19-related global policies is a matter of great concern for the health and well-being of children and adolescents. The effects of policies and regulations implemented to control the COVID-19 outbreak are intricate and multifaceted. Policies that specifically target children and young people, such as school closures and healthcare restrictions, have direct consequences on their social interactions [ 201 ], education [ 202 ], and access to healthcare resources [ 203 ]. Moreover, policy decisions and measures have resulted in numerous social conflicts, further impacting the daily lives of children and adolescents. For example, children often find themselves caught in the midst of disputes between parents, friends, schoolmates, teachers, and activity leaders regarding COVID-19 measures [ 204 ]. Additionally, the social and economic repercussions of policy measures become apparent in later stages of the pandemic, underscoring the direct impact these challenges have on the everyday lives of children. As an illustration, school children from underprivileged families face multiple hurdles due to the pandemic, including financial insecurity and disparities in remote learning caused by a lack of digital devices [ 205 ]. These complex impacts make it difficult to establish a clear link between policies and child health, and limited research has focused on the long-term effects of policies on affected children. Furthermore, as COVID-19 continues to significantly impact the mental health of the general population, countries have developed and revised policies, guidelines, and new initiatives to address the psychological well-being of their citizens. These supportive policies further complicate the discussion surrounding policy impacts.

Evidence of the impact of COVID-19-related policies on children and adolescents is limited. As stated above, current meta-analytical evidence suggests that children may be less affected by certain social settings as a result of policy development, such as the reopening of schools and workplaces. However, the physical and psychological/cognitive effects of the virus may hinder a child’s ability to return to school for several weeks or months. Research on health system utilization in this area primarily focuses on medical resources, such as emergency services and ICU admissions. However, conflicting evidence exists, with only a limited number of studies available. Based on current meta-analytical data, it appears that health system utilization for life-threatening diseases and situations in younger patients is not significantly different from or may even be lower than the pre-pandemic era, except in cases involving younger patients with asthma and obesity. It is important to interpret these results cautiously since medical crowding and inadequate resources may overshadow the utilization of the health system by pediatric COVID-19 patients [ 206 ].

Strengths and weaknesses

The strengths and weaknesses of this umbrella review are quite straightforward. First, one of the strengths is that we provided a comprehensive summary of the current available evidence by reviewing previous meta-analyses on the association between COVID-19 and various health outcomes in children and adolescents. Second, our study protocol was registered in PROSPERO, ensuring transparency and robustness in the planned analysis and results. Considering the worldwide prolonged transmission and evolving nature of COVID-19, this study holds clinical and social importance in shaping preventive strategies for children and adolescents in the post-pandemic era. Next, this study utilized a systematic approach, involving thorough searching, selection, and data extraction conducted by two independent authors with excellent inter-examiner reliability. Methodological quality and evidence classification were assessed using two established criteria, AMSTAR-2 [ 27 ] and evidence grading criteria [ 28 , 29 ] to assess the methodological quality and evidence classification. Without limiting study designs and statistical significance, the current umbrella review filled the knowledge gaps and captured temporal changes in key aspects of COVID-19 in children and adolescents. However, some limitations still exist in this review. Firstly, most of the meta-analyses included in this review are based on observational studies, and the prospective or randomized study designs were not prominently featured. This observational nature of the studies introduces the potential for selection bias, as differences in baseline characteristics and confounding factors can exist among the study populations [ 207 ]. Consequently, these studies only provide associations between variables and cannot account for all possible confounders or factors that may influence the results. Secondly, many studies within this topic rely on self-reported data, which can be subjective and susceptible to recall bias [ 208 ]. The use of self-reporting may lead to inconsistencies or inaccuracies in the data collected. Moreover, the absence of a comparison group in single-arm studies makes it challenging to accurately establish the effects of COVID-19 on children and adolescents. The evaluation of the impact of COVID-19 on age-specific subgroups of children and adolescents is incomplete due to the limited availability of primary and meta-analytical evidence. The lack of age-specific research and comparisons poses challenges for subgroup analyses across various developmental stages, potentially resulting in an incomplete depiction of the differential impacts of COVID-19 across different developmental stages of children and adolescents. In this current study, we chose to present all available meta-analytical data from single-arm studies and categorized them into six predefined COVID-19 condition domains [ 25 ]. This approach allows us to illustrate the main findings of COVID-19 on children and adolescents in a time-dependent manner, considering the dynamic changes in disease severity, clinical manifestations, laboratory and radiological findings, treatment, and outcomes. Although this approach has its limitations, it provides valuable insights that can inform public health measures and interventions targeted at this population. It also emphasizes the need for further controlled studies tailored to address the specific impacts of COVID-19 on children and adolescents.

Implications for practice and research

For practice, the effect estimates combined with different topic domains categorized from primary studies can help practitioners identify high-risk younger patients for COVID-19-related direct and indirect health outcomes. However, when it comes to children and adolescents with rare disease conditions, identifying high-risk groups remains a challenge. These individuals are often underrepresented in primary studies, likely due to decreased medical care utilization during the pandemic. This oversight may result in the neglect of rare disease conditions in both practice and policy decisions. Based on the complete picture of available evidence and the COVID-19 and health domains linking mechanisms, policymakers could better prioritize the prevention and intervention methods for children and adolescents affected by COVID-19 (in)directly. Connecting these individuals with appropriate support services is another key question in COVID-19 management. The effects and evidence summarized in this umbrella review suggests that such services could include medical/surgical management of physical illness and comorbidities, psychotherapy, physio and occupational therapy, and nursing. Multidisciplinary collaboration units dedicated to younger COVID-19 patients or pandemic lockdowns can be invaluable in providing tailored prevention and intervention strategies [ 209 , 210 ]. Shifting the focus of COVID-19 global health emergency management to long-term management, alongside other infectious diseases, can accelerate the implementation of surveillance systems for COVID-19-related health consequences and rehabilitation programs for affected younger patients.

For research, the effect estimates in this review are heavily focused on the physical outcomes, investigating the relationship between COVID-19 and disease severity, clinical manifestations, laboratory/radiological findings, treatment, and outcomes in younger patients. Gaps still exist between available evidence in non-physical outcomes and current clinical practice. Furthermore, some meta-analytical results did not reveal significant associations with many COVID-19-related health conditions, such as the direct effects of long-COVID on the psychological/cognitive well-being and quality of life of younger patients in pairwise studies, which lead to the overall evidence consistency due to poor meta-analytic or methodological reasons. Future studies can provide a more nuanced understanding of how COVID-19 affects children and adolescents at different developmental stages by expanding the scope of research to include a wider range of age groups. This will enable the analysis of the impacts of COVID-19 on various stages of childhood and adolescence, facilitating the development of tailored interventions and guidelines specifically designed for these populations. It is important to note that this does not imply the absence of robust associations, but rather the current body of evidence does not yet support such inferences [ 211 ]. Therefore, updating and transforming this review into a living review would be valuable in incorporating emerging evidence from future meta-analytical studies on the impacts of COVID-19 on children and adolescents.

In summary, this work evaluated the meta-analytical evidence regarding the associations between the (in)direct COVID-19 effects and multiple health and well-being domains of children and adolescents. The findings of this study can serve as a comprehensive evidence map to inform, educate, and train various interested parties, including key stakeholders such as policymakers, patients, and practitioners. It is also important to acknowledge that the majority of the findings and recommendations presented in this study are derived from observational studies that have methodological limitations. Thus, it is crucial to exercise caution when interpreting the results and implementing the implications of this study. Additionally, future research should prioritize the execution of high-quality studies utilizing prospective, long-term, or randomized designs to more comprehensively understand the causal effects of COVID-19, both direct and indirect, on children and adolescents.

Availability of data and materials

All data generated and analyzed that support findings in this study are supplemented in the Supplementary Information. The Rscript snippets for generating descriptive summaries and reanalyzing single-arm studies are available on GitHub using the following link: https://github.com/Piperacillin/Child_COVID19_Umbrellareview .

Abbreviations

Adverse events

A Measurement Tool to Assess Systematic Reviews 2

Coronavirus disease 2019

C-reactive protein

Joanna Briggs Institute

Serum lactate dehydrogenase

Multisystem inflammatory syndrome

Meta-analyses of Observational Studies in Epidemiology

National Institutes of Health

Newcastle-Ottawa Scale

Procalcitonin

Preferred Reporting Items for Systematic Reviews and Meta‐Analyses

Risk of bias

Severe acute respiratory syndrome coronavirus 2

White blood cell

World Health Organization

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (82301106), Natural Science Foundation of Sichuan province (24NSFSC3535), Fundamental Research Funds for the Central Universities and Research and Develop Program, West China Hospital of Stomatology Sichuan University (RD-02–202409).

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Chengchen Duan, Liu Liu, and Tianyi Wang contributed equally to this article.

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State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section of Ren Min Nan Rd., Chengdu, 610041, China

Chengchen Duan, Liu Liu, Tianyi Wang, Guanru Wang, Zhishen Jiang, Honglin Li, Gaowei Zhang, Li Ye, Chunjie Li & Yubin Cao

Department of Conservative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China

Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China

Guanru Wang, Honglin Li, Gaowei Zhang, Li Ye & Chunjie Li

Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China

Department of Evidence-Based Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China

Chunjie Li & Yubin Cao

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The research was conceptualized and designed by Y.C. and L.C. The data were obtained and compiled by T.W., C.D., G.Z., L.Y., and L.L., and analyzed by L.L. and C.D. The manuscript was drafted by L.L., C.D., T.W., and G.W., and critically reviewed for significant intellectual content by L.L., C.D., T.W., Z.J., H.L., C.L., and Y.C. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1..

Differences between protocol and review.

Additional file 2.

PRISMA 2020 checklist.

Additional file 3.

Search strategy.

Additional file 4.

List of excluded studies with justification for exclusion.

Additional file 5.

Detailed information on the characteristics of the included systematic reviews.

Additional file 6.

AMSTAR2 methodological quality assessments.

Additional file 7.

Primary studies data extracted from single-armed meta-analyses of laboratory-confirmed COVID-19.

Additional file 8.

Additional COVID-19-related evidence from single-armed meta-analyses.

Additional file 9.

Effect sizes and forest plots on COVID-19-associated multisystem inflammatory syndrome.

Additional file 10.

Effect sizes and forest plots on newborns from COVID-19-diagnosed mothers.

Additional file 11.

Effect sizes and forest plots on long-COVID.

Additional file 12.

Effect sizes and forest plots on events caused by the COVID-19 vaccine.

Additional file 13.

Effect sizes and forest plots on health impacts during the pandemic.

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Duan, C., Liu, L., Wang, T. et al. Evidence linking COVID-19 and the health/well-being of children and adolescents: an umbrella review. BMC Med 22 , 116 (2024). https://doi.org/10.1186/s12916-024-03334-x

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  • COVID-19, Children and adolescents
  • Comorbidity
  • Mental health
  • Umbrella review

BMC Medicine

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  • Published: 19 March 2024

Unfinished nursing care in healthcare settings during the COVID-19 pandemic: a systematic review

  • Aysun Bayram   ORCID: orcid.org/0000-0003-2038-6265 1 ,
  • Stefania Chiappinotto   ORCID: orcid.org/0000-0003-4829-1831 2 &
  • Alvisa Palese   ORCID: orcid.org/0000-0002-3508-844X 2  

BMC Health Services Research volume  24 , Article number:  352 ( 2024 ) Cite this article

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Unfinished nursing care is becoming increasingly more of a concern in worldwide healthcare settings. Given their negative outcomes, it is crucial to continuously assess those nursing interventions that are commonly postponed or missed, as well as the underlying reasons and consequences. The worldwide COVID-19 pandemic has made it difficult for health facilities to maintain their sustainability and continuity of care, which has also influenced the unfinished nursing care phenomenon. However, no summary of the studies conducted during the COVID-19 pandemic was produced up to now. The main aim of this study was to systematically review the occurrence of, reasons for, and consequences of unfinished nursing care among patients in healthcare settings during the COVID-19 pandemic.

Systematic review registered in PROSPERO (CRD42023422871). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guideline and the Joanna Briggs Institute Critical Appraisal tool for cross-sectional studies were used. MEDLINE-PubMed, the Cumulative Index to Nursing and Allied Health Literature, and Scopus were searched from March 2020 up to May 2023, using keywords established in the field as missed care, unfinished nursing care, or implicit rationing.

Twenty-five studies conducted mainly in European and Asiatic countries were included and assessed as possessing good methodological quality. The following tools were used: the MISSCARE Survey (= 14); the Basel Extent of Rationing of Nursing Care (= 1), also in its revised form (= 2) and regarding nursing homes (= 2); the Perceived Implicit Rationing of Nursing Care (= 4); the Intensive Care Unit-Omitted Nursing Care (= 1); and the Unfinished Nursing Care Survey (= 1). The order of unfinished nursing care interventions that emerged across studies for some countries is substantially in line with pre-pandemic data (e.g., oral care, ambulation). However, some interesting variations emerged at the country and inter-country levels. Conversely, labour resources and reasons close to the emotional state and well-being of nurses were mentioned homogeneously as most affecting unfinished nursing care during the pandemic. None of the studies investigated the consequences of unfinished nursing care.

Conclusions

Two continents led the research in this field during the pandemic: Europe, where this research was already well established, and Asia, where this research is substantially new. While unfinished care occurrence seems to be based on pre-established patterns across Europe (e.g., regarding fundamentals needs), new patterns emerged across Asiatic countries. Among the reasons, homogeneity in the findings emerged all in line with those documented in the pre-pandemic era.

Peer Review reports

Unfinished nursing care (UNC), which is becoming increasingly more of a concern in worldwide healthcare settings, involves the skipped, delayed, or incomplete delivery of nursing interventions needed for the patient and/or the patient’s family [ 1 , 2 ]. The prevalence of UNC, which ranges from 55 to 98% globally [ 1 ], is considered as an accurate indicator of both patient safety and nursing care quality [ 3 , 4 ]. The primary reasons for UNC are issues in communication, labour, and material resources [ 5 ]. The occurrence of UNC has also been associated with staff shortage and factors at both the structural level (e.g., nurses’ roles and experiences) and the process level, such as the stressful work environment, some negative managerial practices, the amount of overtime, and the high and/or complex demand for patient care [ 6 , 7 , 8 , 9 , 10 , 11 ]. In terms of consequences, UNC is linked to poor patient (e.g., pressure sores), nurse (e.g., moral distress), and organisational outcomes (e.g., increased length of stay) [ 5 , 12 , 13 , 14 ]. Given these unfavourable outcomes, it is crucial to continuously assess those nursing interventions that are commonly postponed or missed, as well as the underlying reasons and consequences, to inform evidence-based strategies aimed at decreasing the frequency of UNC.

The worldwide COVID-19 pandemic has made it difficult for health facilities to maintain their sustainability and continuity of care due to the dramatic call to increase the care capacity with limited resources [ 15 , 16 , 17 ]. The staff sector most impacted by the pandemic — especially due to concerns regarding infection — has been recognised as nursing staff delivering direct patient care and thus representing the most crucial element of the health system infrastructure [ 18 ]. In addition to the need to increase the amount of care, nurses have also been impacted by unfamiliar work settings due to changes in the layout of the hospitals, sickness exposure, and urgent deployment from one department to another without the required skills. Therefore, various components (e.g., communication) of nursing care have been compromised by the limited interaction required during the pandemic and the need to be distanced. Nurses’ care capacity has also been negatively impacted by feelings related to the pandemic triggering anxiety, depression, and burnout [ 19 , 20 ]. A rise in the number of nurses layoffs, the increased shortage of nurses, poor working circumstances, negative feelings, and imbalances in the nurse–patient ratio may all have increased the occurrence of UNC during the pandemic [ 21 , 22 ] by further eroding the quality of care [ 23 , 24 ]. Gurkovà et al. [ 25 ] stated that UNC may have increased the risk and adverse effects of the COVID-19 pandemic, resulting in ethical issues and a widespread mistrust in health systems [ 26 ]; moreover, Nash et al. [ 27 ] also stated that healthcare disparities were the consequences of UNC.

However, while the pre-pandemic occurrence of UNC has been well established, with several primary studies and systematic reviews (e.g., [ 28 ]) also investigating the underlying reasons (e.g., [ 29 ]), no summary of the studies conducted during the pandemic has been provided to date. Summarising the evidence produced may highlight the issues experienced during the pandemic in order to prevent them in future epidemiological disasters. It may also provide information on the quality of care in dramatic circumstances and the variations, if any, in the routine care before the pandemic. Finally, it may also set a new baseline in the context of UNC given the profound disruption and changes affecting the healthcare systems, requiring a long-term recovery. Thus, the aim of this review was to systematically review the occurrence of, reasons for, and consequences of UNC among patients in healthcare settings in the face of the COVID-19 pandemic.

To begin with, two researchers (AB, SC) performed a rapid literature search to establish whether any studies had been published on UNC occurrences, their reasons, and consequences among patients during the pandemic. The beginning of the pandemic period was defined as 11 March 2020, according to the declaration by the World Health Organisation [ 30 ].

According to the Population (P), Exposure (E), Comparator (C), Outcomes (O), and Study Design (S) framework [ 31 ], the following were considered: P, patients in any healthcare setting; E, the COVID-19 pandemic period, as started on 11 March 2020 up to 5 May 2023 [ 30 ]; C, none; O, occurrence, reasons, and consequences of UNC, as perceived by nursing staff; and S, any types of quantitative study designs. Consequently, the following research questions were identified: (1) What was the occurrence of the UNC phenomenon among patients during the pandemic? (2) What were the reasons for the UNC during the pandemic? (3) What were the consequences of the UNC among patients during the pandemic? (4) What were the main methodological features of studies designed/conducted during the pandemic?

The systematic review was reported in its methods and findings according to Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) guidelines [ 32 ].

Ethical considerations

The researchers designed a systematic review protocol that was registered in PROSPERO (CRD42023422871).

Inclusion and exclusion criteria

Studies were considered if they (1) regarded the nursing field; (2) focused on UNC occurrence, its reasons, and/or consequences during the pandemic, as perceived by nurses and nursing aides; (3) were published in English, Italian, or Turkish; (4) collected the data using a validated tool/instrument in the UNC field; (5) were conducted after 11 March 2020 during the COVID-19 pandemic up to 5 May 2023 [ 30 ]; and (6) used any types of quantitative designs (randomised controlled trials, non-randomised controlled trials, cohort studies, prospective or retrospective observational studies, cross-sectional studies, longitudinal studies).

Studies were excluded if they (1) did not address UNC data and/or did not involve nurses/nursing aides or care workers in the nursing field; (2) used non-validated tools/instruments measuring UNC or interviews; (3) were conducted in a paediatric setting, due to its specificity not being comparable with the adult field; (4) were designed as qualitative studies, reviews, commentaries, editorials, or books; (5) were written in other languages; or (6) had an abstract/full text that was not accessible.

Search method

MEDLINE-PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Scopus were searched to identify the eligible studies as sources on 5 May 2023. According to the uniqueness of this research, where no MeSH terms have been established and different key words are used [ 1 , 2 ], all synonymous and equivalent keywords established in the field of UNC were used to access the databases. Specifically, the following keywords were used: “nurse”, “nursing”, “missed care”, “missed nursing care”, “unfinished nursing care”, “unfinished care”, “implicit rationing of nursing care”, “implicit rationing”, “rationing of nursing care”, “rationed care”, “prioritization process”, “omitted nursing care”, “task left undone”, and “task undone” using “OR” and “AND” operators (Supplementary Table 1 ).

Quality Appraisal

The Joanna Briggs Quality Appraisal Tool for analytical cross-sectional studies was used in the quality assessment for all eligible studies when they were based on cross-sectional designs [ 33 ]. This tool contains eight items with response options of yes, no, unclear, and not applicable. These items regarded inclusion criteria, subjects and setting description, exposure, standard criteria for measurement of the condition, confounding factors, strategies to deal with confounding factors, outcomes measurement, and statistical analysis. Two researchers (AB, SC) independently assessed the quality of the studies as “Rater 1” and “Rater 2”. In the case of a disagreement, the senior researcher (AP) was consulted to reach a consensus, as summarised analytically in Supplementary Table 2 .

Besides the quality appraisal, to prevent bias, the following strategies were applied: (a) all researchers contributed to the writing of the review protocol; (b) at least two researchers searched the literature, chose the studies, and extracted the data, independently; (c) the senior researcher oversaw the data extraction; and (d) agreement was required before moving on to each next step.

Data extraction and synthesis

All studies that met the inclusion criteria, regardless of the results of their methodological quality, underwent the data extraction and data synthesis. The studies were divided into two groups and shared between two researchers (AB, SC). In primis , the data extraction grid was piloted in one study, and the findings agreed: no changes were required. Then, researchers independently extracted data from the remaining studies by populating the grid with the following data: (1) author(s), year, and country; (2) study aim(s) and design; (3) sample and setting; and (4) period of data collection and tool(s). Then the findings of the quality appraisal were provided (Table  1 ). At the end of data extraction, the researchers rechecked the data. Disagreements were solved with the consultation of the senior researcher (AP) until consensus was reached.

A narrative synthesis process was used to summarise the findings [ 57 ] according to the review questions, applying the following methodology:

Studies conducted during the pandemic and their methodological quality: the researchers conducted a preliminary synthesis to provide an initial description of the main characteristics of the studies and their methodological quality, and similarities and differences across studies were presented by using textual explanations [ 57 ].

The occurrence of UNC: Findings were tabulated according to the tools used in each study, namely the MISSCARE Survey, the Basel Extent of Rationing of Nursing Care (BERNCA) and the Revised BERNCA (BERNCA-R), the Perceived Implicit Rationing of Nursing Care (PIRNCA), the Basel Extent of Rationing of Nursing Care for Nursing Homes (BERNCA-NH), the Intensive Care Unit Omitted Nursing Care instrument (ICU-ONC), and the Unfinished Nursing Care Survey (UNCS). In all tools, participants are required to rank the nursing interventions missed and/or postponed from always to never. Then, according to the following considerations,

the tools used different metrics (Likert from 1 to 5 for MISSCARE Survey and UNCS, from 0 to 4 for BERNCA, from 0 to 3 for PIRNCA, from 1 to 4 for BERNCA-NH, from 1 to 4 for ICU-ONC) and differed in the direction of measures (e.g., from always missed to never missed, e.g., [ 43 ], or the opposite, e.g. [ 50 ]); and

UNC interventions reflect an order [ 58 , 59 ], such as first, second, and third, of interventions missed, expressing a prioritisation process (what should be actualised first and what can be delayed).

Data regarding the position (= order) of each nursing intervention according to the averages documented in the studies were extracted and then ranked according to the position: for example, the average of 3.23 with the MISSCARE Survey [ 35 ], indicating that this was the most unfinished activity, was ranked as first. Then, according to Blackman and colleagues [ 60 ], the first three interventions of high occurrence of being unfinished were identified; from the fourth to the sixth, those of intermediate occurrence; and from the seventh to ninth, those of a low occurrence of UNC.

The UNC reasons: Reasons were summarised based on the following considerations:

Studies using the MISSCARE Survey and the UNCS reported the reasons for UNC item by item, according to the structure of the tool;

Other studies documented the relationships (as correlations, associations) indicating a significant role of some factors in increasing/hindering UNC during the pandemic.

In the first case, the reasons were extracted and analysed in the same manner as UNC activities; in the second, studies (22 out of 25) documenting a statistically significant relationship of given factors with the UNC were extracted and categorised as organisational, work, or individual factors according to the literature in the field [ 29 ]. Of the remaining three studies, which were not focused on the reason for UNC, one was a methodological study that was focused on the psychometric assessment of the tool [ 56 ], one was a comparative study that was focused on the comparison between the data from a COVID-19 sample and a reference sample [ 54 ], and one was a study in which conditions were identified affected by the consequences of UNC [ 48 ].

UNC main consequences: if any, were described narratively.

All researchers were involved in the data analysis and synthesis process to ensure rigour in the process.

The results regarding the included studies are described below, including an exploration of their characteristics and quality and the occurrence of, reasons for, and consequences of UNC.

Search outcomes

In total, 1,389 articles were identified from the electronic databases. The search results were transferred to a reference manager (Mendeley) to organise the data extraction process. First, three steps were followed for the study selection: in the first stage, titles, in the second stage, abstracts, and in the third stage full text of the retrieved studies were screened for their eligibility by two reviewers (AB, SC), independently. In the case of any disagreement, the opinions of a third senior researcher (AP) were consulted during the entire process. Consensus between the researchers was essential for study inclusion.

In the first stage, 726 studies were excluded; from 1,389 studies, 663 articles were retained for abstract screening. Thus, in the second stage, 298 studies were excluded. At this stage, 365 studies met the criteria for next-step screening. Before the full-text screening, 219 duplicated studies were removed, and a visual inspection was conducted by two researchers (AB, SC) to check for duplicates. Then, 146 studies remained for full-text screening, and 122 of them were excluded for different reasons, as reported in Fig.  1 . The references of the excluded reviews were screened by two researchers (AB, SC) to check their eligibility in an independent fashion and then agreed upon. In total, 38 articles were checked, of which 33 were already included, three were not related to UNC, and one was a qualitative study design. At the end of the screening process, 25 studies were included (Fig.  1 ).

figure 1

PRISMA flow chart

Included studies and their quality

Out of the 25 studies included (Table  1 ), 20 used a descriptive cross-sectional design (e.g., [ 34 ]) and five a comparative cross-sectional design confronting the data (a) before and during the pandemic [ 35 ]; (b) or before the pandemic, and the second/third wave [ 38 ]; and (c) of the COVID-19 sample and the reference sample [ 37 , 48 , 54 ]. Most studies were conducted in Europe (= 12, e.g., [ 50 ]) and Asia (= 11, e.g., [ 45 ]). Of the remaining, one was carried out in Africa [ 47 ] and one in Canada [ 53 ]. Study locations ranged from a hospital (e.g., [ 35 ]) to specific hospital settings (tertiary [ 55 ], district [ 51 ], government [ 56 ], private [ 34 ], teaching [ 50 ]) in various types of units (e.g., medical/surgical [ 54 ], urology [ 43 ], cardiology [ 48 ]). In addition, COVID-19 units were included in two studies [ 22 , 37 , 41 ] and nursing homes in another two [ 22 , 40 ].

Studies were published between 2020 and 2023; however, nine of them completed the data collection in 2020 (e.g., [ 52 ]), 10 in 2021 (e.g., [ 47 ]), two between 2020 and 2021 [ 37 , 50 ], one in 2022 [ 55 ], two between 2019 and 2020 [ 35 , 54 ], and one between 2019 and 2021 [ 38 ]. Participants were mainly nurses, and their sample size ranged from 130 [ 42 ] to 672 [ 34 ] in 21 studies; in others, participants were generally identified as “care workers”, ranging from 374 [ 22 ] to 2,700 [ 40 ], while those including nursing assistants and registered nurses together ranged from 43 [ 48 ] to 287 [ 54 ] participants. The MISSCARE Survey tool was the most used (= 14, e.g., [ 44 ]), followed by BERNCA (= 1, [ 46 ]), Revised BERNCA (BERNCA-R) (= 2, [ 51 , 52 ]), BERNCA-NH (= 2, [ 22 , 40 ]), PIRNCA (= 4, e.g., [ 42 ]), ICU-ONC (= 1, [ 53 ]), and UNCS (= 1, [ 37 ]) (Table  1 ).

All studies reported a good methodological quality with minimal bias (Supplementary Table 2 ). Most were ranked positively for at least six (“yes” responses) out of eight questions (= 11; e.g., [ 39 ]), nine studies for at least seven questions (e.g., [ 44 ]), and five for at least five questions (e.g., [ 41 ]). Four studies failed to clarify the strategies to deal with confounding factors (e.g., [ 56 ]), while seven described these strategies unclearly (e.g., [ 51 ]). The settings and study subjects were stated as being unclear in eight studies (e.g., [ 52 ]). Additionally, in one study, the sample inclusion criteria were not detailed, while in another study, the confounding factors were not reported. The objective, standard criteria used to measure the condition were not assessable in any of the qualified studies, since the condition was considered the COVID-19 disease. At the overall level, all except six studies [ 25 , 34 , 42 , 43 , 46 , 55 ] documented the occurrence of and reasons for UNC activities.

The occurrence of UNC

In the 14 studies based on the MISSCARE survey, the most frequent UNC activities were “Ambulation 3 times per day or as ordered”, “Turning patient every two hours”, “Attending interdisciplinary care conferences whenever held”, “Providing mouth care”, and “Patient teaching about procedures, tests and other diagnostic studies”. In particular, “Ambulation 3 times per day or as ordered” was the activity most missed in three studies [ 35 , 38 , 39 ]; it was the second unfinished activity in the study by Al Muharraq et al. [ 36 ] and the third in another three studies ([ 48 ]; in both the COVID-19 sample and the reference sample of von Vogelsang et al. [ 54 ]) (Table  2 , Supplementary Table 3 ). “Turning patient every two hours” was the most frequent UNC activity in two studies (in the COVID-19 sample of Nymark et al. [ 48 ]; in the reference sample of von Vogelsang et al. [ 54 ]) and the second in another three ([ 35 ]; in the reference sample of Nymark et al. [ 48 ]; in the third wave sample of Falk et al. [ 38 ]). This activity was third in another four studies ([ 35 , 36 , 38 ]; second wave [ 47 ]) (Table  2 , Supplementary Table 3 ). However, the first unfinished activity in five studies was “Attending interdisciplinary care conferences whenever held” ([ 36 , 44 , 49 ]; in the reference sample of Nymark et al. [ 48 ]; in the COVID-19 sample of von Vogelsang et al. [ 54 ]) and “Monitoring patient” in one study [ 45 ] (Table  2 , Supplementary Table 3 ). Conversely, the least frequently unfinished activities were “Monitoring intake/output”, “Vital signs assessed as ordered”, “Bedside glucose monitoring”, and “Patient assessments every shift” (Table  2 , Supplementary Table 3 ).

Considering the studies using the PIRNCA tool, the most frequent unfinished interventions were the “Coordination of care and discharge planning” and the least common the “Implementation of prescribed treatment plan” in Schneider-Matyka et al. [ 50 ]. Contrarily, Yuwanto et al. [ 56 ] discovered that “Coordination of care and discharge planning” were the least frequently unfinished activities. The other most frequent UNC activities were listed in Schneider-Matyka et al. [ 50 ] and Yuwanto et al. [ 56 ], respectively, as (i) “Offer emotional or psychological support”, (ii) “Converse with team members”, (iii) “Converse with external agency”, and (i) “Routine skin care”, (ii) “Converse with external agency”, and (iii) “Assist with bowel and bladder elimination”, while the least unfinished were, respectively, (i) “Medication administration”, (ii) “Enteral and parenteral nutrition”, and (i) “Converse with patient regarding discharge”, (ii) “Infection control practices” (Table  3 , Supplementary Table 4 ).

In accordance with Tomaszewska et al. [ 51 ] and Uchmanowicz et al. [ 52 ], who used BERNCA-R, the most common first, second, and third UNC activities were “Education and training”, “Necessary disinfection measures”, and “Monitoring patients as the nurse felt necessary”. The studies identified “Change of the bed linen”, “Skin care”, and “Assist food intake” as the least frequent UNC activities [ 51 ] (Table  4 , Supplementary Table 5 ).

In two studies that used the BERNCA-NH tool, “Social care” and “Emotional support” reported the highest occurrences [ 22 , 40 ]. The most frequent UNC activities were listed in Hackman et al. [ 40 ] as (i) “Cultural activity for residents with contact outside of nursing home”, (ii) “Scheduled single activity with a resident”, and (i) “Scheduled group activity with several residents”; in contrast, the most frequent unfinished activities in Zhang et al. [ 22 ] were (i) “Activating or rehabilitating care”, (ii) “Emotional support”, and (iii) “Scheduled group activity with several residents”. On the other hand, “Assist dressing/undressing”, “Drinking”, “Food intake”, and “Sponge bath/partial sponge bath/skin care” were listed as the least frequent UNC activities [ 22 , 40 ] (Table  4 , Supplementary Table 5 ).

In the remaining two studies, recent tools were used. In the study conducted using the ICU-ONC tool, the most common unfinished activities were “Mobilization every two hours”, “Mouth care for intubated patients”, and “Document treatments and procedures”; those least frequent were “Cardiac monitoring surveillance”, “Flag the presence of signs or symptoms of infection”, and “Titrate intravenous perfusions for hemodynamic targets” [ 53 ] (Table  5 , Supplementary Table 6 ). In the study using the UNCS [ 37 ], the most frequent UNC for both the COVID-19 sample and the reference sample were “Performing bedside glucose monitoring as prescribed”, “Performing clinical handover to adequately inform the next shift nursing team about patients’ conditions”, and “Recording vital signs as planned”, while the least frequently unfinished activities were “Helping patient in need in ambulation”, “Providing passive mobilization/changing position in bedrest patient”, and “Providing mouth care to patients who need it” (Table  6 , Supplementary Table 7 ).

The reasons for UNC

Among the studies using the MISSCARE Survey, four [ 39 , 45 , 49 , 55 ] did not report the reasons item by item. In the remaining, “Inadequate number of staff” (e.g., in Wave 1 and Wave 2 sample of Falk et al. [ 38 ]; [ 25 ]) was reported as the most significant reason in six studies, “Unexpected raise in patient volume and/or acuity” as the first or second reason in four studies (e.g., [ 38 , 48 ]), and “Urgent patient situations” as the first, second, or third in six studies (e.g., [ 41 , 47 ]) (Table  2 , Supplementary Table 3 ). The reasons for UNC that were given least were “Other departments did not provide the needed care”, “Inadequate hand-off from previous shift or sending unit”, “Caregiver is off unit or unavailable”, and “Tension or communication breakdowns with the medical staff/other support departments” (Table  2 , Supplementary Table 3 ).

Regarding the findings from the UNCS [ 37 ], “Priority setting” and “Supervision of nursing aides” were reported as the most frequent factors causing UNC, followed by “Communication”. In particular, the most frequent reasons were “Inaccurate initial priority setting”, “Tension/conflicts within the nursing staff”, and “Inadequate nursing care model (e.g., functional task-oriented model of care)”. The reasons given least were the material and human resources as well as the unpredictability of the workflows (Table  6 , Supplementary File 7 ).

In 22 studies, UNC has been linked to other, additional factors. Among these, organisational factors, insufficient resources, and large hospital facilities were reported as increasing UNC [ 40 , 45 ]; other factors (e.g., adequate staff, the quality of care, the safety of the patients in the unit, a favourable nursing work environment, and the perceived accountability, organisational support, and leadership) hindered the occurrence of UNC (Table  7 ). Among the work-related factors, the type of shift work (afternoon shift [ 35 ]; 12-hour shift [ 41 ]; both day and night shift (not only night shift) [ 47 ]), overtime work, the type of unit, the workloads, and other factors increased the occurrence of UNC, whereas having a few patients to each nurse or COVID-19 patients, or better staffing levels, all decreased the occurrence of UNC (Table  7 ). Moreover, at the individual level, less than 10 years of experience and several other factors close to the nurses’ emotional state and well-being all decreased the occurrence of UNC (Table  7 ).

The Main consequences of UNC

No studies reported the consequences of UNC.

At the overall level, a total of 25 studies conducted mainly in European and Asiatic countries were produced during the pandemic, around 10 studies a year, continuing the tradition of this research field during difficult times for both nurses and healthcare settings. All tools available in the field were used, mostly the MISSCARE Survey, but also, on fewer occasions, BERNCA, also in its revised forms. As previously, mostly cross-sectional studies along with a few comparative studies were produced, suggesting the likelihood of a merely descriptive intent due to the challenging times. The order of UNC interventions that emerged across studies is substantially in line with pre-pandemic data, while some interesting variations emerged at the country and inter-country levels. Labour resources and reasons close to the emotional state and well-being of nurses were mentioned as most affecting UNC during the pandemic. However, none of the studies investigated the consequences of the phenomenon.

The discussion section follows the results structure and includes a reflection on the methodological quality of the studies and UNC occurrence, reasons, and consequences.

Included studies and their methodological quality

Studies released after the World Health Organisation declared the COVID-19 pandemic [ 30 ] as a period characterised by altered working conditions, workloads, and processes compared to those of the pre-pandemic era were included. No UNC differences between COVID-19 and non-COVID-19 patients emerged [ 63 , 64 ], suggesting that the pandemic affected the whole system. Moreover, given the substantial disruption of the routine care processes in the health systems, which may require time to recover, and with the likelihood of not reaching the same levels of the pre-pandemic era, a comprehensive review may contribute to providing a new reference point for future studies in the field of UNC.

Fewer than 10 studies a year were produced, in line with the pre-pandemic era [ 64 , 65 ]; moreover, data collection was performed mainly in 2020 and 2021, suggesting that available findings reflect the first phases of the pandemic. The leading continents in these studies were Europe and Asia, unlike in the past when the United States was the leading country, given that the missed care/left undone concepts were developed there [ 2 ]. Asian and European countries were those firstly and dramatically hit by the pandemic, thus triggering researchers to measure the UNC. However, the setting of the data collection has remained the hospital, as in the pre-pandemic era [ 66 ]: this finding is in line with the expanded capacity required in the hospitals and the recognition of their key role, especially in some waves, in facing the pandemic. Interestingly, several studies involved more units in very different institutions (e.g., [ 47 ]), which seems to suggest that this research line was scaled up during the pandemic from unit-based studies to large healthcare systems, thus embodying a reasonable health service research perspective because the whole system was changed to provide the care, and no one single part was left unaltered.

The study designs were cross-sectional with some comparative examples, as documented in the pre-pandemic era (e.g., [ 29 ]). The turbulent environments may have prevented longitudinal studies (e.g., to discover UNC outcomes). Forty-three [ 48 ] to 2,700 [ 40 ] nurses, nursing assistants, and care workers were involved, the sample sizes mirroring those of the pre-pandemic era [ 66 ]. However, no studies involved midwives, which suggests a lack of evidence in terms of what happened in maternal and paediatric departments.

Four different tools have been used to measure UNC, from those most validated across the world, namely the MISSCARE Survey [ 39 ] to more recent instruments, such as the ICU-ONC [ 53 ]. The different instruments used reflect the trends in this research field, characterised by a range of validated tools, thus preventing comparisons across studies. On the one hand, the utilisation of classic, well-validated tools may have provided accurate data and increased the comparison with pre- and intra-pandemic studies, whereas on the other hand, tools designed for a non-pandemic situation may have failed in their capacity to detect UNC in extraordinary conditions. Moreover, all tools collected UNC data as perceived by nurses, and their perceptions may have been influenced by the stress and the dramatic working conditions they were experiencing, as well as by the desire to do the best for the patients.

The overall quality of the studies was methodologically good: the extraordinary difficulties posed by the pandemic also required new strategies (e.g., to promote study participation among nurses, design protocols, and initiate studies while other priorities are perceived) in conducting research and seem to have been faced appropriately by researchers.

The different UNC activities, in their order, can be discussed around three main perspectives: (1) the instrument used; (2) the intercountry and intra-countries differences; and (3) the state of the evidence in the pre-pandemic era. The order of UNC interventions emerged across studies, for some countries are substantially in line with pre-pandemic data. The MISSCARE Survey studies highlighted that, during the pandemic, nurses firstly postponed or omitted interventions that call for proximity to the patient, such as oral care, or one-on-one interaction, such as ambulation. Studies using the ICU-ONC tool also showed the same trend, suggesting that these two tools can detect actions of care at the bedside. Nursing interventions related to organisation and communication were instead commonly unfinished in studies using the PIRNCA scale. Communication should also be seen as a fundamental care [ 67 , 68 , 69 ], as speaking and listening were most often seen as a nursing necessity during the pandemic. Differently, education, disinfection measures, and monitoring were the most frequent UNC activities in studies employing the BERNCA scale. Likewise, nursing interventions for patient follow-up were frequently unfinished in a study using the UNCS [ 37 ].

The most significant nursing interventions identified during the pandemic were monitoring, educating the patient, and implementing preventive measures against infections. Nurses may have felt that their usual applications were inadequate or incomplete given the growing demand for these interventions, or they may have believed that they would be unable to complete these applications out of fear of failing. Finally, social and rehabilitative nursing interventions were ranked first as unfinished activities in studies using the BERNCA-NH instrument. This reflects the contingencies of the COVID-19 pandemic, which forced residents of nursing homes to remain in their own rooms [ 70 ]. Therefore, at the overall level, it seems that nurses adopted the pre-pandemic patterns of prioritisation (e.g., failing in ensuring fundamental care) with the intent of reducing exposure in patients’ rooms for an extended period and to avoid the source of contagion [ 71 ], and/or due to the fatigue caused by the personal protective equipment worn (e.g., [ 72 ]). The rationed nursing activities did not turn out to be very different from those of the pre-pandemic period (e.g., [ 2 , 73 ]), as also emerged in those studies that included comparative studies [ 35 , 38 ].

However, interesting intra- and inter-country differences have emerged: at the intra-country level, two main patterns are evident. In Sweden, for example, Falk et al. [ 38 ] and von Vogelsan et al. [ 54 ] found that the three most unfinished activities are substantially the same, whereas in Jordan [ 35 , 44 ] and Iran [ 41 , 49 ], the first three unfinished activities differ (Table  2 , Supplementary Table 3 ). Similarly, at the inter-country level, in those studies using the MISSCARE Survey performed across Europe, the unfinished activities seem to have similar trends in the order pattern. Comparing these countries with those where UNC has started to be measured (e.g., Iran, Jordan, Saudia Arabia, Indonesia, Sultanate of Oman), feeding the patient and offering emotional support were not missed immediately, while attending interdisciplinary meetings was unfinished at first. In the two studies using the BERNCA-NH tool, a similar divergence appeared: in the study by Zhang et al. [ 22 ] performed in China, some activities (i.e., providing emotional support and rehabilitation care) were the first to be unfinished, while in Hackman et al. [ 40 ] these were ranked as being missed less often. Examples can also be found in studies using the PIRNCA and performed in Poland [ 50 ] and Indonesia [ 56 ]. On the one hand, this seems to suggest that when the healthcare system is under tremendous pressure, as during the pandemic, the process of prioritisation is based on pre-established patterns (e.g., across Europe; [ 74 ]); on the other hand, different patterns seem to be enacted outside of Europe, mainly in Asiatic countries. Given that these countries are substantially new to measuring UNC, replicating studies to establish whether the emerged patterns are the same as those used in normal conditions is strongly recommended.

Above all, studies produced during the pandemic period report unfinished activities according to the tool used. For example, the MISSCARE Survey was developed in the early 2000s [ 59 ] and is able to measure “basic” nursing activities; therefore, its capacity to detect exactly what happened in the nursing processes during the pandemic should be debated.

First, issues regarding human resources and the increased needs of patients were the most cited reasons in those studies using the MISSCARE tool, while issues among the staff or across departments impacted only a little. This is likely derived from the expanded capacity of the health systems under urgent circumstances [ 75 ] that increased the well-known shortages in resources, whereas facing the pandemic reduced tensions within the staff and across units, promoting a sense of collaboration [ 76 , 77 ]. Moreover, nurses became infected and were not available when quarantined: all these situations seriously disrupted the capacity of nursing care [ 21 , 22 ], threatening the patients’ needs [ 16 , 17 , 78 ]. Conversely, for Cengia et al. [ 37 ], human resources were not an issue in triggering UNC occurrence; however, this is a single study with the UNC survey tool, and although performed in several facilities, its findings may be interpreted from different perspectives: the units involved in the study may have been better equipped during the pandemic to deal with the situation, or nurses may have learnt for several years how to work under pressure, with limited resources, in a sort of “normalised” condition, where working under such conditions was not an issue [ 63 ].

Other potential reasons documented among studies are in line with those documented by Chiappinotto et al. [ 29 ]. However, two new elements emerged at the overall level among studies performed during the pandemic. Firstly, in those cases where the same reason has been documented (e.g., the role of working overtime [ 25 , 39 , 47 ]), no conflicting findings have been reported across studies, suggesting an evident accumulation of knowledge in the same direction. Previously, conflicting findings emerged for the same reasons across studies, in some increasing and in others hindering the occurrence of UNC (e.g., working overtime [ 29 ]). The increased homogeneity of the findings that emerged in the pandemic studies may depend on the same circumstances experienced in all healthcare services across the world. Secondly, several emotional factors at the nurses’ level (e.g., satisfaction, burnout, satisfaction with economic situation, stress) have been investigated and associated with UNC. The focus seems to be the professional and personal well-being of the nurses, reasons that may have a role as antecedents of UNC but that also express the consequences of the unfinished care phenomenon itself as well as the consequences of the exacerbated working conditions during the pandemic.

No UNC consequences have been documented to date confirming the tradition of this research field in which outcomes are under-reported [ 79 ]. In difficult times with turbulent environments, unstable staff, and disconnections between healthcare settings (e.g., hospital and community settings), it would be difficult to link the occurrence of UNC to the different potential outcomes at the patient, nurse, and organisational levels [ 5 , 12 , 13 , 14 ]. However, the occurrence of UNC may have bolstered the negative effects of other widely observed phenomena, such as the decreased accessibility and continuity of care observed during the pandemic, thus indirectly affecting the health outcomes at both the individual and collective levels (e.g., reduced screening, reduced care for cancer patients) [ 80 , 81 ].

Limitations

This review has several limitations. First, databases were searched using well-known established keywords in the field, strictly connected with the conceptual definitions in the field and with the tools measuring the phenomenon. Moreover, given that no MeSH terms have been established in the field, researchers used keywords. Consequently, some studies may have been missed. Second, studies whose data collection period was uncertain or ambiguous (e.g., started before or during the pandemic) were excluded. Moreover, studies not using validated instruments with available reliability and validity data were also excluded, and these decisions may have introduced a selection bias. Furthermore, grey literature was not assessed, introducing additional selection bias. Third, we included only articles written in English, Turkish, or Italian, so the comprehensiveness of this review could have been threatened by the exclusion of other languages. Fourth, in the data analysis and synthesis process, an approach was adopted aiming at ensuring accuracy given the different measurement tools used in the field. Moreover, the data analysis process was conducted in an innovative manner by considering each intervention or reason at the granular level (the order, according to the statistical values) instead of the global level (global scores). This may have provided clarity, but it may have compromised the depiction of a global picture of the phenomenon. No previous similar approaches have been used in this field. Accumulating evidence with additional studies, such as summarising findings in the post-pandemic era, may corroborate the analytical strategy used.

UNC studies produced during the pandemic documented the occurrence of the phenomenon and its reasons mainly in the first and second waves of the COVID-19 pandemic. These studies were conducted mainly in Europe and Asia, which were the first to be dramatically affected by the pandemic. The studies involved multicentre units in the attempt to measure the whole response of the healthcare settings, mainly using the MISSCARE Survey with descriptive intents and using quality, sound research methodologies.

At the overall level, those nursing care activities that were mostly unfinished during the pandemic are substantially the same as those reported in the pre-pandemic era, suggesting that nurses applied the same prioritisation responses in difficult times. However, interesting intra- and inter-country differences emerged: those countries new to measuring unfinished care reported different patterns compared to those seen in Europe and the US, where this research is well established; they also reported intra-country variations, suggesting an interesting new course of research in the field. The new patterns that emerged should be better investigated through post-pandemic studies to discover whether they reflected the decision-making process during difficult conditions or a different prioritisation process.

Across studies, the primary reasons for UNC were listed as labour resources, followed by other specific reasons related to organisational, work, and individual variables. Substantially, the evidence is in line with that previously documented. However, findings are consistent across studies, suggesting that health services experienced similar pressure worldwide. Moreover, several emotional factors have been investigated among nurses, revealing their important role in triggering UNC. This level should be investigated further, considering the long-term consequences of the pandemic on the well-being of the workforce. Given that no studies have attempted to measure the UNC consequences, more efforts are also required in this direction.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

Basel Extend of Rationing of Nursing Care

Basel Extent of Rationing of Nursing Care for Nursing Homes

Basel Extent of Rationing of Nursing Care Revised

Cumulative Index to Nursing and Allied Health Literature

Coronavirus-19

Intensive Care Unit Omitted Nursing Care instrument

Perceived Implicit Rationing of Nursing Care

Preferred Reporting Items for Systematic Reviews and Meta Analysis

Unfinished Nursing Care

Unfinished Nursing Care Survey

World Health Organization

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Bayram, A., Chiappinotto, S. & Palese, A. Unfinished nursing care in healthcare settings during the COVID-19 pandemic: a systematic review. BMC Health Serv Res 24 , 352 (2024). https://doi.org/10.1186/s12913-024-10708-7

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  • The role of COVID-19 vaccines in preventing post-COVID-19 thromboembolic and cardiovascular complications
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  • Núria Mercadé-Besora 1 , 2 , 3 ,
  • Xintong Li 1 ,
  • Raivo Kolde 4 ,
  • Nhung TH Trinh 5 ,
  • Maria T Sanchez-Santos 1 ,
  • Wai Yi Man 1 ,
  • Elena Roel 3 ,
  • Carlen Reyes 3 ,
  • http://orcid.org/0000-0003-0388-3403 Antonella Delmestri 1 ,
  • Hedvig M E Nordeng 6 , 7 ,
  • http://orcid.org/0000-0002-4036-3856 Anneli Uusküla 8 ,
  • http://orcid.org/0000-0002-8274-0357 Talita Duarte-Salles 3 , 9 ,
  • Clara Prats 2 ,
  • http://orcid.org/0000-0002-3950-6346 Daniel Prieto-Alhambra 1 , 9 ,
  • http://orcid.org/0000-0002-0000-0110 Annika M Jödicke 1 ,
  • Martí Català 1
  • 1 Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS , University of Oxford , Oxford , UK
  • 2 Department of Physics , Universitat Politècnica de Catalunya , Barcelona , Spain
  • 3 Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) , IDIAP Jordi Gol , Barcelona , Catalunya , Spain
  • 4 Institute of Computer Science , University of Tartu , Tartu , Estonia
  • 5 Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences , University of Oslo , Oslo , Norway
  • 6 School of Pharmacy , University of Oslo , Oslo , Norway
  • 7 Division of Mental Health , Norwegian Institute of Public Health , Oslo , Norway
  • 8 Department of Family Medicine and Public Health , University of Tartu , Tartu , Estonia
  • 9 Department of Medical Informatics, Erasmus University Medical Center , Erasmus University Rotterdam , Rotterdam , Zuid-Holland , Netherlands
  • Correspondence to Prof Daniel Prieto-Alhambra, Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; daniel.prietoalhambra{at}ndorms.ox.ac.uk

Objective To study the association between COVID-19 vaccination and the risk of post-COVID-19 cardiac and thromboembolic complications.

Methods We conducted a staggered cohort study based on national vaccination campaigns using electronic health records from the UK, Spain and Estonia. Vaccine rollout was grouped into four stages with predefined enrolment periods. Each stage included all individuals eligible for vaccination, with no previous SARS-CoV-2 infection or COVID-19 vaccine at the start date. Vaccination status was used as a time-varying exposure. Outcomes included heart failure (HF), venous thromboembolism (VTE) and arterial thrombosis/thromboembolism (ATE) recorded in four time windows after SARS-CoV-2 infection: 0–30, 31–90, 91–180 and 181–365 days. Propensity score overlap weighting and empirical calibration were used to minimise observed and unobserved confounding, respectively.

Fine-Gray models estimated subdistribution hazard ratios (sHR). Random effect meta-analyses were conducted across staggered cohorts and databases.

Results The study included 10.17 million vaccinated and 10.39 million unvaccinated people. Vaccination was associated with reduced risks of acute (30-day) and post-acute COVID-19 VTE, ATE and HF: for example, meta-analytic sHR of 0.22 (95% CI 0.17 to 0.29), 0.53 (0.44 to 0.63) and 0.45 (0.38 to 0.53), respectively, for 0–30 days after SARS-CoV-2 infection, while in the 91–180 days sHR were 0.53 (0.40 to 0.70), 0.72 (0.58 to 0.88) and 0.61 (0.51 to 0.73), respectively.

Conclusions COVID-19 vaccination reduced the risk of post-COVID-19 cardiac and thromboembolic outcomes. These effects were more pronounced for acute COVID-19 outcomes, consistent with known reductions in disease severity following breakthrough versus unvaccinated SARS-CoV-2 infection.

  • Epidemiology
  • PUBLIC HEALTH
  • Electronic Health Records

Data availability statement

Data may be obtained from a third party and are not publicly available. CPRD: CPRD data were obtained under the CPRD multi-study license held by the University of Oxford after Research Data Governance (RDG) approval. Direct data sharing is not allowed. SIDIAP: In accordance with current European and national law, the data used in this study is only available for the researchers participating in this study. Thus, we are not allowed to distribute or make publicly available the data to other parties. However, researchers from public institutions can request data from SIDIAP if they comply with certain requirements. Further information is available online ( https://www.sidiap.org/index.php/menu-solicitudesen/application-proccedure ) or by contacting SIDIAP ([email protected]). CORIVA: CORIVA data were obtained under the approval of Research Ethics Committee of the University of Tartu and the patient level data sharing is not allowed. All analyses in this study were conducted in a federated manner, where analytical code and aggregated (anonymised) results were shared, but no patient-level data was transferred across the collaborating institutions.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

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WHAT IS ALREADY KNOWN ON THIS TOPIC

COVID-19 vaccines proved to be highly effective in reducing the severity of acute SARS-CoV-2 infection.

While COVID-19 vaccines were associated with increased risk for cardiac and thromboembolic events, such as myocarditis and thrombosis, the risk of complications was substantially higher due to SARS-CoV-2 infection.

WHAT THIS STUDY ADDS

COVID-19 vaccination reduced the risk of heart failure, venous thromboembolism and arterial thrombosis/thromboembolism in the acute (30 days) and post-acute (31 to 365 days) phase following SARS-CoV-2 infection. This effect was stronger in the acute phase.

The overall additive effect of vaccination on the risk of post-vaccine and/or post-COVID thromboembolic and cardiac events needs further research.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

COVID-19 vaccines proved to be highly effective in reducing the risk of post-COVID cardiovascular and thromboembolic complications.

Introduction

COVID-19 vaccines were approved under emergency authorisation in December 2020 and showed high effectiveness against SARS-CoV-2 infection, COVID-19-related hospitalisation and death. 1 2 However, concerns were raised after spontaneous reports of unusual thromboembolic events following adenovirus-based COVID-19 vaccines, an association that was further assessed in observational studies. 3 4 More recently, mRNA-based vaccines were found to be associated with a risk of rare myocarditis events. 5 6

On the other hand, SARS-CoV-2 infection can trigger cardiac and thromboembolic complications. 7 8 Previous studies showed that, while slowly decreasing over time, the risk for serious complications remain high for up to a year after infection. 9 10 Although acute and post-acute cardiac and thromboembolic complications following COVID-19 are rare, they present a substantial burden to the affected patients, and the absolute number of cases globally could become substantial.

Recent studies suggest that COVID-19 vaccination could protect against cardiac and thromboembolic complications attributable to COVID-19. 11 12 However, most studies did not include long-term complications and were conducted among specific populations.

Evidence is still scarce as to whether the combined effects of COVID-19 vaccines protecting against SARS-CoV-2 infection and reducing post-COVID-19 cardiac and thromboembolic outcomes, outweigh any risks of these complications potentially associated with vaccination.

We therefore used large, representative data sources from three European countries to assess the overall effect of COVID-19 vaccines on the risk of acute and post-acute COVID-19 complications including venous thromboembolism (VTE), arterial thrombosis/thromboembolism (ATE) and other cardiac events. Additionally, we studied the comparative effects of ChAdOx1 versus BNT162b2 on the risk of these same outcomes.

Data sources

We used four routinely collected population-based healthcare datasets from three European countries: the UK, Spain and Estonia.

For the UK, we used data from two primary care databases—namely, Clinical Practice Research Datalink, CPRD Aurum 13 and CPRD Gold. 14 CPRD Aurum currently covers 13 million people from predominantly English practices, while CPRD Gold comprises 3.1 million active participants mostly from GP practices in Wales and Scotland. Spanish data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), 15 which encompasses primary care records from 6 million active patients (around 75% of the population in the region of Catalonia) linked to hospital admissions data (Conjunt Mínim Bàsic de Dades d’Alta Hospitalària). Finally, the CORIVA dataset based on national health claims data from Estonia was used. It contains all COVID-19 cases from the first year of the pandemic and ~440 000 randomly selected controls. CORIVA was linked to the death registry and all COVID-19 testing from the national health information system.

Databases included sociodemographic information, diagnoses, measurements, prescriptions and secondary care referrals and were linked to vaccine registries, including records of all administered vaccines from all healthcare settings. Data availability for CPRD Gold ended in December 2021, CPRD Aurum in January 2022, SIDIAP in June 2022 and CORIVA in December 2022.

All databases were mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) 16 to facilitate federated analytics.

Multinational network staggered cohort study: study design and participants

The study design has been published in detail elsewhere. 17 Briefly, we used a staggered cohort design considering vaccination as a time-varying exposure. Four staggered cohorts were designed with each cohort representing a country-specific vaccination rollout phase (eg, dates when people became eligible for vaccination, and eligibility criteria).

The source population comprised all adults registered in the respective database for at least 180 days at the start of the study (4 January 2021 for CPRD Gold and Aurum, 20 February 2021 for SIDIAP and 28 January 2021 for CORIVA). Subsequently, each staggered cohort corresponded to an enrolment period: all people eligible for vaccination during this time were included in the cohort and people with a history of SARS-CoV-2 infection or COVID-19 vaccination before the start of the enrolment period were excluded. Across countries, cohort 1 comprised older age groups, whereas cohort 2 comprised individuals at risk for severe COVID-19. Cohort 3 included people aged ≥40 and cohort 4 enrolled people aged ≥18.

In each cohort, people receiving a first vaccine dose during the enrolment period were allocated to the vaccinated group, with their index date being the date of vaccination. Individuals who did not receive a vaccine dose comprised the unvaccinated group and their index date was assigned within the enrolment period, based on the distribution of index dates in the vaccinated group. People with COVID-19 before the index date were excluded.

Follow-up started from the index date until the earliest of end of available data, death, change in exposure status (first vaccine dose for those unvaccinated) or outcome of interest.

COVID-19 vaccination

All vaccines approved within the study period from January 2021 to July 2021—namely, ChAdOx1 (Oxford/AstraZeneca), BNT162b2 (BioNTech/Pfizer]) Ad26.COV2.S (Janssen) and mRNA-1273 (Moderna), were included for this study.

Post-COVID-19 outcomes of interest

Outcomes of interest were defined as SARS-CoV-2 infection followed by a predefined thromboembolic or cardiac event of interest within a year after infection, and with no record of the same clinical event in the 6 months before COVID-19. Outcome date was set as the corresponding SARS-CoV-2 infection date.

COVID-19 was identified from either a positive SARS-CoV-2 test (polymerase chain reaction (PCR) or antigen), or a clinical COVID-19 diagnosis, with no record of COVID-19 in the previous 6 weeks. This wash-out period was imposed to exclude re-recordings of the same COVID-19 episode.

Post-COVID-19 outcome events were selected based on previous studies. 11–13 Events comprised ischaemic stroke (IS), haemorrhagic stroke (HS), transient ischaemic attack (TIA), ventricular arrhythmia/cardiac arrest (VACA), myocarditis/pericarditis (MP), myocardial infarction (MI), heart failure (HF), pulmonary embolism (PE) and deep vein thrombosis (DVT). We used two composite outcomes: (1) VTE, as an aggregate of PE and DVT and (2) ATE, as a composite of IS, TIA and MI. To avoid re-recording of the same complication we imposed a wash-out period of 90 days between records. Phenotypes for these complications were based on previously published studies. 3 4 8 18

All outcomes were ascertained in four different time periods following SARS-CoV-2 infection: the first period described the acute infection phase—that is, 0–30 days after COVID-19, whereas the later periods - which are 31–90 days, 91–180 days and 181–365 days, illustrate the post-acute phase ( figure 1 ).

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Study outcome design. Study outcomes of interest are defined as a COVID-19 infection followed by one of the complications in the figure, within a year after infection. Outcomes were ascertained in four different time windows after SARS-CoV-2 infection: 0–30 days (namely the acute phase), 31–90 days, 91–180 days and 181–365 days (these last three comprise the post-acute phase).

Negative control outcomes

Negative control outcomes (NCOs) were used to detect residual confounding. NCOs are outcomes which are not believed to be causally associated with the exposure, but share the same bias structure with the exposure and outcome of interest. Therefore, no significant association between exposure and NCO is to be expected. Our study used 43 different NCOs from previous work assessing vaccine effectiveness. 19

Statistical analysis

Federated network analyses.

A template for an analytical script was developed and subsequently tailored to include the country-specific aspects (eg, dates, priority groups) for the vaccination rollout. Analyses were conducted locally for each database. Only aggregated data were shared and person counts <5 were clouded.

Propensity score weighting

Large-scale propensity scores (PS) were calculated to estimate the likelihood of a person receiving the vaccine based on their demographic and health-related characteristics (eg, conditions, medications) prior to the index date. PS were then used to minimise observed confounding by creating a weighted population (overlap weighting 20 ), in which individuals contributed with a different weight based on their PS and vaccination status.

Prespecified key variables included in the PS comprised age, sex, location, index date, prior observation time in the database, number of previous outpatient visits and previous SARS-CoV-2 PCR/antigen tests. Regional vaccination, testing and COVID-19 incidence rates were also forced into the PS equation for the UK databases 21 and SIDIAP. 22 In addition, least absolute shrinkage and selection operator (LASSO) regression, a technique for variable selection, was used to identify additional variables from all recorded conditions and prescriptions within 0–30 days, 31–180 days and 181-any time (conditions only) before the index date that had a prevalence of >0.5% in the study population.

PS were then separately estimated for each staggered cohort and analysis. We considered covariate balance to be achieved if absolute standardised mean differences (ASMDs) were ≤0.1 after weighting. Baseline characteristics such as demographics and comorbidities were reported.

Effect estimation

To account for the competing risk of death associated with COVID-19, Fine-and-Grey models 23 were used to calculate subdistribution hazard ratios (sHRs). Subsequently, sHRs and confidence intervals were empirically calibrated from NCO estimates 24 to account for unmeasured confounding. To calibrate the estimates, the empirical null distribution was derived from NCO estimates and was used to compute calibrated confidence intervals. For each outcome, sHRs from the four staggered cohorts were pooled using random-effect meta-analysis, both separately for each database and across all four databases.

Sensitivity analysis

Sensitivity analyses comprised 1) censoring follow-up for vaccinated people at the time when they received their second vaccine dose and 2) considering only the first post-COVID-19 outcome within the year after infection ( online supplemental figure S1 ). In addition, comparative effectiveness analyses were conducted for BNT162b2 versus ChAdOx1.

Supplemental material

Data and code availability.

All analytic code for the study is available in GitHub ( https://github.com/oxford-pharmacoepi/vaccineEffectOnPostCovidCardiacThromboembolicEvents ), including code lists for vaccines, COVID-19 tests and diagnoses, cardiac and thromboembolic events, NCO and health conditions to prioritise patients for vaccination in each country. We used R version 4.2.3 and statistical packages survival (3.5–3), Empirical Calibration (3.1.1), glmnet (4.1-7), and Hmisc (5.0–1).

Patient and public involvement

Owing to the nature of the study and the limitations regarding data privacy, the study design, analysis, interpretation of data and revision of the manuscript did not involve any patients or members of the public.

All aggregated results are available in a web application ( https://dpa-pde-oxford.shinyapps.io/PostCovidComplications/ ).

We included over 10.17 million vaccinated individuals (1 618 395 from CPRD Gold; 5 729 800 from CPRD Aurum; 2 744 821 from SIDIAP and 77 603 from CORIVA) and 10.39 million unvaccinated individuals (1 640 371; 5 860 564; 2 588 518 and 302 267, respectively). Online supplemental figures S2-5 illustrate study inclusion for each database.

Adequate covariate balance was achieved after PS weighting in most studies: CORIVA (all cohorts) and SIDIAP (cohorts 1 and 4) did not contribute to ChAdOx1 subanalyses owing to sample size and covariate imbalance. ASMD results are accessible in the web application.

NCO analyses suggested residual bias after PS weighting, with a majority of NCOs associated positively with vaccination. Therefore, calibrated estimates are reported in this manuscript. Uncalibrated effect estimates and NCO analyses are available in the web interface.

Population characteristics

Table 1 presents baseline characteristics for the weighted populations in CPRD Aurum, for illustrative purposes. Online supplemental tables S1-25 summarise baseline characteristics for weighted and unweighted populations for each database and comparison. Across databases and cohorts, populations followed similar patterns: cohort 1 represented an older subpopulation (around 80 years old) with a high proportion of women (57%). Median age was lowest in cohort 4 ranging between 30 and 40 years.

  • View inline

Characteristics of weighted populations in CPRD Aurum database, stratified by staggered cohort and exposure status. Exposure is any COVID-19 vaccine

COVID-19 vaccination and post-COVID-19 complications

Table 2 shows the incidence of post-COVID-19 VTE, ATE and HF, the three most common post-COVID-19 conditions among the studied outcomes. Outcome counts are presented separately for 0–30, 31–90, 91–180 and 181–365 days after SARS-CoV-2 infection. Online supplemental tables S26-36 include all studied complications, also for the sensitivity and subanalyses. Similar pattern for incidences were observed across all databases: higher outcome rates in the older populations (cohort 1) and decreasing frequency with increasing time after infection in all cohorts.

Number of records (and risk per 10 000 individuals) for acute and post-acute COVID-19 cardiac and thromboembolic complications, across cohorts and databases for any COVID-19 vaccination

Forest plots for the effect of COVID-19 vaccines on post-COVID-19 cardiac and thromboembolic complications; meta-analysis across cohorts and databases. Dashed line represents a level of heterogeneity I 2 >0.4. ATE, arterial thrombosis/thromboembolism; CD+HS, cardiac diseases and haemorrhagic stroke; VTE, venous thromboembolism.

Results from calibrated estimates pooled in meta-analysis across cohorts and databases are shown in figure 2 .

Reduced risk associated with vaccination is observed for acute and post-acute VTE, DVT, and PE: acute meta-analytic sHR are 0.22 (95% CI, 0.17–0.29); 0.36 (0.28–0.45); and 0.19 (0.15–0.25), respectively. For VTE in the post-acute phase, sHR estimates are 0.43 (0.34–0.53), 0.53 (0.40–0.70) and 0.50 (0.36–0.70) for 31–90, 91–180, and 181–365 days post COVID-19, respectively. Reduced risk of VTE outcomes was observed in vaccinated across databases and cohorts, see online supplemental figures S14–22 .

Similarly, the risk of ATE, IS and MI in the acute phase after infection was reduced for the vaccinated group, sHR of 0.53 (0.44–0.63), 0.55 (0.43–0.70) and 0.49 (0.38–0.62), respectively. Reduced risk associated with vaccination persisted for post-acute ATE, with sHR of 0.74 (0.60–0.92), 0.72 (0.58–0.88) and 0.62 (0.48–0.80) for 31–90, 91–180 and 181–365 days post-COVID-19, respectively. Risk of post-acute MI remained lower for vaccinated in the 31–90 and 91–180 days after COVID-19, with sHR of 0.64 (0.46–0.87) and 0.64 (0.45–0.90), respectively. Vaccination effect on post-COVID-19 TIA was seen only in the 181–365 days, with sHR of 0.51 (0.31–0.82). Online supplemental figures S23-31 show database-specific and cohort-specific estimates for ATE-related complications.

Risk of post-COVID-19 cardiac complications was reduced in vaccinated individuals. Meta-analytic estimates in the acute phase showed sHR of 0.45 (0.38–0.53) for HF, 0.41 (0.26–0.66) for MP and 0.41 (0.27–0.63) for VACA. Reduced risk persisted for post-acute COVID-19 HF: sHR 0.61 (0.51–0.73) for 31–90 days, 0.61 (0.51–0.73) for 91–180 days and 0.52 (0.43–0.63) for 181–365 days. For post-acute MP, risk was only lowered in the first post-acute window (31–90 days), with sHR of 0.43 (0.21–0.85). Vaccination showed no association with post-COVID-19 HS. Database-specific and cohort-specific results for these cardiac diseases are shown in online supplemental figures S32-40 .

Stratified analyses by vaccine showed similar associations, except for ChAdOx1 which was not associated with reduced VTE and ATE risk in the last post-acute window. Sensitivity analyses were consistent with main results ( online supplemental figures S6-13 ).

Figure 3 shows the results of comparative effects of BNT162b2 versus ChAdOx1, based on UK data. Meta-analytic estimates favoured BNT162b2 (sHR of 0.66 (0.46–0.93)) for VTE in the 0–30 days after infection, but no differences were seen for post-acute VTE or for any of the other outcomes. Results from sensitivity analyses, database-specific and cohort-specific estimates were in line with the main findings ( online supplemental figures S41-51 ).

Forest plots for comparative vaccine effect (BNT162b2 vs ChAdOx1); meta-analysis across cohorts and databases. ATE, arterial thrombosis/thromboembolism; CD+HS, cardiac diseases and haemorrhagic stroke; VTE, venous thromboembolism.

Key findings

Our analyses showed a substantial reduction of risk (45–81%) for thromboembolic and cardiac events in the acute phase of COVID-19 associated with vaccination. This finding was consistent across four databases and three different European countries. Risks for post-acute COVID-19 VTE, ATE and HF were reduced to a lesser extent (24–58%), whereas a reduced risk for post-COVID-19 MP and VACA in vaccinated people was seen only in the acute phase.

Results in context

The relationship between SARS-CoV-2 infection, COVID-19 vaccines and thromboembolic and/or cardiac complications is tangled. Some large studies report an increased risk of VTE and ATE following both ChAdOx1 and BNT162b2 vaccination, 7 whereas other studies have not identified such a risk. 25 Elevated risk of VTE has also been reported among patients with COVID-19 and its occurrence can lead to poor prognosis and mortality. 26 27 Similarly, several observational studies have found an association between COVID-19 mRNA vaccination and a short-term increased risk of myocarditis, particularly among younger male individuals. 5 6 For instance, a self-controlled case series study conducted in England revealed about 30% increased risk of hospital admission due to myocarditis within 28 days following both ChAdOx1 and BNT162b2 vaccines. However, this same study also found a ninefold higher risk for myocarditis following a positive SARS-CoV-2 test, clearly offsetting the observed post-vaccine risk.

COVID-19 vaccines have demonstrated high efficacy and effectiveness in preventing infection and reducing the severity of acute-phase infection. However, with the emergence of newer variants of the virus, such as omicron, and the waning protective effect of the vaccine over time, there is a growing interest in understanding whether the vaccine can also reduce the risk of complications after breakthrough infections. Recent studies suggested that COVID-19 vaccination could potentially protect against acute post-COVID-19 cardiac and thromboembolic events. 11 12 A large prospective cohort study 11 reports risk of VTE after SARS-CoV-2 infection to be substantially reduced in fully vaccinated ambulatory patients. Likewise, Al-Aly et al 12 suggest a reduced risk for post-acute COVID-19 conditions in breakthrough infection versus SARS-CoV-2 infection without prior vaccination. However, the populations were limited to SARS-CoV-2 infected individuals and estimates did not include the effect of the vaccine to prevent COVID-19 in the first place. Other studies on post-acute COVID-19 conditions and symptoms have been conducted, 28 29 but there has been limited reporting on the condition-specific risks associated with COVID-19, even though the prognosis for different complications can vary significantly.

In line with previous studies, our findings suggest a potential benefit of vaccination in reducing the risk of post-COVID-19 thromboembolic and cardiac complications. We included broader populations, estimated the risk in both acute and post-acute infection phases and replicated these using four large independent observational databases. By pooling results across different settings, we provided the most up-to-date and robust evidence on this topic.

Strengths and limitations

The study has several strengths. Our multinational study covering different healthcare systems and settings showed consistent results across all databases, which highlights the robustness and replicability of our findings. All databases had complete recordings of vaccination status (date and vaccine) and are representative of the respective general population. Algorithms to identify study outcomes were used in previous published network studies, including regulatory-funded research. 3 4 8 18 Other strengths are the staggered cohort design which minimises confounding by indication and immortal time bias. PS overlap weighting and NCO empirical calibration have been shown to adequately minimise bias in vaccine effectiveness studies. 19 Furthermore, our estimates include the vaccine effectiveness against COVID-19, which is crucial in the pathway to experience post-COVID-19 complications.

Our study has some limitations. The use of real-world data comes with inherent limitations including data quality concerns and risk of confounding. To deal with these limitations, we employed state-of-the-art methods, including large-scale propensity score weighting and calibration of effect estimates using NCO. 19 24 A recent study 30 has demonstrated that methodologically sound observational studies based on routinely collected data can produce results similar to those of clinical trials. We acknowledge that results from NCO were positively associated with vaccination, and estimates might still be influenced by residual bias despite using calibration. Another limitation is potential under-reporting of post-COVID-19 complications: some asymptomatic and mild COVID-19 infections might have not been recorded. Additionally, post-COVID-19 outcomes of interest might be under-recorded in primary care databases (CPRD Aurum and Gold) without hospital linkage, which represent a large proportion of the data in the study. However, results in SIDIAP and CORIVA, which include secondary care data, were similar. Also, our study included a small number of young men and male teenagers, who were the main population concerned with increased risks of myocarditis/pericarditis following vaccination.

Conclusions

Vaccination against SARS-CoV-2 substantially reduced the risk of acute post-COVID-19 thromboembolic and cardiac complications, probably through a reduction in the risk of SARS-CoV-2 infection and the severity of COVID-19 disease due to vaccine-induced immunity. Reduced risk in vaccinated people lasted for up to 1 year for post-COVID-19 VTE, ATE and HF, but not clearly for other complications. Findings from this study highlight yet another benefit of COVID-19 vaccination. However, further research is needed on the possible waning of the risk reduction over time and on the impact of booster vaccination.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

The study was approved by the CPRD’s Research Data Governance Process, Protocol No 21_000557 and the Clinical Research Ethics committee of Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) (approval number 4R22/133) and the Research Ethics Committee of the University of Tartu (approval No. 330/T-10).

Acknowledgments

This study is based in part on data from the Clinical Practice Research Datalink (CPRD) obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. We thank the patients who provided these data, and the NHS who collected the data as part of their care and support. All interpretations, conclusions and views expressed in this publication are those of the authors alone and not necessarily those of CPRD. We would also like to thank the healthcare professionals in the Catalan healthcare system involved in the management of COVID-19 during these challenging times, from primary care to intensive care units; the Institut de Català de la Salut and the Program d’Analítica de Dades per a la Recerca i la Innovació en Salut for providing access to the different data sources accessible through The System for the Development of Research in Primary Care (SIDIAP).

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

  • Data supplement 1

AMJ and MC are joint senior authors.

Contributors DPA and AMJ led the conceptualisation of the study with contributions from MC and NM-B. AMJ, TD-S, ER, AU and NTHT adapted the study design with respect to the local vaccine rollouts. AD and WYM mapped and curated CPRD data. MC and NM-B developed code with methodological contributions advice from MTS-S and CP. DPA, MC, NTHT, TD-S, HMEN, XL, CR and AMJ clinically interpreted the results. NM-B, XL, AMJ and DPA wrote the first draft of the manuscript, and all authors read, revised and approved the final version. DPA and AMJ obtained the funding for this research. DPA is responsible for the overall content as guarantor: he accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.

Funding The research was supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018–11-ST2-004). Funding to perform the study in the SIDIAP database was provided by the Real World Epidemiology (RWEpi) research group at IDIAPJGol. Costs of databases mapping to OMOP CDM were covered by the European Health Data and Evidence Network (EHDEN).

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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  • NATURE PODCAST
  • 17 December 2020

Coronapod: The big COVID research papers of 2020

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

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

Papers discussed

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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An Introduction to COVID-19

Simon james fong.

4 Department of Computer and Information Science, University of Macau, Taipa, Macau, China

Nilanjan Dey

5 Department of Information Technology, Techno International New Town, Kolkata, West Bengal India

Jyotismita Chaki

6 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu India

A novel coronavirus (CoV) named ‘2019-nCoV’ or ‘2019 novel coronavirus’ or ‘COVID-19’ by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, China [1–4]. COVID-19 is a pathogenic virus. From the phylogenetic analysis carried out with obtainable full genome sequences, bats occur to be the COVID-19 virus reservoir, but the intermediate host(s) has not been detected till now.

A Brief History of the Coronavirus Outbreak

A novel coronavirus (CoV) named ‘2019-nCoV’ or ‘2019 novel coronavirus’ or ‘COVID-19’ by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, China [ 1 – 4 ]. COVID-19 is a pathogenic virus. From the phylogenetic analysis carried out with obtainable full genome sequences, bats occur to be the COVID-19 virus reservoir, but the intermediate host(s) has not been detected till now. Though three major areas of work already are ongoing in China to advise our awareness of the pathogenic origin of the outbreak. These include early inquiries of cases with symptoms occurring near in Wuhan during December 2019, ecological sampling from the Huanan Wholesale Seafood Market as well as other area markets, and the collection of detailed reports of the point of origin and type of wildlife species marketed on the Huanan market and the destination of those animals after the market has been closed [ 5 – 8 ].

Coronaviruses mostly cause gastrointestinal and respiratory tract infections and are inherently categorized into four major types: Gammacoronavirus, Deltacoronavirus, Betacoronavirus and Alphacoronavirus [ 9 – 11 ]. The first two types mainly infect birds, while the last two mostly infect mammals. Six types of human CoVs have been formally recognized. These comprise HCoVHKU1, HCoV-OC43, Middle East Respiratory Syndrome coronavirus (MERS-CoV), Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) which is the type of the Betacoronavirus, HCoV229E and HCoV-NL63, which are the member of the Alphacoronavirus. Coronaviruses did not draw global concern until the 2003 SARS pandemic [ 12 – 14 ], preceded by the 2012 MERS [ 15 – 17 ] and most recently by the COVID-19 outbreaks. SARS-CoV and MERS-CoV are known to be extremely pathogenic and spread from bats to palm civets or dromedary camels and eventually to humans.

COVID-19 is spread by dust particles and fomites while close unsafe touch between the infector and the infected individual. Airborne distribution has not been recorded for COVID-19 and is not known to be a significant transmission engine based on empirical evidence; although it can be imagined if such aerosol-generating practices are carried out in medical facilities. Faecal spreading has been seen in certain patients, and the active virus has been reported in a small number of clinical studies [ 18 – 20 ]. Furthermore, the faecal-oral route does not seem to be a COVID-19 transmission engine; its function and relevance for COVID-19 need to be identified.

For about 18,738,58 laboratory-confirmed cases recorded as of 2nd week of April 2020, the maximum number of cases (77.8%) was between 30 and 69 years of age. Among the recorded cases, 21.6% are farmers or employees by profession, 51.1% are male and 77.0% are Hubei.

However, there are already many concerns regarding the latest coronavirus. Although it seems to be transferred to humans by animals, it is important to recognize individual animals and other sources, the path of transmission, the incubation cycle, and the features of the susceptible community and the survival rate. Nonetheless, very little clinical knowledge on COVID-19 disease is currently accessible and details on age span, the animal origin of the virus, incubation time, outbreak curve, viral spectroscopy, dissemination pathogenesis, autopsy observations, and any clinical responses to antivirals are lacking among the serious cases.

How Different and Deadly COVID-19 is Compared to Plagues in History

COVID-19 has reached to more than 150 nations, including China, and has caused WHO to call the disease a worldwide pandemic. By the time of 2nd week of April 2020, this COVID-19 cases exceeded 18,738,58, although more than 1,160,45 deaths were recorded worldwide and United States of America became the global epicentre of coronavirus. More than one-third of the COVID-19 instances are outside of China. Past pandemics that have existed in the past decade or so, like bird flu, swine flu, and SARS, it is hard to find out the comparison between those pandemics and this coronavirus. Following is a guide to compare coronavirus with such diseases and recent pandemics that have reformed the world community.

Coronavirus Versus Seasonal Influenza

Influenza, or seasonal flu, occurs globally every year–usually between December and February. It is impossible to determine the number of reports per year because it is not a reportable infection (so no need to be recorded to municipality), so often patients with minor symptoms do not go to a physician. Recent figures placed the Rate of Case Fatality at 0.1% [ 21 – 23 ].

There are approximately 3–5 million reports of serious influenza a year, and about 250,000–500,000 deaths globally. In most developed nations, the majority of deaths arise in persons over 65 years of age. Moreover, it is unsafe for pregnant mothers, children under 59 months of age and individuals with serious illnesses.

The annual vaccination eliminates infection and severe risks in most developing countries but is nevertheless a recognized yet uncomfortable aspect of the season.

In contrast to the seasonal influenza, coronavirus is not so common, has led to fewer cases till now, has a higher rate of case fatality and has no antidote.

Coronavirus Versus Bird Flu (H5N1 and H7N9)

Several cases of bird flu have existed over the years, with the most severe in 2013 and 2016. This is usually from two separate strains—H5N1 and H7N9 [ 24 – 26 ].

The H7N9 outbreak in 2016 accounted for one-third of all confirmed human cases but remained confined relative to both coronavirus and other pandemics/outbreak cases. After the first outbreak, about 1,233 laboratory-confirmed reports of bird flu have occurred. The disease has a Rate of Case Fatality of 20–40%.

Although the percentage is very high, the blowout from individual to individual is restricted, which, in effect, has minimized the number of related deaths. It is also impossible to monitor as birds do not necessarily expire from sickness.

In contrast to the bird flu, coronavirus becomes more common, travels more quickly through human to human interaction, has an inferior cardiothoracic ratio, resulting in further total fatalities and spread from the initial source.

Coronavirus Versus Ebola Epidemic

The Ebola epidemic of 2013 was primarily centred in 10 nations, including Sierra Leone, Guinea and Liberia have the greatest effects, but the extremely high Case Fatality Rate of 40% has created this as a significant problem for health professionals nationwide [ 27 – 29 ].

Around 2013 and 2016, there were about 28,646 suspicious incidents and about 11,323 fatalities, although these are expected to be overlooked. Those who survived from the original epidemic may still become sick months or even years later, because the infection may stay inactive for prolonged periods. Thankfully, a vaccination was launched in December 2016 and is perceived to be effective.

In contrast to the Ebola, coronavirus is more common globally, has caused in fewer fatalities, has a lesser case fatality rate, has no reported problems during treatment and after recovery, does not have an appropriate vaccination.

Coronavirus Versus Camel Flu (MERS)

Camel flu is a misnomer–though camels have MERS antibodies and may have been included in the transmission of the disease; it was originally transmitted to humans through bats [ 30 – 32 ]. Like Ebola, it infected only a limited number of nations, i.e. about 27, but about 858 fatalities from about 2,494 laboratory-confirmed reports suggested that it was a significant threat if no steps were taken in place to control it.

In contrast to the camel flu, coronavirus is more common globally, has occurred more fatalities, has a lesser case fatality rate, and spreads more easily among humans.

Coronavirus Versus Swine Flu (H1N1)

Swine flu is the same form of influenza that wiped 1.7% of the world population in 1918. This was deemed a pandemic again in June 2009 an approximately-21% of the global population infected by this [ 33 – 35 ].

Thankfully, the case fatality rate is substantially lower than in the last pandemic, with 0.1%–0.5% of events ending in death. About 18,500 of these fatalities have been laboratory-confirmed, but statistics range as high as 151,700–575,400 worldwide. 50–80% of severe occurrences have been reported in individuals with chronic illnesses like asthma, obesity, cardiovascular diseases and diabetes.

In contrast to the swine flu, coronavirus is not so common, has caused fewer fatalities, has more case fatality rate, has a longer growth time and less impact on young people.

Coronavirus Versus Severe Acute Respiratory Syndrome (SARS)

SARS was discovered in 2003 as it spread from bats to humans resulted in about 774 fatalities. By May there were eventually about 8,100 reports across 17 countries, with a 15% case fatality rate. The number is estimated to be closer to 9.6% as confirmed cases are counted, with 0.9% cardiothoracic ratio for people aged 20–29, rising to 28% for people aged 70–79. Similar to coronavirus, SARS had bad results for males than females in all age categories [ 36 – 38 ].

Coronavirus is more common relative to SARS, which ended in more overall fatalities, lower case fatality rate, the even higher case fatality rate in older ages, and poorer results for males.

Coronavirus Versus Hong Kong Flu (H3N2)

The Hong Kong flu pandemic erupted on 13 July 1968, with 1–4 million deaths globally by 1969. It was one of the greatest flu pandemics of the twentieth century, but thankfully the case fatality rate was smaller than the epidemic of 1918, resulting in fewer fatalities overall. That may have been attributed to the fact that citizens had generated immunity owing to a previous epidemic in 1957 and to better medical treatment [ 39 ].

In contrast to the Hong Kong flu, coronavirus is not so common, has caused in fewer fatalities and has a higher case fatality rate.

Coronavirus Versus Spanish Flu (H1N1)

The 1918 Spanish flu pandemic was one of the greatest occurrences of recorded history. During the first year of the pandemic, lifespan in the US dropped by 12 years, with more civilians killed than HIV/AIDS in 24 h [ 40 – 42 ].

Regardless of the name, the epidemic did not necessarily arise in Spain; wartime censors in Germany, the United States, the United Kingdom and France blocked news of the disease, but Spain did not, creating the misleading perception that more cases and fatalities had occurred relative to its neighbours

This strain of H1N1 eventually affected more than 500 million men, or 27% of the world’s population at the moment, and had deaths of between 40 and 50 million. At the end of 1920, 1.7% of the world’s people had expired of this illness, including an exceptionally high death rate for young adults aged between 20 and 40 years.

In contrast to the Spanish flu, coronavirus is not so common, has caused in fewer fatalities, has a higher case fatality rate, is more harmful to older ages and is less risky for individuals aged 20–40 years.

Coronavirus Versus Common Cold (Typically Rhinovirus)

Common cold is the most common illness impacting people—Typically, a person suffers from 2–3 colds each year and the average kid will catch 6–8 during the similar time span. Although there are more than 200 cold-associated virus types, infections are uncommon and fatalities are very rare and typically arise mainly in extremely old, extremely young or immunosuppressed cases [ 43 , 44 ].

In contrast to the common cold, coronavirus is not so prevalent, causes more fatalities, has more case fatality rate, is less infectious and is less likely to impact small children.

Reviews of Online Portals and Social Media for Epidemic Information Dissemination

As COVID-19 started to propagate across the globe, the outbreak contributed to a significant change in the broad technology platforms. Where they once declined to engage in the affairs of their systems, except though the possible danger to public safety became obvious, the advent of a novel coronavirus placed them in a different interventionist way of thought. Big tech firms and social media are taking concrete steps to guide users to relevant, credible details on the virus [ 45 – 48 ]. And some of the measures they’re doing proactively. Below are a few of them.

Facebook started adding a box in the news feed that led users to the Centers for Disease Control website regarding COVID-19. It reflects a significant departure from the company’s normal strategy of placing items in the News Feed. The purpose of the update, after all, is personalization—Facebook tries to give the posts you’re going to care about, whether it is because you’re connected with a person or like a post. In the virus package, Facebook has placed a remarkable algorithmic thumb on the scale, potentially pushing millions of people to accurate, authenticated knowledge from a reputable source.

Similar initiatives have been adopted by Twitter. Searching for COVID-19 will carry you to a page highlighting the latest reports from public health groups and credible national news outlets. The search also allows for common misspellings. Twitter has stated that although Russian-style initiatives to cause discontent by large-scale intelligence operations have not yet been observed, a zero-tolerance approach to network exploitation and all other attempts to exploit their service at this crucial juncture will be expected. The problem has the attention of the organization. It also offers promotional support to public service agencies and other non-profit groups.

Google has made a step in making it better for those who choose to operate or research from home, offering specialized streaming services to all paying G Suite customers. Google also confirmed that free access to ‘advanced’ Hangouts Meet apps will be rolled out to both G Suite and G Suite for Education clients worldwide through 1st July. It ensures that companies can hold meetings of up to 250 people, broadcast live to up to about 100,000 users within a single network, and archive and export meetings to Google Drive. Usually, Google pays an additional $13 per person per month for these services in comparison to G Suite’s ‘enterprise’ membership, which adds up to a total of about $25 per client each month.

Microsoft took a similar move, introducing the software ‘Chat Device’ to help public health and protection in the coronavirus epidemic, which enables collaborative collaboration via video and text messaging. There’s an aspect of self-interest in this. Tech firms are offering out their goods free of charge during periods of emergency for the same purpose as newspapers are reducing their paywalls: it’s nice to draw more paying consumers.

Pinterest, which has introduced much of the anti-misinformation strategies that Facebook and Twitter are already embracing, is now restricting the search results for ‘coronavirus’, ‘COVID-19’ and similar words for ‘internationally recognized health organizations’.

Google-owned YouTube, traditionally the most conspiratorial website, has recently introduced a connection to the World Health Organization virus epidemic page to the top of the search results. In the early days of the epidemic, BuzzFeed found famous coronavirus conspiratorial videos on YouTube—especially in India, where one ‘explain’ with a false interpretation of the sources of the disease racketeered 13 million views before YouTube deleted it. Yet in the United States, conspiratorial posts regarding the illness have failed to gain only 1 million views.

That’s not to suggest that misinformation doesn’t propagate on digital platforms—just as it travels through the broader Internet, even though interaction with friends and relatives. When there’s a site that appears to be under-performing in the global epidemic, it’s Facebook-owned WhatsApp, where the Washington Post reported ‘a torrent of disinformation’ in places like Nigeria, Indonesia, Peru, Pakistan and Ireland. Given the encrypted existence of the app, it is difficult to measure the severity of the problem. Misinformation is also spread in WhatsApp communities, where participation is restricted to about 250 individuals. Knowledge of one category may be readily exchanged with another; however, there is a considerable amount of complexity of rotating several groups to peddle affected healing remedies or propagate false rumours.

Preventative Measures and Policies Enforced by the World Health Organization (WHO) and Different Countries

Coronavirus is already an ongoing epidemic, so it is necessary to take precautions to minimize both the risk of being sick and the transmission of the disease.

WHO Advice [ 49 ]

  • Wash hands regularly with alcohol-based hand wash or soap and water.
  • Preserve contact space (at least 1 m/3 feet between you and someone who sneezes or coughs).
  • Don’t touch your nose, head and ears.
  • Cover your nose and mouth as you sneeze or cough, preferably with your bent elbow or tissue.
  • Try to find early medical attention if you have fatigue, cough and trouble breathing.
  • Take preventive precautions if you are in or have recently go to places where coronavirus spreads.

The first person believed to have become sick because of the latest virus was near in Wuhan on 1 December 2019. A formal warning of the epidemic was released on 31 December. The World Health Organization was informed of the epidemic on the same day. Through 7 January, the Chinese Government addressed the avoidance and regulation of COVID-19. A curfew was declared on 23 January to prohibit flying in and out of Wuhan. Private usage of cars has been banned in the region. Chinese New Year (25 January) festivities have been cancelled in many locations [ 50 ].

On 26 January, the Communist Party and the Government adopted more steps to contain the COVID-19 epidemic, including safety warnings for travellers and improvements to national holidays. The leading party has agreed to prolong the Spring Festival holiday to control the outbreak. Universities and schools across the world have already been locked down. Many steps have been taken by the Hong Kong and Macau governments, in particular concerning schools and colleges. Remote job initiatives have been placed in effect in many regions of China. Several immigration limits have been enforced.

Certain counties and cities outside Hubei also implemented travel limits. Public transit has been changed and museums in China have been partially removed. Some experts challenged the quality of the number of cases announced by the Chinese Government, which constantly modified the way coronavirus cases were recorded.

Italy, a member state of the European Union and a popular tourist attraction, entered the list of coronavirus-affected nations on 30 January, when two positive cases in COVID-19 were identified among Chinese tourists. Italy has the largest number of coronavirus infections both in Europe and outside of China [ 51 ].

Infections, originally limited to northern Italy, gradually spread to all other areas. Many other nations in Asia, Europe and the Americas have tracked their local cases to Italy. Several Italian travellers were even infected with coronavirus-positive in foreign nations.

Late in Italy, the most impacted coronavirus cities and counties are Lombardia, accompanied by Veneto, Emilia-Romagna, Marche and Piedmonte. Milan, the second most populated city in Italy, is situated in Lombardy. Other regions in Italy with coronavirus comprised Campania, Toscana, Liguria, Lazio, Sicilia, Friuli Venezia Giulia, Umbria, Puglia, Trento, Abruzzo, Calabria, Molise, Valle d’Aosta, Sardegna, Bolzano and Basilicata.

Italy ranks 19th of the top 30 nations getting high-risk coronavirus airline passengers in China, as per WorldPop’s provisional study of the spread of COVID-19.

The Italian State has taken steps like the inspection and termination of large cultural activities during the early days of the coronavirus epidemic and has gradually declared the closing of educational establishments and airport hygiene/disinfection initiatives.

The Italian National Institute of Health suggested social distancing and agreed that the broader community of the country’s elderly is a problem. In the meantime, several other nations, including the US, have recommended that travel to Italy should be avoided temporarily, unless necessary.

The Italian government has declared the closing (quarantine) of the impacted areas in the northern region of the nation so as not to spread to the rest of the world. Italy has declared the immediate suspension of all to-and-fro air travel with China following coronavirus discovery by a Chinese tourist to Italy. Italian airlines, like Ryan Air, have begun introducing protective steps and have begun calling for the declaration forms to be submitted by passengers flying to Poland, Slovakia and Lithuania.

The Italian government first declined to permit fans to compete in sporting activities until early April to prevent the potential transmission of coronavirus. The step ensured players of health and stopped event cancellations because of coronavirus fears. Two days of the declaration, the government cancelled all athletic activities owing to the emergence of the outbreak asking for an emergency. Sports activities in Veneto, Lombardy and Emilia-Romagna, which recorded coronavirus-positive infections, were confirmed to be temporarily suspended. Schools and colleges in Italy have also been forced to shut down.

Iran announced the first recorded cases of SARS-CoV-2 infection on 19 February when, as per the Medical Education and Ministry of Health, two persons died later that day. The Ministry of Islamic Culture and Guidance has declared the cancellation of all concerts and other cultural activities for one week. The Medical Education and Ministry of Health has also declared the closing of universities, higher education colleges and schools in many cities and regions. The Department of Sports and Culture has taken action to suspend athletic activities, including football matches [ 52 ].

On 2 March 2020, the government revealed plans to train about 300,000 troops and volunteers to fight the outbreak of the epidemic, and also send robots and water cannons to clean the cities. The State also developed an initiative and a webpage to counter the epidemic. On 9 March 2020, nearly 70,000 inmates were immediately released from jail owing to the epidemic, presumably to prevent the further dissemination of the disease inside jails. The Revolutionary Guards declared a campaign on 13 March 2020 to clear highways, stores and public areas in Iran. President Hassan Rouhani stated on 26 February 2020 that there were no arrangements to quarantine areas impacted by the epidemic and only persons should be quarantined. The temples of Shia in Qom stayed open to pilgrims.

South Korea

On 20 January, South Korea announced its first occurrence. There was a large rise in cases on 20 February, possibly due to the meeting in Daegu of a progressive faith community recognized as the Shincheonji Church of Christ. Any citizens believed that the hospital was propagating the disease. As of 22 February, 1,261 of the 9,336 members of the church registered symptoms. A petition was distributed calling for the abolition of the church. More than 2,000 verified cases were registered on 28 February, increasing to 3,150 on 29 February [ 53 ].

Several educational establishments have been partially closing down, including hundreds of kindergartens in Daegu and many primary schools in Seoul. As of 18 February, several South Korean colleges had confirmed intentions to delay the launch of the spring semester. That included 155 institutions deciding to postpone the start of the semester by two weeks until 16 March, and 22 institutions deciding to delay the start of the semester by one week until 9 March. Also, on 23 February 2020, all primary schools, kindergartens, middle schools and secondary schools were declared to postpone the start of the semester from 2 March to 9 March.

South Korea’s economy is expected to expand by 1.9%, down from 2.1%. The State has given 136.7 billion won funding to local councils. The State has also coordinated the purchase of masks and other sanitary supplies. Entertainment Company SM Entertainment is confirmed to have contributed five hundred million won in attempts to fight the disease.

In the kpop industry, the widespread dissemination of coronavirus within South Korea has contributed to the cancellation or postponement of concerts and other programmes for kpop activities inside and outside South Korea. For instance, circumstances such as the cancellation of the remaining Asian dates and the European leg for the Seventeen’s Ode To You Tour on 9 February 2020 and the cancellation of all Seoul dates for the BTS Soul Tour Map. As of 15 March, a maximum of 136 countries and regions provided entry restrictions and/or expired visas for passengers from South Korea.

The overall reported cases of coronavirus rose significantly in France on 12 March. The areas with reported cases include Paris, Amiens, Bordeaux and Eastern Haute-Savoie. The first coronaviral death happened in France on 15 February, marking it the first death in Europe. The second death of a 60-year-old French national in Paris was announced on 26 February [ 54 ].

On February 28, fashion designer Agnès B. (not to be mistaken with Agnès Buzyn) cancelled fashion shows at the Paris Fashion Week, expected to continue until 3 March. On a subsequent day, the Paris half-marathon, planned for Sunday 1 March with 44,000 entrants, was postponed as one of a series of steps declared by Health Minister Olivier Véran.

On 13 March, the Ligue de Football Professional disbanded Ligue 1 and Ligue 2 (France’s tier two professional divisions) permanently due to safety threats.

Germany has a popular Regional Pandemic Strategy detailing the roles and activities of the health care system participants in the case of a significant outbreak. Epidemic surveillance is carried out by the federal government, like the Robert Koch Center, and by the German governments. The German States have their preparations for an outbreak. The regional strategy for the treatment of the current coronavirus epidemic was expanded by March 2020. Four primary goals are contained in this plan: (1) to minimize mortality and morbidity; (2) to guarantee the safety of sick persons; (3) to protect vital health services and (4) to offer concise and reliable reports to decision-makers, the media and the public [ 55 ].

The programme has three phases that may potentially overlap: (1) isolation (situation of individual cases and clusters), (2) safety (situation of further dissemination of pathogens and suspected causes of infection), (3) prevention (situation of widespread infection). So far, Germany has not set up border controls or common health condition tests at airports. Instead, while at the isolation stage-health officials are concentrating on recognizing contact individuals that are subject to specific quarantine and are tracked and checked. Specific quarantine is regulated by municipal health authorities. By doing so, the officials are seeking to hold the chains of infection small, contributing to decreased clusters. At the safety stage, the policy should shift to prevent susceptible individuals from being harmed by direct action. By the end of the day, the prevention process should aim to prevent cycles of acute treatment to retain emergency facilities.

United States

The very first case of coronavirus in the United States was identified in Washington on 21 January 2020 by an individual who flew to Wuhan and returned to the United States. The second case was recorded in Illinois by another individual who had travelled to Wuhan. Some of the regions with reported novel coronavirus infections in the US are California, Arizona, Connecticut, Illinois, Texas, Wisconsin and Washington [ 56 ].

As the epidemic increased, requests for domestic air travel decreased dramatically. By 4 March, U.S. carriers, like United Airlines and JetBlue Airways, started growing their domestic flight schedules, providing generous unpaid leave to workers and suspending recruits.

A significant number of universities and colleges cancelled classes and reopened dormitories in response to the epidemic, like Cornell University, Harvard University and the University of South Carolina.

On 3 March 2020, the Federal Reserve reduced its goal interest rate from 1.75% to 1.25%, the biggest emergency rate cut following the 2008 global financial crash, in combat the effect of the recession on the American economy. In February 2020, US businesses, including Apple Inc. and Microsoft, started to reduce sales projections due to supply chain delays in China caused by the COVID-19.

The pandemic, together with the subsequent financial market collapse, also contributed to greater criticism of the crisis in the United States. Researchers disagree about when a recession is likely to take effect, with others suggesting that it is not unavoidable, while some claim that the world might already be in recession. On 3 March, Federal Reserve Chairman Jerome Powell reported a 0.5% (50 basis point) interest rate cut from the coronavirus in the context of the evolving threats to economic growth.

When ‘social distance’ penetrated the national lexicon, disaster response officials promoted the cancellation of broad events to slow down the risk of infection. Technical conferences like E3 2020, Apple Inc.’s Worldwide Developers Conference (WWDC), Google I/O, Facebook F8, and Cloud Next and Microsoft’s MVP Conference have been either having replaced or cancelled in-person events with internet streaming events.

On February 29, the American Physical Society postponed its annual March gathering, planned for March 2–6 in Denver, Colorado, even though most of the more than 11,000 physicist attendees already had arrived and engaged in the pre-conference day activities. On March 6, the annual South to Southwest (SXSW) seminar and festival planned to take place from March 13–22 in Austin, Texas, was postponed after the city council announced a local disaster and forced conferences to be shut down for the first time in 34 years.

Four of North America’s major professional sports leagues—the National Hockey League (NHL), National Basketball Association (NBA), Major League Soccer (MLS) and Major League Baseball (MLB) —jointly declared on March 9 that they would all limit the media access to player accommodations (such as locker rooms) to control probable exposure.

Emergency Funding to Fight the COVID-19

COVID-19 pandemic has become a common international concern. Different countries are donating funds to fight against it [ 57 – 60 ]. Some of them are mentioned here.

China has allocated about 110.48 billion yuan ($15.93 billion) in coronavirus-related funding.

Foreign Minister Mohammad Javad Zarif said that Iran has requested the International Monetary Fund (IMF) of about $5 billion in emergency funding to help to tackle the coronavirus epidemic that has struck the Islamic Republic hard.

President Donald Trump approved the Emergency Supplementary Budget Bill to support the US response to a novel coronavirus epidemic. The budget plan would include about $8.3 billion in discretionary funding to local health authorities to promote vaccine research for production. Trump originally requested just about $2 billion to combat the epidemic, but Congress quadrupled the number in its version of the bill. Mr. Trump formally announced a national emergency that he claimed it will give states and territories access to up to about $50 billion in federal funding to tackle the spread of the coronavirus outbreak.

California politicians approved a plan to donate about $1 billion on the state’s emergency medical responses as it readies hospitals to fight an expected attack of patients because of the COVID-19 pandemic. The plans, drawn up rapidly in reaction to the dramatic rise in reported cases of the virus, would include the requisite funds to establish two new hospitals in California, with the assumption that the state may not have the resources to take care of the rise in patients. The bill calls for an immediate response of about $500 million from the State General Fund, with an additional about $500 million possible if requested.

India committed about $10 million to the COVID-19 Emergency Fund and said it was setting up a rapid response team of physicians for the South Asian Association for Regional Cooperation (Saarc) countries.

South Korea unveiled an economic stimulus package of about 11.7 trillion won ($9.8 billion) to soften the effects of the biggest coronavirus epidemic outside China as attempts to curb the disease exacerbate supply shortages and drain demand. Of the 11,7 trillion won expected, about 3.2 trillion won would cover up the budget shortfall, while an additional fiscal infusion of about 8.5 trillion won. An estimated 10.3 trillion won in government bonds will be sold this year to fund the extra expenditure. About 2.3 trillion won will be distributed to medical establishments and would support quarantine operations, with another 3.0 trillion won heading to small and medium-sized companies unable to pay salaries to their employees and child care supports.

The Swedish Parliament announced a set of initiatives costing more than 300 billion Swedish crowns ($30.94 billion) to help the economy in the view of the coronavirus pandemic. The plan contained steps like the central government paying the entire expense of the company’s sick leave during April and May, and also the high cost of compulsory redundancies owing to the crisis.

In consideration of the developing scenario, an updating of this strategy is planned to take place before the end of March and will recognize considerably greater funding demands for the country response, R&D and WHO itself.

Artificial Intelligence, Data Science and Technological Solutions Against COVID-19

These days, Artificial Intelligence (AI) takes a major role in health care. Throughout a worldwide pandemic such as the COVID-19, technology, artificial intelligence and data analytics have been crucial in helping communities cope successfully with the epidemic [ 61 – 65 ]. Through the aid of data mining and analytical modelling, medical practitioners are willing to learn more about several diseases.

Public Health Surveillance

The biggest risk of coronavirus is the level of spreading. That’s why policymakers are introducing steps like quarantines around the world because they can’t adequately monitor local outbreaks. One of the simplest measures to identify ill patients through the study of CCTV images that are still around us and to locate and separate individuals that have serious signs of the disease and who have touched and disinfected the related surfaces. Smartphone applications are often used to keep a watch on people’s activities and to assess whether or not they have come in touch with an infected human.

Remote Biosignal Measurement

Many of the signs such as temperature or heartbeat are very essential to overlook and rely entirely on the visual image that may be misleading. However, of course, we can’t prevent someone from checking their blood pressure, heart or temperature. Also, several advances in computer vision can predict pulse and blood pressure based on facial skin examination. Besides, there are several advances in computer vision that can predict pulse and blood pressure based on facial skin examination.

Access to public records has contributed to the development of dashboards that constantly track the virus. Several companies are designing large data dashboards. Face recognition and infrared temperature monitoring technologies have been mounted in all major cities. Chinese AI companies including Hanwang Technology and SenseTime have reported having established a special facial recognition system that can correctly identify people even though they are covered.

IoT and Wearables

Measurements like pulse are much more natural and easier to obtain from tracking gadgets like activity trackers and smartwatches that nearly everybody has already. Some work suggests that the study of cardiac activity and its variations from the standard will reveal early signs of influenza and, in this case, coronavirus.

Chatbots and Communication

Apart from public screening, people’s knowledge and self-assessment may also be used to track their health. If you can check your temperature and pulse every day and monitor your coughs time-to-time, you can even submit that to your record. If the symptoms are too serious, either an algorithm or a doctor remotely may prescribe a person to stay home, take several other preventive measures, or recommend a visit from the doctor.

Al Jazeera announced that China Mobile had sent text messages to state media departments, telling them about the citizens who had been affected. The communications contained all the specifics of the person’s travel history.

Tencent runs WeChat, and via it, citizens can use free online health consultation services. Chatbots have already become important connectivity platforms for transport and tourism service providers to keep passengers up-to-date with the current transport protocols and disturbances.

Social Media and Open Data

There are several people who post their health diary with total strangers via Facebook or Twitter. Such data becomes helpful for more general research about how far the epidemic has progressed. For consumer knowledge, we may even evaluate the social network group to attempt to predict what specific networks are at risk of being viral.

Canadian company BlueDot analyses far more than just social network data: for instance, global activities of more than four billion passengers on international flights per year; animal, human and insect population data; satellite environment data and relevant knowledge from health professionals and journalists, across 100,000 news posts per day covering 65 languages. This strategy was so successful that the corporation was able to alert clients about coronavirus until the World Health Organization and the Centers for Disease Control and Prevention notified the public.

Automated Diagnostics

COVID-19 has brought up another healthcare issue today: it will not scale when the number of patients increases exponentially (actually stressed doctors are always doing worse) and the rate of false-negative diagnosis remains very high. Machine learning therapies don’t get bored and scale simply by growing computing forces.

Baidu, the Chinese Internet company, has made the Lineatrfold algorithm accessible to the outbreak-fighting teams, according to the MIT Technology Review. Unlike HIV, Ebola and Influenza, COVID-19 has just one strand of RNA and it can mutate easily. The algorithm is also simpler than other algorithms that help to determine the nature of the virus. Baidu has also developed software to efficiently track large populations. It has also developed an Ai-powered infrared device that can detect a difference in the body temperature of a human. This is currently being used in Beijing’s Qinghe Railway Station to classify possibly contaminated travellers where up to 200 individuals may be checked in one minute without affecting traffic movement, reports the MIT Review.

Singapore-based Veredus Laboratories, a supplier of revolutionary molecular diagnostic tools, has currently announced the launch of the VereCoV detector package, a compact Lab-on-Chip device able to detect MERS-CoV, SARS-CoV and COVID-19, i.e. Wuhan Coronavirus, in a single study.

The VereCoV identification package is focused on VereChip technology, a Lab-on-Chip device that incorporates two important molecular biological systems, Polymerase Chain Reaction (PCR) and a microarray, which will be able to classify and distinguish within 2 h MERS-CoV, SARS-CoV and COVID-19 with high precision and responsiveness.

This is not just the medical activities of healthcare facilities that are being charged, but also the corporate and financial departments when they cope with the increase in patients. Ant Financials’ blockchain technology helps speed-up the collection of reports and decreases the number of face-to-face encounters with patients and medical personnel.

Companies like the Israeli company Sonovia are aiming to provide healthcare systems and others with face masks manufactured from their anti-pathogenic, anti-bacterial cloth that depends on metal-oxide nanoparticles.

Drug Development Research

Aside from identifying and stopping the transmission of pathogens, the need to develop vaccinations on a scale is also needed. One of the crucial things to make that possible is to consider the origin and essence of the virus. Google’s DeepMind, with their expertise in protein folding research, has rendered a jump in identifying the protein structure of the virus and making it open-source.

BenevolentAI uses AI technologies to develop medicines that will combat the most dangerous diseases in the world and is also working to promote attempts to cure coronavirus, the first time the organization has based its product on infectious diseases. Within weeks of the epidemic, it used its analytical capability to recommend new medicines that might be beneficial.

Robots are not vulnerable to the infection, and they are used to conduct other activities, like cooking meals in hospitals, doubling up as waiters in hotels, spraying disinfectants and washing, selling rice and hand sanitizers, robots are on the front lines all over to deter coronavirus spread. Robots also conduct diagnostics and thermal imaging in several hospitals. Shenzhen-based firm Multicopter uses robotics to move surgical samples. UVD robots from Blue Ocean Robotics use ultraviolet light to destroy viruses and bacteria separately. In China, Pudu Technology has introduced its robots, which are usually used in the cooking industry, to more than 40 hospitals throughout the region. According to the Reuters article, a tiny robot named Little Peanut is distributing food to passengers who have been on a flight from Singapore to Hangzhou, China, and are presently being quarantined in a hotel.

Colour Coding

Using its advanced and vast public service monitoring network, the Chinese government has collaborated with software companies Alibaba and Tencent to establish a colour-coded health ranking scheme that monitors millions of citizens every day. The mobile device was first introduced in Hangzhou with the cooperation of Alibaba. This applies three colours to people—red, green or yellow—based on their transportation and medical records. Tencent also developed related applications in the manufacturing centre of Shenzhen.

The decision of whether an individual will be quarantined or permitted in public spaces is dependent on the colour code. Citizens will sign into the system using pay wallet systems such as Alibaba’s Alipay and Ant’s wallet. Just those citizens who have been issued a green colour code will be permitted to use the QR code in public spaces at metro stations, workplaces, and other public areas. Checkpoints are in most public areas where the body temperature and the code of individual are tested. This programme is being used by more than 200 Chinese communities and will eventually be expanded nationwide.

In some of the seriously infected regions where people remain at risk of contracting the infection, drones are used to rescue. One of the easiest and quickest ways to bring emergency supplies where they need to go while on an epidemic of disease is by drone transportation. Drones carry all surgical instruments and patient samples. This saves time, improves the pace of distribution and reduces the chance of contamination of medical samples. Drones often operate QR code placards that can be checked to record health records. There are also agricultural drones distributing disinfectants in the farmland. Drones, operated by facial recognition, are often used to warn people not to leave their homes and to chide them for not using face masks. Terra Drone uses its unmanned drones to move patient samples and vaccination content at reduced risk between the Xinchang County Disease Control Center and the People’s Hospital. Drones are often used to monitor public areas, document non-compliance with quarantine laws and thermal imaging.

Autonomous Vehicles

At a period of considerable uncertainty to medical professionals and the danger to people-to-people communication, automated vehicles are proving to be of tremendous benefit in the transport of vital products, such as medications and foodstuffs. Apollo, the Baidu Autonomous Vehicle Project, has joined hands with the Neolix self-driving company to distribute food and supplies to a big hospital in Beijing. Baidu Apollo has also provided its micro-car packages and automated cloud driving systems accessible free of charge to virus-fighting organizations.

Idriverplus, a Chinese self-driving organization that runs electrical street cleaning vehicles, is also part of the project. The company’s signature trucks are used to clean hospitals.

This chapter provides an introduction to the coronavirus outbreak (COVID-19). A brief history of this virus along with the symptoms are reported in this chapter. Then the comparison between COVID-19 and other plagues like seasonal influenza, bird flu (H5N1 and H7N9), Ebola epidemic, camel flu (MERS), swine flu (H1N1), severe acute respiratory syndrome, Hong Kong flu (H3N2), Spanish flu and the common cold are included in this chapter. Reviews of online portal and social media like Facebook, Twitter, Google, Microsoft, Pinterest, YouTube and WhatsApp concerning COVID-19 are reported in this chapter. Also, the preventive measures and policies enforced by WHO and different countries such as China, Italy, Iran, South Korea, France, Germany and the United States for COVID-19 are included in this chapter. Emergency funding provided by different countries to fight the COVID-19 is mentioned in this chapter. Lastly, artificial intelligence, data science and technological solutions like public health surveillance, remote biosignal measurement, IoT and wearables, chatbots and communication, social media and open data, automated diagnostics, drug development research, robotics, colour coding, drones and autonomous vehicles are included in this chapter.

IMAGES

  1. How COVID-19 Prompted a Research Pivot for Two Surgeon-Scientists

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  2. Business Impact of COVID-19 Survey

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  3. UN/DESA Policy Brief #78: Achieving the SDGs through the COVID-19

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  6. Examining COVID-19 versus previous pandemics

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COMMENTS

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

    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 [].The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [], causing massive economic strain ...

  2. Impact of COVID-19 on the social, economic, environmental and energy

    COVID-19 is a worldwide pandemic that puts a stop to economic activity and poses a severe risk to overall wellbeing. The global socio-economic impact of COVID-19 includes higher unemployment and poverty rates, lower oil prices, altered education sectors, changes in the nature of work, lower GDPs and heightened risks to health care workers.

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

    Continued research into the virus is critical to trace the source of the outbreak and provide evidence for future outbreak . Conclusions. The current COVID-19 pandemic is clearly an international public health problem. There have been rapid advances in what we know about the pathogen, how it infects cells and causes disease, and clinical ...

  4. COVID-19 impact on research, lessons learned from COVID-19 research

    The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical ...

  5. A comprehensive SARS-CoV-2 and COVID-19 review, Part 1 ...

    This work was supported by supplemental funds for COVID-19 research from Translational Research Institute of Space Health through NASA Cooperative Agreement NNX16AO69A (T-0404) to AB, and by a ...

  6. COVID-19 pandemic crisis—a complete outline of SARS-CoV-2

    Research has shown that SARS-CoV-2 shows similarities with SARS-CoV and MERS-CoV. ... Conclusion. COVID-19 has emerged as the most terrified and enormous viral infection. According to WHO, the coronavirus might become an endemic disease. Originating from China as a global pandemic, it has influenced people on a large scale.

  7. The Origins of Covid-19

    Key Events in the Effort to Determine the Origins of the Covid-19 Pandemic. The origins story dates back to December 31, 2019, when the World Health Organization (WHO) learned of a cluster of ...

  8. Our COVID-19 Research Summary

    The published literature on COVID now exceeds 211,000 papers, books, and documents, which include: 22,866 observational studies, 19,591 reviews, 1496 meta-analyses and 781 randomized control trials. These publications comprise the backdrop for our research and writing. The project began in the spring of 2020 based on a limited source of cumulative COVID-19 data and has broadened considerably ...

  9. Methodological quality of COVID-19 clinical research

    Fig. 4: Differences in methodological quality scores in COVID-19 compared to historical control articles. A Time to acceptance was reduced in COVID-19 articles compared to control articles (13.0 ...

  10. Frontiers

    COVID-19: Emergence, Spread, Possible Treatments, and Global Burden. The Coronavirus (CoV) is a large family of viruses known to cause illnesses ranging from the common cold to acute respiratory tract infection. The severity of the infection may be visible as pneumonia, acute respiratory syndrome, and even death.

  11. Conclusion

    Conclusion. This research has captured the diversity and complexity of people's experiences. ... there is still concern about the longer term harm and disruption that COVID-19 has caused to people and communities, and worry about the threat of future waves of infection. This report captures a number of specific suggestions for support.

  12. Conclusion

    > Researching in the Age of COVID-19 > Conclusion; Researching in the Age of COVID-19. Volume I: Response and Reassessment. Book contents. Frontmatter. Contents. List of Figures and Tables. Notes on Contributors. ... Helena Vicente and her colleagues in the European Union needed to move planned face-to-face research encounters online, but at a ...

  13. Experience, Perceptions, and Recommendations Concerning COVID-19

    Conclusions: Clinical research professionals perceive that COVID-19-related clinical trial adjustments positively impact multiple aspects of study conduct. Those with greatest experience-both specific to COVID-19-related changes and more generally-are more likely to recommend that these adjustments continue in the future.

  14. Conclusion

    Conclusion; Get access. Share. Cite. Summary. Care and resilience of participants, researchers and others, are rarely discussed in research methods books. This points to perhaps one of the small silver linings of the COVID-19 pandemic: in some research arenas, people have begun to take more care of each other. A global crisis that affects ...

  15. Family perspectives of COVID-19 research

    Background The COVID-19 pandemic has uniquely affected children and families by disrupting routines, changing relationships and roles, and altering usual child care, school and recreational activities. Understanding the way families experience these changes from parents' perspectives may help to guide research on the effects of COVID-19 among children. Main body As a multidisciplinary team ...

  16. A Review of Coronavirus Disease-2019 (COVID-19)

    There have been around 96,000 reported cases of coronavirus disease 2019 (COVID-2019) and 3300 reported deaths to date (05/03/2020). The disease is transmitted by inhalation or contact with infected droplets and the incubation period ranges from 2 to 14 d. The symptoms are usually fever, cough, sore throat, breathlessness, fatigue, malaise ...

  17. Global research on coronavirus disease (COVID-19)

    WHO COVID-19 Research Database. The WHO Covid-19 Research Database is a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued. ...

  18. Beneficial Cardiovascular Outcomes Linked to COVID-19 Vaccination ...

    Conclusion. The research outlined in this article adds to the growing body of evidence that COVID-19 vaccines offer numerous health benefits beyond preventing COVID-19 itself.

  19. Five discoveries about COVID-19 made since the public health ...

    A systematic review of 194 studies with over 700,000 participants reported that on average, 45% of survivors of COVID-19 had at least one unresolved symptom at a mean follow-up of around 4 months ...

  20. 05 Conclusion

    Research paper 16 February 2021 ISBN: 978 1 78413 436 5 Show authors. Joyce Hakmeh Deputy Director, International Security Programme; Co-Editor, Journal of Cyber Policy ... 05 Conclusion. The COVID-19 pandemic and trends in technology. Hide contents. The COVID-19 pandemic and trends in technology ...

  21. Evidence linking COVID-19 and the health/well-being of children and

    The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been spreading globally for more than 3 years [1, 2].As of April 20, 2023, there have been over 765 million confirmed cases and over 6.9 million deaths reported worldwide [].COVID-19 has had varied effects on the health of children and adolescents, both directly and indirectly.

  22. Unfinished nursing care in healthcare settings during the COVID-19

    The worldwide COVID-19 pandemic has made it difficult for health facilities to maintain their sustainability and continuity of care, which has also influenced the unfinished nursing care phenomenon. ... Conclusions. Two continents led the research in this field during the pandemic: Europe, where this research was already well established, and ...

  23. ILO Policy Brief on COVID-19: Conclusion

    International Labour Standards contain guidance for ensuring decent work that is applicable even in the unparalleled context of the COVID 19 crisis. In particular, the Employment and Decent Work for Peace and Resilience Recommendation, 2017 (No. 205) emphasizes that crisis responses need to "ensure respect for all human rights and the rule of ...

  24. The role of COVID-19 vaccines in preventing post-COVID-19 ...

    Objective To study the association between COVID-19 vaccination and the risk of post-COVID-19 cardiac and thromboembolic complications. Methods We conducted a staggered cohort study based on national vaccination campaigns using electronic health records from the UK, Spain and Estonia. Vaccine rollout was grouped into four stages with predefined enrolment periods. Each stage included all ...

  25. COVID-19 Pandemic: Knowledge and Perceptions of the Public and

    Conclusions. The COVID-19 pandemic has affected the world in various ways. The deficiency of information, the need for accurate information, and the rapidity of its dissemination are important, as this pandemic requires the cooperation of entire populations. ... "Your research proposal 'Response of the public and health care providers to a ...

  26. Coronapod: The big COVID research papers of 2020

    Download MP3. In the final Coronapod of 2020, we dive into the scientific literature to reflect on the COVID-19 pandemic. Researchers have discovered so much about SARS-CoV-2 - information that ...

  27. CORACLE (COVID-19 liteRAture CompiLEr): A platform for ...

    Background: During COVID-19 pandemic there emerged a need to efficiently monitor and process large volumes of scientific literature on the subject. Currently, as the pandemic is winding down, the clinicians encountered a novel syndrome - Post-acute Sequelae of COVID-19 (PASC) - that affects over 10% of those who contract SARS-CoV-2 and presents a significant and growing challenge in the ...

  28. Stuck at home: Housing and neighborhood conditions during the COVID-19

    This article analyzes the degree of (dis)satisfaction of residents, with their homes and their neighborhood facilities, during the lockdown imposed by the COVID-19 pandemic. The research, centered on the Lisbon and Porto metropolitan areas (Portugal), methodologically crosses a qualitative analysis (based on a survey carried out among the ...

  29. Simplified COVID‐19 guidance for adults with intellectual and

    Journal of Applied Research in Intellectual Disabilities (JARID) is a learning disabilities journal covering topics ranging from quality of life to medication & services. Abstract Background During the COVID-19 pandemic, the United States' Centers for Disease Control and Prevention (CDC) created guidance documents that were too complex to be ...

  30. An Introduction to COVID-19

    A novel coronavirus (CoV) named '2019-nCoV' or '2019 novel coronavirus' or 'COVID-19' by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, China [1-4]. COVID-19 is a pathogenic virus. From the phylogenetic analysis ...