literature review of covid 19

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

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

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

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

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

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

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

Methodology

Research question.

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

Study design

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

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

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

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

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

Eligibility criteria

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

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

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

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

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

Information sources

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

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

Study selection

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

Data collection process

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

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

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

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

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

Quality assessment in individual reviews

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

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

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

Synthesis of results

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

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

Managing overlapping systematic reviews

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

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

PRISMA flow diagram

Characteristics of included reviews

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

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

Population and study designs

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

Systematic review findings

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

A meta-analysis of the prevalence of mortality

Clinical symptoms

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

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

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

Therapeutic possibilities

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

Laboratory and radiological findings

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

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

Quality of evidence in individual systematic reviews

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Availability of data and materials

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

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Acknowledgments

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

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Israel Júnior Borges do Nascimento

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Dónal P. O’Mathúna

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

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

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

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

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

Additional file 5: Appendix 5.

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

Additional file 6: Appendix 6.

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

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

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• Published: 09 August 2021

More than 50 long-term effects of COVID-19: a systematic review and meta-analysis

• Sandra Lopez-Leon   ORCID: orcid.org/0000-0001-7504-3441 1 ,
• Talia Wegman-Ostrosky   ORCID: orcid.org/0000-0002-3207-6697 2 ,
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• Rosalinda Sepulveda   ORCID: orcid.org/0000-0003-1146-9552 4 ,
• Paulina A. Rebolledo   ORCID: orcid.org/0000-0002-9808-063X 5 , 6 ,
• Angelica Cuapio   ORCID: orcid.org/0000-0002-9451-1914 7 &
• Sonia Villapol   ORCID: orcid.org/0000-0002-6174-4113 8 , 9  

Scientific Reports volume  11 , Article number:  16144 ( 2021 ) Cite this article

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COVID-19 can involve persistence, sequelae, and other medical complications that last weeks to months after initial recovery. This systematic review and meta-analysis aims to identify studies assessing the long-term effects of COVID-19. LitCOVID and Embase were searched to identify articles with original data published before the 1st of January 2021, with a minimum of 100 patients. For effects reported in two or more studies, meta-analyses using a random-effects model were performed using the MetaXL software to estimate the pooled prevalence with 95% CI. PRISMA guidelines were followed. A total of 18,251 publications were identified, of which 15 met the inclusion criteria. The prevalence of 55 long-term effects was estimated, 21 meta-analyses were performed, and 47,910 patients were included (age 17–87 years). The included studies defined long-COVID as ranging from 14 to 110 days post-viral infection. It was estimated that 80% of the infected patients with SARS-CoV-2 developed one or more long-term symptoms. The five most common symptoms were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%), and dyspnea (24%). Multi-disciplinary teams are crucial to developing preventive measures, rehabilitation techniques, and clinical management strategies with whole-patient perspectives designed to address long COVID-19 care.

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Introduction.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected in China in December 2019. Since then, more than 175 million people worldwide have been infected after a year, and over 3.8 million people have died from the coronavirus disease 2019 (COVID-19) 1 . Although unprecedented efforts from the scientific and medical community have been directed to sequence, diagnose, treat, and prevent COVID-19, individuals' lasting effects after the acute phase of the disease are yet to be revealed.

The terminology has been confusing and not standardized. Different authors have used several terms to describe prolonged symptoms following COVID-19 illness, such as “Long COVID-19”, “post-acute COVID-19”, “persistent COVID-19 symptoms”, “chronic COVID-19”, “post-COVID-19 manifestations”, “long-term COVID-19 effects”, “post COVID-19 syndrome”, “ongoing COVID-19”, “long-term sequelae”, or “long-haulers” as synonyms. Most recently, the term “post-acute sequelae of SARS-CoV-2 infection” (PASC), “long-COVID-19”, and “post-acute COVID-19”, has been utilized 2 .

Symptoms, signs, or abnormal clinical parameters persisting two or more weeks after COVID-19 onset that do not return to a healthy baseline can potentially be considered long-term effects of the disease 3 . Although such alteration is mainly reported in severe and critical disease survivors, the lasting effects also occur in individuals with a mild infection who did not require hospitalization 4 . However, it has not yet been established how sex, gender, age, ethnicity, underlying health conditions, viral dose, or progression of COVID-19 significantly affect the risk of developing long-term effects of COVID-19 5 .

Since first reported, there has been a vast amount of social media patient groups, polls, comments, and scientific articles aiming to describe the chronicity of COVID-19. In parallel, hundreds of scientific publications, including cohorts studying specific effects of the disease and lists of case reports, have been described 6 . However, a broad overview of all the possible longstanding effects of COVID-19 is still needed. Therefore, our study aimed to perform a systematic review and meta-analysis of peer-reviewed studies to estimate the prevalence of all the symptoms, signs, or abnormal laboratory parameters extending beyond the acute phase of COVID-19 reported to date.

Database search strategy

The databases used to identify the studies were LitCOVID 7 , which includes all COVID articles in PubMed and Medline) and Embase. The studies classified in this meta-analysis included those published in the year 2020 (strictly before January 1st, 2021).

The search terms or keywords used were: (COVID-19) OR (COVID) OR (SARS-CoV-2) OR (coronavirus) OR (2019-nCoV) AND (long* OR haulers OR post OR chronic OR term OR complications OR recurrent OR lingering OR convalescent OR convalescence OR persist*. Given that LitCOVID includes all articles from MedLine, in the search in Embase, we excluded the articles from MedLine and those not related to COVID-19. The systematic review followed the Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) guidelines 8 , 9 . The registration of the review protocol was not previously done.

Inclusion and exclusion criteria

The inclusion criteria of the search were as follows: (1) to identify peer-reviewed human studies in English that reported symptoms, signs, or (2) laboratory parameters of patients at a post-COVID-19 stage (assessed 2 weeks or more after initial symptoms) in cohorts of COVID-19 patients. All types of studies, including randomized controlled trials, cohorts, and cross-sectional studies, were analyzed only when the cases (numerator) were part of a COVID-19 cohort (denominator). Titles, abstracts, and full texts of articles were independently screened by two authors (S.L.L. and T.W.O.). The complete article was reviewed in case of a difference of opinion on the inclusion based on title or abstract. Disagreement on the inclusion of a full-text article was discussed with all the authors. We exclude letters, editorials, reviewers, and commentaries. The exclusion criteria were: (1) not written in English; (2) have less than 100 patients included in the study. To estimate the prevalence of long-term erects in patients with COVID-19, we needed to include as a denominator the patients with acute COVID-19 (with and without long-term effects). Therefore, it is not possible to include case studies (usually less than 100 persons). The larger the denominator, the greater the reliability and generalizability of the estimate, and the lower the possibility of bias of including only patients that developed long COVID-19. We also exclude non-English language studies due to a lack of robust resources for accurate translation.

Data extraction and analysis

Data were extracted by five review authors (C.P., A.C., P.R., R.S., S.V.), and each study's quality was assessed using the Health States Quality-Controlled data (QCed) by two review authors independently (S.L.L. and T.W.O.). This index is described and recommended by the MetaXL Guidelines. It is specific to evaluate the quality of studies assessing prevalence. Relevant studies were then subjected to full-text screening by the same reviewers. The descriptive variables extracted were country, setting, follow-up time, the severity of COVID-19, sample size, mean age and percentage of gender, outcomes, and names used to describe the long-term effects of COVID-19 (Supplemental Table S1 ).

All the diseases, disorders, symptoms, signs, and laboratory parameters reported total numbers or percentages were included. Outcomes of interest were blood biomarkers and abnormal chest X-ray/CT reported for patients with SARS-CoV-2 infection in any setting. In addition, we assessed symptoms in several distinct systems; neurological, respiratory, gastrointestinal, cardiac, endocrine, dermatological, hepatic, and renal. When two time points were reported in the study, the outcomes assessed after the most extended follow-up were used.

Statistical analysis

For effects reported only in a single study, the prevalence was estimated by dividing the number of patients with each symptom by the total number of COVID-19 patients in the sample multiplied by 100 to calculate the percentage. For effects reported in two or more studies, meta-analyses using a random-effects model were performed using the MetaXL software to estimate the pooled prevalence, which uses a double arcsine transformation 10 . Prevalence with 95% confidence intervals (CI) was presented. Heterogeneity was assessed using I 2 statistics. The Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) 2020 guideline was followed. Given the heterogeneity expected, a random-effects model was used. Heterogeneity was assessed using the I 2 statistics. Values of 25%, 50%, and 75% for I 2 represented low, medium, and high heterogeneity. Sensitivity analyses were performed to assess the contribution of each study. Although none of the included definitions, or effects, were pre-specified, all of the effects and definitions were determined via each identified study. Publication bias in the selected study was evaluated by plotting the funnel plot and subsequent analyses. Each study's quality was assessed and described using the MetaXL Guidelines, which is specific to assess the quality of studies assessing prevalence. A description of what was considered is found in Supplemental Table S1 .

The title and abstract of 18,251 publications were screened. Of these, 82 full publications were reviewed for removal of duplication and initial eligibility assessment of title/abstract of all articles based on the eligibility criteria. Nineteen studies were excluded because they involved less than 100 persons. Thus, a total of 15 studies were selected to be analyzed. The process of study selection is presented in Fig.  1 .

Study selection. Preferred items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. Out of 15,917 identified studies and after application of the inclusion and exclusion criteria, 15 studies were included in the quantitative synthesis.

Characteristics of the included studies

There were eight studies from Europe and UK, three from the USA, one from Australia, China, Egypt, and Mexico (Table 1 ). The number of patient cohorts that were followed up in the studies ranged from 102 to 44,799. Adults ranging from 17 to 87 years of age were included. The patient follow-up time ranged from 14 to 110 days. Ten studies collected information from the patients using self-reported surveys. Two studies collected data from medical records and three by a clinical evaluation. Six out of the 15 studies included only patients hospitalized for COVID-19. The rest of the studies mixed with mild, moderate, and severe COVID-19 patients. There were no studies with overlapping samples. Two meta-analyses showed low heterogeneity ( I 2  < 25%), two showed medium heterogeneity, and the rest had high ( I 2  > 75%).

The populations were well defined. However, most studies were mixed mild, moderate, and severe patients—none of the studies were stratified by different by severity. The observation period was also well defined. However, none of the studies presented their results as stratified by observation. Therefore, it was impossible to identify the source of heterogeneity and it was not possible to assess how long each symptom lasted. Seven of the studies did not describe the system used to record the symptoms in detail, and most were self-reported retrospectively. A high score was given to the studies that administered an interview, included multi-sites surveys, and reported point prevalence. All of the studies received a score of greater than 8 (out of 11 points).

Abnormalities of laboratory parameters

Measurable parameters included 6 elevated laboratory parameters. An abnormal chest X-ray/CT was observed in 34% (95% CI 27–42) of the patients. Markers reported to be elevated were D-dimer (20%, 95% CI 6–39), N-terminal (NT)-pro hormone BNP (NT-proBNP), (11%, 95% CI 6–17), C-reactive protein (CRP) (8%, 95% CI 5–12), serum ferritin (8% 95% CI 4–14), procalcitonin (4% 95% CI 2–9) and interleukin-6 (IL-6) (3% 95% CI% 1–7) (Table 2 , Fig.  2 ).

Long-term effects of coronavirus disease 2019 (COVID-19). The meta-analysis of the studies included an estimate for one symptom or more reported that 80% of the patients with COVID-19 have long-term symptoms. CRP C-reactive protein, CT computed tomography, IL-6 Interleukin-6, NT-proBNP (NT)-pro hormone BNP, OCD Obsessive Compulsive Disorder, PTSD Post-traumatic stress disorder. This figure was created using Biorender.com.

Prevalence of long-term effects in COVID-19 patients

We identified a total of 55 long-term effects associated with COVID-19 in the literature reviewed (Table 2 ). Most of the effects correspond to clinical symptoms such as fatigue, headache, joint pain, anosmia, ageusia, etc. In addition, diseases such as stroke and diabetes mellitus were also present. Table 2 presents the prevalence of all the effects that were reported. It was possible to perform 21 meta-analyses. For the rest, the prevalence was estimated using one cohort. The meta-analysis of the studies (n = 7) that included an estimate for one symptom or more reported that 80% (95% CI 65–92) of the patients with COVID-19 have long-term symptoms.

Overall prevalence of most common symptoms

The 5 most common manifestations were fatigue (58%, 95% CI 42–73), headache (44%, 95% CI 13–78), attention disorder (27% 95% CI 19–36), hair loss (25%, 95% CI 17–34), dyspnea (24%, 95% CI 14–36) (Table 2 , Fig.  2 ). Other symptoms were related to lung disease (cough, chest discomfort, reduced pulmonary diffusing capacity, sleep apnea, and pulmonary fibrosis), cardiovascular (arrhythmias, myocarditis), neurological (dementia, depression, anxiety, attention disorder, obsessive–compulsive disorders), and others were unspecific such as hair loss, tinnitus, and night sweat (Table 2 , Fig.  2 , Supplemental Figure S1 ). A couple of studies reported that fatigue was more common in females, and one study reported that post-activity polypnea and alopecia were more common in females 4 , 24 . The rest of the studies did not stratify their results by age or sex.

This systematic review and meta-analysis shows that 80% (95% CI 65–92) of individuals with a confirmed COVID-19 diagnosis continue to have at least one overall effect beyond 2 weeks following acute infection. In total, 55 effects, including symptoms, signs, and laboratory parameters, were identified, with fatigue, anosmia, lung dysfunction, abnormal chest X-ray/CT, and neurological disorders being the most common (Table 1 , Fig.  2 ). Most of the symptoms were similar to the symptomatology developed during the acute phase of COVID-19. However, given that all of the surveys were predefined, there is a possibility that other effects have not yet been identified. In the following paragraphs, we will discuss the most common symptoms to illustrate how complex each one can be. However, further studies are needed to understand each symptom separately and in conjunction with the other symptoms. The five most common effects were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%), and dyspnea (24%). The recovery from COVID-19 should be more developed than checking for hospital discharge or testing negative for SARS-CoV-2 or positive for antibodies 25 .

Fatigue (58%) is the most common symptom of long and acute COVID-19 23 . It is present even after 100 days of the first symptom of acute COVID-19 4 , 23 . In addition, there are syndromes such as acute respiratory distress syndrome (ARDS), in which it has been observed that after a year, more than two-thirds of patients reported clinically significant fatigue symptoms 26 . The symptoms observed in post-COVID-19 patients, resemble in part the chronic fatigue syndrome (CFS), which includes the presence of severe incapacitating fatigue, pain, neurocognitive disability, compromised sleep, symptoms suggestive of autonomic dysfunction, and worsening of global symptoms following minor increases in physical and/or cognitive activity 27 , 28 , 29 , 30 , 31 . Myalgic encephalomyelitis (ME) or CFS is a complex and controversial clinical condition without established causative factors, and 90% of ME/CFS has not been diagnosed 32 . Possible causes of CFS include viruses, immune dysfunction, endocrine-metabolic dysfunction, and neuropsychiatric factors. The infectious agents related to CFS have been Epstein-Barr virus, cytomegalovirus, enterovirus, and herpesvirus 33 . It is tempting to speculate that SARS-CoV-2 can be added to the viral agents' list causing ME/CFS.

Several neuropsychiatric symptoms have been reported, headache (44%), attention disorder (27%), and anosmia (21%). Other symptoms were reported, which were not included in the publications, including brain fog and neuropathy 34 , 35 . The etiology of neuropsychiatric symptoms in COVID-19 patients is complex and multifactorial. They could be related to the direct effect of the infection, cerebrovascular disease (including hypercoagulation) 36 , physiological compromise (hypoxia), side effects of medications, and social aspects of having a potentially fatal illness 37 . Adults have a double risk of being newly diagnosed with a psychiatric disorder after the COVID-19 diagnosis 37 , and the most common psychiatric conditions presented were anxiety disorders, insomnia, and dementia. Sleep disturbances might contribute to the presentation of psychiatric disorders 38 . Prompt diagnosis and intervention of any neuropsychiatric care is recommended for all patients recovering from COVID-19. An increase in mental health attention models in hospitals and communities is needed during and after the COVID-19 pandemic. Hair loss after COVID-19 could be considered as telogen effluvium, defined by diffuse hair loss after an important systemic stressor or infection. Premature follicular transitions cause it from the active growth phase (anagen) to the resting phase (telogen). It is a self-limiting condition that lasts approximately 3 months, but it could cause emotional distress 39 .

Dyspnea and cough were found in 24% and 19% of patients, respectively (Table 2 , Fig.  2 ). In addition, abnormalities in CT lung scans persisted in 35% of patients even after 60–100 days from the initial presentation. In a follow-up study conducted in China among non-critical cases of hospitalized patients with COVID-19, radiographic changes persisted in nearly two-thirds of patients 90 days after discharge 40 . Although most of the available studies do not include baseline pulmonary dysfunction or radiographic abnormalities, findings indicate improvement or resolution of abnormal CT findings. Previous data from recovered patients with other viral pneumonia 41 , 42 , also found residual radiographic changes. Abnormalities in pulmonary function, such as decreased diffusion capacity for carbon monoxide, were present among 10% of patients in this meta-analysis. Although these findings are not as high as compared to other available studies of survivors with COVID-19 or SARS, where the estimate of lung dysfunction is 53% and 28% respectively 43 , 44 , the reasons behind these differences could be distinct follow-up periods, definitions of pulmonary dysfunction, or characteristics of the patient population. Nevertheless, residual radiographic findings or lung function abnormalities require additional investigation on their clinical relevance and long-term consequences.

The immune-mediated tissue damage in COVID-19 involves cellular and humoral responses, but the immunity to SARS-CoV-2 and the protection to reinfection or a final viral 40 , 45 clearance is unknown. Also, the reason why some patients experience long-term symptoms after COVID-19 is uncertain. This could be partially explained by host-controlled factors that influence the outcome of the viral infection, including genetic susceptibility, age of the host when infected, dose and route of infection, induction of anti-inflammatory cells and proteins, presence of concurrent infections, past exposure to cross-reactive agents, etc. Whether SARS-CoV-2 can cause substantial tissue damage leading to a chronic form of the disease such as the chronic lesions in convalescence observed in other viruses such as human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), and some herpesviruses is still unknown.

One study was excluded because it did not provide a denominator, and therefore it was not possible to estimate the prevalence 46 . In such a study, the authors performed a survey in a Facebook group of patients who previously had COVID-19 and compared the symptoms of those hospitalized with mild to moderate symptoms. They concluded that both groups had symptoms after 3 months of having COVID-19. Symptoms that were not mentioned in any of the articles we studied include sudden loss of body weight, ear pain, eye problems, sneezing, cold nose, burning feeling in the trachea, dizziness, heart palpitations, pain/burning feeling in the lungs, pain between the shoulder blades, Sicca syndrome, vertigo, body aches, and confusion 3 , 12 .

The results assessed in the present study are in line with the current scientific knowledge on other coronaviruses, such as those producing SARS and MERS, both clinical sharing characteristics with COVID-19, including post symptoms. For example, studies on SARS survivors have shown lung abnormalities months after infection. After a 1-year follow-up, a study showed that 28% of the survivors presented decreased lung function and pulmonary fibrosis signs 44 , 47 , 48 . In addition, MERS survivors showed pulmonary fibrosis (33%) 49 . Regarding psychiatric symptoms, a study reported high levels of depression, anxiety, and post-traumatic stress disorder (PTSD) 37 in the long term in patients previously infected with other coronaviruses.

To assure that future healthcare providers, researchers, and educators recognize the effects of long-term COVID19 that are sex- and age-specific related, it is essential to classify the groups according to such variables to make better decisions about prevention diagnosis and disease management.

Limitations of this systematic review and meta-analyses include the small sample size for some outcomes, making it difficult to generalize these results to the general population. The variation in the definition of some outcomes and markers and the possibility of bias. For example, several studies that used a self-reported questionnaire could result in reporting bias. In addition, the studies were very heterogeneous, mainly due to the follow-up time references and the mixture of patients who had moderate and severe COVID-19. All of the studies assessed had performed their internal pre-definition of symptoms, and therefore there is the possibility that essential outcomes were not reported. Another limitation is that, given that COVID-19 is a new disease, it is impossible to determine how long these effects will last. To decrease heterogeneity and better understand the long-term effects of COVID-19, there is a need for studies to stratify by age, previous comorbidities, the severity of COVID-19 (including asymptomatic), and the duration of each symptom. To determine whether these long-term effects either complicate previous diseases or continue COVID-19, there is a need for prospective cohort studies. The baseline characteristics should be well established. To obtain more accurate meta-analyses, there is an urgent need to have a standard definition of long-COVID-19. Currently, post-COVID-19 symptoms that develop during or after COVID-19 are defined if they continue for ≥ 12 weeks (“long-COVID-19”), and not explained by an alternative diagnosis 2 , 6 , 50 . There is a need to standardize biological measures such as peripheral blood markers of genetic, inflammatory, immune, and metabolic function to compare studies. Besides studying pre-defined symptoms and characteristics, an open question should be included. Proper documentation in medical charts by health care providers and the flexibility and collaboration from the patients to report their symptoms are of equal importance.

More evidence and research from multi-disciplinary teams are crucial to understanding the causes, mechanisms, and risks to develop preventive measures, rehabilitation techniques, and clinical management strategies with whole-patient perspectives designed to address the after-COVID-19 care. There is a need for more information about prospective studies to better evaluate the natural course of COVID-19 infection and define the long- COVID-19 syndrome. From the clinical point of view, physicians should be aware of the symptoms, signs, and biomarkers present in patients previously affected by COVID-19 to promptly assess, identify and halt long COVID-19 progression, minimize the risk of chronic effects help reestablish pre-COVID-19 health. Management of all these effects requires further understanding to design individualized, dynamic cross-sectoral interventions in Post-COVID-19 clinics with multiple specialties, including graded exercise, physical therapy, frequent medical evaluations, and cognitive behavioral therapy when required 51 , 52 .

Data availability

All data relevant to the study are included in the article or uploaded as supplementary information. In addition, the datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Acute respiratory distress syndrome

Credible interval

Coronavirus disease 2019

Chronic fatigue syndrome

C-reactive protein

Computed tomography

Human immunodeficiency virus

Hepatitis C virus

Hepatitis B virus

Interleukin-6

Pro hormone BNP

Myalgic encephalomyelitis

Middle East respiratory syndrome

Obsessive Compulsive Disorder

Post-traumatic stress disorder

Severe acute respiratory syndrome coronavirus 2

Severe acute respiratory syndrome

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Figure 2 was created by S.V. using Biorender.com.

This article was funded by Houston Methodist Research Institute (S.V.).

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T.W.O. and C.P. performed the statistical analysis. S.L.L., T.W.O., C.P., R.S., P.R., A.C. and S.V. performed the literature search, collected the data, wrote the manuscript, and made edits. S.L.L. and S.V. were mainly responsible for the interpretation of the data and preparing the final version. S.V. created the figures. All authors provided critical feedback and contributed to the final manuscript. Correspondence and requests for materials should be addressed to S.V.

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Lopez-Leon, S., Wegman-Ostrosky, T., Perelman, C. et al. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep 11 , 16144 (2021). https://doi.org/10.1038/s41598-021-95565-8

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• Evropi Theodoratou , professor of cancer epidemiology and global health 4 5 ,
• Xiaomeng Zhang , research student 4 ,
• Ashmika Motee , research student 4 ,
• Danny Liew , professor of medical outcomes and health economics 1 2 ,
• Dragan Ilic , professor of medical education and public health 1
• 1 School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004 VIC, Australia
• 2 Monash Outcomes Research and health Economics (MORE) Unit, Monash University, VIC, Australia
• 3 Torrens University, VIC, Australia
• 4 Centre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK
• 5 Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
• 6 School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
• Correspondence to: S Talic stella.talic{at}monash.edu
• Accepted 21 October 2021

Objective To review the evidence on the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.

Design Systematic review and meta-analysis.

Data sources Medline, Embase, CINAHL, Biosis, Joanna Briggs, Global Health, and World Health Organization COVID-19 database (preprints).

Eligibility criteria for study selection Observational and interventional studies that assessed the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.

Main outcome measures The main outcome measure was incidence of covid-19. Secondary outcomes included SARS-CoV-2 transmission and covid-19 mortality.

Data synthesis DerSimonian Laird random effects meta-analysis was performed to investigate the effect of mask wearing, handwashing, and physical distancing measures on incidence of covid-19. Pooled effect estimates with corresponding 95% confidence intervals were computed, and heterogeneity among studies was assessed using Cochran’s Q test and the I 2 metrics, with two tailed P values.

Results 72 studies met the inclusion criteria, of which 35 evaluated individual public health measures and 37 assessed multiple public health measures as a “package of interventions.” Eight of 35 studies were included in the meta-analysis, which indicated a reduction in incidence of covid-19 associated with handwashing (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I 2 =12%), mask wearing (0.47, 0.29 to 0.75, I 2 =84%), and physical distancing (0.75, 0.59 to 0.95, I 2 =87%). Owing to heterogeneity of the studies, meta-analysis was not possible for the outcomes of quarantine and isolation, universal lockdowns, and closures of borders, schools, and workplaces. The effects of these interventions were synthesised descriptively.

Conclusions This systematic review and meta-analysis suggests that several personal protective and social measures, including handwashing, mask wearing, and physical distancing are associated with reductions in the incidence covid-19. Public health efforts to implement public health measures should consider community health and sociocultural needs, and future research is needed to better understand the effectiveness of public health measures in the context of covid-19 vaccination.

Systematic review registration PROSPERO CRD42020178692.

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Introduction

The impact of SARS-CoV-2 on global public health and economies has been profound. 1 As of 14 October 2021, there were 239 007 759 million cases of confirmed covid-19 and 4 871 841 million deaths with covid-19 worldwide. 2

A variety of containment and mitigation strategies have been adopted to adequately respond to covid-19, with the intention of deferring major surges of patients in hospitals and protecting the most vulnerable people from infection, including elderly people and those with comorbidities. 3 Strategies to achieve these goals are diverse, commonly based on national risk assessments that include estimation of numbers of patients requiring hospital admission and availability of hospital beds and ventilation support.

Globally, vaccination programmes have proved to be safe and effective and save lives. 4 5 Yet most vaccines do not confer 100% protection, and it is not known how vaccines will prevent future transmission of SARS-CoV-2, 6 given emerging variants. 7 8 9 The proportion of the population that must be vaccinated against covid-19 to reach herd immunity depends greatly on current and future variants. 10 This vaccination threshold varies according to the country and population’s response, types of vaccines, groups prioritised for vaccination, and viral mutations, among other factors. 6 Until herd immunity to covid-19 is reached, regardless of the already proven high vaccination rates, 11 public health preventive strategies are likely to remain as first choice measures in disease prevention, 12 particularly in places with a low uptake of covid-19 vaccination. Measures such as lockdown (local and national variant), physical distancing, mandatory use of face masks, and hand hygiene have been implemented as primary preventive strategies to curb the covid-19 pandemic. 13

Public health (or non-pharmaceutical) interventions have been shown to be beneficial in fighting respiratory infections transmitted through contact, droplets, and aerosols. 14 15 Given that SARS-CoV-2 is highly transmissible, it is a challenge to determine which measures might be more effective and sustainable for further prevention.

Substantial benefits in reducing mortality were observed in countries with universal lockdowns in place, such as Australia, New Zealand, Singapore, and China. Universal lockdowns are not, however, sustainable, and more tailored interventions need to be considered; the ones that maintain social lives and keep economies functional while protecting high risk individuals. 16 17 Substantial variation exists in how different countries and governments have applied public health measures, 18 and it has proved a challenge for assessing the effectiveness of individual public health measures, particularly in policy decision making. 19

Previous systematic reviews on the effectiveness of public health measures to treat covid-19 lacked the inclusion of analytical studies, 20 a comprehensive approach to data synthesis (focusing only on one measure), 21 a rigorous assessment of effectiveness of public health measures, 22 an assessment of the certainty of the evidence, 23 and robust methods for comparative analysis. 24 To tackle these gaps, we performed a systematic review of the evidence on the effectiveness of both individual and multiple public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality. When feasible we also did a critical appraisal of the evidence and meta-analysis.

This systematic review and meta-analysis were conducted in accordance with PRISMA 25 (supplementary material 1, table 1) and with PROSPERO (supplementary material 1, table 2).

Eligibility criteria

Articles that met the population, intervention, comparison, outcome, and study design criteria were eligible for inclusion in this systematic review (supplementary material 1, table 3). Specifically, preventive public health measures that were tested independently were included in the main analysis. Multiple measures, which generally contain a “package of interventions”, were included as supplementary material owing to the inability to report on the individual effectiveness of measures and comparisons on which package led to enhanced outcomes. The public health measures were identified from published World Health Organization sources that reported on the effectiveness of such measures on a range of communicable diseases, mostly respiratory infections, such as influenza.

Given that the scientific community is concerned about the ability of the numerous mathematical models, which are based on assumptions, to predict the course of virus transmission or effectiveness of interventions, 26 this review focused only on empirical studies. We excluded case reports and case studies, modelling and simulation studies, studies that provided a graphical summary of measures without clear statistical assessments or outputs, ecological studies that provided a descriptive summary of the measures without assessing linearity or having comparators, non‐empirical studies (eg, commentaries, editorials, government reports), other reviews, articles involving only individuals exposed to other pathogens that can cause respiratory infections, such as severe acute respiratory syndrome or Middle East respiratory syndrome, and articles in a language other than English.

Information sources

We carried out electronic searches of Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature, Ebsco), Global Health, Biosis, Joanna Briggs, and the WHO COVID-19 database (for preprints). A clinical epidemiologist (ST) developed the initial search strategy, which was validated by two senior medical librarians (LR and MD) (supplementary material 1, table 4). The updated search strategy was last performed on 7 June 2021. All citations identified from the database searches were uploaded to Covidence, an online software designed for managing systematic reviews, 27 for study selection.

Study selection

Authors ST, DG, SS, AM, ET, JR, XL, WX, IME, and XZ independently screened the titles and abstracts and excluded studies that did not match the inclusion criteria. Discrepancies were resolved in discussion with the main author (ST). The same authors retrieved full text articles and determined whether to include or exclude studies on the basis of predetermined selection criteria. Using a pilot tested data extraction form, authors ST, SS, AM, JR, XL, WX, AM, IME, and XZ independently extracted data on study design, intervention, effect measures, outcomes, results, and limitations. ST, SS, AM, and HW verified the extracted data. Table 5 in supplementary material 1 provides the specific criteria used to assess study designs. Given the heterogeneity and diversity in how studies defined public health measures, we took a common approach to summarise evidence of these interventions (supplementary material 1, table 6).

Risk of bias within individual studies

SS, JR, XL, WX, IME, and XZ independently assessed risk of bias for each study, which was cross checked by ST and HW. For non-interventional observational studies, a ROBINS-I (risk of bias in non-randomised studies of interventions) risk of bias tool was used. 28 For interventional studies, a revised tool for assessing risk of bias in randomised trials (RoB 2) tool was used. 29 Reviewers rated each domain for overall risk of bias as low, moderate, high, or serious/critical.

Data synthesis

The DerSimonian and Laird method was used for random effects meta-analysis, in which the standard error of the study specific estimates was adjusted to incorporate a measure of the extent of variation, or heterogeneity, among the effects observed for public health measures across different studies. It was assumed that the differences between studies are a result of different, yet related, intervention effects being estimated. If fewer than five studies were included in meta-analysis, we applied a recommended modified Hartung-Knapp-Sidik-Jonkman method. 30

Statistical analysis

Because of the differences in the effect metrics reported by the included studies, we could only perform quantitative data synthesis for three interventions: handwashing, face mask wearing, and physical distancing. Odds ratios or relative risks with corresponding 95% confidence intervals were reported for the associations between the public health measures and incidence of covid-19. When necessary, we transformed effect metrics derived from different studies to allow pooled analysis. We used the Dersimonian Laird random effects model to estimate pooled effect estimates along with corresponding 95% confidence intervals for each measure. Heterogeneity among individual studies was assessed using the Cochran Q test and the I 2 test. 31 All statistical analyses were conducted in R (version 4.0.3) and all P values were two tailed, with P=0.05 considered to be significant. For the remaining studies, when meta-analysis was not feasible, we reported the results in a narrative synthesis.

Public and patient involvement

No patients or members of the public were directly involved in this study as no primary data were collected. A member of the public was, however, asked to read the manuscript after submission.

A total of 36 729 studies were initially screened, of which 36 079 were considered irrelevent. After exclusions, 650 studies were eligible for full text review and 72 met the inclusion criteria. Of these studies, 35 assessed individual interventions and were included in the final synthesis of results ( fig 1 ) and 37 assessed multiple interventions as a package and are included in supplementary material 3, tables 2 and 3. The included studies comprised 34 observational studies and one interventional study, eight of which were included in the meta-analysis.

Flow of articles through the review. WHO=World Health Organization

Risk of bias

According to the ROBINS-I tool, 28 the risk of bias was rated as low in three studies, 32 33 34 moderate in 24 studies, 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 and high to serious in seven studies. 59 60 61 62 63 64 65 One important source of serious or critical risk of bias in most of the included studies was major confounding, which was difficult to control for because of the novel nature of the pandemic (ie, natural settings in which multiple interventions might have been enforced at once, different levels of enforcement across regions, and uncaptured individual level interventions such as increased personal hygiene). Variations in testing capacity and coverage, changes to diagnostic criteria, and access to accurate and reliable outcome data on covid-19 incidence and covid-19 mortality, was a source of measurement bias for numerous studies ( fig 2 ). These limitations were particularly prominent early in the pandemic, and in low income environments. 47 52 62 63 65 The randomised controlled trial 66 was rated as moderate risk of bias according to the ROB-2 tool. Missing data, losses to follow-up, lack of blinding, and low adherence to intervention all contributed to the reported moderate risk. Tables 1 and 2 in supplementary material 2 summarise the risk of bias assessment for each study assessing individual measures.

Summary of risk of bias across studies assessing individual measures using risk of bias in non-randomised studies of interventions (ROBINS-I) tool

Study characteristics

Studies assessing individual measures.

Thirty five studies provided estimates on the effectiveness of an individual public health measures. The studies were conducted in Asia (n=11), the United States (n=9), Europe (n=7), the Middle East (n=3), Africa (n=3), South America (n=1), and Australia (n=1). Thirty four of the studies were observational and one was a randomised controlled trial. The study designs of the observational studies comprised natural experiments (n=11), quasi-experiments (n=3), a prospective cohort (n=1), retrospective cohorts (n=8), case-control (n=2), and cross sectional (n=9). Twenty six studies assessed social measures, 32 34 35 37 38 39 40 41 42 44 46 47 48 52 53 55 56 57 58 59 60 61 63 64 65 67 12 studies assessed personal protective measures, 36 43 45 49 50 57 58 60 63 66 68 three studies assessed travel related measures, 54 58 62 and one study assessed environmental measures 57 (some interventions overlapped across studies). The most commonly measured outcome was incidence of covid-19 (n=18), followed by SARS-CoV-2 transmission, measured as reproductive number, growth number, or epidemic doubling time (n=13), and covid-19 mortality (n=8). Table 1 in supplementary material 3 provides detailed information on each study.

Effects of interventions

Personal protective measures.

Handwashing and covid-19 incidence —Three studies with a total of 292 people infected with SARS-CoV-2 and 10 345 participants were included in the analysis of the effect of handwashing on incidence of covid-19. 36 60 63 Overall pooled analysis suggested an estimated 53% non-statistically significant reduction in covid-19 incidence (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I 2 =12%) ( fig 3 ). A sensitivity analysis without adjustment showed a significant reduction in covid-19 incidence (0.49, 0.33 to 0.72, I 2 =12%) ( fig 4 ). Risk of bias across the three studies ranged from moderate 36 60 to serious or critical 63 ( fig 2 ).

Meta-analysis of evidence on association between handwashing and incidence of covid-19 using modified Hartung-Knapp-Sidik-Jonkman adjusted random effect model

Meta-analysis of evidence on association between handwashing and incidence of covid-19 using unadjusted random effect model

Mask wearing and covid-19 incidence —Six studies with a total of 2627 people with covid-19 and 389 228 participants were included in the analysis examining the effect of mask wearing on incidence of covid-19 ( table 1 ). 36 43 57 60 63 66 Overall pooled analysis showed a 53% reduction in covid-19 incidence (0.47, 0.29 to 0.75), although heterogeneity between studies was substantial (I 2 =84%) ( fig 5 ). Risk of bias across the six studies ranged from moderate 36 57 60 66 to serious or critical 43 63 ( fig 2 ).

Study characteristics and main results from studies that assessed individual personal protective and environmental measures

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Meta-analysis of evidence on association between mask wearing and incidence of covid-19 using unadjusted random effect model

Mask wearing and transmission of SARS-CoV-2, covid-19 incidence, and covid-19 mortality —The results of additional studies that assessed mask wearing (not included in the meta-analysis because of substantial differences in the assessed outcomes) indicate a reduction in covid-19 incidence, SARS-CoV-2 transmission, and covid-19 mortality. Specifically, a natural experiment across 200 countries showed 45.7% fewer covid-19 related mortality in countries where mask wearing was mandatory ( table 1 ). 49 Another natural experiment study in the US reported a 29% reduction in SARS-CoV-2 transmission (measured as the time varying reproductive number Rt) (risk ratio 0.71, 95% confidence interval 0.58 to 0.75) in states where mask wearing was mandatory. 58

A comparative study in the Hong Kong Special Administrative Region reported a statistically significant lower cumulative incidence of covid-19 associated with mask wearing than in selected countries where mask wearing was not mandatory ( table 1 ). 68 Similarly, another natural experiment involving 15 US states reported a 2% statistically significant daily decrease in covid-19 transmission (measured as case growth rate) at ≥21 days after mask wearing became mandatory, 50 whereas a cross sectional study reported that a 10% increase in self-reported mask wearing was associated with greater odds for control of SARS-CoV-2 transmission (adjusted odds ratio 3.53, 95% confidence interval 2.03 to 6.43). 45 The five studies were rated at moderate risk of bias ( fig 2 ).

Environmental measures

Disinfection in household and covid-19 incidence.

Only one study, from China, reported the association between disinfection of surfaces and risk of secondary transmission of SARS-CoV-2 within households ( table 1 ). 57 The study assessed disinfection retrospectively by asking participants about their “daily use of chlorine or ethanol-based disinfectant in households,” and observed that use of disinfectant was 77% effective at reducing SARS-CoV-2 transmission (odds ratio 0.23, 95% confidence interval 0.07 to 0.84). The study did not collect data on the concentration of the disinfectant used by participants and was rated at moderate risk of bias ( fig 2 ).

Social measures

Physical distancing and covid-19 incidence.

Five studies with a total of 2727 people with SARS-CoV-2 and 108 933 participants were included in the analysis that examined the effect of physical distancing on the incidence of covid-19. 37 53 57 60 63 Overall pooled analysis indicated a 25% reduction in incidence of covid-19 (relative risk 0.75, 95% confidence interval 0.59 to 0.95, I 2 =87%) ( fig 6 ). Heterogeneity among studies was substantial, and risk of bias ranged from moderate 37 53 57 60 to serious or critical 63 ( fig 2 ).

Meta-analysis of evidence on association between physical distancing and incidence of covid-19 using unadjusted random effect model

Physical distancing and transmission of SARS-CoV-2 and covid-19 mortality

Studies that assessed physical distancing but were not included in the meta-analysis because of substantial differences in outcomes assessed, generally reported a positive effect of physical distancing ( table 2 ). A natural experiment from the US reported a 12% decrease in SARS-CoV-2 transmission (relative risk 0.88, 95% confidence interval 0.86 to 0.89), 40 and a quasi-experimental study from Iran reported a reduction in covid-19 related mortality (β −0.07, 95% confidence interval −0.05 to −0.10; P<0.001). 47 Another comparative study in Kenya also reported a reduction in transmission of SARS-CoV-2 after physical distancing was implemented, reporting 62% reduction in overall physical contacts (reproductive number pre-intervention was 2.64 and post-intervention was 0.60 (interquartile range 0.50 to 0.68)). 61 These three studies were rated at moderate risk of bias 40 61 to serious or critical risk of bias 47 ( fig 2 ).

Study characteristics and main results from studies assessing individual social measures

Stay at home or isolation and transmission of SARS-CoV-2

All the studies that assessed stay at home or isolation measures reported reductions in transmission of SARS-CoV-2 ( table 2 ). A retrospective cohort study from the US reported a significant reduction in the odds of having a positive reproductive number (R0) result (odds ratio 0.07, 95% confidence interval 0.01 to 0.37), 41 and a natural experiment reported a 51% reduction in time varying reproductive number (Rt) (risk ratio 0.49, 95% confidence interval 0.43 to 0.54). 58

A study from the UK reported a 74% reduction in the average daily number of contacts observed for each participant and estimated a decrease in reproductive number: the reproductive number pre-intervention was 3.6 and post-intervention was 0.60 (95% confidence interval 0.37 to 0.89). 65 Similarly, an Iranian study projected the reproductive number using serial interval distribution and the number of incidence cases and found a significant decrease: the reproductive number pre-intervention was 2.70 and post-intervention was 1.13 (95% confidence interval 1.03 to 1.25). 55 Three of the studies were rated at moderate to serious or critical risk of bias, 55 58 65 and one study was rated at low risk of bias 41 ( fig 2 ).

Quarantine and incidence and transmission of SARS-CoV-2

Quarantine was assessed in two studies ( table 2 ). 34 59 A prospective cohort study from Saudi Arabia reported a 4.9% decrease in the incidence of covid-19 at eight weeks after the implementation of quarantine. 34 This study was rated at low risk of bias ( fig 2 ). A retrospective cohort study from India reported a 14 times higher risk of SARS-CoV-2 transmission associated with no quarantine compared with strict quarantine (odds ratio 14.44, 95% confidence interval 2.42 to 86.17). 59 This study was rated at moderate risk of bias ( fig 2 ).

School closures and covid-19 incidence and covid-19 mortality

Two studies assessed the effectiveness of school closures on transmission of SARS-CoV-2, incidence of covid-19, or covid-19 mortality ( table 2 ). 44 48 A US population based longitudinal study reported on the effectiveness of state-wide closure of primary and secondary schools and observed a 62% decrease (95% confidence interval −49% to −71%) in incidence of covid-19 and a 58% decrease (−46% to−68%) in covid-19 mortality. 48 Conversely, a natural experiment from Japan reported no effect of school closures on incidence of covid-19 (α coefficient 0.08, 95% confidence interval −0.36 to 0.65). 44 Both studies were rated at moderate risk of bias ( fig 2 ).

School closures and transmission of SARS-CoV-2

Two natural experiments from the US reported a reduction in transmission (ie, reproductive number); with one study reporting a reduction of 13% (relative risk 0.87, 95% confidence interval 0.86 to 0.89) 40 and another reporting a 10% (0.90, 0.86 to 0.93) reduction ( table 2 ). 58 A Swedish study reported an association between school closures and a small increase in confirmed SARS-CoV-2 infections in parents (odds ratio 1.17, 95% confidence interval 1.03 to 1.32), but observed that teachers in lower secondary schools were twice as likely to become infected than teachers in upper secondary schools (2.01, 1.52 to 2.67). 32 All three studies were rated at moderate risk of bias ( fig 2 ).

Business closures and transmission of SARS-CoV-2

Two natural experiment studies assessed business closures across 50 US states and reported reductions in transmission of SARS-CoV-2 ( table 2 ). 40 58 One of the studies observed a significant reduction in transmission of 12% (relative risk 0.88, 95% confidence interval 0.86 to 0.89) 40 and the other reported a significant 16% (risk ratio 0.84, 0.79 to 0.90) reduction. 58 Both studies were rated at moderate risk of bias ( fig 2 ).

Lockdown and incidence of covid-19

A natural experiment involving 202 countries suggested that countries that implemented universal lockdown had fewer new cases of covid-19 than countries that did not (β coefficient −235.8 (standard error −11.04), P<0.01) ( table 2 ). 52 An Indian quasi-experimental study reported a 10.8% reduction in incidence of covid-19 post-lockdown, 56 whereas a South African retrospective cohort study observed a 14.1% reduction in risk after implementation of universal lockdown ( table 2 ). 46 These studies were rated at high risk of bias 52 and moderate risk of bias 46 56 ( fig 2 ).

Lockdown and covid-19 mortality

The three studies that assessed universal lockdown and covid-19 mortality generally reported a decrease in mortality ( table 2 ). 35 38 42 A natural experiment study involving 45 US states reported a decrease in covid-19 related mortality of 2.0% (95% confidence interval −3.0% to 0.9%) daily after lockdown had been made mandatory. 35 A Brazilian quasi-experimental study reported a 27.4% average difference in covid-19 related mortality rates in the first 25 days of lockdown. 42 In addition, a natural experiment study reported about 30% and 60% reductions in covid-19 related mortality post-lockdown in Italy and Spain over four weeks post-intervention, respectively. 38 All three studies were rated at moderate risk of bias ( fig 2 ).

Lockdown and transmission of SARS-CoV-2

Four studies assessed universal lockdown and transmission of SARS-CoV-2 during the first few months of the pandemic ( table 2 ). The decrease in reproductive number (R0) ranged from 1.27 in Italy (pre-intervention 2.03, post-intervention 0.76) 39 to 2.09 in India (pre-intervention 3.36, post-intervention 1.27), 64 and 3.97 in China (pre-intervention 4.95, post-intervention 0.98). 33 A natural experiment from the US reported that lockdown was associated with an 11% reduction in transmission of SARS-CoV-2 (relative risk 0.89, 95% confidence interval 0.88 to 0.91). 40 All the studies were rated at low risk of bias 33 39 to moderate risk 40 64 ( fig 2 ).

Travel related measures

Restricted travel and border closures.

Border closure was assessed in one natural experiment study involving nine African countries ( table 3 ). 62 Overall, the countries recorded an increase in the incidence of covid-19 after border closure. These studies concluded that the implementation of border closures within African countries had minimal effect on the incidence of covid-19. The study had important limitations and was rated at serious or critical risk of bias. In the US, a natural experiment study reported that restrictions on travel between states contributed about 11% to a reduction in SARS-CoV-2 transmission ( table 3 ). 36 The study was rated at moderate risk of bias ( fig 2 ).

Study characteristics and main results from studies that assessed individual travel measures

Entry and exit screening (virus or symptom screening)

One retrospective cohort study assessed screening of symptoms, which involved testing 65 000 people for fever ( table 3 ). 54 The study found that screening for fever lacked sensitivity (ranging from 18% to 24%) in detecting people with SARS-CoV-2 infection. This translated to 86% of the population with SARS-CoV-2 remaining undetected when screening for fever. The study was rated at moderate risk of bias ( fig 2 ).

Multiple public health measures

Overall, 37 studies provided estimates on the effectiveness of multiple public health measures, assessed as a collective group. Studies were mostly conducted in Asia (n=15), the US (n=11), Europe (n=6), Africa (n=4), and South America (n=1). All the studies were observational. The most commonly measured outcome was transmission of disease (ie, measured as reproductive number, growth number, or epidemic doubling time) (n=23), followed by covid-19 incidence (n=19) and covid-19 mortality (n=8). This review attempted to assess the overall effectiveness of the public health intervention packages by reporting the percentage difference in outcome before and after implementation of measures or between regions or countries studied. Eleven of the 37 included studies noted a difference of between 26% and 50% in transmission of SARS-CoV-2 and incidence of covid-19, 70 71 72 73 74 75 76 77 78 79 80 nine noted a difference of between 51% and 75% in SARS-CoV-2 transmission, covid-19 incidence, and covid-19 mortality, 81 82 83 84 85 86 87 88 89 and 14 noted a difference of more than 75% in transmission of SARS-CoV-2, covid-19 incidence and covid-19 mortality. 79 80 89 90 91 92 93 94 95 96 97 98 99 100 For the remaining studies, the overall effectiveness was not assessed owing to a lack of comparators (see supplementary material 3, table 3). Two studies that assessed universal lockdown and physical distancing reported a decrease of between 0% and 25% in SARS-CoV-2 transmission and covid-19 incidence. 79 101 Studies that included school and workplace closures, 91 95 96 isolation or stay at home measures, 80 94 or a combination of both 79 89 93 97 98 99 reported decreases of more than 75% in SARS-CoV-2 transmission. Supplementary material 3, table 2 provides detailed information on each study.

Worldwide, government and public health organisations are mitigating the spread of SARS-CoV-2 by implementing various public health measures. This systematic review identified a statistically significant reduction in the incidence of covid-19 through the implementation of mask wearing and physical distancing. Handwashing interventions also indicated a substantial reduction in covid-19 incidence, albeit not statistically significant in the adjusted model. As the random effects model tends to underestimate confidence intervals when a meta-analysis includes a small number of individual studies (<5), the adjusted model for handwashing showed a statistically non-significant association in reducing the incidence of covid-19 compared with the unadjusted model.

Overall effectiveness of these interventions was affected by clinical heterogeneity and methodological limitations, such as confounding and measurement bias. It was not possible to evaluate the impact of type of face maks (eg, surgical, fabric, N95 respirators) and compliance and frequency of wearing masks owing to a lack of data. Similarly, it was not feasible to assess the differences in effect that different recommendations for physical distancing (ie, 1.5 m, 2m, or 3 m) have as preventive strategies.

The effectiveness of measures such as universal lockdowns and closures of businesses and schools for the containment of covid-19 have largely been effective, but depended on early implementation when incidence rates of covid-19 were still low. 42 52 58 Only Japan reported no decrease in covid-19 incidence after school closures, 44 and other studies found that different public health measures were sometimes implemented simultaneously or soon after one another, thus the results should be interpreted with caution. 32 46 56

Isolation or stay at home was an effective measure in reducing the transmission of SARS-CoV-2, but the included studies used results for mobility to assess stay at home or isolation and therefore could have been limited by potential flaws in publicly available phone data, 41 58 102 and variations in the enforcement of public health measures in different states or regions were not assessed. 55 58 102 Quarantine was found to be as effective in reducing the incidence of covid-19 and transmission of SARS-CoV-2, yet variation in testing and case detection in low income environments was substantial. 59 96 98 Another study reported that quarantine was effective in reducing the transmission of SARS-CoV-2 in a cohort with a low prevalence of the virus, yet it is unknown if the same effect would be observed with higher prevalence. 34

It was not possible to draw conclusions about the effectiveness of restricted travel and full border closures because the number of empirical studies was insufficient. Single studies identified that border closure in Africa had a minimal effect in reducing SARS-CoV-2 transmission, but the study was assessed as being at high risk of bias. 62 Screening for fever was also identified to be ineffective, with only 24% of positive cases being captured by screening. 54

Comparison with other studies

Previous literature reviews have identified mask wearing as an effective measure for the containment of SARS-CoV-2 103 ; the caveat being that more high level evidence is required to provide unequivocal support for the effectiveness of the universal use of face masks. 104 105 Additional empirical evidence from a recent randomised controlled trial (originally published as a preprint) indicates that mask wearing achieved a 9.3% reduction in seroprevalence of symptomatic SARS-CoV-2 infection and an 11.9% reduction in the prevalence of covid-19-like symptoms. 106 Another systematic review showed stronger effectiveness with the use of N95, or similar, respirators than disposable surgical masks, 107 and a study evaluating the protection offered by 18 different types of fabric masks found substantial heterogeneity in protection, with the most effective mask being multilayered and tight fitting. 108 However, transmission of SARS-CoV-2 largely arises in hospital settings in which full personal protective measures are in place, which suggests that when viral load is at its highest, even the best performing face masks might not provide adequate protection. 51 Additionally, most studies that assessed mask wearing were prone to important confounding bias, which might have altered the conclusions drawn from this review (ie, effect estimates might have been underestimated or overestimated or can be related to other measures that were in place at the time the studies were conducted). Thus, the extent of such limitations on the conclusions drawn remain unknown.

A 2020 rapid review concluded that quarantine is largely effective in reducing the incidence of covid-19 and covid-19 mortality. However, uncertainty over the magnitude of such an effect still remains, 109 with enhanced management of quality quarantine facilities for improved effective control of the epidemics urgently needed. 110 In addition, findings on the application of school and workplace closures are still inconclusive. Policy makers should be aware of the ambiguous evidence when considering school closures, as other potentially less disruptive physical distancing interventions might be more appropriate. 21 Numerous findings from studies on the efficacy of school closures showed that the risk of transmission within the educational environment often strongly depends on the incidence of covid-19 in the community, and that school closures are most successfully associated with control of SARS-CoV-2 transmission when other mitigation strategies are in place in the community. 111 112 113 114 115 116 117 School closures have been reported to be disruptive to students globally and are likely to impair children’s social, psychological, and educational development 118 119 and to result in loss of income and productivity in adults who cannot work because of childcare responsibilities. 120

Speculation remains as how best to implement physical distancing measures. 121 Studies that assess physical distancing measures might interchangeably study physical distancing with lockdown 35 52 56 64 and other measures and thus direct associations are difficult to assess.

Empirical evidence from restricted travel and full border closures is also limited, as it is almost impossible to study these strategies as single measures. Current evidence from a recent narrative literature review suggested that control of movement, along with mandated quarantine, travel restrictions, and restricting nationals from entering areas of high infection, are effective measures, but only with good compliance. 122 A narrative literature review of travel bans, partial lockdowns, and quarantine also suggested effectiveness of these measures, 123 and another rapid review further supported travel restrictions and cross border restrictions to stop the spread of SARS-CoV-2. 124 It was impossible to make such observations in the current review because of limited evidence. A German review, however, suggested that entry, exit, and symptom screening measures to prevent transmission of SARS-CoV-2 are not effective at detecting a meaningful proportion of cases, 125 and another review using real world data from multiple countries found that border closures had minimal impact on the control of covid-19. 126

Although universal lockdowns have shown a protective effect in lowering the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality, these measures are also disruptive to the psychosocial and mental health of children and adolescents, 127 global economies, 128 and societies. 129 Partial lockdowns could be an alternative, as the associated effectiveness can be high, 125 especially when implemented early in an outbreak, 85 and such measures would be less disruptive to the general population.

It is important to also consider numerous sociopolitical and socioeconomic factors that have been shown to increase SARS-CoV-2 infection 130 131 and covid-19 mortality. 132 Immigration status, 82 economic status, 81 101 and poverty and rurality 98 can influence individual and community compliance with public health measures. Poverty can impact the ability of communities to physically distance, 133 especially in crowded living environments, 134 135 as well as reduce access to personal protective measures. 134 135 A recent study highlights that “a one size fits all” approach to public health measures might not be effective at reducing the spread of SARS-CoV-2 in vulnerable communities 136 and could exacerbate social and economic inequalities. 135 137 As such, a more nuanced and community specific approach might be required. Even though screening is highly recommended by WHO 138 because a proportion of patients with covid-19 can be asymptomatic, 138 screening for symptoms might miss a larger proportion of the population with covid-19. Hence, temperature screening technologies might need to be reconsidered and evaluated for cost effectiveness, given such measures are largely depended on symptomatic fever cases.

Strengths and limitations of this review

The main strength of this systematic review was the use of a comprehensive search strategy to identify and select studies for review and thereby minimise selection bias. A clinical epidemiologist developed the search strategy, which was validated by two senior medical librarians. This review followed a comprehensive appraisal process that is recommended by the Cochrane Collaboration 31 to assess the effectiveness of public health measures, with specifically validated tools used to independently and individually assess the risk of bias in each study by study design.

This review has some limitations. Firstly, high quality evidence on SARS CoV-2 and the effectiveness of public health measures is still limited, with most studies having different underlying target variables. Secondly, information provided in this review is based on current evidence, so will be modified as additional data become available, especially from more prospective and randomised studies. Also, we excluded studies that did not provide certainty over the effect measure, which might have introduced selection bias and limited the interpretation of effectiveness. Thirdly, numerous studies measured interventions only once and others multiple times over short time frames (days v month, or no timeframe). Additionally, the meta-analytical portion of this study was limited by significant heterogeneity observed across studies, which could neither be explored nor explained by subgroup analyses or meta-regression. Finally, we quantitatively assessed only publications that reported individual measures; studies that assessed multiple measures simultaneously were narratively analysed with a broader level of effectiveness (see supplementary material 3, table 3). Also, we excluded studies in languages other than English.

Methodological limitations of studies included in the review

Several studies failed to define and assess for potential confounders, which made it difficult for our review to draw a one directional or causal conclusion. This problem was mainly because we were unable to study only one intervention, given that many countries implemented several public health measures simultaneously; thus it is a challenge to disentangle the impact of individual interventions (ie, physical distancing when other interventions could be contributing to the effect). Additionally, studies measured different primary outcomes and in varied ways, which limited the ability to statistically analyse other measures and compare effectiveness.

Further pragmatic randomised controlled trials and natural experiment studies are needed to better inform the evidence and guide the future implementation of public health measures. Given that most measures depend on a population’s adherence and compliance, it is important to understand and consider how these might be affected by factors. A lack of data in the assessed studies meant it was not possible to understand or determine the level of compliance and adherence to any of the measures.

Conclusions and policy implications

Current evidence from quantitative analyses indicates a benefit associated with handwashing, mask wearing, and physical distancing in reducing the incidence of covid-19. The narrative results of this review indicate an effectiveness of both individual or packages of public health measures on the transmission of SARS-CoV-2 and incidence of covid-19. Some of the public health measures seem to be more stringent than others and have a greater impact on economies and the health of populations. When implementing public health measures, it is important to consider specific health and sociocultural needs of the communities and to weigh the potential negative effects of the public health measures against the positive effects for general populations. Further research is needed to assess the effectiveness of public health measures after adequate vaccination coverage has been achieved. It is likely that further control of the covid-19 pandemic depends not only on high vaccination coverage and its effectiveness but also on ongoing adherence to effective and sustainable public health measures.

What is already known on this topic

Public health measures have been identified as a preventive strategy for influenza pandemics

The effectiveness of such interventions in reducing the transmission of SARS-CoV-2 is unknown

What this study adds

The findings of this review suggest that personal and social measures, including handwashing, mask wearing, and physical distancing are effective at reducing the incidence of covid-19

More stringent measures, such as lockdowns and closures of borders, schools, and workplaces need to be carefully assessed by weighing the potential negative effects of these measures on general populations

Further research is needed to assess the effectiveness of public health measures after adequate vaccination coverage

Ethics statements

Ethical approval.

Not required.

Data availability statement

No additional data available.

Acknowledgments

We thank medical subject librarians Lorena Romero (LR) and Marshall Dozier (MD) for their expert advice and assistance with the study search strategy.

Contributors: ST, DG, DI, DL, and ZA conceived and designed the study. ST, DG, SS, AM, HW, WX, JR, ET, AM, XL, XZ, and IME collected and screened the data. ST, DG, and DI acquired, analysed, or interpreted the data. ST, HW, and SS drafted the manuscript. All authors critically revised the manuscript for important intellectual content.. XL and ST did the statistical analysis. NA obtained funding. LR and MD provided administrative, technical, or material support. ST and DI supervised the study. ST and DI had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. ST is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: No funding was available for this research. ET is supported by a Cancer Research UK Career Development Fellowship (grant No C31250/A22804). XZ is supported by The Darwin Trust of Edinburgh.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: ET is supported by a Cancer Research UK Career Development Fellowship and XZ is supported by The Darwin Trust of Edinburgh; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.

The lead author (ST) affirms that the manuscript is an honest, accurate, and transparent account of the study reported; no important aspects of the study have been omitted. Dissemination to participants and related patient and public communities: It is anticipated to disseminate the results of this research to wider community via press release and social media platforms.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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SYSTEMATIC REVIEW article

Systematic review of the literature about the effects of the covid-19 pandemic on the lives of school children.

• Department of Didactics of Musical, Plastic and Corporal Expression, University of Jaén, Jaén, Spain

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Methodology

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

Procedure and Search Strategy

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

Inclusion and Exclusion Criteria

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

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

Figure 1 . PRISMA flowchart.

Article Coding

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

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

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

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

Table 1 . Basis of the study.

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

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

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

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

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

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

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

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

Conclusions

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

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

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

Author Contributions

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

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

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.

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

Keywords: children, COVID-19, coronavirus, physical activity, psychology

Citation: Cachón-Zagalaz J, Sánchez-Zafra M, Sanabrias-Moreno D, González-Valero G, Lara-Sánchez AJ and Zagalaz-Sánchez ML (2020) Systematic Review of the Literature About the Effects of the COVID-19 Pandemic on the Lives of School Children. Front. Psychol. 11:569348. doi: 10.3389/fpsyg.2020.569348

Received: 03 June 2020; Accepted: 27 August 2020; Published: 14 October 2020.

Reviewed by:

Copyright © 2020 Cachón-Zagalaz, Sánchez-Zafra, Sanabrias-Moreno, González-Valero, Lara-Sánchez and Zagalaz-Sánchez. 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: Déborah Sanabrias-Moreno, dsmoreno@ujaen.es

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.

Molnupiravir Use Among Patients with COVID-19 in Real-World Settings: A Systematic Literature Review

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• Published: 14 May 2024

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• Julia Richmond DiBello 1 ,
• Valerie T. Raziano   ORCID: orcid.org/0000-0003-4710-8070 2 , 3 ,
• Xinyue Liu   ORCID: orcid.org/0000-0003-1610-1263 1 ,
• Amy Puenpatom 2 ,
• Kathryn Peebles   ORCID: orcid.org/0000-0002-2095-7446 1 ,
• Nazleen F. Khan   ORCID: orcid.org/0000-0003-4012-6671 1 &
• Deanna D. Hill 1  

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Introduction

Molnupiravir (MOV) is an oral antiviral for the treatment of individuals with mild-to-moderate COVID-19 and at high risk of progression to severe disease. Our objective was to conduct a systematic literature review (SLR) of evidence on the effectiveness of MOV in reducing the risk of severe COVID-19 outcomes in real-world outpatient settings.

The SLR was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and using pre-determined population, intervention, comparison, outcome, time, and study design inclusion criteria. Eligible studies were published between January 1, 2021, and March 10, 2023, and evaluated the real-world effectiveness of MOV compared to no treatment in reducing the risk of severe COVID-19 outcomes among outpatients ≥ 18 years of age with a laboratory-confirmed diagnosis of SARS-CoV-2 infection.

Nine studies from five countries were included in the review. The size of the MOV-treated group ranged from 359 to 7818 individuals. Omicron variants of SARS-CoV-2 were dominant in all study periods. Most studies noted differences in the baseline characteristics of the MOV-treated and untreated control groups, with the treated groups generally being older and with more comorbidities. Eight studies reported that treatment with MOV was associated with a significantly reduced risk of at least one severe COVID-19 outcome in at least one age group, with greater benefits consistently observed among older age groups.

Conclusions

In this SLR study, treatment with MOV was effective in reducing the risk of severe outcomes from COVID-19 caused by Omicron variants, especially for older individuals. Differences in the ages and baseline comorbidities of the MOV-treated and control groups may have led to underestimation of the effectiveness of MOV in many observational studies. Real-world studies published to date thus provide additional evidence supporting the continued benefits of MOV in non-hospitalized adults with COVID-19.

Plain Language Summary

COVID-19 continues to be a major source of morbidity and mortality. Throughout the pandemic, many countries authorized various therapies for the treatment of individuals presenting with mild-to-moderate COVID-19 and at high risk of progression to severe disease. Some of these therapies have since been rendered ineffective due to the emergence of Omicron variants in late 2021. The objective of the current study was to conduct a systematic literature review to assess real-world evidence on the effectiveness of molnupiravir, including effectiveness against COVID-19 caused by Omicron variants, to supplement the findings of the MOVe-OUT clinical trial and further inform on the potential clinical benefit and utility of this antiviral agent. Nine studies were included in the systematic literature review. We found that treatment with molnupiravir was effective in reducing the risk of severe outcomes from COVID-19 caused by Omicron variants, especially for older individuals. Differences in the ages and baseline comorbidities of the molnupiravir-treated and control groups may have led to underestimation of the effectiveness of molnupiravir in many observational studies. In summary, real-world effectiveness studies provide additional evidence supporting the continued benefits of molnupiravir in non-hospitalized adults with COVID-19.

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COVID-19 continues to be a major source of morbidity and mortality: in November 2023, the World Health Organization (WHO) reported > 9400 new cases and > 120 deaths attributable to COVID-19 per week [ 1 ]. Due to decreases in testing, monitoring, and surveillance activities over time [ 2 , 3 ], and especially since the WHO declared in May 2023 that the global health emergency had ended [ 4 ], both cases and deaths are likely under-reported; wastewater surveillance data suggest that clinical detection underestimates global cases 2- to 19-fold [ 5 ] and global excess mortality data suggest that the true COVID-19 mortality rate may be up to 2.7-fold higher than stated in official figures [ 6 ].

Severe COVID-19 outcomes—including hospitalization, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV) use, and death—are more common among individuals ≥ 65 years of age, those who are immunocompromised or immunosuppressed, and those with certain other underlying medical conditions [ 7 ]. Many countries have authorized various therapies at different points during the pandemic for the treatment of individuals presenting with mild-to-moderate COVID-19 and at high risk of progression to severe disease, often via expedited mechanisms such as the Emergency Use Authorization status used by the US Food and Drug Administration (FDA) [ 8 ]. These therapies include several monoclonal antibodies (mAbs: casirivimab, imderimab, bamlavimab, etesevimab, and sotrovimab) that have since been rendered ineffective due to the emergence and rapid global dominance of Omicron variants of SARS-CoV-2 in late 2021 [ 8 , 9 ]. The WHO recommended in May 2022 that these treatments be used only for individuals with COVID-19 caused by non-Omicron variants [ 8 ].

Several small-molecule drugs that remain effective against Omicron variants have also been approved for the treatment of mild-to-moderate COVID-19 [ 8 ]. The first to be approved by many regulatory agencies (e.g., the US FDA and the European Medicines Agency [EMA]) was remdesivir (RDV; Veklury®, Gilead Sciences, Inc.), an inhibitor of the SARS-CoV-2 RNA-dependent RNA polymerase enzyme [ 10 , 11 ]. The use of this therapy has been limited because a full treatment course requires daily intravenous administration for at least 3 days, representing a logistical challenge for outpatient use [ 10 , 11 ]. The viral protease inhibitor ritonavir-boosted nirmatrelvir (NRM/r; Paxlovid™, Pfizer) is an alternative treatment for COVID-19 that is taken orally and that has been approved by the FDA and EMA, among others [ 12 , 13 ]. However, NRM/r is contraindicated for individuals with certain pre-existing conditions, including chronic kidney disease (CKD) and liver diseases, and also carries an FDA warning for significant drug–drug interactions caused by the ritonavir component, which can increase the blood concentration of medications that are metabolized by the cytochrome P4503A enzyme [ 12 , 13 ]. There is thus still an urgent need for effective antiviral agents that can be taken orally by individuals at high risk of progression to severe disease and for whom NRM/r is contraindicated.

Molnupiravir (MOV; Lagevrio™, Merck & Co., Inc., Rahway, NJ, USA) is an oral antiviral that has been granted Emergency Use Authorization by the FDA for the treatment of mild-to-moderate COVID-19 in individuals at high risk of progression to severe disease and for whom other available antiviral therapies are not accessible or recommended, for example due to CKD or potential drug–drug interactions with NRM/r [ 14 , 15 , 16 ]. The drug has also been approved or authorized in many other countries [ 17 , 18 , 19 , 20 , 21 , 22 ]. In the MOVe-OUT phase 3 clinical trial, MOV treatment of non-hospitalized adults reduced the risk of 29-day all-cause hospitalization or death by 31% and the risk of 29-day all-cause mortality by 89% compared to placebo [ 23 ]. A 2022 meta-analysis of four studies found no evidence for significant differences between molnupiravir and placebo in terms of all adverse events, serious adverse events, or adverse events leading to death or treatment discontinuation [ 24 ].

The MOVe-OUT clinical trial enrolled participants from May to September 2021—before Omicron variants of SARS-CoV-2 became globally dominant—and included only unvaccinated participants [ 23 ]. The objective of the current research was thus to conduct a systematic literature review (SLR) to assess real-world evidence on the effectiveness of MOV, including effectiveness against COVID-19 caused by Omicron variants, to supplement the findings of the clinical trial and further inform on the potential clinical benefit and utility of this antiviral agent.

Study Inclusion Criteria and Search Strategy

The SLR was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines [ 25 ]. The study used pre-determined population, intervention, comparison, outcome, time, and study design (PICOTS) inclusion criteria (Table  1 ) [ 26 ]. We included real-world studies published between January 1, 2021 and March 10, 2023 that evaluated the effectiveness of molnupiravir (MOV) against severe outcomes of COVID-19 (primarily hospitalization and/or death) among outpatients ≥ 18 years of age with a diagnosis of SARS-CoV-2 infection, as confirmed by nucleic acid amplification or rapid antigen test. Consistent with the MOVe-OUT clinical trial [ 23 ], eligible studies compared outcomes between individuals who received MOV and those who did not receive any authorized COVID-19 treatment. We excluded studies of populations with specific medical conditions other than SARS-CoV-2 infection.

The literature search terms used to identify potentially eligible studies from the Embase and Scopus databases were developed in collaboration with a medical sciences librarian (Table S1, Supplementary Material). Additional potentially eligible COVID-19-related studies were identified via supplementary ongoing literature surveillance to capture pre-prints and other publication types that are not indexed in the study databases.

Study Selection

All studies identified in the initial literature search were imported into Rayyan, a collaborative platform for the conduct of SLR [ 27 ]. Duplicates were discarded, and the title and abstract of each unique study were reviewed independently by two epidemiologists. We excluded studies that did not meet the pre-determined PICOTS inclusion criteria based on this initial review. The full text of each remaining study was then assessed against the PICOTS criteria by the two independent reviewers, and ineligible studies were excluded. Any discrepancies between the two reviewers would have been resolved via discussion with a third independent member of the study team, but no such discrepancies arose at any stage of the study selection process.

Assessment of Risk of Bias

All included studies were assessed independently by two reviewers for risk of bias across seven domains (confounding, selection bias, intervention, deviations from intended interventions, missing data, outcome measurement, and selection of reported result) using the cohort-type study version of the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool [ 28 ]. In this framework, a low risk of bias indicates that the study was comparable to a well-performed randomized controlled trial (RCT) in a given domain, while a critical risk of bias in any domain indicates that a study was not likely to provide useful evidence [ 28 ]. Per study-specific criteria, failure to control for immortal time bias due to differences in index dates and follow-up periods between the MOV-treated and control groups resulted in a moderate risk for selection bias, while studies that did not monitor adherence to MOV therapy were assigned a rating of moderate risk in the deviation from intended intervention domain. Finally, studies assessing COVID-19-related hospitalization/mortality as outcomes, or those that had the potential for outcome under-ascertainment based on the data sources used, were judged to have a moderate risk of bias in the outcome measurement domain. An overall risk of bias was then assigned based on the scores in each individual domain. Again, any discrepancies between the two reviewers would have been resolved via discussion with a third independent member of the study team, but no such discrepancies arose.

Data Extraction

A single reviewer extracted data on study design, setting, and outcomes from the full text of each study (including all available supplementary material) into a standardized data abstraction form. The accuracy of all abstracted information was then independently confirmed by a second reviewer; any discrepancies were resolved as above. Statistical significance was defined as a risk measure (hazard ratio [HR], odds ratio [OR], or relative risk [RR]) with a 95% CI that was entirely > 1 or entirely < 1.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Study Characteristics

A total of 412 potentially eligible studies were identified: 267 from Embase and 145 from Scopus (Fig.  1 ). After excluding 69 duplicates, 343 documents underwent an initial title and abstract screen, of which 287 were excluded. The most common reasons for exclusion were preclinical study ( n  = 83), review article (52), RCT (30), drug other than MOV (22), and safety study (21). The remaining 56 studies, plus an additional three records identified via supplementary literature surveillance, underwent full-text review. Fifty records were excluded, with the most common reason being non-population-based study (17), active comparator only (14), and no eligible outcomes (8).

Flow chart of systematic literature review to identify, screen, and select eligible real-world studies evaluating the effectiveness of molnupiravir. PICOTS population, intervention, comparator, outcome, time, and study design. A Additional documents were identified via ongoing literature surveillance

Nine studies (six peer-reviewed studies and three pre-prints) met all PICOTS criteria and were included in the SLR (Table  2 ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Two of the three pre-prints have subsequently been published as peer-reviewed articles [ 38 , 39 ]. The peer-reviewed version of Paraskevis et al . has a different title and abstract compared to the pre-print version, but the data appear to be the same [ 33 , 39 ]. As the study period and results in the peer-reviewed version of Bajema et al . have been updated compared to the pre-print version, we revised the analysis to incorporate the published version of Bajema et al . rather than the pre-print [ 30 , 38 ].

One of the nine included studies was conducted in Greece [ 33 ], three in Hong Kong [ 34 , 35 , 37 ], two in Israel [ 29 , 32 ], one in the UK [ 31 ], and two in the US [ 30 , 36 , 38 ]. Some studies from the same country used the same data source (electronic health records from the Hong Kong Hospital Authority in three studies [ 34 , 35 , 37 ], the Clalit Health System in two Israeli studies [combined with another data source in one of these studies] [ 29 , 32 ], and the US Veterans Health Administration COVID-19 Shared Data Resource in two studies [combined with other data sources in one of these studies]) [ 30 , 36 , 38 ]. However, studies using the same data source had different study periods and methodologies. The study periods were of different lengths, but all began between December 16, 2021 and February 26, 2022 and ended between February 28 and October 20, 2022; Omicron variants of SARS-CoV-2 were thus dominant during all study periods and in all study locations [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 ].

The size of the MOV-treated study population ranged from 359 to 7818, and all study populations had age-related and/or other risk factors for progression to severe COVID-19 [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Prior immunity to SARS-CoV-2 (defined in various ways based on vaccination and/or previous infection) was assessed directly in seven studies [ 29 , 30 , 31 , 32 , 33 , 35 , 36 , 38 ] and inferred from age- and sex-stratified population-level vaccination data in one study [ 37 ]. The proportion of the MOV-treated group with prior SARS-CoV-2 immunity ranged from 16.1% in a Hong Kong-based study [ 35 ] to 98.2% in the UK study [ 31 ]. The most common MOV treatment initiation window was ≤ 5 days after a positive test (five studies) [ 29 , 32 , 35 , 36 , 37 ]; a 3-day window was used in one study [ 33 ], a 7-day window in two studies [ 31 , 34 ], and a 10-day window in one study [ 30 , 38 ]. All studies used cohort designs with longitudinal data, patient-level follow-up, and robust statistical methods (e.g., multivariate regression or propensity score-based approaches) to address potential confounding [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].

Assessment of Study Bias

Two studies had a serious overall risk of bias, assigned in both cases due to an assessment of serious risk of bias in the confounding domain (Table  3 ) [ 31 , 33 ]. In the case of Paraskevis et al., the serious risk of confounding was assigned because the authors were unable to obtain information on comorbidities among the untreated control group [ 33 ]. The authors attempted to address this limitation by excluding individuals < 65 years of age and by matching treated individuals to controls by age, since the number of comorbidities generally increases with age [ 33 ]. Nevertheless, comorbidities are an important risk factor for severe COVID-19 outcomes [ 7 ], and thus the study was assigned a serious risk of bias in the confounding domain. In the other study with a serious risk of confounding bias, Evans et al. did not use matching or propensity score adjustment for comparisons between the MOV-treated and control groups [ 31 ]. Although no statistical tests of differences between the treated and control groups were reported, the authors noted differences between the two groups that could potentially bias the study results in favor of MOV: the treated group were on average younger than the control group (mean age 53 versus 57 years), had fewer comorbidities (e.g., 74.6 vs. 62.8% had a Charlson comorbidity index of 0–10), and had a higher degree of prior immunity (36.3 vs. 17.6% had received ≥ 4 doses of a SARS-CoV-2 vaccine) [ 31 ]. The study reported univariate association analyses showing that younger individuals (i.e., those < 60 years of age) and those who had received ≥ 4 vaccine doses were more likely to avoid hospital admission or death within 28 days than were older or less vaccinated individuals, respectively [ 31 ]. Both studies had a low or moderate risk of bias in all other domains.

The remaining seven studies were determined to have a moderate overall risk of bias. The domains in which these seven studies were found to have a moderate, rather than a low, risk of bias were confounding (all seven studies; moderate is the lowest possible risk of confounding bias for a non-randomized study [ 28 ]), deviations from intended intervention (five studies [ 30 , 34 , 35 , 36 , 37 , 38 ]), outcomes measurement (four studies [ 29 , 32 , 35 , 36 ]), and selection bias (three studies [ 34 , 35 , 37 ]).

Seven studies were appropriately designed to accurately classify all time at risk of the outcome for both the MOV-treated and the control groups, and thereby mitigated the risk of immortal time bias. However, two of the Hong Kong-based studies did not account for immortal time bias due to differences in index dates and follow-up periods between the MOV-treated and control groups [ 34 , 37 ]. Six studies accounted for other key confounders (e.g., age, prior immunity, and time since last vaccine dose). One of the three exceptions was Paraskevis et al . , where (as noted above) the authors were not able to adjust for any differences in comorbidity distribution between the treated and control groups [ 33 ]. In addition, two of the three studies conducted in Hong Kong either did not report SARS-CoV-2 vaccination status [ 34 ] or inferred the proportion of each group who were fully vaccinated from age-, sex-, and index-date-matched population-level data, rather than from individual vaccination status data [ 37 ].

Effectiveness of Molnupiravir in Real-World Studies

Most studies noted differences in the baseline characteristics of the unmatched MOV-treated and untreated control groups. The treated group was on average older than the unmatched control group in five studies [ 29 , 32 , 34 , 36 , 37 ], younger in two studies [ 31 , 35 ], and of similar age in two studies [ 30 , 33 , 38 ]. The treated group had more baseline comorbidities than the control group in six studies [ 29 , 32 , 34 , 35 , 36 , 37 ], fewer comorbidities in one study [ 31 ], and a similar baseline comorbidity distribution in one study [ 30 , 38 ]; baseline comorbidity comparisons were not reported in one study due to a lack of data for the control group [ 33 ].

Seven studies reported the effectiveness of MOV in reducing the risk of hospitalization [ 29 , 30 , 33 , 34 , 35 , 36 , 37 , 38 ], seven reported effectiveness in reducing the risk of mortality [ 29 , 30 , 32 , 33 , 34 , 35 , 36 , 38 ], four reported composite hospitalization/mortality outcomes [ 30 , 31 , 33 , 36 , 38 ], and four reported other outcomes such as severe disease, ICU admission, IMV use, and/or other composite outcomes [ 30 , 32 , 35 , 37 , 38 ] (Table  4 ). Three studies reported COVID-19-related outcomes [ 29 , 32 , 33 ], five reported all-cause outcomes [ 30 , 31 , 34 , 36 , 37 , 38 ], and one reported a mixture of both [ 35 ]. The outcomes measurement period generally ranged from 28 to 35 days, with the exception of a 10-day COVID-19-related hospitalization outcome in one study [ 33 ].

The overall pattern among the outcomes of the included studies was that MOV was effective in reducing the risk of severe COVID-19 outcomes, particularly among older age groups: eight of the nine studies reported a statistically significantly reduced risk of hospitalization, death, or composite outcomes in ≥ 1 age group [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 38 ] (Table 4 , Fig.  2 ). Of the six studies that reported hospitalization outcomes in the overall study population, three reported that MOV was associated with a significantly reduced risk of all-cause or COVID-19-related hospitalization compared to controls; two others reported a numerically (i.e., non-statistically significantly) lower risk [ 30 , 33 , 34 , 35 , 36 , 38 ]. Three other studies that reported hospitalization outcomes stratified by age group found that MOV was associated with a significantly reduced risk of hospitalization in older age groups (≥ 65 years of age in Arbel et al . ; 75–79 and ≥ 80 years of age in Paraskevis et al . ; and > 60 years of age in Wong et al . ) but a numerically higher risk of hospitalization among younger age groups [ 29 , 33 , 35 ]. However, Yip et al . reported a numerically higher risk of 30-day all-cause hospitalization among the overall MOV-treated group compared to the control group [ 37 ].

Summary of the real-world effectiveness of molnupiravir in reducing the risk of hospitalization and/or mortality, stratified by age group (in years)

Five of the six studies that assessed mortality outcomes for the overall study population reported that treatment with MOV was associated with significantly decreased risk of all-cause or COVID-19-related death compared to controls, while the other study observed a numerically lower risk [ 30 , 32 , 33 , 34 , 35 , 36 , 38 ]. Two studies reported mortality outcomes stratified by age group; as with the hospitalization outcomes, treatment with MOV was associated with a significantly reduced risk among older age groups (≥ 65 years of age in Arbel et al . and > 60 years of age in Wong et al . ), but a significantly (Arbel) or numerically (Wong) increased risk among younger individuals [ 29 , 35 ]. Treatment with MOV was also associated with a significantly reduced risk of a composite hospitalization/mortality outcome in four studies [ 30 , 31 , 33 , 36 , 38 ]. One of these studies also reported this composite outcome stratified by age group and observed a numerically reduced risk of all-cause hospitalization or mortality compared to controls in both the 18–64 years and the ≥ 65 years age groups [ 30 , 38 ].

Four studies reported outcomes other than hospitalization or mortality. Bajema et al . found that in the overall study population, MOV treatment was associated with a numerically higher risk of all-cause ICU admission and a numerically lower risk of all-cause IMV use compared to controls [ 30 , 38 ]. Yip et al . reported a composite outcome combining all-cause mortality, ICU admission, and IMV use, and found that overall, in individuals ≥ 70 years of age, and in those ≥ 60 years of age with comorbidity, treatment with MOV was associated with a numerically higher risk; in contrast, individuals < 70 years of age and treated with MOV had a slightly numerically lower risk of this outcome compared to controls [ 37 ]. In Najjar-Debbiny et al ., the risk of both severe COVID-19 and a severe COVID-19/COVID-19-related mortality composite outcome was numerically lower among all MOV-treated individuals than among controls; for the composite outcome, MOV treatment was associated with a significantly higher risk among individuals ≤ 75 years of age and a significantly lower risk among those > 75 years of age [ 32 ]. Finally, Wong et al . found that treatment with MOV was associated with a significantly lower risk of in-hospital disease progression in the overall population and among those > 60 years of age, but a numerically higher risk among those ≤ 60 years of age [ 35 ].

In this SLR, we identified nine real-world studies that assessed the effectiveness of MOV among non-hospitalized adults at high risk of progression to severe COVID-19, compared to controls who were not treated with any approved antiviral agent. The studies were conducted in multiple locations with populations at different levels of baseline risk for severe COVID-19 outcomes and with different levels of prior immunity. All studies took place when Omicron variants of SARS-CoV-2 were dominant worldwide [ 40 ]. Overall, the evidence from eight of the nine included real-world, Omicron-era studies was consistent with the conclusion of the MOVe-OUT clinical trial that MOV is effective in reducing the risk of the most severe consequences of COVID-19 among non-hospitalized adults, particularly among older age groups.

All of the included real-world study populations had potentially relevant differences to the MOVe-OUT clinical trial population that may have affected the estimation of MOV effectiveness [ 23 ]. For instance, in contrast to the clinical trial (from which vaccinated individuals were excluded), the study populations in all nine of the included manuscripts had some (highly variable) degree of prior immunity to SARS-CoV-2, via vaccination and/or previous infection. The MOVe-OUT inclusion criteria also specified laboratory confirmation of SARS-CoV-2 infection and onset of symptoms ≤ 5 days before randomization [ 23 ]. In contrast, the treatment initiation window in all nine of the included real-world studies was based on date of positive SARS-CoV-2 test rather than date of onset of symptoms; the length of the window varied between studies, from ≤ 3 to < 10 days following a positive test. Studies with longer treatment initiation windows may have included participants who were not treated with MOV in accordance with the drug’s FDA approval, which states that treatment should begin ‘as soon as possible after a diagnosis of COVID-19 has been made, and within 5 days of symptom onset’ [ 15 ].

Our risk of bias assessment identified several other factors that may also have affected the estimation of the effectiveness of MOV in the reviewed studies, including potential baseline differences between treated and control groups. In most studies that compared the baseline characteristics of the unmatched MOV-treated and control groups, the MOV-treated group was generally older and/or had more comorbidities than the respective control group [ 29 , 32 , 34 , 35 , 36 , 37 ]. These studies, as well as a study that was not able to compare baseline comorbidities between the MOV-treated and control groups [ 33 ], may therefore have underestimated the effectiveness of MOV. Indeed, Yip et al . (who reported non-significant increases in risk associated with MOV treatment) noted that the effectiveness of MOV may have been underestimated because local treatment guidelines at the time of the study restricted the use of MOV to individuals at the highest level of risk for severe COVID-19 outcomes [ 37 ].

Further, although MOV is indicated for individuals with mild-to-moderate COVID-19, none of the included studies required that members of both the MOV-treated and the control groups have mild-to-moderate illness on the index date, and none adjusted for baseline severity of illness. The direction of any resulting bias is unknown, although studies where the MOV-treated group included a higher proportion of individuals with symptomatic or more severe cases of COVID-19 would likely underestimate the effectiveness of MOV. In addition, two of the Hong Kong-based studies did not account for immortal time bias; differences in index dates and follow-up periods between the MOV-treated and control groups may therefore have resulted in overestimation of the effectiveness of MOV in these studies [ 34 , 37 ].

The use of potentially heterogeneous and/or subjective outcome measures may also have affected the effectiveness estimates of some studies. For instance, Yip et al . noted that non-mortality outcomes may have been subject to surveillance bias, with MOV-treated patients being monitored more closely than controls [ 37 ]. Similar issues may have occurred in other studies that reported non-mortality outcomes, since the criteria for decisions regarding hospitalization, ICU admission, and IMV use can vary depending on factors such as local treatment guidelines, individual patients’ comorbidities and general medical history, individual physician discretion, and the availability of hospital/ICU beds and IMV equipment. Even among studies that reported COVID-19-specific mortality outcomes, the determination of a COVID-19-related death may vary depending on local guidelines, the criteria used by specific study protocols, and other factors such as individual physician or coroner discretion. All-cause mortality outcomes are not subject to these potential biases, but could still result in an underestimation of the effectiveness of MOV in those studies where the treated group had more comorbidities than the control group and therefore a presumably higher risk of non-COVID-19-related death.

The overall generalizability of the findings of the SLR is unknown for several reasons, including small numbers of outcome events in some studies. For example, one of the two studies that reported a significantly increased risk of severe COVID-19 outcomes among younger age groups treated with MOV reported a small number ( n  = 4) of COVID-19-related deaths in the younger age group [ 29 ]; the absolute number of events was not reported for subgroup analyses in the other study [ 32 ]. In addition, we excluded studies of the effectiveness of MOV in populations with specific medical conditions other than SARS-CoV-2 infection. However, in several jurisdictions (including the US) the use of MOV is recommended for populations with an increased risk of progression to severe COVID-19 outcomes, for example due to CKD, immunocompromised status, or other medical conditions [ 7 ]. Although some individuals with these conditions were recruited into the included studies, their outcomes were not assessed separately. Filling this knowledge gap should be a priority area for future research, to provide evidence on how best to protect high-risk individuals with underlying medical conditions from COVID-19-related hospitalization, death, and other severe clinical outcomes. Finally, the heterogenous designs and populations of the included studies, including differences in the level of prior immunity through vaccination and/or infection, precluded any direct comparisons of their outcomes; a meta-analysis was considered but deemed to be infeasible due to the lack of overlapping estimates for the study outcomes within distinct study populations. Future research should include rigorous network meta-analyses to aid in the ongoing assessment of the real-world effectiveness of MOV.

In conclusion, this SLR identified and assessed nine geographically diverse studies of the effectiveness of MOV in real-world populations and during a different phase of the pandemic compared to the MOVe-OUT clinical trial, with Omicron variants of SARS-CoV-2 dominant in all studies. In general, the included studies found that MOV was effective at reducing the risk of the most serious consequences of COVID-19 in real-world settings, particularly for older populations. While several factors may have influenced each study’s estimation of MOV effectiveness, including the level of prior immunity among the study population, the clearest pattern was that the treated groups in many studies were older and had more baseline comorbidities than the untreated control groups, which may have resulted in underestimation of the effectiveness of MOV. Overall, these real-world data provide additional clinical evidence in support of the continued benefits of MOV in treating COVID-19 caused by Omicron variants of SARS-CoV-2, especially for older individuals.

Data Availability

No new data were generated during this study.

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Conceptualization: Julia Richmond DiBello, Xinyue Liu, Amy Puenpatom, and Deanna D. Hill. Literature review: Julia Richmond DiBello, Xinyue Liu, Amy Puenpatom, Kathryn Peebles, Nazleen Khan, and Deanna D. Hill. Data analysis: Julia Richmond DiBello and Xinyue Liu. Writing- original draft preparation: Julia Richmond DiBello, Valerie T. Raziano, Xinyue Liu, Amy Puenpatom, and Deanna D. Hill. Writing- review and editing: Julia Richmond DiBello, Valerie T. Raziano, Xinyue Liu, Amy Puenpatom, Kathryn Peebles, Nazleen Khan, and Deanna D. Hill. All authors read and approved the final manuscript.

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Julia Richmond DiBello, Valerie T. Raziano, Xinyue Liu, Amy Puenpatom, Kathryn Peebles, Nazleen F. Khan, and Deanna D. Hill are employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and shareholders of Merck & Co., Inc., Rahway, NJ, USA.

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Richmond DiBello, J., Raziano, V.T., Liu, X. et al. Molnupiravir Use Among Patients with COVID-19 in Real-World Settings: A Systematic Literature Review. Infect Dis Ther (2024). https://doi.org/10.1007/s40121-024-00976-5

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Systematic review and meta-analysis of Tuberculosis and COVID-19 Co-infection: Prevalence, fatality, and treatment considerations

Roles Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

¶ ‡ QW, YC, and XL are joint first authors of this paper and they contributed equally.

Affiliations School of Public Health, Peking University, Beijing, China, Brown School, Washington University in St Louis, St Louis, Missouri, United States of America

Roles Data curation, Formal analysis, Investigation, Validation

Affiliation Jinan Municipal Center for Disease Control and Prevention, Jinan, Shandong Province, China

Roles Data curation, Formal analysis, Investigation, Validation, Writing – review & editing

Roles Writing – review & editing

Affiliation School of Public Health, Peking University, Beijing, China

Affiliation Centre for Global Health Economics, University College London, London, United Kingdom

Affiliation Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

Roles Funding acquisition, Project administration, Resources, Software, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

• Quan Wang, 
• Yanmin Cao, 
• Xinyu Liu, 
• Yaqun Fu, 
• Jiawei Zhang, 
• Yeqing Zhang, 
• Lanyue Zhang, 
• Xiaolin Wei, 

• Published: May 13, 2024
• https://doi.org/10.1371/journal.pntd.0012136
• Reader Comments

Tuberculosis (TB) and COVID-19 co-infection poses a significant global health challenge with increased fatality rates and adverse outcomes. However, the existing evidence on the epidemiology and treatment of TB-COVID co-infection remains limited.

This updated systematic review aimed to investigate the prevalence, fatality rates, and treatment outcomes of TB-COVID co-infection. A comprehensive search across six electronic databases spanning November 1, 2019, to January 24, 2023, was conducted. The Joanna Briggs Institute Critical Appraisal Checklist assessed risk of bias of included studies, and meta-analysis estimated co-infection fatality rates and relative risk.

From 5,095 studies screened, 17 were included. TB-COVID co-infection prevalence was reported in 38 countries or regions, spanning both high and low TB prevalence areas. Prevalence estimates were approximately 0.06% in West Cape Province, South Africa, and 0.02% in California, USA. Treatment approaches for TB-COVID co-infection displayed minimal evolution since 2021. Converging findings from diverse studies underscored increased hospitalization risks, extended recovery periods, and accelerated mortality compared to single COVID-19 cases. The pooled fatality rate among co-infected patients was 7.1% (95%CI: 4.0% ~ 10.8%), slightly lower than previous estimates. In-hospital co-infected patients faced a mean fatality rate of 11.4% (95%CI: 5.6% ~ 18.8%). The pooled relative risk of in-hospital fatality was 0.8 (95% CI, 0.18–3.68) for TB-COVID patients versus single COVID patients.

TB-COVID co-infection is increasingly prevalent worldwide, with fatality rates gradually declining but remaining higher than COVID-19 alone. This underscores the urgency of continued research to understand and address the challenges posed by TB-COVID co-infection.

Author summary

Tuberculosis (TB) and COVID-19, both highly infectious diseases, have posed significant global health challenges, particularly in low/middle-income countries (LMICs) with limited medical resources. Our research highlights that TB-COVID co-infection remains a substantial concern, impacting regions with varying TB burdens. The predominant treatment approach for TB-COVID co-infection has not notably evolved since our earlier study in 2021. It typically involves a combination of the recommended TB regimen and standard COVID-19 treatment. Our analysis consistently shows that individuals with TB-COVID co-infection are at heightened risk of hospitalization, protracted recovery periods, and accelerated mortality compared to those with sole COVID-19 infections. Remarkably, we found limited information on the post-COVID-19 condition of co-infected patients. One study indicated a higher prevalence of anxiety symptoms, highlighting the potential psychological toll of TB-COVID co-infection. Although the fatality rate has gradually decreased, it remains notably higher than that of COVID-19 alone. Our findings underscore the urgent need for global collaboration to address the complex challenges posed by TB-COVID co-infection, particularly in countries with limited medical resources.

Citation: Wang Q, Cao Y, Liu X, Fu Y, Zhang J, Zhang Y, et al. (2024) Systematic review and meta-analysis of Tuberculosis and COVID-19 Co-infection: Prevalence, fatality, and treatment considerations. PLoS Negl Trop Dis 18(5): e0012136. https://doi.org/10.1371/journal.pntd.0012136

Editor: Dileepa Ediriweera, University of Kelaniya Faculty of Medicine, SRI LANKA

Received: September 5, 2023; Accepted: April 5, 2024; Published: May 13, 2024

Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: LY was supported by grant from National Natural Science Foundation of China [72174010] and Natural Science Foundation of Beijing Municipality [M22033]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The ongoing COVID-19 pandemic has created an unprecedented healthcare crisis, especially in low/middle-income countries (LMICs) where medical resources are severely limited [ 1 , 2 ]. Unfortunately, these countries are also heavily burdened by tuberculosis (TB), with their populations being the main victims of this disease [ 3 ]. World Health Organization (WHO) has emphasized that the COVID-19 pandemic has not only disrupted TB services and response but also reversed years of progress made in the fight against tuberculosis [ 4 , 5 ]. Consequently, more people have fallen ill with TB and experienced higher mortality rates, timely diagnosis rates have decreased, and global spending on essential TB services has significantly declined [ 6 ].

A systematic review, encompassing evidence from 2019 to mid-2021, revealed a consistent upward trend in the absolute number of co-infected patients. Furthermore, an increasing number of countries reported co-infected patients, including both high-income countries and LMICs [ 7 ]. TB, as one of the world’s deadliest infectious diseases, comes second only to COVID-19 in terms of its impact[ 8 ]. Some experts believe that TB-COVID co-infection is associated with a poorer prognosis and a higher risk of mortality[ 9 , 10 ]. It is crucial to note that despite an exhaustive review, we did not encounter a universally accepted definition for TB–COVID co-infection. In this context, our systematic analysis provides a preliminary characterization, defining TB–COVID co-infection as a state arising from both ongoing and past infections involving M . tuberculosis and SARS-CoV-2. It’s essential to emphasize that while latent TB infection and TB disease (or active TB) present significant clinical distinctions, our usage of ’TB’ in this study encompasses all forms of M . tuberculosis infection, spanning latent, active, cured, and current states.

While there have been studies that have synthesized evidence on co-infection, they have primarily relied on case reports and case series, providing relatively weak support for epidemiology and treatment [ 11 , 12 ]. Consequently, there remains a dearth of information regarding the treatment and outcomes of TB-COVID co-infection, and a lack of consensus regarding its epidemiological status. This study serves as an update to our previous systematic review, which collected and pooled evidence as of the middle of 2021[ 7 ]. In this updated systematic review, we aim to summarize the latest epidemiological data on TB-COVID co-infection, discuss fatality rates, and explore possible clinical outcomes.

This systematic review follows the PRISMA guidelines ( S1 Table ) [ 13 ]. The study was registered in PROSPERO’s database with the registration number CRD42021253660.

Search strategy

We conducted a comprehensive search using six electronic databases: MEDLINE, Web of Science, ProQuest, Scopus, Cochrane database, and Embase. To maximize the scope of our search, we also employed the Grey Matters Checklist to identify relevant grey literature [ 14 ]. The literature search was conducted until January 24, 2023. Medical Subject Heading (MeSH) terms, title/abstract, topic, or subject words were used in the selected databases. The search formula included the terms "TB" AND "COVID-19". For "TB," key terms such as "tuberculosis," "TB," "tuberculos*," "mycobacterium tuberculosis," and "m.tuberculosis" were used. For "COVID-19," the key terms used were "COVID-19" and "SARS-COV-2".

Eligibility criteria of included studies

This systematic review included epidemiological and fatality data on TB-COVID co-infection from cohort studies, cross-sectional studies, and experimental research, excluding case reports, series, reviews, editorials, and clinical guidelines. Studies with sample sizes less than 20 were also excluded to reduce potential bias. Two reviewers (QW and XL) independently screened and selected studies using Covidence. Non-English and non-Chinese articles were translated to English using TranslateGo (Hangzhou Qingxun Science and Technology Co., China). Manual reference screening ensured study inclusivity. Conflicts were resolved by a third author (LY), and duplicates were managed across similar studies. We would like to stress that, unlike our previous work in 2021, we did not include case reports or case series in this study. Building on the insights from our earlier research, we found that these study types contributed little to our understanding of the topic, and they did not provide sufficient data for estimating fatality rates, prevalence status, or determining best practices in treatment.

Data extraction, quality assessment, and analysis

Relevant data, including authors, publication dates, study design, location, sample size, settings, epidemiological and treatment information, and clinical outcomes, were extracted. Prevalence rates of co-infection were prioritized for epidemiological data, along with total and hospitalized fatality rates. The total fatality rate represents the proportion of patients documented as deceased among all TB-COVID co-infected individuals, irrespective of whether they received treatment. On the other hand, the hospitalized fatality rate pertains to the proportion of patients documented as deceased among all TB-COVID co-infected individuals who underwent hospitalization. Treatment details, including drugs and ICU utilization, were also collected. The quality of included studies was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Study Reporting Prevalence Data [ 15 ].

Location data from all studies identified the reporting countries and regions of TB-COVID co-infection cases. Prevalence and fatality rates were chronologically listed for temporal trends analysis.

Random-effects meta-analysis calculated pooled fatality rates and relative risks (RR) of fatality between TB-COVID co-infection and single COVID-19 patients. Forest plots displayed point estimates and 95% confidence intervals (CIs), while I 2 assessed heterogeneity. P values < 0.05 indicated statistical significance.

Egger’s tests assessed publication bias, and sensitivity analyses assessed robustness by omitting studies one at a time. Subgroup analyses explored LMICs vs. high-income countries and active TB vs. previous TB status. Stata 17 (StataCorp LLC, USA) performed calculations.

A comprehensive search strategy utilizing the building blocks approach was executed to identify pertinent studies. After an extensive search, we retrieved 1,792 records from MEDLINE, 2,863 from Web of Science, 2,404 from ProQuest, 2,928 from Scopus, 1,314 from the Cochrane database, 1,962 from Embase, and 61 from Grey Matters Checklist (refer to S2 Table for details). Upon importing these records into Covidence, 8,229 duplicate records were identified and subsequently removed, resulting in 5,095 records available for title and abstract screening. In this phase, 4,391 records were excluded. The remaining 704 records entered the full-text review process, during which 38 potentially relevant records were identified. Ultimately, 689 out of 704 records and 36 out of 38 records were excluded, and 17 retrospective studies were included for analysis; no experimental studies were identified in the search. The entire process is visually presented in Fig 1 .

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https://doi.org/10.1371/journal.pntd.0012136.g001

As of the search date, our analysis identified TB-COVID co-infection cases reported in 38 countries or regions, including Argentina, Belarus, Belgium, Brazil, Chile, China, France, Republic of Guinea, India, Italy, Mexico, Niger, Pakistan, Panama, Peru, Philippines, Portugal, Romania, Russia, Singapore, Spain, Switzerland, UK, Australia, Canada, Colombia, Greece, Honduras, Lithuania, the Netherlands, Oman, Paraguay, Serbia, Slovakia, South Africa, Turkey, Thailand, and USA. Among the studies included, there was one notable study conducted by the TB/COVID-19 Global Study Group in 2022, which involved TB-COVID patients from 172 centers in 34 countries. The remaining 16 studies reported patients within a single region or country [ 16 ].

Regarding the prevalence rate of TB-COVID co-infection, two studies provided information. The first study, conducted by the Western Cape Department of Health in collaboration with the National Institute for Communicable Diseases, analyzed data from the Western Cape Provincial Health Data Centre. They found a prevalence rate of approximately 0.04% among individuals aged 20 years or above in the Western Cape Province until 1 June 2020. After testing criteria changed, the prevalence rate increased to approximately 0.06% until 9 June 2020 [ 17 ]. The second study, led by Nabity in 2021, identified 6371 co-infected patients among all California residents between September 3, 2019, and December 31, 2020, resulting in a prevalence rate of approximately 0.02% [ 18 ]. The S3 Table provides detailed information on these two studies.

Among the studies included in our analysis, only a limited number of studies provided information on the treatment of TB-COVID co-infection. Upon comparing our findings with our previous study conducted in 2021, we did not identify any new treatments that have emerged. The most commonly utilized treatment approach involved the use of first-line anti-TB treatment (ATT) drugs, including rifampicin, isoniazid, ethambutol, and pyrazinamide, which were administered in the majority of cases. In terms of antiviral drugs, lopinavir, ritonavir, and arbidol were the three most frequently prescribed medications. Notably, the use of hydroxychloroquine (HCQ) has become limited, as it has been demonstrated to have no benefit in the treatment of TB-COVID co-infection [ 19 ]. Three studies included in our review highlighted the utilization of Intensive Care Units (ICUs) in the management of TB-COVID co-infection. The reported ICU admission rates varied from 1.3% to 31.8% [ 20 – 22 ]. Additionally, Wang discussed the usage of Paxlovid, an antiviral therapeutic for COVID-19 treatment, and emphasized its contraindication in patients receiving rifampicin, one of the first-line agents for TB treatment, due to drug interactions as Paxlovid is a strong cytochrome P450 3A4 inhibitor. Consequently, Paxlovid was not deemed suitable for treating patients with active TB-COVID co-infection undergoing ATT [ 21 ].

Several studies have indicated that TB-COVID co-infected patients face increased risks of hospitalization, longer time-to-recovery in elderly patients, and shorter time-to-death compared to individuals with single COVID-19 infection [ 21 , 23 – 25 ]. Parolina’s study highlighted various factors associated with an increased risk of developing severe COVID-19 in TB patients, including female gender, smoking, fever, dyspnea, disseminated TB, having three or more co-morbidities, and patient age[ 26 ]. Wang emphasized that despite the milder nature of infections with the Omicron variant compared to earlier variants, patients with TB-COVID co-infection do not exhibit the mild disease course observed in the general population [ 21 ]. Notably, the majority of patients in Wang’s study, 142 out of 153 co-infected individuals, were classified as nonsevere, with 10 being asymptomatic [ 21 ]. This may be attributed to lung parenchyma damage resulting from pulmonary remodeling due to persistent cavitation, fibrosis, or bronchiectasis, which is present in approximately 50% of cured TB patients and may increase susceptibility to COVID-19 and mortality rates [ 25 ]. The presence of dual lung damage following both TB and COVID-19 necessitates careful follow-up of patients with post-tuberculosis lung disease who have experienced COVID-19 pneumonia [ 25 ]. These findings underscore the complex interactions and challenges associated with TB-COVID co-infection. The coexistence of two lung diseases can lead to heightened severity and poorer outcomes, warranting specialized management approaches and continued monitoring of affected individuals. For more detailed information, please refer to S4 Table .

Fatality rate

A total of 17studies were included in our analysis, reporting data on the fatality rate of TB-COVID co-infection. The reported fatality rates among the total patient population varied widely, ranging from 0% to 23.6%. Similarly, the in-hospital fatality rates also showed considerable variation, ranging from 0% to 27.3% ( Table 1 ).

https://doi.org/10.1371/journal.pntd.0012136.t001

To further explore the impact of active TB and previous TB on fatality rates, we collected and analyzed information specific to these subgroups ( Table 2 ). Among co-infected patients with concurrent TB disease (active TB), the reported fatality rates ranged from 7.6% to 23.6% for the total patient population, and for hospitalized active TB-COVID patients, the fatality rates ranged from 0% to 27.3%. Regarding previous TB-COVID patients, the fatality rates ranged from 4.9% to 14.5% for the total patient population, and for hospitalized patients, the fatality rates ranged from 0% to 24.0%. Please refer to S4 Table and S5 Table , and S6 Table for comprehensive and detailed information about the included studies.

https://doi.org/10.1371/journal.pntd.0012136.t002

Quality assessment of included studies

We employed the JBI Critical Appraisal Checklist for Study Reporting Prevalence Data as a tool to assess the quality of the 17 included studies. The checklist consisted of 9 questions covering various aspects such as sampling method, sample size, study subjects and setting, analysis method, and participant response. Each question was evaluated using one of the four options: yes, no, unclear, or not applicable. In total, 10 studies reached more than 70% of ‘yes’ scores, 6 studies reached from 50% to 69% of ‘yes’ scores, and 1 study was below 50%. Upon further analysis, it was identified that the sample frame, sampling method, and sample size were the areas most frequently identified as having a higher risk of bias within the included studies. Check S7 Table and S1 Fig for assessment result of each study.

Meta-analysis of fatality rates

Among all included studies, the pooled fatality rate of TB-COVID co-infection among total patients was estimated to be 7.1% (95% CI, 4.0%-10.8%). However, when examining the results by country income status, significant variations were observed. In high-income countries (HICs), the pooled fatality rate was higher, with a result of 10.2% (95% CI, 9.4%-10.9%) based on two studies that included a total of 6,569 individuals. On the other hand, in low- and middle-income countries, the pooled fatality rate was lower at 5.8% (95% CI, 2.0%-11.3%), based on five studies involving 2,888 individuals ( Fig 2 ). The GTN’s study provided three cohorts: total co-infected patients, co-infected patients in Europe, and co-infected patients outside of Europe. Considering that most included countries in Europe are HICs and most countries outside of Europe are LMICs, we placed these two cohorts in the HICs and LMICs subgroups, respectively. The results of Egger’s test indicated no evidence of publication bias across all the included study groups, as well as within the low- and middle-income countries subgroup ( S8 Table and S2 Fig ). To assess the robustness of our pooled results, we performed sensitivity analyses by systematically omitting one study at a time. These analyses consistently demonstrated the stability and reliability of our pooled estimates ( S9 Table and S3 Fig ).

https://doi.org/10.1371/journal.pntd.0012136.g002

The estimated fatality rate among hospitalized patients with TB-COVID co-infection was 11.4% (95% CI, 5.6%-18.8%). It is important to note that significant heterogeneity was detected among the studies and groups analyzed. Unlike the total fatality rate, the results for low- and middle-income countries (LMICs) were similar to those of high-income countries (HICs) in terms of fatality rate among hospitalized patients. The pooled result for LMICs was 11.1% (95% CI, 4.0%-20.9%) based on eight studies involving 985 individuals. In comparison, the pooled result for HICs was 10.9% (95% CI, 5.9%-17.1%) based on four studies involving 148 individuals. These findings suggest a comparable fatality rate among hospitalized TB-COVID co-infection patients in both LMICs and HICs. For detailed results, please refer to Fig 3 . Based on the results of Egger’s tests, publication bias was observed in all included study groups. However, no evidence of publication bias was found within the 2 subgroups ( S10 Table and S4 Fig ). Furthermore, the sensitivity analysis, which involved systematically omitting one study at a time, demonstrated that the exclusion of any particular study did not significantly alter the pooled results. This finding supports the robustness and reliability of our study findings ( S11 Table and S5 Fig ).

https://doi.org/10.1371/journal.pntd.0012136.g003

In our subgroup analysis based on TB status (active/previous), the pooled results revealed significant differences in the fatality rates between active TB-COVID infection and previous TB- COVID infection ( Fig 4 ). For total fatality rate, the pooled estimate for active TB-COVID infection was 10.6% (95% CI, 7.9%-13.6%), which was higher compared to previous TB-COVID infection with a pooled estimate of 5.7% (95% CI, 4.7%-6.7%). Regarding in-hospital fatality rate, the estimated pooled result for active TB-COVID infection was 9.8% (95% CI, 2.8%-19.8%) based on eight studies involving 739 individuals. In contrast, the in- hospital fatality rate for previous TB-COVID infection was higher, with a pooled estimate of 21.0% (95% CI, 16.7%-25.6%). Furthermore, Egger’s tests were conducted to assess publication bias, and the results can be found in S12 Table and S6 Fig . Additionally, sensitivity analyses were performed, and the results demonstrated the stability and robustness of the study findings (refer to S13 Table and S7 Fig ).

A: Total active TB-COVID co-infection patients; B: Total previous TB-COVID co-infection patients; C: Hospitalized active TB-COVID co-infection patients; D: Hospitalized previous TB-COVID co-infection patients.

https://doi.org/10.1371/journal.pntd.0012136.g004

Meta-analysis of relative risk

Three studies included in our analysis provided results on the relative risk (RR) of in-hospital fatality between TB-COVID patients and single COVID patients. The pooled analysis, which involved a total of 1285 patients, suggested that TB infection might potentially reduce the fatality risk with a relative risk estimate of 0.8 (95% CI, 0.18–3.68) ( S8 Fig ). Regarding publication bias, the results of Egger’s test indicated no evidence of publication bias in the included studies ( S9 Fig ). However, the sensitivity analysis revealed some instability in the pooled results, suggesting the need for caution in interpreting these findings ( S14 Table and S10 Fig ).

This updated systematic review collected relevant studies up until January 24, 2023, and included a total of 17 studies. In comparison to a previous study that identified co-infection cases in 12 countries or regions based on population studies, our review expanded the scope and identified an additional 18 countries or regions reporting TB-COVID co-infection. This finding suggests that despite COVID-19 no longer being classified as a Public Health Emergency of International Concern by the WHO, the prevalence of TB-COVID co-infection remains significant in both high and low TB-burden countries or regions.

A notable contribution to the field is a large-scale study led by Nabity in 2021, which provided an updated prevalence rate estimate of approximately 0.02% in California, USA [ 18 ]. In contrast, an earlier study conducted in West Cape Province, South Africa in early 2020 reported a prevalence rate of 0.06%. The discrepancy in prevalence rates between these two regions suggests that the burden of TB-COVID co-infection may vary across different geographic locations. Factors such as differences in TB prevalence, COVID-19 incidence, and the effectiveness of TB and COVID-19 control measures implemented in each region may contribute to these variations.

In terms of treatment, our analysis revealed that the treatment approach for TB-COVID co-infection has not undergone significant changes since our previous study in 2021. The predominant strategy employed in the included studies involved the administration of first-line anti-TB drugs, which is in accordance with the established standard treatment protocol for TB. Despite our comprehensive review of the available literature, we did not identify any experimental studies that could provide specific guidance on the best practices for managing TB-COVID co-infection. Only a limited number of studies made any mention of adjustments to treatment regimens based on the unique characteristics of co-infected patients. As a result, the current approach to treatment for TB-COVID co-infection appears to be a combination of the recommended TB regimen and the standard treatment for COVID-19.

The studies that reported ICU utilization in the context of TB-COVID co-infection provided insights into the severity of the disease and the clinical management required. The wide range of reported ICU admission rates, ranging from 1.3% to 31.8%, highlights the heterogeneity in disease presentation and underscores the need for specialized care for individuals with severe forms of co-infection. These findings emphasize the importance of tailored management approaches that address the complex interactions between TB and COVID-19. Consistent findings across multiple studies indicate that individuals with TB-COVID co-infection face a higher risk of hospital admission, longer time-to-recovery, and shorter time-to-death compared to individuals with single COVID-19 infection [ 21 , 23 – 25 ]. These observations underscore the unique challenges posed by the coexistence of TB and COVID-19 and emphasize the necessity for tailored management strategies that effectively address both diseases.

Another important aspect to consider in the context of TB-COVID co-infection is the potential development of Post-COVID-19 condition (PCC), commonly known as long COVID [ 34 ]. PCC refers to a range of persistent symptoms and health issues that can affect individuals even after recovering from acute COVID-19 infection [ 35 ]. It has been observed that PCC can significantly impact a person’s daily functioning, employability, and overall well-being. Moreover, it has been associated with an increased risk of developing new health conditions and the utilization of healthcare services, which can further strain the individual’s financial stability [ 34 ]. However, it is worth noting that the current evidence regarding PCC specifically in the context of TB-COVID co-infection is scarce. We only identified one study that mentioned the proportion of individuals experiencing long-lasting symptoms after COVID-19 infection in conjunction with previous tuberculosis (PTB) treatment [ 36 ]. This study reported that over time, the proportion of individuals with persistent symptoms decreased, although a significant proportion, approximately one in six, still experienced ongoing symptoms. Furthermore, this group exhibited a higher prevalence of anxiety symptoms, underscoring the potential psychological impact of TB-COVID co-infection. The recurrence of pulmonary tuberculosis and the need for psychological support for individuals with a history of both COVID-19 and pulmonary TB after discharge warrant additional attention and investigation [ 36 ].

The meta-analyses conducted on the overall fatality rate of TB-COVID co-infection revealed an estimated rate of 7.1%, which is lower than our previous study’s estimate of 13.9%. This difference could potentially be attributed to the emergence of new SARS-CoV-2 variants that may exhibit milder clinical manifestations. However, it is important to note that the fatality rate of TB-COVID co-infection remains higher than that of COVID-19 alone, which was estimated at 0.68% by mid of 2020[ 37 ]. Subgroup analyses based on high-income countries and low- and middle-income countries showed a higher fatality rate in high-income countries (10.2%) compared to LMICs (5.8%). It is crucial to recognize that multiple confounding factors may contribute to this observed discrepancy. For instance, lower vigilance and delayed time-to-diagnosis in outpatient clinics, particularly in higher-income countries with traditionally lower TB burdens, could play a role. Another potential factor is the higher frequency of COVID-19 testing in high-income countries, which might dilute the numbers of identified active TB-COVID infection. Additionally, the average age of co-infected patients tends to be higher in HICs, and given that age is a proven risk factor for COVID-19 mortality, this demographic difference could contribute to the observed higher fatality rate. These findings underline the importance of considering various contextual factors when interpreting fatality rates and emphasize the need for further research to elucidate the complex dynamics at play. In terms of in-hospital fatality rates, the results were similar between high-income countries (11.1%) and LMICs (10.9%), further supporting the assumption mentioned above.

Our subgroup analysis based on TB status (active/previous) revealed significant differences in the fatality rates between active TB-COVID infection and previous TB-COVID infection. These findings highlight the differential risks and outcomes associated with active and previous TB in the context of COVID-19 co-infection. The reasons for these differences may be multifactorial. Active TB-COVID infection may impose a greater burden on the immune system and respiratory function, leading to increased susceptibility to severe COVID-19 illness and poorer outcomes. In contrast, individuals with previous TB may have partially developed immunity or residual lung damage, which could potentially confer some level of protection or adaptation against severe COVID-19. We acknowledge the variability in the status of TB infection extracted from the included origin studies, as there was no uniform standard criterion across different studies. Active TB is a complex disease with a lengthy treatment regimen, which is commonly defined as disease that occurs in someone infected with Mycobacterium tuberculosis . It is characterized by signs or symptoms of active disease, or both, and is distinct from latent tuberculosis infection, which occurs without signs or symptoms of active disease [ 38 ]. The absence of consistent definitions or criteria may have contributed to the heterogeneity observed in the meta-analysis.

An intriguing trend in current TB-COVID research centers around a significant focus on the pandemic’s impact on TB care services. Global studies have demonstrated a substantial adverse effect on the delivery, accessibility, and utilization of TB care services [ 39 ]. Comparing 2020 to 2019, there was an 18% reduction in global tuberculosis case detection, dropping from 7.1 million to 5.8 million cases, with up to a 24% decrease in the ten worst-affected countries with a high tuberculosis burden [ 5 ]. This service disruption in TB care has led to a consequential increase in additional tuberculosis-related deaths. From a critical thinking perspective, we posit that this impact might contribute to an augmentation in our estimated TB-COVID fatality rate in two crucial ways. Firstly, the reduction in tuberculosis case detection may result in fewer identified TB-COVID co-infected patients. This is particularly significant as COVID-related deaths are usually more rigorously recorded in many countries, and during this process, the TB infection can also be documented. Secondly, the disruption in TB care services might result in insufficient treatment for numerous co-infected individuals, potentially contributing to preventable deaths. This concern is particularly pronounced in LMICs, where healthcare services are often limited and of lower quality [ 40 , 41 ]. Additionally, the decrease in discovered cases of TB could contribute to a lower total number of identified co-infected patients.

In our analysis, we observed a relative risk (RR) value suggesting that TB-COVID co-infection might reduce the fatality risk compared to single COVID-19 infection. This finding may initially seem counterintuitive given that TB is a known risk factor for severe respiratory illness and mortality. It’s essential to emphasize that the groups with TB-COVID co-infection and those with single COVID-19 infection did not exhibit precisely homogeneous patient characteristics, including differences in age, gender, comorbidities, and treatment modalities. For instance, studies by Parolina and Sereda reported a higher proportion of male patients in the TB-COVID co-infection group compared to the single COVID-19 infection group [ 26 , 31 ]. Also of note is that Sy’s 2020 study, employing propensity score matched sampling, suggested that co-infected patients experienced higher fatality rates [ 24 ]. However, due to the limited information available regarding the specific details of the included patient groups, we cannot deduce the underlying reasons for this counterintuitive RR. Therefore, readers are advised to approach this finding with caution and interpret it within the acknowledged limitations we have outlined.

As a systematic review focused on TB-COVID co-infection, understanding how TB impacts COVID-19 is as crucial as comprehending how COVID-19 impacts TB. However, given the prominence of COVID-19 as a research topic, many studies at the individual level tend to emphasize the perspective of COVID-19 infection. While we did encounter studies exploring how COVID-19 impacts TB, these primarily delved into microbiological mechanisms or the pandemic’s disruption of TB service delivery. Immunologically, a shared dysregulation of immune responses in COVID-19 and TB has been identified, indicating a dual risk posed by co-infection in worsening COVID-19 severity and favoring TB disease progression [ 42 , 43 ]. Notably, for some severe COVID-19 patients, corticosteroid use can induce immunosuppression [ 44 ], significantly increasing the risk of new secondary infections and/or reactivation of existing quiescent TB infections [ 45 , 46 ]. From the TB service perspective, the COVID-19 pandemic has substantially impacted the normal delivery of TB services, exerting a negative influence on TB patients [ 39 ]. However, some studies suggest a potential reduction in Mycobacterium tuberculosis transmission during the pandemic, potentially lowering TB fatality rates [ 47 , 48 ]. Unfortunately, the current evidence is limited, and the impact of the pandemic on TB remains conflicting and inconclusive. We cautiously posit that COVID-19 exerts a negative influence on individuals already carrying Mycobacterium tuberculosis .

In our assessment of study quality, two critical bias factors emerged: insufficient sample size and unappreciated sample frame. Insufficient sample size refers to studies with limited participants, hampering findings’ generalizability. With relatively lower prevalence for TB-COVID co-infection compared to individual TB or COVID-19, obtaining a sizeable co-infected cohort, especially where TB and COVID-19 are rarer, becomes challenging. Limited sample size may curtail statistical power and precision, potentially biasing prevalence estimates. Unappreciated sample frame denotes studies unintentionally selecting populations misrepresenting the target group. Poorly described sampling or inclusion criteria misaligned with intended population characteristics can lead to biases. In TB-COVID co-infection, ensuring representation of individuals with both conditions, not biased subgroups, is vital. Incorrect sample framing may introduce biases and limit findings’ applicability.

While we recognize that a randomized controlled trial (RCT) stands as the gold standard for investigating treatments or risk factors, we contend that diverse study designs can offer valuable contributions to this field. In light of our current findings, we advocate for the consideration of a comparable sampling frame, such as the utilization of propensity score matched sampling in future studies. This approach allows for the creation of balanced groups, resembling the random assignment achieved in an RCT, thus minimizing selection bias and improving the internal validity of observational studies. Furthermore, we propose a more comprehensive description of patients’ baseline conditions and treatment regimens in subsequent research endeavors. This detailed information holds the potential to mitigate bias significantly. A thorough account of patients’ characteristics and treatment variables enhances the ability to control for confounding factors, providing a clearer understanding of the associations under investigation. Employing such strategies not only bolsters the robustness of observational studies but also facilitates the comparability of findings across different research designs.

Several limitations should be acknowledged in the interpretation of our findings. First, we did not include “comorbidity” as a keyword and MeSH term in the searching process, which might have resulted in the omission of relevant studies taking TB as a kind of comorbidity of COVID-19 patients. Second, the observational design precludes establishing causation, and although we employed rigorous statistical methods to control for confounding factors, residual confounders may persist. Third, the generalizability of our results may be influenced by the predominantly retrospective and multicentric nature of the included studies. Variability in healthcare settings, patient populations, diagnostic criteria, and treatment approaches across different regions and countries could impact the external validity of our findings. Additionally, the lack of uniformity in reporting across studies may have introduced inconsistencies in our data synthesis. Furthermore, the limited availability of detailed information on certain variables, such as socioeconomic status, comorbidities, and M . tuberculosis infection status, restricted our ability to conduct more granular subgroup analyses. As mentioned earlier, distinctions exist among latent, active, cured, and current M . tuberculosis infections. However, due to insufficient details, we faced considerable challenges in differentiating between these states. Finally, the evolving landscape of the COVID-19 pandemic and variations in healthcare infrastructure over time may have influenced treatment strategies and outcomes. Despite these limitations, our study provides valuable insights into the landscape of TB-COVID co-infection, emphasizing the need for further research to address these complexities comprehensively.

In conclusion, the fatality rate of co-infection declined gradually and still stayed higher than COVID-19 alone, underscoring the heightened vulnerability in co-infected individuals. Addressing this challenge requires targeted measures such as heightened awareness campaigns, improved screening strategies for TB infection, and the provision of comprehensive long COVID care for co-infected patients. Collaboration on a global scale may be beneficial in addressing the challenges posed by TB-COVID co-infection, particularly in regions with limited medical resources.

Supporting information

S1 table. the preferred reporting items for systematic reviews and meta-analyses (prisma) 2020 checklist..

https://doi.org/10.1371/journal.pntd.0012136.s001

S2 Table. Search strategies to identify studies reporting the prevalence status, treatment and outcomes of tuberculosis and COVID-19.

https://doi.org/10.1371/journal.pntd.0012136.s002

S3 Table. Studies reported prevalence rate (n = 2).

https://doi.org/10.1371/journal.pntd.0012136.s003

S4 Table. Detailed basic information of included studies (n = 17).

Detailed basic information of included case reports (n = 17).

https://doi.org/10.1371/journal.pntd.0012136.s004

S5 Table. The fatality rates of active and previous TB-COVID co-infection (n = 11).

The fatality rates of active TB-COVID co-infection (n = 11).

https://doi.org/10.1371/journal.pntd.0012136.s005

S6 Table. The fatality rates of previous TB-COVID co-infection (n = 3).

https://doi.org/10.1371/journal.pntd.0012136.s006

S7 Table. Quality assessment of each included study.

https://doi.org/10.1371/journal.pntd.0012136.s007

S8 Table. Egger’s test on total fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s008

S9 Table. Sensitives analysis on MA of total fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s009

S10 Table. Egger’s test on MA of In-hospital fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s010

S11 Table. Sensitives analysis on MA of In-hospital fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s011

S12 Table. Egger’s test on MA of active/previous TB-COVID co-infection fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s012

S13 Table. Sensitives analysis on MA of In-hospital fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s013

S14 Table. Sensitives analysis on RR of in-hospital fatality between TB-COVID patients and single COVID patients.

https://doi.org/10.1371/journal.pntd.0012136.s014

S1 Fig. Quality assessment of included studies (N = 17).

https://doi.org/10.1371/journal.pntd.0012136.s015

S2 Fig. Egger’s test on MA of total fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s016

S3 Fig. Sensitives analysis on MA of total fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s017

S4 Fig. Egger’s test on MA of In-hospital fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s018

S5 Fig. Sensitives analysis on MA of In-hospital fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s019

S6 Fig. Egger’s test on MA of hospitalized Active TB-COVID co-infection patients fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s020

S7 Fig. Sensitives analysis on MA of hospitalized Active TB-COVID co-infection patients fatality rate.

https://doi.org/10.1371/journal.pntd.0012136.s021

S8 Fig. Relative risk of in-hospital Fatality between TB-COVID co-infection and Single COVID-19 co-infection.

https://doi.org/10.1371/journal.pntd.0012136.s022

S9 Fig. Egger’s test on RR of in-hospital fatality between TB-COVID patients and single COVID patients.

https://doi.org/10.1371/journal.pntd.0012136.s023

S10 Fig. Sensitives analysis on RR of in-hospital fatality between TB-COVID patients and single COVID patients.

https://doi.org/10.1371/journal.pntd.0012136.s024

Acknowledgments

We would like to express our sincere thanks to Dr. Lusine Abrahamyan at University of Toronto for her kind help. We also want to present our best wishes to the front-line medical worker all over the world, and we believe their work of integrity and selflessness is key to ending the COVID-19 pandemic.

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Association between COVID-19 Infection or Vaccination Outcomes and Methylenetetrahydrofolate Reductase Gene Polymorphism: A Systematic Review of the Literature

Affiliations.

• 1 Internal Medicine Department, Gastroenterology Division, University Hospital of Split, 21000 Split, Croatia.
• 2 Department of Health Studies, University of Split, 21000 Split, Croatia.
• 3 School of Medicine, University of Split, 21000 Split, Croatia.
• 4 Internal Medicine Department, Endocrinology Division, University Hospital of Split, 21000 Split, Croatia.
• 5 Internal Medicine Department, Division of Emergency and Intensive Medicine with Clinical Pharmacology and Toxicology, University Hospital of Split, 21000 Split, Croatia.
• PMID: 38138914
• PMCID: PMC10744904
• DOI: 10.3390/jpm13121687

Background: Thrombosis is a detrimental sequala of COVID-19 infection; thus, prophylactic anti-coagulant therapy has been deemed mandatory in treatment unless serious contraindications are present. Susceptibility to thromboembolic events in COVID-19, or following COVID-19 vaccination, is likely attributable to an interplay of factors, including a patient's baseline clinical status and comorbidities, alongside genetic risk factors. In Europe, 8-20% of the population are homozygous for the MTHFR (methylene tetrahydrofolate reductase) variant, which compromises folate metabolism and elevates homocysteine levels. While heightened homocysteine levels are considered a risk factor for thromboembolic events, the precise clinical significance remains a contentious issue. However, recent research suggests elevated homocysteine levels may predict the course and severity of COVID-19 infection. Given the lack of reliable biomarkers predictive of COVID-19 thrombotic risk existing in practice, and the accessibility of MTHFR screening, we established two main outcomes for this study: (1) to determine the association between hereditary MTHFR mutations and COVID-19 severity and thromboembolic events and (2) to determine the link between MTHFR variants and adverse thrombotic events following COVID-19 vaccination.

Methods: The review was conducted in accordance with PRISMA guidelines. Medline, Scopus, and Web of Science databases were searched from pandemic inception (11 March 2020) to 30 October 2023. Eligibility criteria were applied, and data extraction performed.

Results: From 63 citations identified, a total of 14 articles met the full inclusion criteria (8 of which were cross-sectional or observational studies, and 6 were case studies or reports). Among the eight observational and cross-sectional studies evaluating the relationship between MTHFR variants (C667T; A1298C) and thromboembolic events in COVID-19 infection, four studies established a connection ( n = 2200), while the remaining four studies failed to demonstrate any significant association ( n = 38).

Conclusions: This systematic review demonstrated a possible association between the MTHFR gene variants and COVID-19 severity, thromboembolic events, and adverse events following vaccination. However, the paucity of robust data precluded any firm conclusions being drawn. Further prospective trials are required to determine the connection between the MTHFR gene variant and COVID-19 infection and vaccination outcomes.

Keywords: COVID-19 infection; MTHFR A1298C; MTHFR C667T; SARS-CoV-2; methylenetetrahydrofolate reductase gene polymorphisms; vaccine.

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Side effects of COVID-19 vaccines: a systematic review and meta-analysis protocol of randomised trials

Kleyton santos medeiros.

1 Health Sciences Postgraduate Program, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil

2 Instituto de Ensino, Pesquisa e Inovação, Liga Contra o Câncer, Natal, Rio Grande do Norte, Brazil

Ana Paula Ferreira Costa

Ayane cristine alves sarmento, cijara leonice freitas, ana katherine gonçalves.

3 Department of Obstetrics and Gynecology, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Associated Data

Introduction.

SARS-CoV-2 is responsible for a large number of global COVID-19 cases. Strategies such as social isolation, personal hygiene and frequent hand washing have been implemented; however, a protective vaccine is required to achieve sufficient herd immunity to SARS-CoV-2 infection to ultimately control the COVID-19 pandemic. To meet the urgent need for a vaccine, a reduction in the development schedule has been proposed from 10–15 years to 1–2 years. For this reason, this systematic review and meta-analysis protocol aims to compare the side effects, safety and toxicity of COVID-19 vaccines available globally, including their combinations.

Methods and analysis

We will select randomised controlled trial-type studies that evaluate the side effects of the COVID-19 vaccine. PubMed, Web of Science, Embase, CINAHL, PsycINFO, LILACS, SCOPUS, ClinicalTrials.gov, International Clinical Trials Registry Platform (ICTRP), medRxiv.org, biorxiv.org, preprints.org and the Cochrane Library will be searched for eligible studies until December 2021. Three reviewers will independently screen and select studies, assess methodological quality and extract data. A meta-analysis will be performed, if possible, and the Grading of Recommendations, Assessment, Development and Evaluations summary of findings will be presented.

Ethics and dissemination

This study will review published data, and thus it is unnecessary to obtain ethical approval. The findings of this systematic review will be published in a peer-reviewed journal.

PROSPERO registration number

CRD42021231101.

Strengths and limitations of this study

• Four authors (KSM, APFC, ACAS, CLF) will select the articles independently using titles and abstracts.
• To the best of our knowledge, there are no existing reviews regarding the side effects of COVID-19 vaccines.
• The DerSimonian and Laird method may underestimate the true between-study variance, potentially producing overly narrow CIs for the mean effect. This fact is a limitation, so the collection of studies will be done with care and the assumptions of the analytical methods will be assessed.

SARS-CoV-2 is responsible for a large number of global COVID-19 cases. It is a highly transmissible virus among humans that has become a significant public health issue. 1 Symptoms include fever, dry cough, fatigue, shortness of breath, chills, muscle pain, headache, gastric disorders and weight loss, often leading to death. 2

Strategies such as social isolation, personal hygiene and frequent hand washing have been implemented; however, a protective vaccine is required to achieve sufficient herd immunity to SARS-CoV-2 infection to ultimately control the COVID-19 pandemic. 3 To meet the urgent need for a vaccine, a reduction in the development schedule has been proposed from 10–15 years to 1–2 years. 4

SARS-CoV-2 is an RNA virus with a high mutation rate, and that on the envelope surface has three important structural proteins that can be identified: spike protein (S), envelope protein (E) and membrane protein (M). Most innovative vaccines have focused their efforts on inducing an immune response against the S protein. Attenuated virus vaccines are based on weakened microorganisms, effective in stimulating the immune system. The inactivated ones (dead microorganisms) are more stable than the attenuated ones, but they have a short duration of immunological memory that requires the association of adjuvants. mRNA vaccines are stable—and can be easily produced in large quantities. Vaccines against COVID-19 differ in composition and mechanism of action, which may be relevant for their safety and efficacy, being essential for the success and eradication of this infection. 5 6 The viral vector (mRNA) vaccine encodes full-length S protein ectodomains of SARS-CoV-2, which contains both T and B cell epitopes that can induce cellular and humoral immune responses against viral infection. 7

Assessing the safety, efficacy and side effects of the vaccine is urgently needed, and has been heavily scrutinised by the leading medical agencies around the world, like the Centers for Disease Control and Prevention and the Food and Drug Administration. Developing any vaccine needs to ensure that safety risks are identified and quantified against potential benefits. Among the potential risks raised in the context of COVID-19, vaccine development is the security and effectiveness of immune responses elicited by a vaccine. Here, this systematic review protocol aims to assess the side effects, safety and toxicity of vaccines against COVID-19.

This systematic review and meta-analysis protocol aims to compare the side effects, safety and toxicity of COVID-19 vaccines available globally, including their combination.

Review question

What are the rates of adverse reactions (local and systemic) to COVID-19 vaccines?

The meta-analysis protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines. 8 9 This protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO).

Eligibility criteria

The inclusion criteria involved: (1) randomised controlled trial (RCT)-type studies that evaluated the side effects of the COVID-19 vaccine; (2) experiments involving human beings; (3) studies evaluating the safety, immunogenicity and efficacy parameters of the vaccines; (4) studies that presented similar vaccination protocols; (5) studies published since January 2020 until December 2021; and (6) studies published in any language.

The exclusion criteria were as follows: (1) observational studies, and (2) case reports, meeting abstracts, review papers and commentaries.

Patients, intervention, comparison, outcome strategy and types of studies

• Patients: healthy adults aged 18 years or older who were HIV negative and previously SARS-CoV-2 infection free.
• Intervention: COVID-19 vaccine or a combination of vaccines against COVID-19.
• Comparator/control: placebo.
• Outcome: safety, tolerability and immunogenicity of the COVID-19 vaccine or the combination of vaccines against COVID-19.
• Types of studies: RCTs.

Information sources

The following databases will be searched: Medline / PubMed, Web of Science, Embase, CINAHL, PsycINFO, Latin American and Caribbean Health Sciences Literature (LILACS), SCOPUS, ClinicalTrials.gov, International Clinical Trials Registry Platform (ICTRP), medRxiv.org, biorxiv.org, preprints.org and Cochrane Central Controlled Trials Registry. Furthermore, eligible studies may also be selected from the reference lists of retrieved articles.

Patient and public involvement

The individual patient data will not be presented. A literature search will be carried out from defined databases. No patient will be involved in the study planning and application process during neither the analysis nor the dissemination of results.

Search strategy

Our keyword search will be based on Medical Subject Headings according to the following combination: (COVID-19 OR SARS-CoV-2 OR 2019-nCoV OR coronavirus) AND (vaccines OR vaccination OR COVID-19 vaccine OR SARS-CoV-2 vaccine OR BNT162 vaccine OR mRNA-1273 vaccine OR COVID-19 aAPC vaccine OR INO-4800 vaccine OR LV-SMENP-DC COVID-19 vaccine OR Ad5-nCoV vaccine OR ChAdOx1 COVID-19 vaccine OR MNA SARS-CoV-2 S1 subunit vaccines OR PittCoVacc OR Inactivated novel coronavirus 2019-CoV vaccine Vero cells OR Inactivated Vaccines OR SARS-CoV-2 inactivated vaccines OR Viral Vaccines OR Gam-COVID-Vac vaccine OR Ad26.COV2.S vaccine OR EpiVacCorona vaccine) AND (Toxicity OR Vaccine Immunogenicity OR side effects OR adverse events) AND (randomized controlled trial OR double blind method OR clinical trial) ( table 1 ). A list of vaccines available at WHO was also used.

Medline search strategy

Study records

Four researchers (KSM, APFC, ACAS, CLF) performed the selection of the studies of interest. Titles and abstracts will be read independently, and duplicate studies will be excluded. The same authors analysed the selected texts to assess the compliance with the inclusion criteria. A fifth reviewer, AKG, solves the discrepancies. The flow chart of this study is shown in figure 1 .

Flow diagram of the search for eligible studies on the side effects, safety and toxicity of the COVID-19 vaccine. CENTRAL, Cochrane Central Register of Controlled Trials.

Data collection process and management

A standardised data extraction form was developed and tested. Data from each included study will be extracted independently by two reviewers (ACAS and APFC), and any subsequent discrepancies will be resolved through discussion with a third reviewer (AKG). The data extracted will include information on authors, the year of publication, study location, type of study, main objectives, population, type of vaccine, follow-up of participants, rates of systemic events, gastrointestinal symptoms, injection site-related adverse effects and serious vaccine-related adverse events ( table 2 ). Furthermore, participant characteristics (eg, mean age, gender) and results for immunogenicity will be collected.

Adverse events of COVID-19 vaccines

The study authors will be contacted in case of missing data and/or to resolve any uncertainties. In addition, any additional information will be recorded. All data entries will be checked twice. If we find a set of articles with similar characteristics based on the information in the data extraction table, we will perform a meta-analysis using a random-effects model. If there are data that are not clear in some articles, the corresponding author will be contacted for possible clarification.

Risk of bias in individual studies

Three authors (KSM, ACAS, APFC) will independently assess the risk of bias in the eligible studies using the Cochrane risk-of-bias tool. 10 The Risk of Bias 2 tool 11 will be used to assess the risk of bias. Bias is assessed as a judgement (high, low or unclear) for individual elements from five domains (selection, performance, attrition, reporting and others).

Data will be entered into the Review Manager software (RevMan V.5.2.3). This software allows the user to enter protocols; complete reviews; include text, characteristics of the studies, comparison tables and study data; and perform meta-analyses. For dichotomous outcomes, we extracted or calculated the OR and 95% CI for each study. In case of heterogeneity (I 2 ≥50%), the random-effects model will be used to combine the studies to calculate the OR and 95% CI using the DerSimonian-Laird algorithm 12 .

Data synthesis and analysis

To grade the strength of evidence from the included data, we will use the Grading of Recommendations, Assessment, Development and Evaluation 13 approach. The summary of the assessment will be incorporated into broader measurements to ensure the judgement of the risk of bias, consistency, directness and precision. The quality of the evidence will be assessed based on the risk of bias, indirectness, inconsistency, imprecision and publication bias.

The COVID-19 pandemic represents one of the most significant global public health crises of this generation. Lockdown, quarantine, contact tracing and case isolation are suggested as effective interventions to control the epidemic; however, they may present different results in different contexts because of the specific features of the COVID-19. The lack of implementation of continued interventions or effective treatments further contributes to discovering and using effective and safe vaccines. 14 15

For all these reasons, scientists worldwide entered a race to find a vaccine candidate useful in fighting the new coronavirus pandemic. Nevertheless, it is essential to note that a vaccine’s production is not easy and quick. Before being released to the population, a vaccine must go through three phases of clinical trials that prove its safety and effectiveness. More volunteers are recruited at each stage, and the researchers analyse the test results to ensure that a vaccine can be licensed. 16–18

One hundred and seventy-three vaccines were in preclinical development and 64 in clinical trials until 20 January 2021. On 31 December 2020, the WHO listed the mRNA vaccine against COVID-19 for emergency use, making this Pfizer/BioNTech immuniser the first to receive WHO emergency validation from the beginning outbreak. Already, in January 2021, emergency approval was granted to nine vaccines by regulatory authorities in different parts of the world. 14 19

With the starting vaccination, several studies were carried out to ascertain the safety of these vaccines, since they were produced in record time. 20–22 Currently, one systematic review about the thematic showed that of 11 published clinical trials of COVID-19 vaccines included in the study, adverse reactions reported were considered mild to moderate with few severe reactions which were unrelated to the test vaccine. Common adverse events were pain at the site of injection, fever, myalgia, fatigue and headache. Serious adverse events (SAE) were reported in four trials: COVID-19 Vaccine AstraZeneca (AZD1222)—168 SAEs with only three related to the vaccine; Ad26.COV2.S—four with none related to the testing vaccine; five with Comirnaty (BNT162b1) vaccine and one with Covaxin (BBV152) vaccine. 19

One limitation about the COVID-19 vaccine safety tested until now is that clinical trials of the safety and effectiveness have had low inclusion of vulnerable groups, for example, older persons, the first population to receive the whole vaccine. That’s why pharmacovigilance postmarketing is necessary to surveillance of new drugs, as a critical aspect of evaluating medicine safety and effectiveness, particularly in risk groups.

Other prevention approaches are likely to emerge in the coming months, including antiviral agents, drugs may be to decrease disease progression, monoclonal antibodies, hyperimmune globulin and convalescent titre. If proven effective, these approaches could be used in high-risk individuals, including healthcare workers, other essential workers and older adults. 23–26 It is essential to maintain protective measures such as washing hands frequently with soap and water or gel alcohol and covering the mouth with a forearm when coughing or sneezing.

For all the reasons mentioned above, this review is necessary and essential. The latter is a well-defined protocol registered with PROSPERO, well planned to include the largest possible number of vaccines, a significant number of vaccinated patients, thus providing safe and reliable results regarding the use of vaccines.

Supplementary Material

Contributors: KSM, ACAS and APFC contributed to the design of this review. KSM and ACAS drafted the protocol manuscript. APFC and AKG revised the manuscript. KSM, AKG and APFC developed the search strategies. KSM, CLF and ACAS implemented the search strategies. KSM, CLF, ACAS and APFC tracked the potential studies, extracted the data and assessed the quality. In case of disagreement between the data extractors, AKG advised on the methodology and worked as a referee. KSM completed the data synthesis. All authors approved the final version for publication.

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

Competing interests: None declared.

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.

Ethics statements

Patient consent for publication.

Not applicable.