- 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
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%.
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.
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.
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.
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.
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.
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
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
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 ].
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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We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.
This research received no external funding.
Authors and affiliations.
University Hospital and School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
Israel Júnior Borges do Nascimento & Milena Soriano Marcolino
Medical College of Wisconsin, Milwaukee, WI, USA
Israel Júnior Borges do Nascimento
Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA
Dónal P. O’Mathúna
School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland
Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany
Thilo Caspar von Groote
Department of Sport and Health Science, Technische Universität München, Munich, Germany
Hebatullah Mohamed Abdulazeem
School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia
Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
Cochrane Croatia, University of Split, School of Medicine, Split, Croatia
Ana Marusic, Irena Zakarija-Grkovic & Tina Poklepovic Pericic
Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia
Cochrane Brazil, Evidence-Based Health Program, Universidade Federal de São Paulo, São Paulo, Brazil
Vinicius Tassoni Civile & Alvaro Nagib Atallah
Yorkville University, Fredericton, New Brunswick, Canada
Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
Nicola Luigi Bragazzi
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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.
Correspondence to Livia Puljak .
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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 : 04 June 2021
DOI : https://doi.org/10.1186/s12879-021-06214-4
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- Review Article
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- Published: 01 November 2023
The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID
- Jingwei Li 1 na1 ,
- Yun Zhou 1 na1 ,
- Jiechao Ma 2 na1 ,
- Qin Zhang 1 , 3 na1 ,
- Jun Shao 1 ,
- Shufan Liang 1 ,
- Yizhou Yu ORCID: orcid.org/0000-0002-0470-5548 4 ,
- Weimin Li ORCID: orcid.org/0000-0003-0985-0311 1 &
- Chengdi Wang ORCID: orcid.org/0000-0002-5284-2889 1
Signal Transduction and Targeted Therapy volume 8 , Article number: 416 ( 2023 ) Cite this article
- Immunological disorders
- Infectious diseases
There have been hundreds of millions of cases of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the growing population of recovered patients, it is crucial to understand the long-term consequences of the disease and management strategies. Although COVID-19 was initially considered an acute respiratory illness, recent evidence suggests that manifestations including but not limited to those of the cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, and musculoskeletal systems may persist long after the acute phase. These persistent manifestations, also referred to as long COVID, could impact all patients with COVID-19 across the full spectrum of illness severity. Herein, we comprehensively review the current literature on long COVID, highlighting its epidemiological understanding, the impact of vaccinations, organ-specific sequelae, pathophysiological mechanisms, and multidisciplinary management strategies. In addition, the impact of psychological and psychosomatic factors is also underscored. Despite these crucial findings on long COVID, the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate, and well-designed clinical trials should be prioritized to validate existing hypotheses. Thus, we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.
The coronavirus disease 2019 (COVID-19) pandemic has brought about an unprecedented scale of burden on health care systems. 1 , 2 More than 770 million cases of COVID-19 and over 6.9 million fatalities have been documented since the outbreak. 3 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, gains entry into host cells by combining with angiotensin-converting enzyme 2 (ACE2). 4 , 5 Subsequently, SARS-CoV-2 undergoes replication and provokes damage to multiple organs/tissues, resulting in a complex array of clinical manifestations and potential long-term sequelae. 6 , 7
Long COVID, also referred to as ongoing symptomatic COVID-19 and post-acute sequelae of COVID-19 (PASC), is defined as symptoms of COVID-19 that persist for between 4 and 12 weeks or a post-acute syndrome at over 12 weeks after the onset of acute symptoms that cannot be attributed to any other illnesses 8 , 9 , 10 (Fig. 1 ). According to recent research, patients with long COVID exhibit impairment of multiple organs, which manifests as a range of symptoms, including persistent fatigue, diarrhea, dyspnea, limited exercise tolerance, endocrine abnormalities, taste and smell dysfunction, and depression 11 , 12 , 13 , 14 , 15 , 16 (Fig. 2 ). Furthermore, long COVID has been observed in a diverse spectrum of COVID-19 regardless of the mild or severe illness. 17 , 18 , 19 Thus, it could be conceivably hypothesized that a more devastating effect could occur in the long COVID period than in the acute period of COVID-19. Nevertheless, the pathophysiological mechanisms and effective therapeutic choices regarding long COVID are unknown.
Timeline and multi-organ damage of long COVID. Long COVID is defined as the ongoing symptoms of COVID-19 patients between 4 and 12 weeks, or the post-acute syndrome over 12 weeks after the acute symptoms onset. In addition, the commonly involved organs and biological mechanisms are outlined
Multi-system symptoms/manifestations of long COVID. Long COVID has served as a multi-organ disease which can damage respiratory system, cardiovascular system, neuropsychic system, digestive system, circulatory system, musculoskeletal system, and genitourinary systems. ME/CFS myalgic encephalomyelitis/chronic fatigue syndrome, POTS postural orthostatic tachycardia syndrome
Even so, multiple hypotheses concerning the pathophysiology of long COVID have been recently proposed (Fig. 3 ). The prevailing theories for underlying mechanisms comprise persisting viral reservoirs, 20 sustained inflammation, 21 host microbiome factors, 22 , 23 persistent autoimmune responses, 24 and endothelial dysfunction and subsequent blood clotting. 25 Nevertheless, these studies regarding mechanistic hypotheses are mostly at the preliminary stage, and further research regarding the pathophysiology of long COVID is urgently needed.
The potential pathophysiological mechanisms of long COVID. The main hypothesized pathophysiological mechanisms for long COVID include viral reservoir, gut microbiome dysbiosis, endothelial dysfunction, autoimmunity, and inflammation
Herein, we thoroughly describe the current understanding concerning the epidemiology, prevalent manifestations, pathophysiological mechanisms, and potential diagnostic tools and therapeutic options of long COVID. Furthermore, the present obstacles that need to be solved to advance long COVID research are discussed.
To comprehensively understand the epidemiology, mechanisms, and management of long COVID, extensive literature searches were conducted of reputable databases, including PubMed, the Web of Science, EMBASE, and the Cochrane Library. The search spanned from January 2020 to June 2023, ensuring a thorough collection of relevant articles. The search terms involved “long COVID”, “post-acute sequelae of COVID-19”, “epidemiology”, “symptom”, “mechanism”, “management”, and their relative terms. Two authors screened the studies independently via the titles and abstracts. Afterwards, a meticulous review of the full texts of the eligible studies was performed. All included studies were written in English. Disagreements were reassessed by a third reviewer. Ultimately, the literature was read through and discussed by all authors to yield reliable conclusions.
Epidemiology of long COVID
As the patient population recovering from acute COVID-19 continues to grow, recent studies have increasingly focused on investigating the post-acute effects of SARS-CoV-2 infection. 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 An international observational study of individuals from 56 countries assessed the outcomes of 3762 confirmed or suspected COVID-19 patients within 6 months of infection via an online survey. 26 The overwhelming majority of patients (65.2%) had at least one symptom at 6 months, of which fatigue (80%), post-exertional weakness (73.3%), and cognitive impairment (58.4%) were common clinical manifestations. 26
Long COVID symptoms have also been recorded in studies conducted in China, providing significant data on the prevalence and characteristics of these symptoms. Long-term sequelae were evaluated in a cohort study comprising 1733 COVID-19 patients who had been discharged through the utilization of a range of assessments, including questionnaires, physical examinations, blood tests, a 6-minute walk test, ultrasonography, pulmonary function assessments, and imaging of the chest. 27 At 6 months after the onset of symptoms, muscle weakness or fatigue was observed in 63% of cases, followed closely by sleep disorders (26%) and depression/anxiety (23%). 27 In an identical cohort throughout a 2-year follow-up after acute SARS-CoV-2 infection, the morbidity rate of long COVID exhibited a substantial decline, dropping from 68% at 6 months to 55% after a 2-year period. 28 Analogously, fatigue or muscle weakness was the prevailing manifestation, with an incidence of 30%. 28 Sleep difficulties, hair loss, dizziness, and palpitations had a high incidence of over 10%. 28 Although longitudinal improvements have been witnessed among studies, the prevalence of long COVID sequelae remains stubbornly high, implying that these sequelae may last long and lead to a tremendous burden on patients.
Studies from European countries have proclaimed similar results. 29 , 30 , 31 , 32 , 33 , 34 In a French cohort with 150 confirmed COVID-19 patients, long COVID symptoms were collected via electronic medical records or telephone follow-up. 29 At 60 days after symptom onset, 66% of patients had at least one sequela. Among these symptoms, the most frequent manifestation was asthenia (40%), followed by dyspnea (30%) and anosmia/ageusia (23%). 29 According to a prospective cohort study from Spain, 50.9% of COVID-19 patients suffered from long COVID symptoms 10 to 14 weeks after illness onset. 30 Furthermore, more detailed information regarding respiratory manifestations was assessed by spirometry or chest radiology. Reduced pulmonary function was noted in 9.3% of cases, and abnormal imaging results were found in 18.9% of patients. 30 Analogously, data from the northern Netherlands involving 4231 COVID-19 patients and 8462 matched controls revealed that 21.4% of COVID-19 individuals and 8.7% of controls experienced persistent symptoms, including dyspnea, chest pain, painful muscles, lump in throat, ageusia/anosmia, heavy extremities, tingling extremities, paresthesia, and fatigue. 32 In another study from the United Kingdom, a telephone screening instrument was designed to attain the post-acute COVID-19 sequelae and quality of life of patients with or without intensive care unit (ICU) care. 31 Emerging COVID-19-related fatigue had the highest prevalence of 72% in ICU patients and 60.3% in non-ICU patients, and the alarming incidence of breathlessness as well as psychological distress was also emphasized. 31
In an observational cohort study reporting 60-day outcomes from 38 hospitals in Michigan, the United States, medical records and telephone follow-up were adopted to aggregate longer-term consequences. 35 After a 60-day period following discharge, 488 (41.8%) patients completed the follow-up, of whom 159 (32.6%) had persistent symptoms related to COVID-19, 75 (15.4%) had a sustained cough, 81 (16.6%) had wheezing/chest tightness/breathlessness, 44 (9.0%) experienced difficulty in ambulation, and 112 (23.0%) suffered from breathlessness when walking up stairs. 35 In addition, according to the Morbidity and Mortality Weekly Report (MMWR) from the United States, 38.2% of participants with SARS-CoV-2 infection versus 16.0% of controls had long-term sequelae. 36 Notably, the occurrence of long COVID was significantly more prevalent in individuals aged ≥65 years (45.4%) than in patients aged 18–64 years (35.4%). 36 Thus, prevention strategies for long COVID sequelae should be emphasized, particularly in elderly individuals with COVID-19.
The aforementioned evidence has significantly enhanced our understanding of the manifestations of long COVID and aided in the identification of individuals at a heightened risk of developing sequelae following SARS-CoV-2 infection. Due to a number of factors, such as follow-up time, heterogeneity in self-reported data, variations in SARS-CoV-2 strains, and race and ethnicity, there are differences in long COVID epidemiology in distinct investigations. In addition, symptomatic differences are present in hospitalized and non-hospitalized individuals. Recent studies have indicated that participants allocated to general wards or ICUs had a higher likelihood of experiencing long COVID symptoms than individuals who were not hospitalized. 37 , 38 However, hospitalization seems to be the only risk factor for complication probability rather than the severity of long COVID. 39 These outcomes indicate that a large number of patients are likely to have long COVID, and efficient prevention and treatment strategies are warranted for combating numerous clinical manifestations. Moreover, the association between plentiful hazard factors and morbidity of long COVID remains to be investigated.
Impact of variants and vaccines on long COVID
Since the COVID-19 pandemic, multiple variants have emerged with enhanced transmissibility, which may contribute to the increased number of patients with severe illness and even long COVID manifestations. 40 , 41 , 42 The data from the Israeli nationwide health care organization revealed that long COVID symptoms associated with infections with all variants, including prototype, Alpha, and Delta variants, remained consistent. 43 Nevertheless, another study revealed that infection with the prototype variant was associated with a higher prevalence of long COVID symptoms than infection with the Alpha or Delta variant. 44 Among these variants, dyspnea was found to be more prevalent in patients infected with the prototype variant, while hair loss was more commonly observed in patients infected with the Delta variant. 44 Interestingly, the prevalence of fatigue was found to be similar across both variants. 44 Compared to these variants, the current data suggested that the Omicron variant possibly led to fewer clinical manifestations of long COVID. 45 A study from Eastern India revealed that approximately 8.2% of patients infected with the Omicron variant self-reported experiencing long COVID manifestations, which is relatively lower than estimates for those infected with the Delta variant. 46 Similarly, 4.5% of patients with an Omicron variant infection and 10.8% of those infected with the Delta variant in an observational study in the United Kingdom (UK) had long COVID. 47 Despite the relatively low morbidity, the increased transmissibility of the Omicron variant may result in a larger number of potential patients with long COVID. 48 Thus, it is essential to develop medications with special effects against long COVID to alleviate its clinical manifestations.
The approved vaccines have been proven to be highly effective in preventing COVID-19, especially severe illness. 49 , 50 Notably, these vaccines also exhibit the capacity to prevent long COVID. 51 In participants who received an mRNA or adenovirus vector COVID-19 vaccine, 12.8% and 8.8% decreases in long COVID morbidity were initially observed after one dose and two doses, respectively. 52 Similarly, in a study involving 739 COVID-19 participants from Italy, the prevalence of long COVID was found to be 41.8% in unvaccinated individuals, 30.0% in those with one dose, 17.4% in those with two doses, and 16.0% in those with three doses, showing a correlation between the number of vaccine doses and long COVID prevalence. 53 Compared to one dose, two vaccination doses also lower the risk of a larger range of manifestations, including myocarditis, myalgia, cerebral hemorrhage, anosmia, and interstitial lung disease within six months after infection, among other symptoms. 54 In addition to reducing morbidity, a sentinel cohort study conducted in the United Kingdom emphasized the crucial role of vaccination in lowering the mortality rate among long COVID patients, 55 further implying the importance of vaccination. However, the data from the Israeli nationwide health care organization indicated that, among the sequelae of long COVID, the administration of a vaccine led to merely a reduced prevalence of dyspnea in cases with a breakthrough infection. 43 The differences in vaccines and races may be the underlying cause of the inconsistent results above. Despite the potential protective effect of vaccines, a higher antibody titer is correlated with worse sequelae, 56 suggesting that an excessive immune response should be considered during vaccination.
Notably, increasing reinfection is extensively associated with the additional morbidity of long COVID sequelae involving cardiovascular, pulmonary, endocrine, hematological, gastrointestinal, mental, urinary, neurological, and musculoskeletal disorders. 57 Therefore, adopting adequate strategies to prevent reinfection is necessary. Although vaccine administration holds a remarkably protective effect, the risk of long COVID remains high. 57 , 58 Moreover, breakthrough SARS-CoV-2 infection (BTI), which refers to the occurrence of SARS-CoV-2 infection within 14 days of vaccination, increases the risk of experiencing various long COVID manifestations, including coagulation and hematologic, cardiovascular, kidney, gastrointestinal, neurologic, metabolic, and musculoskeletal disorders. 59 Thus, reliance on vaccines is incapable of optimally mitigating the persistent sequelae of COVID-19, and prevention strategies for SARS-CoV-2 infection and treatment options for sequelae should be emphasized to enhance the quality of life of patients.
Symptoms and possible mechanisms
The clinical manifestations of the neuropsychiatric system, which primarily include memory loss, sensorimotor aberration, cognitive disorder, paresthesia, loss of smell or taste, dizziness, and audiovestibular symptoms, 60 , 61 , 62 , 63 , 64 , 65 , 66 seem to be the prominent features of long COVID, severely impeding the daily activities of patients. Among multiple neuropsychiatric symptoms, a meta-analysis indicated that fatigue and cognitive manifestations were the most prevalent symptoms of long COVID, with proportions of 32% and 22%, respectively. 67 Cognitive disorder is a complicated neuropsychological syndrome characterized by the impairment of thinking, perceiving, and remembering. 68 According to a recent study of 236,379 patients, the estimated incidences of parkinsonism, dementia, anxiety, and psychotic syndromes among cognitive disorders following six months of SARS-CoV-2 infection were 0.11%, 0.67%, 17.39%, and 1.40%, respectively. 69 Within the field of cognitive symptoms, approximately a quarter of patients suffer from “brain fog”, a newly proposed syndrome encompassing attention deficits, processing speed issues, language fluency difficulties, memory problems, and executive function disorders. 70 , 71 Even though these symptoms have low morbidity, cognitive impairments still require sufficient attention. A cohort study with a follow-up period up to 2 years after COVID-19 onset implied that common psychiatric manifestations such as mood disorders and anxiety returned to normal within 2 months, whereas the risks of brain fog, dementia, epilepsy, and psychotic disorders were still increasing at the end of 2 years. 72 Additionally, sufficient emphasis should be placed on children due to the higher risk of persistent seizures/epilepsy and psychotic disorders compared with adults. 72 Therefore, fully understanding the pathophysiological mechanisms of these manifestations is critical for prevention and treatment.
There are several possible hypotheses for their pathophysiology. The first explanation is the existence of an active-virus reservoir in the nervous system and neuronal injury. 73 , 74 , 75 An in vitro experiment assessed SARS-CoV-2 infection in nerve cells and brain organoids and found that viral proteins and infectious particles of the virus were profiled in brain organoids. 73 Simultaneously, cortical neurons and neural precursor cells were verified to be the direct binding site of SARS-CoV-2, 73 suggesting that the virus probably invades the human brain and contributes to neuropsychiatric symptoms such as anosmia, ageusia, encephalitis, and Guillain‒Barre syndrome. Among nonhuman primates, sporadic SARS-CoV-2 has been detected in the brains of infected animals. 74 Moreover, the pathophysiological processes of neuroinflammation, hypoxia, and microhemorrhages have also been observed, 74 providing novel perspectives on the neuropsychiatric manifestations of long COVID. Aside from animal experiments, complete autopsies and sampling from the central nervous system of COVID-19 patients have shown that viral replication widely occurs in tissues/organs, including the brain, at 7 months after acute infection. 75 In ORF6 and ORF10 of the SARS-CoV-2 proteome, two amyloidogenic subsequences with self-assembled structures were screened to have high toxicity for neurons by means of nanoscale imaging, spectroscopy, molecular modeling, kinetic assays, and X-ray scattering, 76 triggering the neurologic symptoms of patients and further supporting the impact of persistent SARS-CoV-2 in neuropsychiatric symptoms of long COVID.
Neuroinflammation has also been confirmed as an imperative mechanism for the neuropathology of long COVID. 77 An investigation based on the UK Biobank comprising 401 COVID-19 participants showed that a reduction in brain size as well as reduced gray matter thickness in the parahippocampal gyrus and orbitofrontal cortex were noted via magnetic resonance imaging (MRI) at 141 days after infection. 78 Similar structural changes were also observed in a German study, 79 indicating the possibility of neuroinflammatory events and degenerative hallmarks in patients with long COVID. To elucidate the underlying mechanisms, an in vivo experiment illustrated that inflammation-associated cytokines/chemokines such as CCL11 might promote hippocampal microglial activity and inhibit neurogenesis, which was comparable to the neuropathophysiology of cancer therapy and contributed to cognitive impairment. 80 , 81 Inflammasome activation induced by viral infection also facilitates the activation of transforming growth factor beta (TGF-β) signaling as well as oxidative overload, thus resulting in Alzheimer’s disease-like features and cognitive disorders. 82 Furthermore, a study on golden hamsters revealed immune cell activation and the production of proinflammatory cytokines in the olfactory bulb and olfactory epithelium, which even existed one month after SARS-CoV-2 clearance. 83 These outcomes underscore the role of neuroinflammation in persistent neuropsychiatric symptoms.
Injury to blood vessels is likewise involved in the pathophysiology of the neuropsychiatric manifestations of long COVID. The impaired vessel density of retinal capillary microcirculation was shown to be more noticeable in long COVID patients than in controls, 84 validating that blood vessel damage might promote persistent symptoms. Furthermore, endothelial dysfunction has been proven to facilitate long COVID. A retrospective study revealed that elevated levels of endothelial cell markers, such as von Willebrand factor (VWF) antigen and VWF propeptide, were observed in COVID-19 patients after an average of 68 days following infection, 85 signifying sustained endothelial dysfunction in long COVID patients. Similarly, another study indicated that patients with COVID-19 developed endothelial dysfunction over 6 months after discharge compared with healthy participants. 86 Nevertheless, the impact of endothelial dysfunction on cerebrovascular manifestations of long COVID merits further investigation.
Myalgic encephalomyelitis/chronic fatigue syndrome
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a neuroimmune illness characterized by intolerance to systemic exertion and chronic fatigue that cannot be alleviated via rest. 87 , 88 The diagnostic criteria of ME/CFS comprise substantial impairment in daily activities for a minimum of 6 months and profound fatigue of new/definite onset that cannot be relieved by rest, accompanied by post-exertional malaise and unrefreshing sleep, cognitive impairment and/or orthostatic intolerance. 89 For most patients with ME/CFS, ‘infectious-like’ manifestations involving fever, respiratory and digestive symptoms, myalgia, and lymphadenopathy universally emerge before illness onset. 60 , 87 , 89
Fatigue syndromes after confirmed infection that might meet the diagnostic criteria of ME/CFS have been reported for multiple pathogens, including Ebola virus, Epstein-Barr virus, herpesvirus-6, SARS-CoV, and Mycoplasma pneumoniae . 90 , 91 , 92 , 93 , 94 The potential correlation between long COVID and ME/CFS has also been commented by many researchers. A systematic review of 21 studies revealed that the clinical manifestations of ME/CFS exhibited many overlaps with long COVID. 95 The presence of ME/CFS has also been observed in both children and adults with confirmed long COVID. 96 , 97 Among participants over 18 years old with long COVID, 58.7% met the criteria for ME/CFS. 96 In a cross-sectional survey, 40% of children and adolescents with COVID-19 were reported to have documented ME/CFS. 97
The pathophysiological processes of ME/CFS could be summarized as initial immune and inflammatory responses, vascular dysregulation, and autonomic/metabolic adaptation. 89 , 98 The widespread inflammatory response triggered by SARS-CoV-2 infection has been reported in patients with varying degrees of illness severity, 80 , 99 which could occur in the nervous system and contribute to neuropsychiatric symptoms. 80 , 83 The interaction networks of ME/CFS and long COVID, including inflammatory cytokines comprising interleukin (IL)-6 and IL-1B, common genes, and microRNAs, have recently been depicted, 100 revealing the potential mechanism of ME/CFS in the occurrence of long COVID and further emphasizing the significance of the inflammatory response. Meanwhile, the dysregulation of endothelin-1 has been found in long COVID patients and cases presenting exertion intolerance and persistent fatigue, suggesting the emergence of endothelial dysfunction and its effect on ME/CFS. 101 Moreover, metabolic disorders of energy probably promote exercise intolerance and nervous symptoms. The dysregulation of mitochondrial membrane potential and plasma metabolites related to mitochondria-dependent lipid catabolism are manifested in patients with long COVID, 102 , 103 perhaps conducive to ME/CFS development. Intriguingly, a study implementing machine learning to assess antibody-binding data revealed that the microbiota-immune axis was involved in the pathophysiology of ME/CFS, 104 suggesting that the microbiota could serve as a novel direction for investigating ME/CFS in post-COVID-19 syndromes.
Under certain circumstances, several diseases, such as mast cell activation syndrome, postural orthostatic tachycardia syndrome (POTS), intracranial hypertension, and craniocervical obstructions, are generally comorbid with ME/CFS. 105 , 106 , 107 Quite a few studies have also documented the corresponding manifestations in patients with long COVID. 26 , 95 However, the comorbid diseases of ME/CFS in long COVID remain enigmatic and worth further investigation.
Cardiovascular system symptoms remain one of the dominating persistent manifestations of long COVID. The prevalence of cardiac symptoms differs among current studies. 26 , 108 , 109 , 110 In a study integrating data for individuals from 56 countries, the main symptoms of chest pain, palpitations, and fainting occurred in 86.04% of individuals with long COVID. 26 For COVID-19 patients, a study of 153,760 patients showed that the burden of cardiovascular outcome per 1000 persons was 49.37 for dysrhythmia, 2.44 for inflammatory heart disease, and 18.47 for ischemic heart disease at 12 months after the COVID-19-positive test. 110 Furthermore, POTS, a specific subtype of cardiovascular manifestation, was observed in approximately 30% of patients with. 111
Several potential hypotheses of cardiovascular manifestations caused by long COVID have been proposed recently. Cardiac injury could occur in the acute or post-acute phase after viral infection. During the acute stage, the elevation of cardiac markers such as cardiac troponin signifies myocardial ischemia and injury. 112 Regarding the mechanism, it has been confirmed that direct viral toxicity plays a crucial role in mediating myocardial injury by affecting angiotensin-converting enzyme 2 (ACE2) receptors. 113 A pathological analysis of autopsy indicated that cardiac thrombi were observed in 78.6% of patients, and microthrombi in small muscular arteries, capillaries, and arterioles were noted in 64.3% of patients with myocyte necrosis. 114 Thus, thrombosis is also an essential mechanism resulting in myocardial injury. The potential explanation of thrombosis is that SARS-CoV-2 directly targets pericytes with high ACE2 expression and consequently induces endothelial cell dysfunction, 115 , 116 contributing to increased microvascular endothelial permeability and ultimately leading to thrombotic complications in microcirculation. Furthermore, the upregulation of cytokines IL-6, IL-1, and tumor necrosis factor (TNF)-α might also trigger endothelial dysfunction and platelet activation to facilitate thrombosis, 99 which could eventually cause myocardial damage.
The mechanisms of persistent cardiac injury in the post-acute and chronic disease stages are still unclear. One possible mechanism is that the persistent presence of the virus may contribute to the development of cardiac symptoms in individuals with long COVID. Several studies have revealed that SARS-CoV-2 is detectable in plasma, stool, urine, and multiple organs for more than 4 months, 117 , 118 , 119 causing persistent damage. Apart from viral persistence, autoantibodies also exert vital effects on cardiovascular system symptoms. Multiple studies have verified the high level of autoantibodies in patients with long COVID, such as antibodies against ACE2, β2-adrenoceptors, and M2 receptors. 60 , 117 , 120 , 121 Hence, autoimmunity against cardiac antigens may cause chronic damage to the cardiovascular system.
Respiratory symptoms such as cough and dyspnea are common clinical manifestations of long COVID. 26 , 122 , 123 , 124 An Italian study demonstrated that 43.4% of COVID-19 patients had persisting dyspnea at 60 days following onset. 122 In addition, an international study reported that approximately 40% and 20% of patients with long COVID had dyspnea and dry cough over 6 months, respectively. 26 This evidence shows that the respiratory manifestations of COVID-19 may persist for an extended duration and be worth heeding.
On account of acute respiratory disease, quite a number of COVID-19 patients, especially those with severe/critical disease, may suffer from diffuse alveolar damage, severe endothelial injury, and thrombosis, 125 accordingly causing substantial and persistent injury to the lungs. Nevertheless, many patients with mild/moderate illness develop small airway dysfunction in the post-acute phase via quantitative chest computed tomography (CT) evaluation. 126 A probable explanation is persistent SARS-CoV-2 in lung tissue. 119 Thus, direct viral toxicity could constantly cause damage to the respiratory system. In some COVID-19 patients with ongoing dyspnea, increased expression levels of cytokines such as IL-1β, IL-6, and IL-8 have been observed, 99 , 127 which may also contribute to pulmonary fibrosis. Furthermore, the dynamics of the airway immune microenvironment could induce respiratory manifestations. In assessments of the immune-proteomic landscape between healthy controls and long COVID patients, the expression levels of proteins correlated with apoptosis and epithelial injury have been shown to be higher in long COVID patients, and ongoing cytotoxic T-cell activation probably facilitated long-term lung injury. 128 Likewise, another study revealed a positive correlation between SARS-CoV-2-specific T cells and systemic inflammation, while a negative correlation was observed with lung function, 129 signifying that SARS-CoV-2-specific T cells may play an essential role in respiratory symptoms.
The gastrointestinal symptoms of long COVID mainly include heartburn, gastrointestinal disorders, constipation, loss of appetite, and abdominal pain. 130 , 131 , 132 , 133 An online survey of individuals from 56 countries showed that 20.5% of participants with long COVID experienced persistent diarrhea, and 13.7% of long COVID patients had loss of appetite, even 7 months after infection. 26 A prospective study of 749 COVID-19 survivors revealed that gastrointestinal symptoms were experienced by approximately 29% of participants after a period of 6 months following viral infection. 134 Among them, the leading digestive manifestations included reflux/heartburn (16.3% of cases), constipation (11.1% of cases), diarrhea (9.6% of cases), abdominal pain (9.4% of cases), and nausea/vomiting (7.1% of cases). 134 Thus, the effect of digestive system manifestations on the quality of life of patients with long COVID cannot be neglected.
The probable hypotheses of the mechanisms regarding gastrointestinal symptoms comprise the persistent viral component or active virus and alterations in gut microbiota. Multiple studies have been undertaken to validate persistent SARS-CoV-2 in the gastrointestinal tract and found that viral RNAs and proteins in stool and gut tissue could be detected up to 12 months after diagnosis. 135 , 136 , 137 By performing an endoscopy study, it was observed that a significant number of patients still had detectable levels of the nucleocapsid protein of SARS-CoV-2 in the gut epithelium even 7 months after the initial infection. 137 Furthermore, most patients with antigen persistence were reported to have long COVID, which did not occur in the participants without persistent antigens. 137
The gut microbiota is considered a complicated ecosystem that significantly affects human health and multiple diseases. 138 Recent evidence has also revealed that the gut microbiota exerts a critical function in gastrointestinal symptoms of long COVID. 22 , 139 , 140 , 141 The microbiome of long COVID patients is characterized by elevated levels of Bacteroides vulgatus and Ruminococcus gnavus , along with a decreased abundance of Faecalibacterium prausnitzii , 22 which may facilitate the digestive manifestations of long COVID. Intriguingly, the gut microbiota has recently been validated to have predictive potential. Opportunistic pathogens of the gut are related to respiratory symptoms, while nosocomial pathogens such as Actinomyces naeslundii and Clostridium innocuum may contribute to neuropsychiatric manifestations, and butyrate-producing bacteria present an inverse association with long COVID. 22 Analogously, patients with persistent reduction in pulmonary diffusing capacity for carbon monoxide have an altered gut microbiota composition characterized by the reduced abundance of dozens of bacterial taxa and increased levels of several taxa involving Veillonella . 142 Therefore, the gut microbiome should serve as a promising research focus to elucidate the mechanisms of long COVID among multiple systems beyond the digestive system.
Vascular and organ impairment
Despite initial recognition as a disease of the respiratory system, COVID-19 is capable of inducing multiple systemic and organic lesions. Damage to the circulatory system includes endothelial dysfunction, subsequent bleeding and thrombogenesis among long COVID patients. The level of arterial stiffening in the aorta, carotid artery, and brachial artery in COVID-19 patients was shown to be notably superior to that in the control group, 143 indicating that the impairment of great vessels is a universal phenomenon in COVID-19. In regard to microcapillaries, participants with long COVID presented persistent microclots that were resistant to fibrinolysis, 144 which exert a crucial role in blocking microcirculation. In addition, persistent capillary rarefication was found in patients at 18 months following SARS-CoV-2 infection, 25 emphasizing the impaired microcirculation in individuals with long COVID. Vascular impairment at different sites might result in diverse manifestations. Neurovascular damage and endothelial cell activation, accompanied by subsequent platelet aggregation and microthrombi, have been reported in COVID-19 patients, 145 which possibly causes sustained neuropsychiatric symptoms. With respect to the cardiovascular system, microthrombi are the major reason for myocardial damage in COVID patients. 114 Furthermore, the risk of lung embolism, deep venous thromboembolism, and bleeding was still dramatically elevated after a long infection period compared to a control period, 146 highlighting the role of vascular impairment in multisystem damage.
Recent evidence has focused on the multiorgan impairment of long COVID. Compared to controls, German patients who recovered from mild/moderate COVID-19 illness have been observed to have a higher risk of long-term sequelae, which includes multiorgan damage associated with renal, thrombotic, cardiac, and pulmonary functions. 147 Furthermore, an observational cohort study noted that 70% of individuals with long COVID exhibited evidence of damage to at least one organ. 148 Of them, the lungs (11%), heart (26%), kidneys (4%), pancreas (40%), liver (28%), and spleen (4%) suffered from mild impairment, with 29% of patients presenting with multiorgan damage. 148 For certain organic impairments, the symptoms may be specific. Elevation of aminotransferases serves as the representative symptom of long COVID patients with liver impairment, which is perhaps caused by viral infection of the liver and supported by pathological manifestations. 149 Analogously, SARS-CoV-2 has been observed to potentially have a direct impact on the pancreas by binding to ACE2 receptors and causing pancreatitis, diabetes, and pancreatic exocrine dysfunction. 150 , 151 , 152 For kidney impairment, a study of 478 follow-up patients reported a reduced estimated glomerular filtration rate in 29.7% of patients who had no manifestations of acute kidney injury. 153 However, the correlation between long COVID and chronic kidney disease is still obscure. 154
Although studies exploring the potential correlation between long COVID and reproductive manifestations are relatively insufficient, reproductive symptoms have been frequently reported in individuals experiencing the post-acute syndrome of COVID-19. Menstrual alterations are the leading morbidity of reproductive symptoms in women with COVID-19. In a study involving 1031 women, 53% of participants experienced worse premenstrual symptoms, 18% had new onset of menorrhagia, and 30% had new dysmenorrhea after the COVID-19 pandemic. 155 In the CoVHORT study, approximately 16% of COVID-19 participants had alterations in their menstrual cycle, which encompassed irregular menstruation, infrequent menstruation, and heightened premenstrual symptoms, persisting for a prolonged period. 156 In addition, ovarian impairment, including reproductive endocrine disorder and decreased ovary reserve, was observed in COVID-19 patients. 157 Mechanistically, follicular fluid from women who had previously been infected with SARS-CoV-2 exhibited decreased levels of IL-1β and vascular endothelial growth factor (VEGF), which subsequently influenced the expression of estrogen receptor β and the migration of endothelial cells, 158 perhaps contributing to ovarian dysfunction. Nonetheless, another observational study held an inverse outcome that the function of the ovary was not adversely affected following SARS-CoV-2 infection, and the menstrual alterations might be attributed to inflammation or psychological factors. 159 Thus, in-depth investigation with a larger sample size on the relationship between long COVID and ovarian impairment is necessary.
Regarding the male reproductive system, recent studies have exhibited the impairment of spermatogenic function and sperm quality in patients with COVID-19, which was potentially led by direct SARS-CoV-2 attack or immune activation. 160 , 161 A large retrospective study compared the risk of many manifestations of long COVID in 486,149 cases who experienced the SARS-CoV-2 infection with 1,944,580 participants without evidence of infection and indicated that the morbidity of reproductive symptoms such as ejaculation difficulty and reduced libido was increased in COVID-19 patients. 162 As another typical reproductive manifestation, erectile dysfunction was likewise documented in patients experiencing COVID-19. 163 To elucidate its biological mechanism, transmission electron microscopy was conducted on penile tissue and revealed that persistent viral particles of SARS-CoV-2 were found to remain in the vascular endothelial cells of the penis, potentially playing a role in the development of erectile dysfunction. 163
It is worth noting that patients with well-documented ME/CFS commonly have several reproductive manifestations, including menstrual cycle fluctuations, polycystic ovary syndrome (PCOS), and hyperprolactinemia. 164 , 165 , 166 Because over 50% of patients with long COVID have the classic symptoms of ME/CFS, 96 some of them probably also have reproductive comorbidities. Therefore, future studies should lay emphasis on the comorbidity with reproductive manifestations and ME/CFS in long COVID patients to better elucidate its pathophysiology.
The musculoskeletal manifestations of long COVID, including musculoskeletal pain, sarcopenia and decreased skeletal muscle mass, have recently attracted much attention. 130 , 167 , 168 , 169 , 170 , 171 The Linköping COVID-19 Study reported that 28.5% of individuals had weakness in the extremities, and 10.5% of individuals had muscle weakness among patients with long COVID symptoms. 172 In addition, another study indicated that approximately 18.59% and 15.09% of survivors developed joint pain and myalgia at 6 months following hospitalization, respectively. 173 In spite of the relatively low morbidity, these symptoms are classified into one of four major subphenotypes (nervous and musculoskeletal system manifestations) of long COVID and supported as core symptoms by 92% of patients with long COVID and their family members/caregivers, 169 , 174 which signifies that delving deeply into their pathophysiological mechanisms might assist in the precise management of long COVID.
Similar to other systems, the direct attack of SARS-CoV-2 is considered a significant contributing factor to musculoskeletal system disorders. Multiple cellular types in skeletal muscle tissue, such as pericytes and smooth muscle cells, have been validated to express AEC2 and TMPRSS2 via analysis of transcriptional data, 175 which indicates that SARS-CoV-2 has the potential to directly invade these cells, leading to detrimental effects on the musculoskeletal system. However, the autopsy studies found that the load of SARS-CoV-2 was low or even negative in tissue samples of COVID-19 patients who exhibited obvious musculoskeletal symptoms, 176 , 177 , 178 demonstrating that the directly viral attack is difficult to comprehensively elucidate the musculoskeletal manifestations of long COVID. Moreover, inflammation and microvascular injury are also hypothesized to be crucial pathophysiological mechanisms. Systemic inflammation, characterized by persistently elevated levels of cytokines, including IFN-γ, TNF-α, IL-6, and IL-10, has been observed in numerous COVID-19 patients and could persist for over 6 months following SARS-CoV-2 infection. 179 , 180 In this way, inflammatory cytokines potentially trigger abnormal catabolic pathways and result in muscle wasting. 181 Interestingly, the studies of muscle biopsy showed that musculoskeletal injury ought to be the secondary outcome of microvascular damage, and substantial persistent circulating microclots with inflammatory molecules were profiled in patients with long COVID. 144 , 178 Hence, the synergistic effect between inflammation and vascular injury could be an important element for maintaining musculoskeletal injury. Notably, other than catabolic pathways, inflammatory cytokines also promote the sensitization of the nervous system, offering a novel perspective for understanding the musculoskeletal symptoms of long COVID. 182
Increasing evidence has pointed out the importance of hypoxia in skeletal muscle alterations. 167 For patients with hypoxia, metabolic alterations and muscle wasting are common characteristics. 167 Some patients with acute respiratory distress syndrome (ARDS) have been reported to develop skeletal muscle weakness and were unable to fully recover even after a 5-year follow-up after discharge. 183 Intriguingly, a similar finding has been observed in hospitalized COVID-19 patients, especially those assigned to the ICU, 184 , 185 which might be interpreted as hypoxia being widespread in patients with severe COVID-19 and probably facilitating skeletal muscle weakness. In mechanism, the hypoxia-induced factor (HIF) and related regulatory factors that stimulate hypoxia can delay muscle regeneration and cause the loss of skeletal muscle. 186 Nevertheless, further elucidation is required to understand the underlying mechanisms of hypoxia in long COVID. Apart from the mechanisms above, several other factors, such as muscle disuse, malnutrition and medication, might also contribute to the reduced muscle mass and should be considered to explain the musculoskeletal manifestations of long COVID.
While the complete understanding of the immunobiology of long COVID remains elusive, the possible mechanisms in multiple systems have underscored its importance. The dominant hypotheses include persistent SARS-CoV-2 and corresponding antigens and/or nucleic acids involved in the inflammatory response, persistent autoantibodies triggering autoimmunity during the post-acute stage, and an imbalance in the microbiome and immune microenvironment. 60 , 136 , 187 , 188 , 189 Several studies have confirmed the presence of SARS-CoV-2 RNAs and proteins in the cardiovascular system, brain, lung tissue, plasma, and urine for a long time after initial infection, 60 , 117 , 118 , 119 which probably results in chronic inflammation and multiple systemic manifestations. In a study of 37 long COVID participants, the circulating spike emerged in more than 70% of patients with ongoing cardiovascular, gastrointestinal, neuropsychiatric, systemic, and musculoskeletal symptoms, 136 supporting the explanation that a reservoir of active SARS-CoV-2 or its components persists in COVID-19 patients. Nonetheless, the limitation of sample size makes it difficult to ensure reliability, and future research with larger sample sizes is necessitated to be conducted.
Autoimmunity plays a vital role in the development of long COVID. Multiple recent studies have documented the upregulation of autoantibodies such as antibodies to ACE2, anti-nuclear autoantibodies, and immunomodulatory factors (including complement components, chemokines, cytokines, and cell-surface proteins) in patients with persistent symptoms following SARS-CoV-2 infection. 120 , 121 , 190 , 191 , 192 An observational study indicated that the upregulation of IgG antibodies against ACE2 was found in 1.5% of patients with COVID-19, 191 implying the widespread existence of autoantibodies to ACE2. However, the duration of increased levels of anti-ACE2 antibodies and their relationship with long COVID still require further elucidation. In terms of anti-nuclear autoantibodies, the expression of anti-nuclear/extractable-nuclear antibodies (ANAs/ENAs) was higher in COVID-19 patients at 6 months after recovery than in healthy participants or patients with other respiratory infections, and persistent positive titers were relatively associated with the severity of clinical manifestations, 192 implying that anti-ANA/ENA antibodies could facilitate long COVID. Furthermore, the elevated level of specific autoantibodies against immunomodulatory factors has been reported to contribute to particular immune-cell population depletion and worse clinical outcomes, 121 and multiomics data analysis of 309 COVID-19 patients revealed cross-correlations between neutralizing antibodies against SARS-CoV-2 and autoantibodies. 120 For instance, there was a negative correlation between the expression of anti-nuclear and anti-IFN-α2 autoantibodies and the levels of anti-SARS-CoV-2 IgG, 120 suggesting a potential interconnection between these factors and the development of long COVID. Intriguingly, several autoantibodies have been found to be correlated with disorders of specific organs. For instance, long COVID patients with neurological manifestations show a higher level of nucleocapsid protein IgG against SARS-CoV-2, while some other autoantibodies have been associated with sputum production and gastrointestinal symptoms, 120 manifesting their potential as diagnostic biomarkers.
The dynamics in the immune microenvironment could also be associated with long COVID. 193 For patients who go on to develop long COVID, TNF-α and IFN-γ-induced protein 10 are significantly elevated during early recovery (<90 days), while IL-6 is upregulated among patients with long COVID, 194 demonstrating that persistent immune activation potentially leads to long COVID and could be utilized to develop novel remedies. An in-depth study indicated that long COVID patients had a typical immune microenvironment featuring activated innate immunocytes, poor abundance of naïve lymphocytes (T and B cells), and overexpression of IFN-β and IFN-λ1 at 8 months following infection. 195 Furthermore, the patterns of plasma-cell-related expression and monocyte alterations at the acute stage of COVID-19 were also correlated with persistent symptoms, 196 , 197 providing early insights into the etiologies and pathophysiology of long COVID. However, there remain numerous unknown fields concerning the immune microenvironment of long COVID, and further investigation may shed light on its prevention and treatment.
Psychological disorders and long COVID
Although most studies on long COVID are predominantly concerned with somatic manifestations and enrich the understanding of its pathophysiology to a certain extent, 7 , 42 , 60 the effect of psychological and psychosomatic factors has been almost neglected. In fact, psychological disorders may affect a substantial proportion of those who develop long COVID. 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 A previous study reported that both COVID-19 patients and control individuals develop clinical manifestations consistent with PASC. 198 Despite the relatively superior morbidity among COVID-19 patients, no evidence of autoimmunity, abnormal immune activity or persistent infection was found in the entire study population. 198 Hence, the underlying contributing factors rather than the somatic factors mentioned above deserve to be underlined. Significantly, individuals with perceived fatigue in the long COVID group did not exhibit any additional objective fatigability compared to those without long COVID symptoms, 199 signifying that mental factors probably serve as critical modifiers or even causes of long COVID. Analogously, another study showed that 64% of patients after mild COVID-19 with persistent neurological symptoms met the criteria of somatic symptom disorder (SSD), 200 and psychiatric conditions were confirmed to contribute to the prolonged duration of COVID-19 symptoms, 206 which suggests that psychosomatic disorders should be emphasized when investigating the mechanisms of long COVID. Consequently, the identification of psychological or psychosomatic disorders is encouraged to assist in multidisciplinary care and better cure long COVID. 201 , 207
With the accumulation of clinical evidence, the mechanisms of psychological/psychosomatic disorders in long COVID have also been highlighted. Psychiatric consequences have been proven to affect the immune system via multiple pathways, such as the hypothalamic-pituitary adrenal (HPA) axis and microglial activation, which subsequently might delay the recovery of COVID-19. 208 , 209 In addition, somatic symptoms have been verified to lead to worse mental health and poor quality of life, while mental disorders could aggravate the physical symptoms of long COVID patients in turn. 134 , 210 Under these circumstances, the positive feedback loop between somatic symptoms and psychological disorders probably exacerbates both mental and physical manifestations, which further facilitates long COVID. 134 However, although the importance of psychological disorders has been underlined, the mechanisms are still unclear. There are several reasons why psychological disorders in long COVID have not yet been well studied. First, it is difficult for clinicians to investigate the underlying pathology by ascribing psychological disorders. 211 Second, the explanations of physical symptoms from the perspective of psychology are usually considered a stigma for patients. 212 Third, the roles of psychosomatic medicine in other diseases have not yet been clarified, 213 making it more difficult to elucidate the progression of long COVID. Therefore, thoroughly illustrating the psychological mechanisms of long COVID remains an enormous challenge. Notably, a cross-sectional analysis of 26,823 participants suggested that self-reported SARS-CoV-2 infection was correlated with the presence of long COVID symptoms, while seropositivity was merely associated with anosmia instead of other physical symptoms. 202 Thus, it is speculated that persistent somatic symptoms may be related more to self-reported infection than to laboratory-confirmed SARS-CoV-2 infection. Consequently, misdiagnosis might occur, and patients with a diagnosis of long COVID are prone to experiencing multilayered stigmas, further exacerbating their mental symptoms. 214 Therefore, a careful evaluation is indispensable to prevent the manifestations caused by psychological disorders being incorrectly ascribed to PASC or long COVID.
Despite the huge challenge of elucidating its psychological mechanisms, it is assured that psychological and psychosomatic factors exert an indispensable role in the progression of long COVID manifestations, and adopting multidisciplinary therapeutic strategies, including psychotherapy, is potentially beneficial for persistent symptoms.
Management of long COVID
Diagnostic tools for long covid.
For early detection of long COVID, routine auxiliary inspections such as medical imaging techniques and laboratory examinations are currently indispensable. Among them, echocardiography, cardiac biomarkers and MRI are utilized to assess cardiac injury and arrhythmia, 215 and chest imaging examination and pulmonary function tests are adopted for evaluating lung damage and the degree of dyspnea. 216 However, for certain neuropsychiatric manifestations, such as fatigue and cognitive impairment, the evidence is mainly based on self-report of patients rather than objective tests. 26 , 217 Although (18)F-FDG PET has a satisfactory performance in predicting hyposmia, cognitive impairment, and insomnia via the identification of bilateral hypometabolism of the bilateral rectal or orbital gyrus in previous research, 218 no apparent change in glucose metabolism of the brain has been observed in another study. 219 Therefore, it is necessary to develop more accurate laboratory tests to enhance the sensitivity and specificity for predicting long COVID.
In addition to traditional examination methods, the microbiome has recently been reported to be associated with long COVID. 104 , 220 , 221 , 222 Early research into the gut microbiome suggested that opportunistic gut pathogens were correlated with persistent respiratory manifestations. The common nosocomial pathogens, including Actinomyces naeslundii and Clostridium innocuum , were related to fatigue and neuropsychiatric symptoms ( p < 0.05). 22 Simultaneously, multikingdom microbiota identified by metagenomic-based clustering exhibit potential utility in long COVID prediction. 141 Moreover, the alteration of tens of bacterial taxa, including Veillonella , has been validated to be related to fibrosis and could be utilized to diagnose the long-term pulmonary manifestations of long COVID. 142 In a similar manner, the abundance of certain oral microbiota, including members of the Veillonella and genera Prevotella , is significantly increased in long COVID patients, which exhibits the potential for predicting persistent symptoms after SARS-CoV-2 infection. 222 The available evidence indicates that the composition of the microbiome can be potentially used to predict the occurrence of long COVID, even specific symptoms.
Liquid biopsy research into the clinical manifestations of long COVID has shown satisfactory prognostic and diagnostic potential. 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 For example, kynurenine has been validated to be upregulated in the serum and saliva of long COVID-19 patients over 20 weeks, 223 and vascular transformation biomarkers, including P-SEL and ANG-1, have excellent specificity and sensitivity for long COVID and may serve as promising biomarkers for long COVID diagnosis and monitoring. 228 In addition, several biomarkers have the capacity to predict neuropsychiatric manifestations that are difficult to detect in traditional inspection. Neurofilament light chain, glial fibrillary acidic protein, SARS-CoV-2 nucleocapsid antigen, and immune-inflammation indicators in plasma have been verified to be correlated with persistent depression/anxiety and cognitive impairment. 225 , 227 The neurofilament light and 14-3-3 protein from cerebrospinal fluid were significantly related to neurologic disability at 18 months after neurologic symptom onset. 224 Remarkably, extracellular vesicles derived from neurocytes contain the crucial components particularly relevant to occult neural damage and indicate the capacity for monitoring the neuropsychiatric manifestations of long COVID. 229 , 231
The relevance of noncoding RNAs to many human diseases, such as malignant tumors, cardiovascular disorders, and infectious diseases, including COVID-19, has been well studied. 233 , 234 In a pilot study, miR-29a-3p, miR-155-5p and miR-146a-3p exhibited excellent performance in COVID-19 diagnosis, and miR-29a-3p and miR-146a-3p could be utilized to distinguish the post-acute phase of SARS-CoV-2 infection from the acute phase. 235 Hence, noncoding RNAs are also hypothesized to possess the potential to detect and monitor long COVID.
Although the diagnostic performance of liquid biopsy for long COVID has been observed, most of the evidence is based on preliminary studies with limited sample sizes. Therefore, the importance of exploring biomarkers suitable for clinical practice cannot be overemphasized.
Remedies for long COVID
To help clinicians better manage long COVID manifestations, the National Institute for Health and Care Excellence (NICE) of England developed guidelines on the care of patients with persistent effects of COVID-19. 9 , 236 Although there is no documented evidence regarding effective remedies for patients with long COVID, abundant therapeutic regimens based on previous experiences for certain symptoms and pilot studies have been proposed as the means to effectively address the post-acute symptoms of COVID-19 9 , 237 (Table 1 ) (Fig. 4 ).
Multidisciplinary management of long COVID. Multidisciplinary management including diagnostic tools and treatment options based on previous experience and pilot studies is essential for recovery of long COVID patients. AI artificial intelligence, MRI magnetic resonance imaging, TMS transcranial magnetic stimulation, UC-MSC umbilical cord-derived mesenchymal stem cell
Respiratory symptoms are frequently reported among individuals with long COVID, which mainly manifest as persistent dyspnea and cough. 26 The exercise tolerance test and chest radiograph are recommended in the NICE and ESCMID guidelines to assist in illness evaluation and management. 9 , 236 , 238 To better control pulmonary manifestations, multidisciplinary approaches should be applied. The recommendations for dyspnea suggest that self-management, including avoiding pollutants, stopping smoking, and regular exercise, could relieve exacerbated dyspnea. 42 , 239 , 240 For long COVID patients, previous studies showed that inspiratory muscle training and music-based breathing training elicited clinical improvements in chest symptoms and difficult breathing, 241 , 242 which should be recommended for COVID-19 patients. Additional therapeutic regimens proposed for managing the respiratory symptoms of long COVID based on pilot studies also facilitate symptomatic relief. Human umbilical cord-derived mesenchymal stem cell (UC-MSC) administration for patients with COVID-19 has revealed an excellent outcome in symptoms and lung lesion improvement at 1 year after infection compared to a control group. 243 Furthermore, the COVID-Rehab study proposed a cardiopulmonary rehabilitation program to treat individuals with long COVID. 244
For cardiovascular symptoms, the treatment options for other cardiovascular disorders/syndromes can be used for reference in long COVID. In accordance with the NICE guideline, exercise tolerance tests should be applied routinely for heart function measurement. 42 , 236 β-adrenergic blockers have been amply validated to benefit patients with cardiac arrhythmias, acute coronary syndromes, and angina 42 , 245 ; thus, it could be implied that β-adrenergic blockers might possess potential efficacy in the treatment of cardiovascular symptoms associated with long COVID. As a special type of cardiovascular symptom, POTS featured as orthostatic intolerance and the excessive increase of heart rate while standing is commonly reported in long COVID. 26 , 246 The treatment of POTS mainly consists of nonpharmacological interventions such as health education and exercise training as well as pharmacological treatments including β-blockers and vasoactive agents. 246 Therefore, rational usage of these approaches is perhaps beneficial for long COVID patients. However, few studies have explored the therapeutic effects of these interventions in long COVID.
Chronic fatigue holds the dominant place in morbidity of long COVID, which even accounts for 80% of patients with long COVID. 26 Because of the lack of specific treatment, the NICE guideline initially recommended self-management for relieving fatigue. 236 Nevertheless, patients with long COVID fatigue are commonly comorbid with ME/CFS; thus, active therapeutic strategies are necessary. Multidisciplinary strategies have also been proposed to manage ME/CFS in current guidelines, which mainly involve energy management, personalized exercise or physical activity, personalized sleep management, and dietary management. 247 Hence, these strategies are likely to be effective for managing the ME/CFS symptoms of long COVID. Nevertheless, it should be noted that ME/CFS manifestations cannot currently be cured, and the strategies mentioned merely control symptoms. 247 Furthermore, many studies have investigated certain remedies in long COVID patients. A clinical trial evaluated the therapeutic effect of anhydrous enol-oxaloacetate and found that it significantly reduced the fatigue of ME/CFS in long COVID patients. 248 However, the data for the control group is from a historical trial and meta-analysis, and further study of oxaloacetate with a rigorous design is warranted. In addition, oxygen-ozone autohemotherapy has exerted the ability to alleviate fatigue and pain in at least 67% of COVID patients, and hyperbaric oxygen therapy with 10 sessions yielded a substantial improvement in fatigue, cognition, executive function, attention, verbal function, and information processing. 249 , 250 In general, these remedies documented in pilot studies demonstrate a favorable effect on ME/CFS of long-COVID and deserve in-depth study.
Moreover, multiple additional interventions have been applied to tackle the gastrointestinal, circulatory, neurologic, musculoskeletal and even multiorgan manifestations of long COVID. As a research hotspot of the digestive system, the gut microbiome has long been considered a potential direction for the treatment of gastrointestinal diseases. A previous study proved that 5-hydroxytryptamine signaling mediated by gut microbiome dysregulation probably contributed to the gastrointestinal manifestations of long COVID. 251 Thus, certain agents targeting the gut microbiome may alleviate post-COVID manifestations. In this manner, SIM01, a microbiota-derived formula including xylooligosaccharide, galactooligosaccharides, resistant dextrin, and Bifidobacteria strains, relieved the gut dysbiosis and symptoms of COVID-19 patients, 252 further validating the effect of the gut microbiome on treating digestive manifestations. Endothelial dysfunction and persistent plasma microclots have been found in the circulatory system, which potentially lead to damage to multiple organs, including the brain, lung, and heart. 144 , 253 Therefore, active anticoagulant therapy is essential for COVID-19 patients in a hypercoagulable state to reduce the risk of thrombogenesis. For oxidation reduction and endothelial function improvement, the administration of L-arginine plus vitamin C for COVID-19 patients was applied in the LINCOLN study and significantly reduced the incidence of long COVID symptoms such as asthenia, dyspnea, chest tightness, dizziness, headache, and concentration difficulty. 253 , 254 In the meanwhile, low-dose naltrexone exhibited a promising effect on reducing the incidence of thrombotic complications. 255 Interestingly, naltrexone has also been mentioned in the treatment of ME/CFS. 60 The evidence above demonstrates the potential of naltrexone for long COVID treatment. H1 and H2 antihistamines are commonly recommended for mast cell activation syndrome to mitigate the symptoms, but cognitive decline caused by H1 blockers should be noted. 256 Various strategies have been proposed for the treatment of neuropsychiatric manifestations. For instance, obvious improvement in fatigue, cognitive function, and depressive symptoms has been observed after transcranial magnetic stimulation (TMS) in long COVID patients. 257 With regard to persistent olfactory disorders, a recent study indicated that the combination of nasal irrigation (with ambroxol, betamethasone, and rinazine) and systemic prednisone represented an excellent curative effect to improve dysosmia in long COVID patients, 258 and palmitoylethanolamide plus luteolin also ameliorated neuropsychiatric manifestations, including memory and olfactory dysfunction. 259 For musculoskeletal pain, regular exercise was suggested to reduce pain and improve physical function. 260 It is remarkable that these physical manifestations are perhaps aggravated by psychological symptoms; thus, psychotherapy interwoven with therapy of somatic consequences is of utmost importance to long COVID treatment. 261 , 262
In addition to the L-arginine plus vitamin C mentioned above, nutrients, TGF-β inhibitors, and antiviral therapies exhibit a potential effect on alleviating multisystem or multiorgan symptoms. 254 , 263 , 264 , 265 , 266 The proper intake of nutrients is strongly recommended to maintain metabolism and overcome disease during the COVID-19 pandemic. 264 Among nutrients, Morinda citrifolia and fermented Carica papaya are hypothesized to diminish the manifestations of long COVID via redox balancing, pro-energy, and immune-modulating mechanisms. 263 Regrettably, clinical outcomes are still lacking, so research concerning these nutrients in long COVID therapy is warranted. On account of persistent SARS-CoV-2 in multiple organs, 42 , 60 antiviral therapies perhaps exert promising effects on long COVID symptom relief. With respect to antiviral agents, nirmatrelvir dramatically mitigates the progression of COVID-19 without additional safety concerns. 267 Furthermore, patients administered nirmatrelvir presented a 26% reduction in morbidity associated with long COVID symptoms of fatigue, heart disease, blood clots, dyspnea, and cognitive impairment. 265 Thus, nirmatrelvir should be routinely recommended to COVID-19 patients. TGF-β inhibitors, modulators of immunity and fibrosis, have also demonstrated favorable potential in attenuating long-term COVID symptoms. 266 Nevertheless, their curative effects need further validation.
Overall, current studies have presented a diverse array of therapeutic options for combating long COVID. Nevertheless, the majority of them are based on previous experience in similar diseases and pilot studies with crude designs. Although hundreds of clinical trials have been registered (Table 2 ), few of them have been widely used in clinical practice. Accordingly, there is an urgent need for well-designed trials with large sample sizes to investigate the potential impact of updated therapeutic regimens, including nutrients, antiviral agents, and anticoagulants, on addressing the challenges of long COVID.
Artificial intelligence (AI) in long COVID management
In the past few decades, AI technologies have developed rapidly and paved the way for precise diagnosis and clinical decision-making for multiple diseases, including malignant tumors, respiratory diseases, and pediatric diseases, on the basis of medical image and electronic health record (EHR) data. 268 , 269 , 270 In the field of COVID-19, state-of-the-art AI models have also been applied for rapid diagnosis and severe illness prediction. 271 , 272 The above studies demonstrate that AI has the ability to tackle complex clinical tasks and empower personalized medicine.
In spite of the relatively young discipline, cutting-edge AI technologies have been utilized in the management of patients with long COVID. On the one hand, AI technologies enable the accurate identification of long COVID. Liquid biopsy is deemed a potent tool for disease diagnosis and monitoring. A recent study conducted a high-definition single-cell assay to compare long COVID patients with normal donors and identified specific cellular/acellular events of long COVID. 273 Using these events, a machine learning classifier was developed to separate long COVID patients from healthy controls, with an accuracy over 90%. 273 Apart from blood examination, constructing models based on EHR data could also enable accurate diagnosis. 109 , 274 , 275 The XGBoost models trained with diagnostic information, health-care utilization, demographics, and medications attained areas under the curve (AUCs) of 0.92 (whole patients), 0.85 (non-hospitalized individuals), and 0.90 (hospitalized individuals) for long COVID identification. 275 Interestingly, the symptoms in the acute period after SARS-CoV-2 infection were verified to be related to long COVID, 109 , 276 and a random forest model created using the manifestations at 7 days plus personal characteristics and comorbidities had the capacity to predict individuals with persistent symptoms, 109 which offers a powerful tool for the early identification of individuals with a high risk of long COVID. Moreover, a multiclass machine-learning model based on fecal metagenomic data was also reported to hold promising potential in diagnosing PACS. 221
On the other hand, AI technologies have been utilized for the stratification of long COVID patients. Researchers have recently proposed an unsupervised machine learning model for semantic phenotypic clustering utilizing EHR data that divides long COVID patients into 6 subtypes, including multisystem symptoms plus laboratory abnormalities (Cluster 1), pulmonary disorders (Cluster 2), neuropsychiatric manifestations (Cluster 3), cardiovascular manifestations (Cluster 4), pain/fatigue (Cluster 5), and multisystem-pain symptoms (Cluster 6). 277 Consequently, this method enables the rational allocation of clinical resources and precision clinical management. Nevertheless, another study based on machine learning analysis suggested that long COVID patients should be classified into 4 subphenotypes consisting of renal and cardiac phenotype; respiratory, anxiety and sleep phenotype; musculoskeletal and nervous phenotype; and respiratory and digestive phenotype. 169 These differences may be derived from the data bias, selection of cluster number, and variety of machine learning models. Thus, a large sample set from multiple centers is essential for precise subdivision of long COVID phenotypes. Moreover, the instruments for measuring the symptom burden of long COVID individuals have also exhibited the capacity for assessing the effect of interventions and enable precise clinical management. 278 , 279 , 280 However, they are presented in the form of questionnaires, and AI-based patterns should be constructed.
As an essential instrument for the diagnosis and prognosis evaluation of pulmonary diseases, CT examination also exhibits the potential for long COVID assessment. 281 , 282 , 283 , 284 , 285 , 286 The CT abnormalities among patients with persistent manifestations following acute COVID-19 manifest as subpleural bands and ground-glass opacity (GGO) at 3 months after infection as well as fibrosis without obvious GGOs at 6 months after infection. 283 , 285 Even worse, fibrotic changes could persist for 1 year in some individuals. 281 Thus, the early identification and dynamic evaluation of CT abnormalities is of paramount importance. Although qualitative CT approaches have been applied for assessment of the sequential change in long-term pulmonary manifestations of long COVID, they mostly rely on manual assessment. 282 , 284 , 287 Therefore, establishing AI-based models to automatically assess the CT images of long COVID patients is extremely meaningful to improve the efficiency of doctors. Moreover, the current AI models for long COVID management are concentrated on single modality, which inevitably diminishes the synergistic effect among multiple clinical modalities. Indeed, multimodal integration (MMI) via AI has been substantiated to enhance the robustness and accuracy of models, 288 , 289 , 290 lighting the path to clinical practice of AI production. Hence, further studies harnessing MMI technologies to integrate EHR, laboratory examination, and medical images may enable the individualized management of patients with long COVID (Fig. 5 ).
The workflow of multimodal integration (MMI) for precise management of long COVID. MMI systems could identify associations among multimodal data and output the outcomes including diagnosis and dynamic evaluation of patients to empower the precise management. AI artificial intelligence, GGO ground-glass opacity, NLP natural language processing
Conclusions and future directions
The ongoing symptoms and post-acute sequelae of patients with SARS-CoV-2 infection have been increasingly brought to the forefront, which could contribute to multi-system manifestations encompassing cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, and musculoskeletal symptoms. Moreover, advances in diagnosis and remedies for long COVID have been witnessed recently. Nevertheless, many problems left over from the past still require enough attention and urgently need to be addressed.
The first is the accessibility and accuracy of laboratory tests for SARS-CoV-2. The data from a previous study suggested that only 1–3% of patients had laboratory-confirmed COVID-19 in the first wave due to technical limitations. 291 For non-hospitalized individuals with mild to moderate symptoms of COVID-19, PCR or antigen testing might not be applied. 60 In addition, despite being the gold standard for diagnosis, the omission diagnostic rate of nucleic acid detection is still worth noting. Approximately 38% of cases underwent an omission diagnosis at symptom onset, and the false-negative rate was still 2% on days 22–24. 292 , 293 However, the majority of patients with nonsevere illness recovered from COVID-19 at that time, possibly resulting in missed diagnosis of numerous long COVID patients. In consideration of the accessibility of self-tests, antigen tests have also been recommended. However, only 64% of confirmed COVID-19 individuals experienced seroconversion, implying a considerably high proportion of omission diagnosis via antigen testing of COVID-19. 294 The prevalence of nonseroconversion is more remarkable in mild COVID-19 patients and children. 295 , 296 Among the diverse severity spectrum, the antigen levels of 22.2% mild and 2.6% severe patients with COVID-19 could not be profiled even after an 8-month follow-up. 295 To improve the detection rate, cases with COVID-19-related manifestations should receive timely and sustained inspection. Since the combination of IgG and IgM showed promising sensitivity of up to 96%, 297 the combination of IgG/IgM, antigen and PCR tests can be assumed to be a precise approach for COVID-19 diagnosis. By these means, long COVID may be better monitored.
In addition, a lack of knowledge and health-care education for sequelae of COVID-19 probably impedes personalized management. For instance, ME/CFS and POTS are common long COVID sequelae that have rarely been studied previously. Only 5.6% of US medical schools cover the clinical, curriculum, and research criteria for ME/CFS, 298 suggesting that the proportion might be even lower in developing countries. Furthermore, a cross-sectional survey reported that 75% of POTS cases were misdiagnosed due to the complexity of the illness. 299 Therefore, health facilities, medical schools, and government agencies should educate research/health workers on these sequelae and pursue the establishment of a dedicated discipline focused on long COVID research and care to enable the precise management of long COVID manifestations. Moreover, as recommended by researchers from Johns Hopkins University, the establishment of COVID-19 clinics equipped with multidisciplinary specialists is also crucial to afford integrated care for long COVID symptoms. 300 Nonetheless, multidisciplinary clinics in low-/middle-income countries remain challenging due to limited resource availability and medical professionals. 301
Despite the rapid progress in epidemiological understanding, pathophysiological mechanism and management strategies of long COVID, the available evidence is principally based on pilot studies with finite sample sizes and treatment of similar diseases. 60 , 302 , 303 To address these problems, future research concerning the critical clinical, epidemiological, serological, and pathological characteristics will assist in understanding the pathophysiology of long COVID. Ultimately, it is imperative that a diverse range of long COVID patients of various races and ethnicities should be meaningfully engaged in clinical trials to facilitate the clinical applications of novel interventions.
There are several strengths in this review. First, we comprehensively summarize the epidemiological understanding, impact of vaccinations and variants, multiorgan manifestations and corresponding pathophysiological mechanisms, and multidisciplinary management of long COVID. Second, as abnormal laboratory examination results were not found in many patients and a considerable percentage of patients suffering from long COVID symptoms meet the criteria for SSD, 198 , 200 the correlation between psychological or psychosomatic factors and long COVID manifestations is underlined in this review. Third, the major challenges concerning biological knowledge gaps and efficient remedies that need to be addressed are also proposed to move the research field of long COVID forward. Despite the advantages above, weaknesses still exist. Multiple therapeutic strategies have presented promising effects on alleviating symptoms of long COVID, but most of them might be overly optimistic because the current studies are mainly based on small sample sizes without rigorous scientific validation. In fact, these measures may bear a risk of serious side effects without medical advice. Hence, correct guidance is necessary to prevent patients with long COVID from being misled by social media platforms. Furthermore, among patients infected with the Omicron variant and/or vaccinated, the morbidity associated with long COVID is noticeably lower, and most of them are resolved in several months. 43 , 45 Thus, some findings may currently be overrated. However, as the transmissibility of the Omicron variant increases, 48 , 304 a larger proportion of patients are likely to develop long COVID. Consequently, sufficient attention still needs to be paid to the pathophysiological mechanisms and therapeutic options of long COVID.
Given the multisystemic illness of long COVID, numerous clinical manifestations, including impairment of multiple organs/systems, vascular damage, dysautonomia, and ME/CFS, have been observed in an extensive number of patients. Simultaneously, the foreseeable requirement of health care for sequelae of COVID-19 will continue to grow due to the global pandemic. However, the diagnostic and treatment strategies remain inadequate currently. To address these challenges, a combination of existing laboratory tests, suitable health education, outpatient infrastructure, and clinical trials with rigorous designs are clearly warranted.
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This study was supported by the Science and Technology Project of Sichuan (2022ZDZX0018, 2020YFG0473, 2023NSFSC1889); Science and Technology Project of Chengdu (2023-YF09-00007-SN); Sichuan University from “0” to “1” Innovation Project. We acknowledged the BioRender.com for the support of figures design.
These authors contributed equally: Jingwei Li, Yun Zhou, Jiechao Ma, Qin Zhang
Authors and Affiliations
Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Med-X Center for Manufacturing, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
Jingwei Li, Yun Zhou, Qin Zhang, Jun Shao, Shufan Liang, Weimin Li & Chengdi Wang
AI Lab, Deepwise Healthcare, Beijing, China
Department of Postgraduate Student, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
Department of Computer Science, The University of Hong Kong, Hong Kong, China
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C.W., W.L. and Y.Y. conceived the manuscript; J.L., Y.Z., J.M., and Q.Z. wrote the initial draft; J.L. and C.W. contributed to the figure preparation and making; J.S., S.L., Y.Y., W.L. and C.W. revised the manuscript. All authors have read and approved the manuscript.
Correspondence to Yizhou Yu , Weimin Li or Chengdi Wang .
W.L. is one of the Associate Editors of Signal Transduction and Targeted Therapy, but he has not been involved in the process of the manuscript handling. All authors listed declared that there is no conflict of interest.
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Li, J., Zhou, Y., Ma, J. et al. The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID. Sig Transduct Target Ther 8 , 416 (2023). https://doi.org/10.1038/s41392-023-01640-z
Received : 07 February 2023
Revised : 04 August 2023
Accepted : 04 September 2023
Published : 01 November 2023
DOI : https://doi.org/10.1038/s41392-023-01640-z
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Our COVID-19 Research Summary - 2021
The published literature on COVID now exceeds 211,000 papers, books, and documents, which include: 22,866 observational studies, 19,591 reviews, 1496 meta-analyses and 781 randomized control trials. These publications comprise the backdrop for our research and writing. The project began in the spring of 2020 based on a limited source of cumulative COVID-19 data and has broadened considerably. Here is what we have learned.
Our objectives remain to
- Describe trends and the geographic extent of the pandemic, including associated predictors
- Evaluate the effectiveness of vaccinations and exposure limitations.
- Provide public health perspectives.
“We know that people’s behavior, the mode of transmission, and the virus‘s characteristics all play a role but we don’t have a clear quantitative understanding of how all of these forces interact. With COVID, the biggest wild card has been human behavior.”
Dr. R. Rosenfeld , Head, Department of Machine Learning, Carnegie Mellon
Our findings along with other COVID studies are observational , focusing on numbers rather than people. Since the annual COVID mortality rate is about is only about 1 in 700, achieving reliable statistics by state would require a cohort approaching a million subjects, each of which would have to be tracked over time in order to estimate exposures. Observational studies are thus the only practical option and uncertainties about causality vs. association are inevitable.
COVID-19 trends continue to defy analysis in large part because of unpredictable variants, the latest of which is still unfolding. However, some aspects remain and will continue to dominate:
- Daily cases, deaths, and incidence of long-haul COVID can be reduced more than 10-fold by vaccination, notwithstanding deterioration over time that requires boosting.
- Acceptance of vaccination remains a personal choice - a choice that may be associated with personal characteristics including income, education, and political perspective.
- COVID-19 comprises a major cause of death in the United States and may continue to do so.
Findings In 2021 .
January . We showed that COVID-19 cases increased 10-fold, by 30% per month, during the 2020-21 winter; deaths generally followed suit, while case fatality rates (CFRs) decreased up to 10-fold. The Northeast region had shifted from worst to best, so that urban predictor variables like population density were no longer important. Regional rates coalesced in January.
February . The regional trend analyses showed declining cases but not deaths, with steady CFRs. We reported that “No plausible hypotheses have been advanced for the order-of-magnitude increases in cases and deaths since September”, now referred to as the “winter surge”. We compared urban and rural rates and noted the shift towards higher cases, deaths, and CFRs in rural areas.
March . We found that cases and deaths declined while CFRs increased 5-fold. Regional trends, which had ranged 4-fold for cases and deaths now coalesced. We analyzed short-term deaths and found a strong day-of-week effect, probably due to reporting error, but no evidence of important holiday surges.
April . We tried explaining the cyclical behavior of cases in terms of “susceptibles;” predicting an underlying trend of 5000 new cases per million per month. By contrast, the average case rate is now about 8000 cases per million per month - 53 million cases in total. However, after vaccinations got underway and prior to the Delta variant, new cases dropped to levels similar to those at the beginning of the pandemic. CFR’s ranged from about 0.015 in northern regions to 0.05 in the Southwest by April. We showed that Caucasian and mixed-race subjects had far lower COVID death rates than persons of color, and that COVID death rates increased with age at the same rate as non-COVID deaths.
May . COVID rates remained low in May. Comparing states, we reported significant relationships between COVID rates and political preference along with situational factors like household crowding. An increase in Republican voters of 60 percentage points, used as a marker of political perspective, was associated with a doubling of cumulative cases.
June . We revisited our previous consideration of airborne virus transmission, which had been espoused by CDC and the epidemiological community. We estimated ventilation rates and concluded that exposures in a small apartment were likely worse than in subways or aircraft. We also revisited urban-rural differences in more detail and showed that regional COVID rates had continued to coalesce.
July . We did a detailed analysis of vaccination rates and benefits. Daily vaccination rates peaked in April, at about 50% higher in the Northeast than elsewhere. We showed a strong significant decrease in daily state-level cases associated with full vaccination. We estimated unvaccinated case rates to be hundred-folds higher than with full vaccinations. We compared vaccination effects with education, and air pollution concluding that such personal characteristics could also be important. We also showed a negative state-level relationship between voting Republican and cumulative vaccination rates. Interestingly, vaccination rates correlated with COVID rates in 2020 before mass vaccinations began, and vaccinations at this time, also apparently, reduced mortality not associated with COVID. “ Could the decision to vaccinate have been more critical than the vaccination itself? ”
August . We reported that COVID case rates showed a sharp upturn, followed by death, likely due to the arrival of the Delta variant. Death rates and cases had decreased steadily until July to about 30 per million or 10,000 per day – a level the CDC considered as a “tolerable” endemic. We have not had these low levels since then. CFRs peaked in July, growing six-fold with substantial geographic variability.
September . We examined cyclical variations in daily infection rates and found substantial heterogeneity. State-level vaccination rates predicted both cases and death; and complete vaccination decreased case and death rates about 100-fold, even in the presence of the Delta variant.
October . We compared October’s COVID rates with those of the 2018-19 influenza to obtain a public health perspective. Total COVID-19 and influenza cases were similar at about 30 million and both were controlled by vaccination. Compare to influenza, COVID hospitalizations were 4-fold higher and deaths were 20-fold higher - COVID is clearly the more serious disease. We concluded that 178,000 lives may have been saved by COVID-19 vaccination.
November . We continued examining vaccine effects and found no difference in the real-world effectiveness of Pfizer or Moderna vaccines. We found that COVID vaccinations were associated with reduced non-COVID deaths by 3-fold. We built an empirical mathematical model of the temporal variation of cases that fit the existing data very well but grossly underestimated the current situation. We predicted that full vaccinations for the U.S. might reach 72% in the next year, but with a range of 50-90% among states.
December . Cases began a sharp upward trend at years end, with deaths lagging behind. Regional gradients shifted, with Northwest highest and Northeast lowest. Vaccination rates continued to increase slowly, led by the Northeast. Previous beneficial effects of vaccination had been overshadowed by the severity of the Delta variant. We reported that vaccine effectiveness appears to decrease substantially over time. Long-haul COVID, neglected by the epidemiology community, was inversely associated with vaccination rates and the socioeconomic factors underlying vaccine reluctance or refusal. We estimated trends and the contributions of immunity acquired from previous infection, which we found to be statistically modest.
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Toward Inclusive Learning Design pp 183–194 Cite as
The Effects of COVID-19 on Student Achievement Gap: A Literature Review
- Meika C. Billings Dopwell 9 ,
- Halimat Ipesa-Balogun 9 &
- Mashiur Rahaman 10
- First Online: 10 November 2023
Part of the Educational Communications and Technology: Issues and Innovations book series (ECTII)
In this chapter, we will discuss a review of the literature that describes the impact of social inequality and the digital divide on the student achievement gap during the COVID-19 pandemic. Research has shown that students’ socioeconomic backgrounds often influence their access to instruction. While discrepancies in access can affect virtual class attendance, they can also impact the quality of student participation. This investigation of the literature begins by delineating the state of the achievement gap before COVID-19. Afterward, there is a discussion of specific factors that contribute to the state of the achievement gap during the pandemic. Finally, the chapter discusses recommendations for future researchers, school systems, teachers, and related service providers. This chapter will give stakeholders insight into the associated COVID-19 effects on student learning in K-12 settings across the United States and guidance about areas of future investigation.
- Digital divide
- Social inequality
- Achievement gap
- K-12 education
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Meika C. Billings Dopwell & Halimat Ipesa-Balogun
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Matthew M. Schmidt
College of Education, University of Memphis, Memphis, TN, USA
Andrew A. Tawfik
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Billings Dopwell, M.C., Ipesa-Balogun, H., Rahaman, M. (2023). The Effects of COVID-19 on Student Achievement Gap: A Literature Review. In: Hokanson, B., Exter, M., Schmidt, M.M., Tawfik, A.A. (eds) Toward Inclusive Learning Design. Educational Communications and Technology: Issues and Innovations. Springer, Cham. https://doi.org/10.1007/978-3-031-37697-9_15
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A Comprehensive Literature Review on the Clinical Presentation, and Management of the Pandemic Coronavirus Disease 2019 (COVID-19)
- 1 Medicine, National University of Ireland Galway, Galway, IRL.
- 2 Orthopaedics, University Hospital Galway, Galway, IRL.
- PMID: 32269893
- PMCID: PMC7138423
- DOI: 10.7759/cureus.7560
Coronavirus disease 2019 (COVID-19) is a declared global pandemic. There are multiple parameters of the clinical course and management of the COVID-19 that need optimization. A hindrance to this development is the vast amount of misinformation present due to scarcely sourced manuscript preprints and social media. This literature review aims to presents accredited and the most current studies pertaining to the basic sciences of SARS-CoV-2, clinical presentation and disease course of COVID-19, public health interventions, and current epidemiological developments. The review on basic sciences aims to clarify the jargon in virology, describe the virion structure of SARS-CoV-2 and present pertinent details relevant to clinical practice. Another component discussed is the brief history on the series of experiments used to explore the origins and evolution of the phylogeny of the viral genome of SARS-CoV-2. Additionally, the clinical and epidemiological differences between COVID-19 and other infections causing outbreaks (SARS, MERS, H1N1) are elucidated. Emphasis is placed on evidence-based medicine to evaluate the frequency of presentation of various symptoms to create a stratification system of the most important epidemiological risk factors for COVID-19. These can be used to triage and expedite risk assessment. Furthermore, the limitations and statistical strength of the diagnostic tools currently in clinical practice are evaluated. Criteria on rapid screening, discharge from hospital and discontinuation of self-quarantine are clarified. Epidemiological factors influencing the rapid rate of spread of the SARS-CoV-2 virus are described. Accurate information pertinent to improving prevention strategies is also discussed. The penultimate portion of the review aims to explain the involvement of micronutrients such as vitamin C and vitamin D in COVID19 treatment and prophylaxis. Furthermore, the biochemistry of the major candidates for novel therapies is briefly reviewed and a summary of their current status in the clinical trials is presented. Lastly, the current scientific data and status of governing bodies such as the Center of Disease Control (CDC) and the WHO on the usage of controversial therapies such as angiotensin-converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs) (Ibuprofen), and corticosteroids usage in COVID-19 are discussed. The composite collection of accredited studies on each of these subtopics of COVID-19 within this review will enable clarification and focus on the current status and direction in the planning of the management of this global pandemic.
Keywords: ace2; ards; chloroquine; covid-19; lopinavir and ritonavir; mrna-1273 vaccine; pandemic; remdesivir (gs-5734); sars-cov-2; severe acute respiratory infection.
Copyright © 2020, Kakodkar et al.
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A literature review of the economics of COVID‐19
1 Department of Economics, University of Ottawa, Ottawa Ontario, Canada
Table B: COVID‐19 ‐ Timeline
Figure A: Cumulative COVID‐19 Cases and Deaths – Global Pandemic (as on 30 November 2020)
Table C: Cumulative Cases: Top 10 Countries (as of 30 November 2020)
The goal of this piece is to survey the developing and rapidly growing literature on the economic consequences of COVID‐19 and the governmental responses, and to synthetize the insights emerging from a very large number of studies. This survey: (i) provides an overview of the data sets and the techniques employed to measure social distancing and COVID‐19 cases and deaths; (ii) reviews the literature on the determinants of compliance with and the effectiveness of social distancing; (iii) mentions the macroeconomic and financial impacts including the modelling of plausible mechanisms; (iv) summarizes the literature on the socioeconomic consequences of COVID‐19, focusing on those aspects related to labor, health, gender, discrimination, and the environment; and (v) summarizes the literature on public policy responses.
The World was gripped by a pandemic over the first half of 2020, of which the second wave emerged in the Fall. It was identified as a new coronavirus (severe acute respiratory syndrome coronavirus 2, or SARS‐CoV‐2), and later renamed as Coronavirus Disease‐19 or COVID‐19 (Qiu et al., 2020 ). While COVID‐19 originated in the city of Wuhan in the Hubei province of China, it has spread rapidly across the World, resulting in a human tragedy and in tremendous economic damage. By the end of November 2020, there had been close to 63 million reported cases of COVID‐19 globally and over 1.4 million deaths.
Pandemics are anything but new, and they have had severe, adverse economic impacts in the past; COVID‐19 is not expected to be any different (see the Online Appendix for a brief history of past pandemics and their socioeconomic consequences). Given the rapid spread of COVID‐19, countries across the World have adopted several public health measures intended to prevent its spread, including social distancing (Fong et al., 2020 ). According to Mandavilli ( 2020 ), this strategy saved thousands of lives, both during other pandemics, such as the Spanish flu of 1918, and more recently a flu outbreak that occurred in Mexico City in 2009. As part of social distancing measures, businesses, schools, community centers, and nongovernmental organization (NGOs) were required to close down, mass gatherings have been prohibited, and lockdown measures have been imposed in many countries, allowing travel only for essential needs. 1 The goal of these measures is to facilitate a “flattening the curve,” that is, a reduction in the number of new daily cases of COVID‐19 in order to halt their exponential growth and, hence, reduce pressure on medical services (John Hopkins University, 2020 ).
The spread of COVID‐19 has resulted in a considerable slowdown in economic activities. According to an early forecast of The World Bank ( 2020 ), global GDP in 2020 relative to 2019 is forecasted to fall by 5.2%. Similarly, the OECD ( 2020 ) forecasts a fall in global GDP by 6 to 7.6%, depending on whether or not a second wave of COVID‐19 emerges. In its latest forecast, the International Monetary Fund ( 2020 ) projected a contraction of 4.4% in light of the stronger than expected recoveries in advanced economies which lifted lockdowns during May and June of 2020. This was mainly the result of the unprecedented fiscal, monetary, and regulatory responses in these countries that helped to maintain household disposable income, protect cash flows for firms, and support credit provisions.
The economic implications will be wide ranging and uncertain, with different effects expected on labor markets, production supply chains, financial markets, and GDP levels. The negative effects may vary by the stringency of the social distancing measures (e.g., lockdowns and related restrictions), their length of implementation, and the degree of compliance with them. In addition, the pandemic and the subsequent interventions may well lead to higher levels of mental health distress, increased economic inequality, and particularly harsh effects on certain socio‐demographic groups.
The goal of this piece is to survey the emerging and already vast literature on the economic consequences of COVID‐19, and to synthesize the insights contained in a growing number of studies. Figure 1 illustrates the number of National Bureau of Economic Research (NBER) working articles that have been released related to the pandemic between March and November of 2020. 2 By the end of November 2020, there had been 247 articles related to COVID‐19. Similarly, 204 discussion articles on the pandemic were released by the IZA Institute of Labor Economics (IZA) from March to November of 2020. 3
COVID‐19 publications in 2020 in the NBER working paper series. [Color figure can be viewed at wileyonlinelibrary.com ]
Source : Authors’ compilation drawn from the NBER website
This article will focus on five broad areas: (i) the measurement of the spread of COVID‐19 and social distancing activities, (ii) the effectiveness and compliance with social distancing regulations, (iii) the economic impacts of COVID‐19 and the mechanisms giving rise to them, (iv) the socioeconomic consequences of lockdowns, and (v) the policy measures and regulations that have been implemented in response to the pandemic. One topic that we do not cover explicitly is the interface between COVID‐19 and financial markets. This omission is due partly to space constraints, but also to the fact that the outcomes in financial markets that are related to COVID‐19 are extremely volatile, and therefore, any analysis contained in our survey would be ephemeral.
The rest of the article is structured as follows. Section 2 provides an outline of the measurement of COVID‐19 spread and of social distancing actions by documenting and describing the most popular data sources. Section 3 discusses the socioeconomic determinants and the effectiveness of social distancing activities. Section 4 focuses on the economic and financial impacts including modelling of the plausible behavioral mechanisms. Section 5 reviews the literature on the socioeconomic consequences of social distancing measures, focusing on the labor‐related, health‐related, gender‐related, discriminatory, and environmental aspects. Section 6 consists of a summary of the economic impact of the policy responses. Section 7 provides the conclusion.
2. MEASUREMENT OF COVID‐19 AND SOCIAL DISTANCING ACTIONS
2.1. measurement of covid‐19 spread.
Before reviewing the potential economic impact and socioeconomic consequences, it is important to contextualize the data related to COVID‐19, without which it would not be possible to assess the scope of the pandemic. Timely and reliable data inform us of how and where the disease is spreading, what impact the pandemic has on the lives of people around the World, and to what extent the counter measures that are taken are successful (Roser et al., 2020 ).
Four key indicators are: (i) the total number of tests carried out, (ii) the number of confirmed COVID‐19 cases, (iii) the number of confirmed COVID‐19 deaths, and (iv) the number of people who have recovered from COVID‐19. These numbers are provided by different local, regional, and national health agencies/ministries across countries. However, for research and educational purposes, the data are accumulated by the Center for Systems Science and Engineering at Johns Hopkins University. 4 The database provides the figures as well as visual maps of the distribution of cases across the World. They are reported at the provincial level for China, at the city level for the United States of America, Australia, and Canada, and at the country level for all other countries (Dong et al., 2020 ). The data are corroborated with the WHO, 5 the Center for Disease Control (CDC) in the United States, and the European Center for Disease Control (ECDC).
Based on these figures, the Case Fatality Rate (CFR) is calculated as the number of confirmed deaths divided by the number of confirmed cases, which gives the mortality rate. 6 However, Roser et al. ( 2020 ) caution against taking the CFR numbers at face value to assess mortality risks, 7 because the CFR is based on the number of confirmed cases. Due to limited and sporadic testing capacities, not all COVID‐19 cases can be confirmed. Moreover, the CFR reflects the incidence of the disease in a particular context at a particular point in time. Therefore, CFRs are subject to changes over time and are sensitive to the location and population characteristics.
Recent studies indicate that there are large measurement errors associated with COVID‐19 case numbers. Using data on influenza‐like illnesses (ILI) from the CDC, Silverman et al. ( 2020 ) show that ILIs can be a useful predictor of COVID‐19 cases in the United States. The authors find that there was an escalation in the number of ILI patients during March of 2020. These cases could not be properly identified as COVID‐19 cases due to the lack of testing capabilities during the early stages of the pandemic's progression. The authors suggest that the surge in ILIs may have corresponded to 8.7 million new COVID‐19 cases between March 8 and March 28, most of which were probably not diagnosed. Based on imputation, that figure suggests that almost 80% of all actual cases in the United States during that time period were never diagnosed.
While the dataset mentioned above focuses on counts and tests, the COVID Tracking Project 8 in the United States provides additional data on patients who have been hospitalized, are in intensive care units (ICUs), and are on ventilator support for each of the 50 states. It also grades each state on data quality. Recently, it has included the COVID Racial Data Tracker , 9 which shows the race and the ethnicity of individuals affected by COVID‐19. All of these combined measures and statistics provide a more comprehensive perspective of the spread of the pandemic in the United States.
2.2. Measurement of social distancing
Compared to measuring the spread of the virus, social distancing is not easy to quantify. We determined from the literature that there are three main techniques that are employed: (i) developing and calculating measures of the mobility of the population, (ii) modelling proxies, and (iii) calculating indices. Proxies and indices are based on data related to the observed spread of infection and to the implementation of social distancing policies, respectively. On the other hand, the movements of people are based on their observed travelling patterns. Mobility measures have been used extensively in recent months to discern mobility patterns during the pandemic (Nguyen et al., 2020 ). However, mobility data providers have slight differences in their methodologies. Table 1 provides a summary of how different mobility data providers compile their data.
Social distancing— Mobility measures and how they work
Mobility data are more dynamic and are available at a daily frequency. They can also be used to measure the effect of social distancing on other variables, such as adherence to shelter‐in‐place policies or labor employment patterns (Gupta et al., 2020 ). They also offer key insights into human behavior. For example, “Safegraph” data suggest that social activity in the United States started declining substantially and rapidly well before lockdown measures were imposed (Farboodi et al., 2020 ).
Outside of the United States, a large number of studies have relied on Google LLC Community Mobility Reports. For China, mobility has been mostly measured using data from Baidu Inc. For example, Kraemer et al. ( 2020 ) document how COVID‐19 spread in China using Baidu Inc. data. They investigate travel history from Wuhan to other cities in China, finding that the spatial distribution of cases in other cities was correlated with individual peoples’ travel histories. However, after the implementation of social distancing measures in these cities, the correlation no longer held. Therefore, the authors conclude that local lockdowns rather than travel restrictions helped to mitigate the spread and transmission of COVID‐19 in cities outside Wuhan. See Coelho et al. ( 2020 ) for an examination of the spread of COVID‐19 in Brazil using daily air travel statistics from the Official Airline Guide to measure mobility.
Mobility data do have their own limitations and are not frequently used in the case of epidemics, even though they might be useful (Oliver et al., 2020 ). Mobility data are a proxy for time spent in different locations. They do not allow one to determine the situational context of the contacts that are reported, which are needed to understand the spread of COVID‐19, that is whether they occur in the workplace or in the general community (Martín‐Calvo et al., 2020 ). Those two situations involve different levels of the inherent risk of transmission. In regards to the productive activities of the individuals that are tracked, information on the context is also indeterminate. For those who are working virtually from their homes, for instance, these measures do not capture the value‐added stemming from the time that they allocate to their jobs in the labor market. It is also likely that the quality of these measures can deteriorate when overall unemployment rates and job disruptions are high (Gupta et al., 2020 ). 10 Telecom operator data are deemed to be more representative than locational data, as the former are not limited to people with smartphones, GPS locators, and histories of travel using GPS location (Lomas, 2020 ).
Social media has also been used to measure mobility patterns. Galeazzi et al. (2020) analyze the effect of lockdowns in France, Italy, and United Kingdom on national mobility patterns by exploiting geolocalized data observed from 13 million Facebook users. The authors predictably find that people transition toward localized, short‐ range mobility patterns instead of international, long‐range patterns. However, mobility patterns display heterogeneity across countries. In France and the United Kingdom, mobility is more “concentrated” around huge, central metropolises that are largely disconnected from the provinces, which helps to reduce transmission of the virus. In Italy, on the other hand, the population is more “distributed” across clusters around four major cities that remain interconnected, thus permitting persistent spread.
3. SOCIAL DISTANCING: DETERMINANTS, EFFECTIVENESS, AND COMPLIANCE
A large range of social distancing policies have been implemented, ranging from full‐scale lockdowns to voluntary self‐compliance measures. 11 For example, Sweden imposed relatively light restrictions (Juranek & Zoutman, 2020 ). Large‐scale events were prohibited, and restaurants and bars were restricted to table service only; however, private businesses were generally allowed to operate freely. The population was encouraged to stay at home if they were feeling unwell and to limit social interactions if possible (T.M. Andersen et al., 2020 bib18 ).
People tend to adopt social distancing practices when there is a specific incentive to do so in terms of risk to health and financial cost (Makris, 2020 ). Maloney and Taskin ( 2020 ) attribute voluntary, cooperative actions to either fear of infection or to a sense of social responsibility. Stringent social distancing measures tend to be implemented in countries with a greater proportion of elderly residents, a higher population density, a greater proportion of employees working in vulnerable occupations, higher degrees of democratic freedom, a higher incidence of international travel, and greater distances from the Equator (e.g., Jinjarak et al., 2020 ). Appealing to a game theoretic approach, Cui et al. ( 2020 ) argue that states sharing economic ties will be “tipped” to reach a Nash equilibrium, whereby all other states comply with shelter‐in‐place policies. 12
Social distancing policy determinants have been linked to political party characteristics, political beliefs, and partisan differences (Baccini & Brodeur, 2021 ; Barrios & Hochberg, forthcoming; Murray & Murray, 2020 ). Barrios and Hochberg (forthcoming) correlate the risk perception for contracting COVID‐19 with partisan differences. They find that, in the absence of the imposition of social distancing, counties in the United States which had higher vote shares for Donald Trump are less likely to engage in social distancing. This persists even when mandatory stay‐at‐home measures are implemented across states. Allcott et al. ( 2020 ) find a similar pattern. In addition, the authors show through surveys that Democratic and Republican supporters have different risk perceptions about contracting COVID‐19, and hence hold divergent views regarding the importance of following social distancing measures. These stylized facts make it hard to estimate the causal effect of COVID‐19 on electoral outcomes (Baccini et al., 2021 ).
Researchers are trying to determine the effectiveness of social distancing policies in reducing social interactions and ultimately infections and deaths. Abouk and Heydari ( 2021 ) show that reductions in outside‐the‐home social interactions in the United States are driven by a combination of governmental regulations and voluntary measures, with a strong causal impact for the implementation of statewide stay‐at‐home orders, but more moderate impacts for nonessential business closures and limitations placed on bars/restaurants. Ferguson et al. ( 2020 ) argue that multiple interventions are required in order to have a substantial desired impact on transmission. The optimal mitigation strategy, which is a combination of case isolations, home quarantining, and social distancing of high‐risk groups, would reduce the number of deaths by half and the demand for beds in ICUs by two‐thirds in the United Kingdom and the United States.
Some studies focus on the impact of social distancing on COVID‐19 cases, hospitalizations, etc. For example, Fang et al. ( 2020 ) argue that if lockdown policies had not been imposed in Wuhan, then the infection rates would have been 65% higher in cities outside of Wuhan. Hartl et al. ( 2020 ) show that growth rate of COVID‐19 cases in Germany dropped from 26.7 to 13.8% within 7 days after implementation of lockdowns in the country. Greenstone and Nigam ( 2020 ) project that 3 to 4 months of adherence to social distancing regulations would reduce the number of cases in the United States by 1.7 million by October of 2021, 630,000 of which would translate into averted overcrowding of ICUs in hospitals. Friedson et al. ( 2020 ) argue that early intervention in California helped to reduce significantly the numbers of COVID‐19 cases and deaths during the first 3 weeks following its enactment. Note that this set of interventions falls well short of an economic shutdown.
Similarly, Dave, Friedson, Matsuzawa, Sabia, et al. ( 2020 ) find that counties in Texas that adopted shelter‐in‐place orders earlier than the statewide shelter‐in‐place order experienced a 19 to 26% fall in the rates of COVID‐19 case growth 2 weeks after implementation of such orders. M. Andersen et al. ( 2020 ) find that temporary paid sick leave, a federal mandate enacted in the United States, which allowed private and public employees 2 weeks of paid leave, led to increased compliance with stay‐at‐home orders. On a more global scale, Hsiang et al. ( 2020 ) show that social distancing interventions prevented or delayed around 62 million confirmed cases, corresponding to the aversion of roughly 530 million total infections in China, South Korea, Italy, Iran, France, and the United States within 7 days.
Another important related issue is the determinants of compliance behavior (e.g., Coelho et al., 2020 ; Fan et al., 2020 ). The documented socioeconomic determinants of the degree of compliance with social distancing (lockdowns or safer‐at‐home orders) include, among other factors, income level, trust, and social capital, public discourse, and to some extent, news channel viewership. The degree of ethnic diversity is another documented socioeconomic determinant of social distancing (Egorov et al., 2021 ). Galasso et al. ( 2020 ) rely on survey data from eight OECD countries and provide evidence that women are more likely than men to agree with restrictive public policy measures and to comply with them. Chiou and Tucker ( 2020 ) show that Americans living in higher‐income regions with access to high‐speed internet are more likely to comply with social distancing directives. Coven and Gupta ( 2020 ) find that residents of low‐income neighborhoods in New York City comply less with shelter‐in‐place activities during non‐working hours. According to the authors, this pattern is consistent with the fact that low‐income populations are more likely to be front line, “essential” workers and are also are more likely to make frequent retail shopping visits for essentials, making for two compounded effects. People with lower income levels, less flexible work arrangements (e.g., the inability to work remotely), and a lack of accessible interior space outside of bedrooms are less likely to engage in social distancing (Papageorge et al., 2020 ). Last, Bonaccorsi et al. ( 2020 ) analyze the heterogeneous impacts of lockdowns by socioeconomic conditions of people in Italy. Using mobility data from Facebook, they provide evidence that mobility reduction is higher in municipalities which have stronger fiscal capacity and also those which have lower per‐capita income levels. The authors conclude that the pandemic has disproportionately affected poor individuals within municipalities with strong fiscal capacity in Italy.
Individual beliefs and social preferences should also be taken into consideration, as they affect behavior and compliance. Based on an experimental setup with participants in the United States and the United Kingdom, Akesson et al. ( 2020 ) conclude that individuals overestimated the infectiousness of COVID‐19 relative to expert suggestions. If they were exposed to expert opinion, individuals were prone to correct their beliefs. However, the more infectious COVID‐19 was deemed to be, the less likely they were to undertake social distancing measures. This was perhaps due to the belief that the individual will contract COVID‐19 regardless of his/her social distancing practices. Briscese et al. ( 2020 ) model the impact of “lockdown extension” on compliance using a representative sample of residents from Italy. The authors find that if a given hypothetical extension is shorter than expected (i.e., a positive surprise), the residents are more willing to engage in self‐isolation. Therefore, to ensure compliance, these authors suggest that it is imperative for the government or local authorities to work on communication and to manage peoples’ expectations. Campos‐Mercade et al. ( 2021 ) examine the relationship between social preferences and social distancing compliance. The authors find that people who exhibit prosocial behavior (in this instance individuals who claim that they do not want to expose others to risks) are more likely to follow social distancing measures and other health‐related guidelines.
Bargain and Aminjonov ( 2020 ) demonstrate that residents in European regions with high levels of trust decrease their mobility related to non‐necessary activities compared to regions with lower levels of trust. Brück et al. ( 2020 ) document a negative relationship between being in contact with sick people and trust in people and institutions. Similarly, Brodeur et al. ( 2020 ) find that counties in the United States exhibiting relatively more trust in others decrease their mobility significantly once a lockdown policy is implemented. They also provide evidence that the estimated effect on postlockdown compliance is especially large if people tend to place trust in the media, and relatively smaller if they tend to trust in science, medicine, or government.
Researchers also think about this chain of causality in reverse. Aksoy Eichengreen, and Saka ( 2020 ) find that individuals’ degrees of exposure to epidemics (especially during the ages 18 to 25) has a negative effect on their confidence in political institutions. These individuals are also less likely to have confidence in their health care systems during the times of pandemics. Barrios et al. ( 2021 ) and Durante et al. ( 2021 ) provide evidence that regions with stronger civic culture engaged in more voluntary social distancing. Aksoy, Ganslmeier, and Poutvaara ( 2020 ) find that a high level of public attention (measured through the share of Google shares containing matters related to COVID‐19) has a significant correlation with the timing of implementation of social distancing measures. This relationship is mostly applicable for countries with high quality of institutions. Last, Bartscher et al. ( 2020 ) show that higher levels of social capital (proxied through voter turnout in parliamentary elections) lead to fewer cases per capita accumulated from mid‐March to mid‐May in selected European countries and United Kingdom.
Daniele et al. ( 2020 ) investigate the effect of the COVID‐19 shock on sociopolitical attitudes as opposed to the impact of latter on the spread of the virus. Employing a randomized survey flow design for 8,000 respondents in Germany, Italy, Netherlands and Spain, the authors find that COVID‐19 has led to a deterioration in the levels of interpersonal and institutional trust. It has also lowered support for the European Union in general and for social welfare spending financed by taxes. The authors conclude that these results are driven by the “economic insecurity” rather than the “health” dimensions resulting from the crisis.
Simonov et al. ( 2020 ) analyze the causal effect of cable news viewership on social distancing compliance. The authors examine the average partial effect of Fox News viewership, a news channel that has mostly refuted expert recommendations from leaders of the United States and global public health communities on the severity of COVID‐19 and on compliance, and find that a 1 percentage point increase in Fox News viewership reduced the propensity to stay at home by 8.9 percentage points. Bursztyn et al. ( 2020 ) show that greater exposure to the Hannity show compared to the Tucker Carlson Tonight show in Fox News is associated with larger COVID‐19 case numbers and deaths. This is because the former TV host downplayed the importance of COVID‐19, while the latter provided a serious warning on the same topic during early February. The variation between the messages in the two shows led to changes in behavior in response to COVID‐19.
Table 2 provides a summary of the literature related to the determinants (i.e., factors which influence implementation of social distancing as a policy measure), compliance with social distancing (i.e., whether people are actually following social distancing measures), and their effectiveness (i.e., evidence of success in reducing COVID‐19 cases).
Determinants, compliance and effectiveness of social distancing measures: Summary of studies
4. MACROECONOMIC IMPACTS AND PLAUSIBLE MECHANISMS
4.1. plausible mechanisms for macroeconomic impact.
To understand the potential negative economic impact of COVID‐19, it is important to comprehend the economic transmission channels through which the shocks will adversely affect the economy. According to Carlsson‐Szlezak et al. ( 2020a , b ), there are three main transmission channels. The first is the direct impact, which is related to reduced consumption of goods and services. Prolonged lengths of the pandemic and the concomitant social distancing measures might reduce consumer confidence by keeping consumers at home, wary of discretionary spending, and pessimistic about their long‐term economic prospects. The second one is the indirect impact working through financial market shocks and their effects on the real economy. Household wealth will likely fall, savings will increase, and consumption spending will decrease further. The third consists of supply‐side disruptions; as restrictions halt or hamper production activities, they will negatively impact supply chains, labor demand, and employment, leading to prolonged periods of lay‐offs and rising unemployment. In particular, Baldwin ( 2020 ) discusses the expectation shock by which a “wait‐and‐see” attitude is adopted by economic agents. The author argues that this is common during economic climates characterized by uncertainties, as there is less confidence in markets and in engaging in economic transactions. Ultimately, the intensity of the shock is determined by the underlying epidemiological properties of the virus, consumer behaviour, and firm behavior in the face of adversity and uncertainty, and public policy responses. To understand the implications of the spread of the virus and the consequent social distancing measures on economic activities, a number of researchers have integrated canonical epidemiology models such as the susceptible, infected, resolved model (SIR) with macroeconomic models (see the Online Appendix for a detailed review of these models).
Gourinchas ( 2020 , p. 33) summarizes the effect on the economy by stating: “A modern economy is a complex web of interconnected parties: employees, firms, suppliers, consumers, and financial intermediaries. Everyone is someone else's employee, customer, lender, etc.” Due to the very high degrees of interconnectiveness and specialization of productive activities, a breakdown in the supply chains and the circular flows will have cascading effects. Baldwin ( 2020 ) describes the impact of COVID‐19 and subsequent social distancing measures on the macroeconomy within a circular flow framework.
It is also important to understand the processes that generate recoveries from economic crises. Carlsson‐Szlezak et al. ( 2020a ) explain different types of recovery in the aftermath of negative shocks through the concept of “shock geometry.” There are three broad scenarios of economic recoveries, which we mention in ascending order of their severity. First, there is the most optimistic one labelled “V‐shaped,” whereby aggregate output is displaced and quickly recovers to its pre‐crisis path. Second, there is the “U‐shaped” path, whereby output drops swiftly but does not return swiftly to its precrisis path. The gap between the former trajectory of output and the actual one remains large for quite some time, but recovery eventually occurs. Third, in the case of the very grim “L‐shaped” path, output drops and reaches a trough, but subsequent growth rates remain very low. The gap between the former and the new output paths continues to widen. Another scenario of economic recovery often mentioned is the “K‐shaped” one, which occurs when, following a recession, different parts of the economy recover at different rates, times, or magnitudes.
Carlsson‐Szlezak et al. ( 2020b ) state that after previous pandemics, such as the 1918 Spanish Influenza, the 1958 Asian Influenza, the 1968 Hong Kong influenza, and the 2002 SARS outbreak, economies have tended to experience “V‐shaped” recoveries. However, the pattern for the COVID‐19 economic recovery is not expected to be straightforward. This is because the effects on employment due to social distancing measures and lockdowns are expected to be much larger. According to Gourinchas ( 2020 ), during a short period, as much as 50% of the working population might not be able to find work. Moreover, even if no containment measures are implemented, a recession would occur anyway, fueled by the precautionary and/or risk‐averse behavior of households and firms faced with the uncertainty of dealing with a pandemic as well as with an inadequate public health response (Gourinchas, 2020 ).
Guerrieri et al. ( 2020 ) show that in a multisector model with certain assumptions, such as incomplete markets, low substitutability across sectors, and liquidity‐constrained consumers, COVID‐19 imparts a supply shock which works through lockdowns, layoffs, firm closures, etc. The subsequent impact would be a drop in aggregate demand and a demand‐deficient recession, that is, a “Keynesian supply shock.”
Baqaee and Farhi ( 2020 ) analyze the impact through a disaggregated Keynesian model comprised of multiple sectors, factors of production, and input‐output linkages with different features, such as nominal wage rigidities and credit constraints. They find that negative supply shocks are stagflationary, whereas negative demand shocks are deflationary. The policy implications are somewhat ambiguous. Policies that boost aggregate supply (e.g., providing subsidies to businesses, relaxing lockdowns, etc.) might not be effective in increasing demand in certain demand‐constrained sectors. Similarly, demand‐inducing policies (e.g., lower interest rates, more generous social insurance, etc.) might lead to supply shortages and inflationary pressures in certain sectors.
4.2. Quantitative macroeconomic impacts
As the pandemic unfolds, many researchers have been thinking about the economic impact from a historical perspective. Ludvigson et al. ( 2020 ) try to quantify the macroeconomic impact of costly disasters (natural and manmade) and translate them into estimates of the impact of COVID‐19. They find that in a fairly conservative scenario, pandemics, such as COVID‐19, are tantamount to large, multiple‐period exogenous shocks. Using a “costly disaster” index, the authors find that COVID‐19 is constituted of multi‐period shocks in the United States, which leads to a 12.75% drop in industrial production, a 17% loss in service employment, sustained and drastic reductions in air travel, and macroeconomic uncertainties which linger for up to 5 months. Jordà et al. ( 2020 ) analyze the rate of return on the real natural interest rate (the level of real returns on safe assets resulting from the demand and supply of investment capital in a noninflationary environment) from the 14 th century to 2018. Theoretically, a pandemic is supposed to induce a downward negative shock to the real natural interest rate. This is because investment demand decreases due to excess capital per labor unit (i.e., a scarcity of labor being utilized), while savings flows increase due to either precautionary reasons or to replace lost wealth. The authors find that the natural rate of interest may be about 2 percentage points lower than it would otherwise have been some 20 years after the pandemic, and only return to counterfactual levels after 40 years.
Analysis based on historical data, however, might not be relevant in this case. According to Baker et al. ( 2020 ), COVID‐19 has led to massive spikes in uncertainty, and there are no close historical parallels. Because of the speed of evolution and timely requirements of data, the authors suggest that one should utilize forward‐looking uncertainty measures to ascertain its impact on the economy. They formulate the uncertainty measure from the Standard & Poor's 500 Volatility Index (VIX) and the news‐based economic policy uncertainty (EPU) index developed by Baker et al. ( 2016 ). Using a real business cycle model, the authors find that a COVID‐19 shock leads to a year‐over‐year contraction of GDP by 11% in fourth quarter of 2020. According to the authors, more than half of the contraction is caused by COVID‐19‐induced uncertainty. Based on a similar approach, Altig et al. ( 2020 ) conduct an analysis of different forward‐looking uncertainty measures during the pandemic. Coibion et al. ( 2020a ) use surveys to assess the macroeconomic expectations of households in the United States. They find that it is primarily lockdowns, rather than the infections themselves, that lead to declines in consumption spending and employment, lower inflationary expectations, increased uncertainty, and lower mortgage payments being made.
Eichenbaum et al. ( 2020 ) model the interactions between economic decisions and the spread of the virus. They find that, without any mitigation measures, aggregate consumption falls by 9.3% over a 32‐week period. On the other hand, labor supply or hours worked follow a U‐shaped pattern, with a peak decline of 8.25% in the 32nd week from the start of the pandemic. These reductions decrease peak infection rates and death tolls from 7% and 0.30% to 5% and 0.26% respectively, but worsen the magnitude of the recession. Infected people fail to internalize the impact of their choices on the spread of the virus. Therefore, the optimal containment policy increases the severity of the recession but saves lives. 13
Mulligan ( 2020 ) assesses the opportunity cost of “shutdowns” in order to document the macroeconomic impact of COVID‐19. Within the National Accounting Framework for the United States, the author extrapolates the welfare loss stemming from “nonworking days,” the fall in the labor‐capital ratio resulting from the absence/layoff of workers, and the resulting idle capacity of workplaces. After accounting for dead‐weight losses stemming from fiscal stimulus, the replacement of normal import and export flows with black market activities, and the effect on nonmarket activities (lost productivity, missed schooling for children and young adults), the author finds the welfare loss to be approximately $7 trillion per year of shutdown. Medical innovations, such as vaccine development, contact tracing, and workplace risk mitigation can help to offset the welfare loss by around $2 trillion per year of shutdown.
Other researchers have examined the supply side. Bonadio et al. ( 2020 ) use a quantitative framework to simulate a global lockdown as a contraction in labor supply for 64 countries. The authors find that the average decline in real GDP constitutes a major contraction in economic activity, with a large share attributed to disruptions in global supply chains. Elenev et al. ( 2020 ) model the impact of COVID‐19 as a fall in worker productivity and as a decline in labor supply, which both adversely affect firm revenue. The fall in revenue and the subsequent non‐repayment of debt‐servicing obligations spur a wave of corporate defaults, which might also bring down financial intermediaries. Céspedes et al. ( 2020 ) formulate a minimalist economic model in which the virus also leads to losses in productivity. The authors predict a vicious cycle triggered by the loss of productivity causing lower collateral values, in turn limiting the amount of borrowing activity, subsequently leading to decreased employment, followed by a further decline in productivity. The shock is thus magnified through an “unemployment and asset price deflation doom loop” (see Fornaro & Wolf, 2020 ).
Consumption pattern responses and debt responses from pandemic shocks had not been analyzed prior to COVID‐19 (Baker et al., 2020 ). Using transaction‐level household data, these authors find that households sharply increased their spending during the initial period in specific sectors such as retail and food spending. These increases, however, were followed by a decrease in overall spending. Similarly, Chang and Meyerhoefer ( 2020 ) show that consumers in Taiwan have increased food purchases from online platforms. Binder ( 2020 ) conduct an online survey of 500 United States consumers to investigate their concerns and responses related to COVID‐19, which indicated those items of consumption on which they were spending either more or less. They find that 28% of the respondents in that survey postponed future travel plans, and that 40% forewent food purchases. Interestingly, Binder ( 2020 ) finds from the surveys that consumers tend to associate graver concerns about COVID‐19 with higher inflationary expectations, a sentiment which serves as a proxy for “pessimism” or “bad times.”
Clemens and Veuger ( 2020 ) focus on the declines in government sales and income tax collections across US states. According to the authors, COVID‐19 has led to a substantial decline in consumption levels compared to income levels. This pattern is unlike the case in previous recessions, during which income decreased more than consumption. The authors find that the COVID‐19 pandemic will reduce the states’ tax collections by $42 billion in the second quarter of 2020. For fiscal year 2021, the authors anticipate an overall decline in sales and income tax revenues of $106 billion with heterogenous losses across US states.
McKibbin and Fernando ( 2020 ) estimate the aggregate economic costs. Using a hybrid DSGE/CGE global model, the authors model COVID‐19 as a negative shock to labor supply, consumption spending, financial markets, but as a positive shock to government expenditure, particularly stemming from health‐related expenditures. The authors outline seven different scenarios and provide a range of estimates of the increase in mortality and the fall in GDP for a number of countries across the world. In the case of the most contained outbreak, the number of deaths reaches around 15 million, while the reduction in global GDP is around $2.4 trillion in 2020.
Eppinger et al. ( 2020 ) use a quantitative international trade model with input‐output linkages for 43 countries to assess the impact of COVID‐19 supply shock on global value chains. They find that due to the supply shock, China experienced a welfare loss of 30% with moderate (positive or negative) spillover to other countries. Estimating a simulation consisting of a counterfactual scenario described as “without global value chains,” the authors find that welfare losses are reduced for some countries by as much as 40%, while they are magnified for others.
The economic impact of shocks, such as pandemics, is usually measured with aggregate time series data. However, these datasets are available only after a certain lag. In order to analyze the economic impact at a higher frequency, Lewis et al. ( 2020 ) developed a weekly economic index (WEI) using 10 different economic variables to track the economic impact of COVID‐19 in the United States. These authors report that between March 21 and March 28, the WEI declined by 6.19%. This was driven by a decline in consumer confidence, a fall in fuel sales, a rise in unemployment insurance (UI) claims, and changes in other variables. Similarly, Demirguc‐Kunt et al. ( 2020 ) estimate the economic impact of social distancing measures via three high‐frequency proxies (electricity consumption, nitrogen dioxide emissions, and mobility records). The authors find that social distancing measures led to a 10% decline in economic activity (as measured by electricity usage and emissions) across European and Central Asian countries between January and April. Chetty et al. ( 2020 ) develop a real‐time economic tracker using daily statistics on consumption, employment, business revenue, job postings, and other variables. The authors show that the initial slowdown in economic activity was partly driven by reductions in consumption by high‐income individuals. These spending shocks negatively affected business revenues catering to high‐income individuals. Subsequently, low‐income individuals working for these businesses lose much of their incomes and reduce their consumption levels. Kapteyn et al. ( 2020 ) tracked a representative sample of 7,000 respondents in Los Angeles County, California every 2 weeks to assess the impact of COVID‐19 over time.
Brinca et al. ( 2020 ) estimate the labor demand and supply shocks occurring in different sectors in the US economy employing a Bayesian structural vector autoregression model. They find that the decrease in work hours can be attributed to negative labor supply shock, a result that they suggest has important policy design implications. A negative labor supply shock is directly related to the lockdown and might be mitigated once such policies are lifted.
5. SOCIOECONOMIC CONSEQUENCES OF COVID‐19
We now review studies documenting the socioeconomic consequences of COVID‐19 and the ensuing lockdowns. Social distancing and lockdown measures have been shown to adversely affect labor markets, mental health and well‐being, racial inequality, and gender‐related outcomes. The environmental implications, while likely to be positive overall, also deserve careful analysis.
5.1. Labor market outcomes
A large number of studies document the effects on the variables of hours of work and job losses (e.g., Kahn et al., 2020 ). The major increases in unemployment observed in the United States are driven partly by lockdowns and social distancing policies (Rojas et al., 2020 ). Accounting for cross‐state variation in the timing of business closures and stay‐at‐home mandates in the United States, Gupta et al. ( 2020 ) find that the employment rate in the United States falls by about 1.7 percentage points for every extra 10 days that a state experienced a stay‐at‐home mandate during the period of March 12th to April 12th.
Coibion et al. ( 2020b ) find that the level of unemployment and job losses in the United States is more severe than one might judge based on the rise in UI claims, which is to be expected given the low coverage rate of the UI regimes in the United States. They also project a severe fall in the labor participation rate in the long run accompanied by an increase in the number of “discouraged workers” (jobless workers who have stopped actively searching for work, effectively withdrawing from the labor force). This phenomenon might be due to the disproportionate impact of COVID‐19 on the older population. Aum et al. ( 2020a , 2021b ) find that an increase in infections leads to a drop in local employment even in the absence of lockdowns in South Korea, whose government did not mandate them. This estimated impact was higher for countries, such as the United States and the United Kingdom, where mandatory lockdown measures were imposed.
Adams‐Prassl et al. ( 2020a ) analyze the inequality of the distributions of job and income losses based on the type of job held and on individual characteristics for the United States and the United Kingdom. The authors find that workers who can perform none of their employment tasks from home are more likely to lose their job. This study also finds that younger individuals and people without a university education were significantly more likely to experience drops in their income. Yasenov ( 2020 ) finds that workers with lower levels of education, younger adults, and immigrants are concentrated in occupations whose tasks are less likely to be performed from home. Similarly, Alstadsæter et al. ( 2020 ) find that the pandemic shock in Norway has a strong socioeconomic gradient, as it has disproportionately affected the financially vulnerable population, including parents with younger children.
Béland, Brodeur, and Wright ( 2020 ) discuss heterogeneous effects across occupations and workers in the United States, showing that occupations that have a higher share of workers working remotely were less affected by COVID‐19. On the other hand, occupations with relatively more workers working in proximity to others were more affected. They also find that occupations classified as “more exposed to disease” are less affected, which is possibly due to the number of essential workers in these occupations. Based on these results, it can be reasonably expected that workers might change (or students might select different) occupations in the medium term. Bui et al. ( 2020 ) focus on the impact of COVID‐19 on older workers in the United States. Using CPS data, they show that older workers who are over 65 years of age, especially women, are facing higher unemployment in this COVID‐19 recession compared to previous ones.
Kahn et al. ( 2020 ) show that firms in the United States dramatically reduced job vacancies from the second week of March 2020 and thereafter. The authors find that the job vacancy declines occurred simultaneously with increasing UI claims. Notably, the labor market declines (proxied through reductions in job vacancies and increases in UI claims) were uniform across states, with no notable differences across states which experienced the spread of the pandemic, or implemented stay‐at‐home orders, earlier than others. The study also finds that the reductions in job vacancies were uniform across industries and occupations, except for those in front line jobs, such as nursing. Baert et al. ( 2020a ) investigate the impact of COVID‐19 on career prospects through surveys conducted in Belgium. They document concerns that were expressed about job losses and missing out on promotions, especially among migrant workers.
Fairlie ( 2020 ) analyzes the impact of COVID‐19 on the number of small businesses in the United States. Using the April 2020 CPS data, the author finds that the number of active business owners declined by 22% between February and April 2020. While most major industries faced large drop in business, the authors also find that female and immigrant‐owned businesses were disproportionately affected.
With the enforcement of social distancing measures, work from home has become increasingly prevalent. The degree to which economic activity is impaired by such social distancing measures depends largely on the capacity of firms to maintain business processes from the homes of workers (Alipour et al. 2020 ; Papanikolaou & Schmidt, 2020 ). Additionally, working from home or working remotely are much more common and are thought to cause lower productivity losses in industries that are staffed by better educated and better paid workers (Bartik et al., 2020 ). Brynjolfsson et al. ( 2020 ) find that the increase in cases per 100k individuals is associated with a significant rise in the fraction of workers switching to remote work and the fall in the fraction of workers commuting to work in the United States. Interestingly, the authors find that people working from home are more likely to claim UI (if they are laid off) than people who are still commuting to work and are likely working in industries providing essential services.
Dingel and Neiman ( 2020 ) analyze the feasibility of jobs that can be done from home. They find that 37% of jobs can be feasibly performed from home. A different but related context for the feasibility of work from home is the extent to which the job involves face‐to‐face (F2F) interaction. According to Avdiu and Nayyar ( 2020 ), the job‐characteristic variables of home‐based work (HBW) and F2F interaction differ along three main dimensions, namely: (i) temporal (short run vs. medium run); (ii) the primary channel of effects (supply and demand of labor for the occupation/tasks); and (iii) the relevant margins of adjustment (intensive vs. extensive). They argue that the supply of labor in industries with HBW capabilities and low F2F interactions (e.g., professional, scientific, and technical services) might be the least affected. Nevertheless, those industries and occupations with HBW capabilities and high F2F interactions are likely to experience negative productivity shocks. As lockdown restrictions are lifted, industries with low HBW capabilities and low F2F interactions (e.g., manufacturing, transportation, and warehousing) might be able to recover relatively quickly. The risk of infection through physical proximity can be mitigated by wearing personal protective equipment (PPE) and by taking other relevant precautionary measures. However, those industries with low HBW capabilities and high F2F interactions (e.g., accommodation and food services, arts entertainment and recreation) are likely to experience slower recoveries, as consumers might be apprehensive about patronizing them, for example, cinemas and restaurants. Using a web survey in Belgium, Baert et al. ( 2020b ) find that a majority of respondents thought that teleworking and digital conferencing were here to stay and will become more common in the postpandemic period.
From the firm's perspective, there are large short‐term effects of temporary closures, such as the (perhaps permanent) loss of productive workers and declines in job postings, all of which are characterized by strong heterogeneity across industries. Bartik et al. ( 2020 ) survey a small number of firms in the United States and document that several of them have temporarily closed shop and reduced their number of employees compared to January 2020. The surveyed firms were not optimistic about the efficacy of the fiscal stimulus implemented by the federal government of the United States. Campello et al. ( 2020 ) find that job losses have been more severe for industries with highly concentrated labor markets (i.e., where hiring is dominated by a few employers), nontradable sectors (e.g., construction, health services), and credit‐constrained firms. Hassan et al. ( 2020 ) discern a pattern of heterogeneity with respect to firm resilience across industries around the World. Based on earnings call reports, they provide evidence that some firms are expecting increased business opportunities in the midst of the global disruption (e.g., firms which make medical supplies or others whose competitors are facing negative impressions after the outbreak due to their association with regions where case numbers are high). Barrero et al. ( 2020 ) measure the reallocation of labor in response to the pandemic‐induced demand response (e.g., increased hiring by delivery companies, delivery‐oriented restaurant/fast food chains, technology companies).
To conclude this subsection, a large number of studies try to predict labor market outcomes by exploiting high frequency data (e.g., Adams‐Prassl et al. 2020a , Chetty et al., 2020 ). For instance, Bartik et al. ( 2020 ) and Kurmann et al. ( 2020 ) rely on worker‐firm matched daily data drawn from “Homebase,” a scheduling and time clock software provider, to construct real‐time data for small businesses. Other studies have also used high‐frequency electricity market data to estimate the short‐run impacts of COVID‐19 on economic activity (e.g., Fezzi & Fanghella, 2020 ).
5.2. Health outcomes
The impact of the pandemic on physical health and mortality has been documented in many studies (e.g., Goldstein & Lee, 2020 ; Lin & Meissner, 2020 ). Knittel and Ozaltun ( 2020 ) document a positive correlation between the share of elderly population, the incidence of commuting via public transportation, and the number of COVID‐19 deaths in the United States. In contrast, the authors provide evidence that obesity rates, the number of ICU beds per capita, and poverty rates are not related to the death rate. Chatterji and Li ( 2021 ) document the effect of the pandemic on the US health care sector. The authors find that it is associated with a 67% decline in the total number of outpatient visits per provider by the week of April 12th‐18th 2020 relative to the same week in prior years. This might have negative health consequences, especially among individuals with chronic health conditions. Hermosilla et al. ( 2020 ) show that COVID‐19 has crowded out non‐COVID‐19‐related health care demands in China. Others, such as Alé‐Chilet et al. ( 2020 ), explore the drop in emergency cases in hospitals around the world.
Nevertheless, during a crisis, such as the COVID‐19 pandemic, it is common for everyone to experience increased levels of distress and anxiety, particularly the sentiment of social isolation (American Medical Association, 2020 ). A growing number of studies document worsening mental health status and levels of well‐being (Adams‐Prassl et al. 2020b ; Brodeur, Clark, Fleche, & Powdthavee, 2021 ; Davillas & Jones, 2020 ; de Pedraza et al., 2020 , and Tubadji et al., 2020 ). According to Lu et al. ( 2020 ), social distancing or lockdown measures are likely to affect psychological well‐being through a lack of access to essential household supplies, discriminatory treatment, or exclusion by neighbors. They assert that maintaining a positive attitude (in terms of severity perceptions, the credibility of real‐time updates of information, and confidence in social distancing measures) can help reduce depression. Hamermesh ( 2020 ) also provides evidence that, adjusted for numerous demographic and economic variables, happiness levels during the COVID‐19 pandemic are affected by how people spend time and with whom. In the opposite case, using an experimental set‐up, Bogliacino et al. ( 2020 ) find that a negative shock triggered by COVID‐19 lowers cognitive functionality and increases risk aversion and the propensity to punish others, that is negative reciprocity. Public mental health is also affected by the cognitive bias related to the diffusion of public death toll statistics (Tubadji et al., 2020 ). These needs are all the less likely to be addressed given the lower levels of provision of health care and social work services.
Using the Canadian Perspective Survey Series, Béland, Brodeur, Mikola, and Wright ( 2020 ) find that those who missed work not due to COVID‐19, and those who were already unemployed, showed declines in mental health. Using panel data in the United Kingdom, Etheridge and Spantig ( 2020 ) report a large deterioration in the state of mental health, with much larger effects for women.
The implementation of lockdown policy also adversely affected public mental health. Armbruster and Klotzbücher ( 2020 ) demonstrate that there were increases in the demand for psychological assistance (through helpline calls) due to lockdown measures imposed in Germany. The authors find that these calls were mainly driven by mental health issues such as loneliness and depression. Brodeur, Clark, Fleche, & Powdthavee et al. ( 2021 ) show that there has been a substantial increase in the search intensity on Google for “boredom” and “loneliness” during the postlockdown period in nine Western European countries and the United States during the first few weeks of lockdowns. Using experimental surveys, Codagnone Bogliacino, Gómez, and Charris et al. ( 2020 ) find that about 43% of the population in Italy, Spain, and United Kingdom are at high risk of developing mental health problems; not only because of the negative economic shock, but also due to conditions of long‐standing economic weakness and vulnerability in those countries.
Fetzer et al. ( 2020 ) find that there has been broad public support for COVID‐19 containment measures. However, some of the respondents believe that the general public fails to adhere to health measures, and that the governmental response has been insufficient. These respondents have a tendency to exhibit a poorer state of mental health. If governments are seen to take decisive actions, however, then the respondents altered their perceptions about governments and other citizens, which in turn improved their state of mental health.
5.3. Gender and racial inequality
A growing literature points out that COVID‐19 has had an unequal impact between genders and across races in OECD countries; specifically, women and racial minorities, such as African‐Americans and Latinos, have been unduly and adversely affected. While it is thought that prior recessions typically affected men more than women, many studies provide evidence that COVID‐19 has large negative effects on women's labor market outcomes (Adams‐Prassl et al., 2020a ; Forsythe et al., 2020 ; Yasenov, 2020 ). Alon et al. ( 2020 ) argue that women's employment is concentrated in sectors such as health care and education. Moreover, the closure of schools and daycare centers led directly to increased childcare needs, which would have a negative impact on working and/or single mothers. For example, based on household surveys in Spain, Farré et al. ( 2020 ) find that while men increased their participation in household work and childcare duties during lockdowns, the burden of these tasks fell disproportionately on women.
Couch et al. ( 2020a ) examine the variation in unemployment shocks among minority groups in the United States. The authors find that Latino groups were disproportionately affected by the pandemic. They attribute the difference to an unfavorable occupational distribution (e.g., Latino workers tend to work in nonessential services) and to lower skill levels among them. Borjas and Cassidy ( 2020 ) determine that the COVID‐19 shock led to a fall in employment rates of immigrant men compared to native men in United States, which was in contrast to the historical pattern observed during previous recessions. The immigrants’ relatively high rate of job loss was attributed to the fact that immigrants were less likely to hold jobs that could be performed remotely from home. The likelihood of being unemployed during March 2020 was significantly higher for racial and ethnic minorities in the United States (Montenovo et al., 2020 ). In a similar vein, McLaren ( 2020 ) finds that minorities’ population shares in a county strongly correlate with COVID‐19‐related deaths in the United States. After controlling for the factors of education, jobs, and travel patterns, the correlation holds for the African‐American and the First‐Nations populations. The author shows that these racial disparities between African‐Americans, First Nations peoples, and others can be partially attributed to differentials in public transit usage patterns.
Couch et al. ( 2020b ) find that COVID‐19 has unduly affected women compared to men in the United States. Using employment to population ratios and number of hours from the CPS data, the authors find that women with school‐age children faced greater declines in employment and work hours compared to men between April and August 2020. The reductions in work hours and employment can be explained by additional childcare responsibilities, job and skill characteristics, and lower numbers of women involved in “essential” industries.
Schild et al. ( 2020 ) find that COVID‐19 occasioned a rise of Sino‐phobia across the internet, particularly when western countries started showing signs of infection. Bartos et al. ( 2020 ) document the causal effect of economic hardships on hostility against certain ethnic groups in the context of COVID‐19 using an experimental approach. The authors find that the COVID‐19 pandemic magnifies sentiments of hostility and discrimination against foreigners, especially those from Asia.
5.4. Environmental outcomes
The global lockdown and the considerable slowdown of economic activities are expected to have a positive effect on the environment (Almond et al., 2020 ; Cicala et al., 2020 ). He et al. ( 2020 ) show that lockdown measures in China led to a remarkable improvement in air quality. The Air Quality Index and the fine particulate matter (PM 2.5 ) concentrations were brought down by 25% within weeks of the lockdown, with larger effects recorded in colder, richer, and more industrialized cities. Similarly, Almond et al. ( 2020 ) focused on air pollution and the release of greenhouse gases in China during the post‐COVID‐19 period. They determined that, while nitrogen dioxide (NO 2 ) emissions fell precipitously, sulphur dioxide emissions (SO 2 ) did not decrease. For China as a whole, PM 2.5 emissions fell by 22%; however, ozone concentrations increased by 40%. These variations show that there is not necessarily an unambiguous improvement in air pollution due to the economic slowdown. The reduction can be attributed to less travel in personal vehicles causing lower nitrous oxide (NO 2 ) emissions.
Brodeur, Cook, & Wright ( 2021 ) examine the causal effect of “safer‐at‐home” policies on air pollution across US counties. They find that “safer‐at‐home” policies decreased air pollution (measured as PM 2.5 emissions) by almost 25% on average, with larger effects for populous counties. Cicala et al. ( 2020 ) focus on the health and mortality benefits of reduced vehicle travel and electricity consumption in the United States due to stay‐at‐home policies, suggesting that reductions in emissions from less travel and from lower electricity usage reduced deaths by over 360 per month.
On the other hand, Andree ( 2020 ) focuses on the effect of pollution on cases, finding that PM 2.5 levels are a highly significant predictor of COVID‐19 incidence using data from 355 municipalities in the Netherlands. In terms of COVID‐19‐related deaths, Knittel and Ozaltun ( 2020 ) find no evidence that pollution levels are related to death rates in the United States.
Based on the research discussed in section 5 above, Table 3 provides a summary of these strands of the literature dealing with the socioeconomic and environmental outcomes resulting from social distancing actions, stay‐at‐home orders, and/or lockdowns including a listing of the statistical measures and methodologies that were utilized.
Socioeconomic outcomes of COVID‐19 lockdowns: Summary of studies
6. POLICY MEASURES
The economic literature deals with a wide assortment of policy measures. We organize our presentation into six broad topics: (i) the types of policy measures, (ii) the determinants of government policy, (iii) optimal testing methods, (iv) the lockdown measures and their associated factors, (v) the lifting of the lockdown measures, and (vi) the economic stimulus measures.
To mitigate the negative effects of public health controls on the economy and to sustain and promote public welfare, governments all around the World have implemented a variety of policies within a very short time frame. These include fiscal, monetary, and financial policy measures (Gourinchas, 2020 ). The economic measures vary across counties in terms of breadth and scope, and they target households, firms, health systems, and/or banks (Weder di Mauro, 2020 ).
Using a database of economic policies implemented by 166 countries, Elgin et al. ( 2020 ) employ the technique of Principal Component Analysis (PCA) to develop their COVID‐19 Economic Stimulus Index. The authors correlate the standardized index with predictors of governmental response, such as population characteristics (e.g., median age), public health‐related measures (e.g., the number of hospital beds per capita), and economic variables (e.g., GDP per capita). They find that the economic stimulus is larger for countries with higher COVID‐19 infections, older median ages, and higher GPD‐per‐capita levels. In addition, the authors develop a “Stringency Index,” which includes measures such as school closures and travel restrictions. They find that the “Stringency Index” is not a significant predictor of their economic stimulus index, which suggests that public health measures do not drive economic stimulus measures (Weder di Mauro, 2020 ).
On a similar note, Porcher ( 2020 ) has created an index of public health measures using the PCA technique. The index is based on 10 common public health policies implemented across 180 countries to mitigate the spread of COVID‐19. The index is designed to measure the stringency of the public health response across countries. The author finds that, abstracting from the COVID‐19 case numbers and deaths, countries which have better public‐health systems and effective governance tend to have less stringent public health measures.
C. Cheng et al. ( 2020 ) develop the “CoronaNet–COVID‐19 Government Response Database,” which accounts for policy announcements made by countries globally since 31 December 2019. The information that is contained in this data base is categorized according to: (i) type of policy, (ii) national versus subnational enforcement, (iii) people and geographic region targeted by the policy, and (iv) the time frame within which it is implemented. Table 4 provides a description of the government response database for 125 countries. Counts are tabulated according to 15 types of interventions for two variables: cumulative number of policies (of that type) implemented and the number of countries which have implemented it. It also displays that average value for the degree of enforcement.
Summary statistics of COVID‐19 government response dataset
Source: C. Cheng et al. ( 2020 ).
There is substantial variation across policy measures. The policy most governments have implemented is “external border restriction,” that is, restricting access to entry through ports. It has been imposed by 186 countries; the second most common policy measure, implemented by 169 countries, is “school closures.” However, in terms of the frequency of implementation across all countries, the type of “obtaining or securing health resources” has the highest level. This includes the provision of materials (e.g., face masks), personnel (e.g., doctors, nurses), and infrastructure (e.g., hospitals). The second most frequently implemented policy is “restrictions on nonessential businesses.” In terms of stringency of policy enforcement, “emergency declaration” and the formation of a “new task force” or an “administrative reconfiguration to tackle pandemic” are implemented with 100% stringency.
Due to these major differences between policy responses across countries and over time, the authors use a dynamic Bayesian item‐response approach to measure the implied economic, social, and political cost of implementing a particular policy over time. They also develop a supplementary measure labelled the “Policy Activity Index,” which assigns a higher rank for policy measures to countries that are more willing to implement a “costly” policy. Based on that index, the authors determine that school closure is the costliest to implement followed by mandatory business closure and social distancing policies. Moreover, internal border restrictions are viewed as more costly compared to external border restriction.
The topic of optimal testing methods has received a great deal of attention in the media and, to some extent, in academia. A well‐known proposal defended by Paul Romer and many others is a comprehensive “test and isolate” policy, which would effectively reduce the effective reproduction number and allow the economy to operate more openly. 14 Taipale et al. ( 2020 ) formalize this proposal and argue that the epidemic would collapse at a sufficient rate of testing and isolation, and that concurrent testing would outperform random sampling of individuals. Other proposals for optimal testing include regular testing of people in groups that are more likely to be exposed to COVID‐19 (e.g., Cleevely et al., 2020 ; Gollier & Gossner, 2020 ), multi‐stage group testing (e.g., Eberhardt et al., 2020 ), and testing on exit from quarantine instead of upon entry (e.g., Wells et al. 2020 ).
The topic of optimal lockdown policies has been investigated mostly by using epidemiology macroeconomic models, some of which are oriented around the dichotomy between the case in which the choices (and responses) are all made by private agents and the case in which the choices are made by a social planner (Acemoglu et al., forthcoming; Alvarez et al., forthcoming; Berger et al., forthcoming; Bethune & Korinek 2020 ; Eichenbaum et al., 2020 ). Jones et al. ( 2020 ) argue that in contrast to private agents, the social planner will seek to front‐load mitigation strategies: that is to impose strict lockdowns from the beginning to reduce the spread of infection and let the economy to fall into a deep recession. This is because their model's set‐up not only considers the concomitant health care costs and congestion in hospitals, but also rightly considers the fact that workers need time to become productive for a work‐from‐home situation. The outcomes are dependent on the assumed values of the parameters that are inputted into these models. The optimal policy choice reflects the rate of time preference, epidemiological factors, the value of statistical life, the rate at which death rate increases in the infected population, the hazard rate for a vaccine discovery, the learning effects in the health care sector, and the severity of output losses due to a lockdown (Gonzalez‐Eiras & Niepelt, 2020 ). The intensity of the lockdown depends on the gradient of the fatality rate as a function of the number of infected individuals and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown and shortens the duration of the optimal lockdown (Alvarez et al., forthcoming). Chang and Velasco ( 2020 ) argue that the optimality of policies depends on peoples’ expectations. For instance, fiscal transfers must be large enough to induce people to stay at home in order to reduce the degree of contagion; otherwise they might not change their behavior in efforts to reduce the risk of infection. Their analysis also contains a critique of the use of SIR models, as the parameters used in that class of models (which remain fixed in value) would shift as individuals change their behavior in response to policy. Kozlowski et al. ( 2020 ) investigate the scarring effect on perceptions (i.e. the change in belief about the probability of an extreme but negative or tail‐risk event) of COVID‐19, and find that revisions in belief about tail‐risk events among economic agents will lead to a larger and more persistent negative impact on the economy in the long run.
When the daily death rates and case numbers decline, policies regarding reopening the economy are of primary importance. Gregory et al. ( 2020 ) describe the lockdown measure as a “loss of productivity,” whereby relationships between employers and laborers are suspended, terminated, or continued. They further explain that postpandemic, the speed and the type (V‐shaped or L‐shaped) of recovery depend on at least three factors: (i) the fraction of workers who, at the beginning of the lockdown, enter unemployment while maintaining a relationship with their employer, (ii) the rate at which inactive relationships between employers and employees dissolve during the lockdown, and (iii) the rate at which workers who, at the end of the lockdown, are not recalled by their previous employer can find new, stable jobs elsewhere (Gregory et al., 2020 ).
Harris ( 2020 ) points out the importance of utilizing several status indicators (e.g., results of partial voluntary testing, guidelines for eligibility of testing, daily hospitalization rates) in order to decide upon the course of action on reopening the economy. Kopecky and Zha ( 2020 ) state that decreases in deaths are either due to implementation of social distancing measures or to herd immunity; it is hard to identify and disentangle those factors using standard SIR models. They argue that with the “identification problem,” there will be considerable uncertainty about the conditions for restarting the economy. Only comprehensive testing can help resolve this ambiguity by quickly and accurately identifying new cases so that future outbreaks could be contained by isolation and contact‐tracing measures (Kopecky & Zha, 2020 ).
Agarwal et al. ( 2020 ) rely on synthetic control methods to investigate the effect of counterfactual mobility restriction interventions in United States. Using the daily death data from different countries, the authors create different “synthetic mobility United States” variables. These are applied to predict a counterfactual scenario and to understand the trade‐off between different levels of mobility interventions on death levels in United States. They find that a small decrease in mobility reduces the number of deaths; however, after registering a 40% drop in mobility, the benefits derived from mobility restrictions (in terms of the number of deaths) diminish. Using a counterfactual scenario, the authors find that lifting severe mobility restrictions but retaining moderate mobility restrictions (e.g., by imposing limitations in retail and public transport locations) might effectively reduce the number of deaths in United States. Others, such as Rampini ( 2020 ), make the case for the sequential lifting of lockdown measures for the younger population at the initial stages, followed by the older population at later stages. The authors state that the lower effect on the younger population group is a fortunate coincidence, and thus, the economic consequences of interventions can be greatly reduced by adopting a sequential approach. Oswald and Powdthavee ( 2020 ) make a similar case for releasing the younger population from mobility restrictions first on the grounds of higher economic efficiency (as they are more likely to be in the labor force) and their greater resilience against infections.
As some US states reopened, some researchers turned their focus on the immediate consequences. Nguyen et al. ( 2020 ) find that 4 days after reopening, mobility has increased by 6 to 8%, with greater increases across states which were late adopters of lockdown measures. These findings have important implications for the resurgence of cases, hospital capacity utilization, and further deaths. Dave, Friedson, Matsuzawa, and McNichols et al. ( 2020 ) analyze the effect of lifting the shelter‐in‐place order in Wisconsin, after the Wisconsin Supreme Court abolished it, on social distancing and the number of cases and find no statistically significant impact. W. Cheng et al. ( 2020 ) find that employment activity in the United States increased in May due to reopening in some states, mainly as a result of people who resumed working at their previous job. However, they find that the longer employees are separated from their firms, the more their re‐employment probabilities decline.
In regards to the aggregate macroeconomy, Gourinchas ( 2020 ) states that without substantial, timely, and stimulative macroeconomic intervention, the output lost from the economic downturn will be greatly amplified, especially as economic agents react to the negative shock by reducing consumption spending, investment spending, and engaging in lower credit transactions. The author suggests that there should be cross‐regional variation in government responses based on country characteristics. With high amounts of government debt and historically low interest levels existing in most developed countries, Bianchi et al. ( 2020 ) recommend a coordinated monetary and fiscal policy to address the COVID‐19 economic fallout. They recommend that fiscal policy should be used to enact an emergency budget with a ceiling placed on the debt‐to‐GDP ratio. This measure would increase aggregate spending, raise the inflation rate, and reduce real interest rates. The monetary authorities would need to coordinate with fiscal policy authorities by adopting an above‐normal inflation target. In the long run, governments would try to balance the budget, and future monetary policy would aim to bring inflation back to normal levels.
Bigio et al. ( 2020 ) focus on the cases for government transfers versus credit subsidy policies. They determine that the optimal mix between them depends on the level of financial development in the economy. According to these authors, economies with a developed financial system should utilize stimulative credit policies. On the other hand, developing economies should engage in more transfer spending. Guerrieri et al. ( 2020 ) show that the optimal economic policy response for the “Keynesian supply shock” induced by COVID‐19 would be to combine expansionary monetary policy and bolster social insurance programs for employees in the affected sectors. Unconventional policies, such as wage subsidies, helicopter drops of liquid assets, equity injections, and loan guarantees, can keep the economy in a full employment, high‐productivity equilibrium (Céspedes et al., 2020 ). These policies can break the cycle of negative feedback loops between corporate defaults and potential insolvency of financial intermediaries, which could culminate in a meltdown in financial markets (Elenev et al., 2020 ). Didier et al. ( 2020 ) discuss the policy menu, priorities, and trade‐offs of providing direct financing to firms.
Chetty et al. ( 2020 ) analyze the causal effect of policies implemented in the United States on households and businesses. They find that stimulus payments delivered through the Coronavirus Aid, Relief, and Economic Security (CARES) Act increased consumption spending, and that this spending was directed toward durable goods, which require low physical interaction at various stages of production. As a result, this spending is not directed toward small‐ and medium‐size businesses whose revenues were very adversely affected. On the other hand, loans to small businesses from the Paycheck Protection Program did little to restore employment among businesses. According to their analysis, the economic recovery depends on restoring consumer confidence and targeting income replacement programs rather than uniform lump‐sum stimulus payments.
Codagnone, Bogliacino, and Gómez, Folkvord et al. ( 2020 ) focus on the expectations of stakeholders with regards to the postlockdown period. Using an experimental survey in Spain, Italy, and United Kingdom, the authors find that exposure to the COVID‐19 shock and the ensuing lockdown led to pessimistic expectations about job opportunities, greater drawdowns of savings than before, and a deterioration in social relations which might be instrumental in finding job opportunities in the long run. The authors conclude that the fiscal policy measures might be insufficient in managing these expectations amidst uncertainties. They call on policy makers to draft contingency plans for exiting the lockdown—not only in terms of public expenditures earmarked for postlockdown operations, but also in terms of public health strategies to tackle a second wave of COVID‐19.
This study delved into the research related to the economics of COVID‐19 that has been released over a short time period. Our primary aim is to synthesize and to bring coherence and structure to the very rapidly growing body of relevant scientific evidence. By providing an annotated list of dozens of articles along with a brief capsule of their content, we hope to facilitate further research in the many strands of the COVID‐related literature. For readers who are interested in this critically important and pressing topic, this piece also provides an informative summary of the state of knowledge at the time of writing.
Before covering the impacts of COVID‐19, we documented the most popular data sources that are exploited to measure the known cases and deaths resulting from COVID‐19, as well as the social distancing activities. We first pointed out that the numbers of reported cases and deaths are subject to measurement error due to many factors, including testing capacity constraints and lags. Mobility measures that are based on GPS coordinates emitted from cell phones have been used extensively to measure social distancing. However, there are certain caveats that apply, particularly in terms of privacy concerns and the representativeness of data. The article also reviewed separate research related to social distancing activity itself, particularly in regards to its determinants, its efficacy in mitigating the spread of COVID‐19, and compliance with these orders. Going forward, social distancing actions and their measurements will continue to figure prominently in academic research and policy development.
We divided our coverage of the impact of the macroeconomy into two subsections, the first of which deals with the propagation mechanisms. The stay‐at‐home orders have very adverse effects on supply chains as well as on employment, which in turn causes drastic declines in consumption spending for many goods and services. The resultant declines in consumer and investor confidence reinforce negative multiplier effects in a downward spiral between labor and output markets, which can be partially attenuated by stimulative fiscal and monetary policies. Since the trajectory for the macroeconomy depends critically on the degree of spread of the virus itself, some researchers have integrated that element into their models. We reviewed the three potential “shapes” for the macroeconomic recovery: the highly optimistic yet implausible “V” path, the somewhat favorable “U” path, and the pessimistic yet more likely “L” path.
The second aspect of the macroeconomic impact of COVID‐19 that we discussed involves the forecasts. It is thought that the lockdown and social distancing measures wreak greater economic harm than the spread of the virus itself. The tremendous uncertainty regarding the path of the virus is compounded with a high degree of economic uncertainty such that these projections are subject to very wide confidence intervals and constant revisions. Some articles have attempted to address the longer‐term negative impacts on macro variables such as capital formation, productivity, and government finances. Other studies have focused on changes in patterns of consumption, employment, savings, and consumer debt by exploiting real‐time data.
In terms of the socioeconomic consequences of COVID‐19, we focus on the impact of the pandemic and the social distancing measures on outcomes in four areas: the labor market, mental health and well‐being, racial and gender inequality, and the environment. In terms of the labor market outcomes, research has shown that there is a high degree of heterogeneity in the pattern of job losses. The pandemic has caused a major shift toward work from home and away from positions involving F2F interactions with either the public or coworkers. Due to technological features and the nature of the services rendered, there are only a certain number of jobs that can be “feasibly” done from home and do not require F2F interactions. This contributes to the disproportionate effect of the pandemic on workers in certain industries and occupations, many of which have a relatively high concentration of lower‐skilled and/or less educated workers.
Social distancing measures have led to serious deteriorations in the levels of mental health, family stress, and domestic violence. Health care services for non‐COVID patients have been crowded out in many instances. There has been a marked rise in observed racial discrimination and sentiments of hostility toward certain ethnic groups. A growing number of studies also document that women have been adversely affected by the loss of child care and educational services for their children. The only seemingly positive consequence of social distancing/lockdown measures is the decrease in air pollution levels and the incidence of accidents involving motor vehicles. However, the impacts on the environment are multi‐faceted, and thus there remains a fair amount of ambiguity.
The goal of our piece was to survey and summarize the findings of the literature on the economics of COVID‐19. This was a very challenging task, as the literature is growing and evolving fast, and the pandemic is far from over at the time of writing. There are a few qualifications that are worth mentioning. First, very few of the research articles surveyed have undergone normal scientific review processes. Second, we mostly did not comment on methodology, which necessitates caution in interpretations. Finally, due to time as well as space constraints, we offer little in the way of critical analysis. Nonetheless, we hope this survey will facilitate further research in the many strands of the COVID‐related literature.
Table A: Major Pandemics: Historical Timeline
Brodeur A, Gray D, Islam A, Bhuiyan S. A literature review of the economics of COVID‐19 . Journal of Economic Surveys . 2021; 35 :1007–1044. 10.1111/joes.12423 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
1 Social distancing (or physical distancing) is defined as maintaining physical space between yourself and other people residing outside one's home. To practice social/physical distancing: (i) stay at least 6 feet (about 2 arms’ lengths) from other people, (ii) do not gather in groups, and (iii) avoid crowded places and mass gatherings.
2 The list of NBER working article is available at this URL: https://www.nber.org/wp_covid19.html
3 The list of IZA discussion articles is available at this URL: https://covid‐19.iza.org/publications
4 See the link for the numbers and visual representation. Retrieved from https://coronavirus.jhu.edu
5 See WHO COVID‐19 Dashboard: https://covid19.who.int.
6 Refer to Johns Hopkins University ( 2020b ) for CFR data across countries.
7 See the link for further details: https://ourworldindata.org/mortality‐risk‐covid .
8 See the link for further details: https://covidtracking.com/data .
9 See the link for further details: https://covidtracking.com/race .
10 Mobility measures track work locations based on movements to a workplace from a reference point such as their home. However, if a person works from home or becomes unemployed, there will not be a distinct workplace reference point. Hence, the quality of mobility measures is expected to deteriorate.
11 The WHO Health System Response Monitor provides a cross country analysis and other details: https://analysis.covid19healthsystem.org/ .
12 According to the authors, if all members of a set choose to implement shelter‐in‐place policies, then the best response for agents is to follow. Hence, even in the absence of a federal mandate, the members of this “tipping set” can drive all others to adopt shelter‐in‐place policies.
13 The interaction between economic and epidemiological models is described in more details in the Online Appendix.
14 Further details on the “test and isolate” policy is available at the URL: https://paulromer.net/covid‐sim‐part1.
15 See the link for further details: https://www.google.com/covid19/mobility .
16 See the link for further details: https://www.unacast.com/covid19 .
17 See the link for further details: https://www.safegraph.com/dashboard/covid19‐commerce‐patterns .
18 See the link for further details: http://research.baidu.com/Blog/index‐view?id=13 .
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- Open access
- Published: 07 November 2023
24-Hour movement behaviours research during the COVID-19 pandemic: a systematic scoping review
- Danqing Zhang 1 na1 ,
- Sitong Chen 2 na1 ,
- José Francisco López-Gil 3 ,
- Jintao Hong 4 ,
- Fei Wang 5 &
- Yang Liu 1 , 6
BMC Public Health volume 23 , Article number: 2188 ( 2023 ) Cite this article
Many studies examining 24-hour movement behaviours based on the 24-Hour Movement Guidelines (24HMG) have been published during the COVID-19 pandemic. However, no comprehensive reviews summarized and synthesized the evidence concerning studies using 24HMG. The aim of this scoping review was to synthesize the evidence from the 24HMG studies published during the pandemic.
Three electronic databases (Web of Science, PubMed, EBSCO) were utilized to conduct a literature search. The search procedure adhered to the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Initially, a total of 1339 research articles published in peer-reviewed journals were screened. After eliminating 461 duplicates, 878 articles remained. The titles and/or abstracts of these articles were then cross-checked, and 25 articles were included. Subsequently, two authors independently assessed full-text of articles based on the pre-defined inclusion and exclusion criteria, resulting in the final selection of 16 articles that met the inclusion criteria. Study characteristics (e.g., study population, study design, measurement) were extracted and then summarized. According to the Viable Integrative Research in Time-use Research (VIRTUE) epidemiology, the included studies were further classified into different but interrelated study domains (e.g., composition, determinants, health outcomes).
The majority of included articles focused on children and adolescents as study population. This study primarily demonstrated that a low prevalence of meeting the 24HMG among children and adolescents during the COVID-19 pandemic. There has been a decline in the percentage of individuals meeting the 24HMG compared to the pre-COVID-19 period. The majority of included studies focused on sociodemographic factors when examining the correlates of meeting the 24HMG, while a few studies assessed factors of other domains, such as social, cultural, and environmental aspects.
The COVID-19 pandemic had an impact on healthy 24-hour movement behaviours in children and adolescents. In conjunction with the studies conducted during the COVID-19 pandemic, more studies were encouraged to explore the correlates of meeting the 24HMG and the associated health benefits in wider ranges of populations.
Peer Review reports
The coronavirus disease 2019 (COVID-19) causes a high morbidity and mortality rate and severely affects the world [ 1 ]. The World Health Organization (WHO) announced COVID-19 as a pandemic in March 2020. To prevent and limit the possible spread of COVID-19, the governments of some countries issued a series of restrictive measures [ 2 , 3 , 4 , 5 ], including the suspension of school, work organized sports activities and meetings (though allowing for outdoor activities), and implementing national quarantine, restricting the movement of the entire population [ 6 , 7 ]. The COVID-19 pandemic has particularly affected people’s lives and health behaviour [ 3 ], including home isolation and restrictions on activity accessibility, resulting in significant alterations in daily activities [ 8 , 9 ]. In addition, the COVID-19 pandemic also increased kinds of risk disease [ 10 , 11 ]. These circumstances were similar to those of prior disasters [ 12 ]. One of the possible reasons for this was the change in lifestyle behaviour after the disaster [ 2 ]. Confronted with unparalleled challenges and disruptions to their daily lives, individuals often adapt their routines, habits, and activities. These adaptations can manifest in diverse aspects of lifestyle behavior, including decreased physical activity (PA), modified dietary patterns, and disrupted sleep schedules. Consequently, such modifications in lifestyle behavior can exert a profound and enduring influence on individuals’ overall health and well-being. Additional research is warranted to acquire a comprehensive understanding of the precise changes in lifestyle behavior triggered by the COVID-19 pandemic.
Not surprisingly, it has been well documented that the COVID-19 pandemic has significant impacts on individuals’ PA [ 14 , 15 , 16 ], sedentary behaviour (SB) [ 17 , 18 , 19 ] and sleep [ 20 , 21 , 22 ] (these three behaviours were collectively called 24-hour movement behaviours). During the COVID-19 pandemic, people’s PA levels have shown massive declines [ 14 , 15 , 16 ], while SB has shown substantial increases [ 17 , 18 , 19 ], primarily owing to social-distancing and lockdown measures [ 19 ]. In terms of sleep, studies have shown a longer duration of sleep time and daytime sleepiness [ 20 , 21 , 22 ] and adverse changes to sleep patterns and bedtime routines during the home confinement period [ 20 ]. These negative changes influence individuals’ health and wellbeing [ 3 ].
Given that PA, SB and sleep are co-dependent health behaviours and their combined health effects should be given more research attention rather than focusing on either of them, it is recommended that researchers integrate PA, SB and sleep for efficient health promotion [ 23 , 24 ]. A well-developed paradigm for 24-hour movement behaviours research was to adopt the 24-hour movement guidelines (24HMG). Researchers have developed and launched the Canadian 24HMG for populations across the life course [ 24 , 25 , 26 ]. The guidelines mainly have quantifiable recommendations on PA, SB and sleep, supported by robust scientific evidence. Based on this, an increasing number of studies have begun using 24HMG to study PA, SB and sleep in combination [ 27 , 28 , 29 ], as it can help provide an integrative perspective to study movement behaviours at the population level.
Some studies have been conducted using the 24HMG before the COVID-19 pandemic, which examined the prevalence of meeting the 24HMG [ 30 , 31 , 32 , 33 , 34 , 35 ] and the secular trends [ 28 , 31 , 36 , 37 , 38 , 39 ], correlates of meeting the 24HMG [ 30 , 32 , 33 , 35 , 40 ], and the associations between meeting the 24HMG and health outcomes [ 28 , 41 , 42 , 43 , 44 ]. According to the Framework for Viable Integrative Research in Time-Use (VIRTUE) Epidemiology, those studies can be categorized into some research areas, time-use compositions, determinants and health outcomes [ 45 ].
Given the importance of integrating PA, SB and sleep, a number of studies have investigated the prevalence of meeting the 24HMG during COVID-19 [ 7 , 46 , 47 , 48 ]. Furthermore, some of the studies repeatedly measured the prevalence of meeting the 24HMG prior to and during COVID-19, enabling researchers to examine the trends of 24HMG adherence. Jáuregui et al. found that the prevalence of meeting the 24HMG was significantly lower than that before COVID-19 [ 49 ]. Another study by Angel et al. also had similar research findings, indicated that the percentage of participants meeting the 24HMG has decreased from 3.3 to 0.2% [ 50 ]. Despite these studies, there was no synthesized study to review the changes in the prevalence of meeting the 24HMG before and during the pandemic. Such studies were needed not only because of 24-hour movement behaviours associated health outcomes but also to assist with the development of public health interventions when confronting similar public health events.
In addition to the changes in the prevalence of meeting the 24HMG, little was known about which factors (categorization of factors based on VIRTUE framework) were associated with the integrated 24-hour movement behaviours during the COVID-19 pandemic. Therefore, the main aim of this review was to synthesize the evidence concerning research using the 24HMG during the COVID-19 pandemic.
This study aimed to conduct a systematic scoping review to summarize the evidence concerning the 24HMG research conducted during the COVID-19 pandemic.
Data source and search strategy
To ensure a nonbiased and complete review, we searched the following electronic databases from 1 to 2020 to 30 November 2022: Web of Science, EBSCO, and PubMed. Several keywords were employed for the literature search in each database: “24-h*”, “24 hour”, “24-hour”, “Movement Behavio*”, “Sleep*”, “Screen”, “Physical Activity”, “Guideline*”, “recommendation*”, “COVID-19” “Coronavirus Disease”, “Coronavirus”, “SARS-CoV-2” and “nCoV”. In the Web of Science, EBSCO, and PubMed, we divided all search terms into three categories: (24-h* OR 24 h OR 24-hour OR Movement Behavio* OR Sleep* OR Screen OR Physical Activity) AND (Guideline* OR recommendation*) AND (COVID-19 OR Coronavirus Disease OR Coronavirus OR SARS-CoV-2 OR nCoV). Due to the differences in databases, field tags of “Title”, “Abstract” and “Title/Abstract” were used in combination during document retrieval (Supplementary material). To obtain the final number of studies included in this review, all of the retrieved articles were independently screened and assessed by two authors. If there were any differences regarding inclusion, a third author was invited to join the discussion and make a decision.
The inclusion criteria for screening articles were as follows: (1) documents contained search terms and were published from 1 to 2020 to 30 November 2022; (2) study sample related to human beings, and they were acceptable if the subjects were in poor physical condition (disability or disorder); (3) the results reported combined 24-hour movement behaviours using the guidelines (PA or SB and sleep) focused on people who had COVID-19 at the time of the study; and (4) articles written in English.
The exclusion criteria were as follows: (1) studies that met the inclusion criteria but had duplicates between databases; (2) case studies, master’s/doctoral dissertations, conference papers and abstracts, reviews, brief reports and letters, protocol, commentary, and qualitative study; and (3) studies that did not report the percentage adherence to the 24HMG during the COVID-19 pandemic.
Data extraction and data items
The following information was extracted and summarized from the final included studies by two authors: (1) basic information of study (authors, published year, published journal, author countries/organization); (2) sample characteristics of study (sociodemographic, subjects, age group and sex of subjects, simple size and population, countries/country of study conducted); (3) study design and method (study design: cross-sectional study/longitudinal study, survey method: single/mixed method); (4) measurements (self-report, interview, device-based measurement, proxy report); (5) categorizations of research areas in line with the VIRTUE framework (including three types of content in this study: outcomes, correlates/determinants, time-use composition of 24-hour movement behaviours with COVID-19); (6) findings (prevalence of meeting % the 24HMG during COVID-19 and changes % of meeting the 24HMG with COVID-19). EXCEL 2019 was used to categorize these variables.
Coding of studies and summary
The code “D- * ” indicates studies that reported a significant decrease (%) in meeting the 24HMG between before and during the COVID-19 pandemic period. The code “D-” indicates studies that reported a nonsignificant decrease (%) or reported decreased outcomes (%) but did not report the p value. The code “I + ” indicates studies that reported a nonsignificant increase (%). The code “NC” indicates studies that reported no change (%) in meeting the 24HMG between before, and during the COVID-19 pandemic period. The “Summary” contains a code to summarize the state of studies for meeting the 24HMG. If the study contained fewer than three outcomes, the trend could not be summarized.
The search procedure followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 51 ], and the flowchart was presented in Fig. 1 . A total of 1339 articles were searched from three databases (392 articles from EBSCO, 594 articles from PubMed and 353 articles from Web of Science). A total of 878 articles remained after removing 461 duplicates by checking the title and/or abstract. Furthermore, based on the inclusion and exclusion criteria, two authors screened 25 full-text articles for the final selection. Finally, 16 studies [ 5 , 6 , 46 , 47 , 49 , 50 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ] met the inclusion criteria for this review.
PRISMA flowchart for study selection
Characteristics of studies and sample
Figure 2 illustrated populations from nineteen different countries extracted from fifteen included studies. Five studies focused on populations from Canada [ 5 , 6 , 47 , 54 , 55 ]. Three studies targeted populations from Spain [ 50 , 59 , 60 ] and China [ 58 , 60 , 61 ]. Two studies included populations from Bangladesh [ 57 , 60 ], the United States [ 49 , 60 ], Saudi Arabia [ 52 , 53 ], Sweden [ 56 , 60 ]. In addition, three studies included populations from different countries [ 49 , 59 , 60 ], of which populations from fourteen countries (Australia, India, Indonesia, Malaysia, Morocco, Pakistan, Sri Lanka, Vietnam, Japan, Chile, Mexico, Sweden, China and Brazil) were all extracted from one study [ 60 ], López-Gil’s study [ 59 ] included populations from Spain and Brazil, and Jáuregui’s study [ 49 ] included populations from Chile, Mexico, and the United States.
Number of publications involved in studies using 24-hour movement guidelines during the COVID-19 pandemic by country from the included studies of this review
The characteristics of the studies included in this review were summarized in Table 1 . In terms of study design, ten studies (62.5%) were cross-sectional designs [ 5 , 6 , 49 , 52 , 53 , 55 , 56 , 57 , 58 , 59 , 61 ], and four studies (25.0%) were repeated cross-sectional designs [ 47 , 50 , 52 , 54 ]. Two studies (12.5%) were longitudinal in design [ 46 , 60 ]. In regard to sample size, two studies (12.5%) had a sample size under 100 [ 54 , 57 ], four studies (25.0%) had a sample size between 100 and 1000 [ 46 , 50 , 56 , 60 ], and ten studies (62.5%) had a sample size above 1000 [ 5 , 6 , 47 , 50 , 52 , 53 , 54 , 55 , 59 , 61 ]. Regarding age groups, seven studies (43.8%) included preschool students (under 5 ys) [ 46 , 49 , 56 , 57 , 59 , 60 , 61 ]. Ten studies (62.5%) included children (aged 5–13 ys) [ 5 , 6 , 47 , 50 , 52 , 53 , 54 , 55 , 59 , 61 ], and seven studies (43.8%) included adolescents (aged 14–17 ys) [ 5 , 6 , 47 , 50 , 54 , 55 , 59 ]. Only one study (6.3%) targeted adults (18 + ys) [ 58 ]. Fifteen studies (93.8%) assessed the general population [ 5 , 6 , 46 , 47 , 49 , 50 , 52 , 53 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ], and only one study (6.3%) assessed people with disability or disorder [ 54 ].
Measurement and Assessment of studies
The measurement and assessment methods used in the studies were provided in Fig. 3 . Eleven studies used a single method [ 5 , 6 , 47 , 49 , 50 , 52 , 53 , 55 , 58 , 59 , 61 ], of which Nine studies applied a proxy report assessment [ 5 , 6 , 47 , 49 , 52 , 53 , 55 , 59 , 61 ] and two studies applied a self-report measurement [ 50 , 58 ]. Additionally, ten studies used mixed methods, of which five studies applied a proxy report assessment [ 46 , 54 , 56 , 57 , 60 ], three studies [ 46 , 56 , 57 ] used a device-based measurement (accelerometer), and two studies [ 50 , 58 ] applied an interview measurement.
Number of studies according to the measurement and assessment used. Notes: single method refers to only use one measure tool in study survey; mixed methods refer to use ≥ 2 measure tools in study survey
Categorization of studies using the 24-hour movement guidelines
Table 2 shows that the studies from the perspective of the VIRTUE framework. Three research areas (outcomes, correlates/determinants, and time-use composition) were considered in this review. In regard to outcomes, ten studies (62.5%) included sociodemographics (e.g., gender/sex, age, region, urban/rural, country income level) [ 5 , 47 , 49 , 52 , 53 , 55 , 56 , 57 , 59 , 60 ]. Three study [ 6 , 54 , 60 ] included behaviours/lifestyle (e.g., outdoor activity) [ 6 ], disability [ 54 ], environment (e.g., presence of outdoor space within house compound) [ 60 ], and social and cultural factors (e.g., the parent’s concern about the child’s movement behaviour, receiving any support from their childcare center, the parent’s perceived ability to support the child in having healthy movement behaviours, the parent’s perceived level of stress, the parent’s perceived level of exhaustion) [ 60 ]. Regarding the research area of correlates/determinants, two studies focused on psychological factors (e.g., depression, anxiety and stress) [ 58 , 61 ]. In the research area of time-use composition, all included studies assessed the prevalence of meeting the 24HMG. Eight studies (50.0%) included trends in meeting the 24HMG [ 46 , 47 , 49 , 50 , 52 , 54 , 59 , 60 ], of which two studies [ 46 , 50 ] included different numbers of participants meeting the 24HMG.
Levels and chagnes in the prevalence of meeting the 24-hour movement guidelines
As shown in Fig. 4 , seventeen outcomes were extracted from sixteen included studies (one study included two outcomes). Thirteen (76.5%) results (from twelve studies) [ 5 , 6 , 47 , 49 , 50 , 52 , 53 , 54 , 55 , 57 , 59 , 60 ] showed that the prevalence of meeting the 24HMG was under 5% during the COVID-19 pandemic, of which two studies reported a percentage less than 1% [ 50 , 54 ]. Four studies reported that more than 10% of the population (13.4%, 15.1%, 19.4%, and 27.9%, respectively) met the 24HMG during the COVID-19 pandemic period [ 46 , 56 , 58 , 61 ]. During the pandemic, the highest prevalence of meeting the 24HMG was 27.0% [ 58 ], and the lowest was 0.0% [ 54 ].
Prevalence of meeting 24-hour movement guidelines during the COVID-19 reported by studies. Note: CI: IOA = cluster 1: increase outdoor activity; C2: DOA = cluster 2: decrease outdoor activity
As shown in Table 3 , eight outcomes from seven studies reported that the percentage of the population meeting the 24HMG was decreased [ 46 , 47 , 49 , 50 , 52 , 59 , 60 ], of which half of the studies [ 47 , 49 , 50 , 52 ] reported a significant decrease from prior to and during the COVID-19 pandemic. Three studies showed a decrease of more than 3% [ 49 , 50 , 59 ]. One study reported a percentage of 0.0% with no change [ 54 ]. Nonsignificant increases were reported both in children and youth (only girls) [ 47 ].
The aim of this review was to synthesize the evidence from studies using the 24HMG during the COVID-19 pandemic, presenting a knowledge map and research landscape. The main findings of this review were as follows: first, the number of studies using the 24HMG was very limited (n = 16), with most of the studies targeting children and adolescents as study population, and most of the studies used subjective measures and were cross-sectional (n = 14) and conducted in Western countries. Second, most studies found that during the COVID-19 pandemic, the prevalence of meeting the 24HMG was very low (11 studies reported below 4%), and it declined greatly compared with the prevalence before the COVID-19 pandemic. Third, according to the VIRTUE framework, studies focusing on the prevalence of meeting the 24HMG and the trends, as well as sociodemographic correlates of the prevalence, were dominant across the included studies. Evidence synthesized from this review can help inform future research development and policymakers to implement effective approaches against public health events.
Overall status of studies and study characteristics
The findings of this review demonstrated a very limited number of studies using the 24HMG during the COVID-19 pandemic. Compared with studies investigating the prevalence of meeting the PA, SB, or sleep guidelines [ 30 , 31 , 32 , 33 , 34 , 35 ] in insolation, the number was largely lower, which in part reflects less research attention and interest in 24HMG studies. Some possible reasons can be proposed. The first one was that research within 24HMG has had a relatively short history compared with PA, SB and sleep studies in insolation, so the number of 24HMG studies cannot be as large as possible. Second, it might be difficult to collect data on PA, SB and sleep simultaneously during the COVID-19 pandemic. Despite the limited number, those included studies could also advance the knowledge around health behaviour research during the pandemic and provide evidence to refine the 24HMG in the future.
The majority of the studies included in this review were cross-sectional and conducted in Western countries. Similar findings on PA or SB studies in insolation were found [ 7 ], which is consistent with the current review. This was likely because a cross-sectional study may be the most economical and feasible study design during the COVID-19 pandemic period. During the isolation period, it was very challenging to conduct longitudinal and interventional studies on 24-hour movement behaviours owing to social distancing and lockdown. Additionally, some measures against the pandemic also had impacts on measures used by the studies included in this review. As observed in this review, most of the included studies used subjective measures to collect data on movement behaviours, which was likely due to social distancing and lockdown as well [ 8 , 9 ]. However, with the increasing use of device-based measures of movement behaviours, such as mobile phones or some other individually used technological devices (e.g., smartphones, wearable activity monitors) [ 62 , 63 ], future research should consider utilizing these devices to capture more accurate data on movement behaviours [ 7 , 63 ].
Researchers from Western countries were the main contributors to 24HMG studies during the COVID-19 pandemic. Previous studies have shown that research in PA [ 15 ] and SB research [ 7 ], and even sleep research [ 34 ], mainly comes from Western countries [ 7 , 34 , 64 ], which can to some extent explain this finding in our review. Such a bias has been observed in other health-related fields [ 65 ]. Similarly, research bias was also found in terms of the study population. In the current review, all the included studies focused on children and adolescents. This was likely due to two reasons, of which the first was that children and adolescents were the prioritized studied population in PA-related research [ 66 ] and the second was that the first 24HMG was designed for children and adolescents [ 24 ]. However, the first 24HMG for adults was released in 2020 [ 24 ]. Collectively, the above reasons could explain why the included studies prioritized children and adolescents rather than adults or other study populations.
Low levels of 24-hour movement behaviours
The results indicated a low prevalence of meeting the 24HMG (most of the included studies reported below 5%) in children and adolescents during the COVID-19 pandemic, and a significant decline in the prevalence was found compared with the pre-COVID-19 pandemic. This finding was expected on the basis of previous studies and evidence bases. Owing to social distancing and lockdown policies during the COVID-19 pandemic, individuals’ low levels of PA [ 14 , 15 , 16 ], high levels of SB [ 17 , 18 , 19 ] and worse sleep [ 50 ] jointly contributed to the low prevalence of meeting the 24HMG in children and adolescents. The implementation of restrictions has resulted in a rise in “stay-at-home” holidays. A previous study indicated that children’s sleep, SB, and PA exhibit lower levels of regulation on unstructured days (e.g., school holidays) in comparison to structured days (e.g., school days) [ 67 ]. Furthermore, this lack of regulation has been associated with a rise in the prevalence of children having electronic screen devices in their bedrooms, leading to inadequate sleep duration and PA [ 52 ].
Despite four studies reported that the prevalence of meeting the 24HMG over 10% [ 46 , 56 , 58 , 61 ], overall low level was still exhibited. The potential reasons could be attributed to variations of survey population and study recruitment, with three studies specifically examining preschool children and one study concentrating on college students. In the case of preschool children, their limited awareness of the pandemic might influence the outcomes. Conversely, college students may have established similar daily routines, whether residing in dormitories or at home. Additionally, the study reported that the samples were recruited using a convenience sampling procedure [ 58 ], which may also influence the research findings.
Change of 24-hour movement behaviours
Our findings mainly suggested a significant decline in the prevalence of meeting the 24HMG before and after the COVID-19 pandemic, even though one study reported the findings of increasing prevalence in some subgroups of children and adolescents [ 47 ]. This difference may be attributed to variations in virus transmission waves and the implementation of strict lockdown policies across different countries, as well as differences in the timing of measurements in studies [ 47 , 50 ]. These findings indirectly illustrated the impact of pandemic restrictions on individuals’ 24-hour cycle behavior. There has been a perceived decline in young people’s behaviors following the COVID-19 pandemic, with significant changes observed in PA levels, recreational screen time, and sleep duration [ 50 ]. Given the measures against the COVID-19 pandemic, children and adolescents’ access to activity facilities and opportunities was reduced [ 69 ]. Moreover, owing to home quarantine, increased SB, including recreational screen time and domestic sitting time, has been observed [ 7 ]. These factors may also negatively impact on sleep [ 70 ]. Furthermore, the closure of schools during both strict and mild confinement reduces children and adolescents’ access to and opportunities for PA, such as physical education classes and organized PA [ 71 , 72 ]. Additionally, the COVID-19 pandemic has also been associated with a decrease in outdoor playtime among children and adolescents [ 5 , 73 ]. Spending less time outdoors has a substantial impact on PA and SB, further reducing the prevalence of meeting the 24HMG [ 5 ]. As the restrictions imposed due to the COVID-19 pandemic are gradually lifted, it is crucial to address and mitigate the negative impacts on 24-hour movement behaviours in youth. Future research should focus on understanding the long-term consequences of the pandemic on children and adolescents’ movement behaviors. This includes investigating the effectiveness of interventions aimed at promoting PA, reducing SB, and improving adherence to the 24HMG. Furthermore, it is imperative to investigate strategies that enhance access to recreational facilities, encourage outdoor play, and offer organized PA opportunities amidst persistent public health challenges. This will enable the development of evidence-based interventions and policies aimed at promoting the health and well-being of children and adolescents in the aftermath of the pandemic.
Research topics of the 24HMG studies during the pandemic
On the basis of the VIRTUE framework formulated by Pedisic et al. [ 45 ], we examined the research topic of included studies conducted during the COVID-19 pandemic. The findings suggested that studies on time-use compositions and correlates were predominant. This situation can also be observed in PA, SB and sleep epidemiology research in insolation [ 13 , 74 ], which was similar to our review. One possible explanation for this finding is that these two domains of study can be conducted with relatively low testing burden and are easier for researchers to design and perform. Given the constraints and limitations imposed by the pandemic, it is understandable that researchers gravitated towards areas where data collection and analysis could be carried out more easily. Among the time-use composition studies, numerous investigations have reported changes in the prevalence of meeting the 24HMG before and during the COVID-19 pandemic. However, it is equally important to direct research efforts towards examining the changes in post COVID-19 conditions. Such investigations would provide valuable insights into the impact of the pandemic on population health and inform strategies for the future. By expanding research beyond the immediate effects of the pandemic, we can gain a comprehensive understanding of the long-term implications on individuals’ time-use compositions and their adherence to the 24HMG. This knowledge will be crucial for developing targeted interventions and policies that promote healthier time-use behaviors in the post-pandemic era.
In terms of the correlates examined in the 24HMG studies conducted during the COVID-19 pandemic, the majority focused on sociodemographic factors, while a few studies assessed factors of other domains, such as social, cultural, and environmental factors. This finding is consistent with previous studies [ 34 ], partly because of relatively easy data collection on sociodemographic factors. Furthermore, this finding was also similar to the evidence from 24HMG research conducted before or after the COVID-19 pandemic [ 7 ]. This suggests that the emphasis on sociodemographic factors in 24HMG studies is not solely influenced by the pandemic but has been a prevailing trend in the field. Based on our review and the existing literature, it is evident that future studies should aim to explore a broader range of factors influencing 24HMG from different domains. By expanding the scope of investigation beyond sociodemographic factors, researchers can gain a more comprehensive understanding of the complex interplay between activity behavior and various contextual factors. This will contribute to a more nuanced understanding of the relationship between 24HMG and its determinants, ultimately informing interventions and strategies to improve individuals’ health and well-being.
Two study treated 24-hour movement behaviours as correlated and assessed their association with mental health outcomes among Chinese populations of preschool and university students [ 58 , 61 ]. In contrast, the number of studies conducted before or after the COVID-19 pandemic largely exceeded the number. Despite the limited number, evidence can also be used for future refinement and update of the 24HMG for children and adolescents. Based on prior evidence has demonstrated that the low prevalence of meeting the 24HMG was in part responsible for undesirable health outcomes in children and adolescents, such as psychological outcomes (e.g., depression and anxiety) [ 58 ] and physical outcomes (e.g., cardiometabolic risk and adiposity) [ 68 ]. Future research can adopt longitudinal designs to examine the long-term effects of 24-hour movement behaviours on mental health in different age group (preschool students, children and adolescents, and adults) By tracking individuals’ behaviors and mental health outcomes (at different stages before, during and after pandemic), researchers can gain insights into the potential causal relationships and identify whether these associations persist over time. This research will contribute to providing valuable recommendations for the future development of human behavior and psychology.
Strength and limitations
This study’s strengths included a comprehensive review of the prevalence of 24HMG during the pandemic and its analysis of the changes in 24HMG before and after the outbreak. Additionally, it provides a summary of the research topics based on the VIRTUE framework. However, there were some limitations that should be acknowledged. Firstly, most of the studies were cross-sectional studies, which had an impact on the change of meeting 24HMG during the COVID-19 pandemic. Secondly, the included English studies of this review were only searched in three common databases, written in other languages articles were not included. Thirdly, this study did not incorporate COVID-19 policies, however, it is beneficial to analyse the impact of policies on meeting 24HMG in the future study. Additionally, this study did not classify and review adherence to 24HMG among genders, countries with varying socioeconomic status, and different age groups. Future targeted reviews (e.g., focusing on children and adolescents) are also valuable as these would facilitate interventions or policy development. Finally, this study just summarized the research topics of 24-hour movement behaviours during the COVID-19 based on the VIRTUE framework. Future studies are recommended to explore (through systematic review, meta-analysis., etc.) the results of relationships between different factors and 24-hour movement behaviors based on VIRTUE framework.
This review summarized the evidence from studies using 24HMG during the COVID-19 pandemic, offering a knowledge base for future research and policy development. Based on the findings, the COVID-19 may tend to have a negative impact on the prevalence of meeting 24HMG among different age-group populations. According to the study characteristics and research domains, studies using the 24HMG have a large space for improvement in terms of study design, measurement protocols and study domains (e.g., correlates and health outcomes).
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
24-Hour Movement Guidelines
Viable Integrative Research in Time-use Research
- Sedentary behaviour
World Health Organization
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This study was supported by grants from the National Social Science Foundation of China (No. 19BTY077), the Program for Overseas High-level talents at Shanghai Institutions of Higher Learning, and Shanghai Key Laboratory of Human Performance (Shanghai University of Sport, No.11DZ2261100).
Danqing Zhang and Sitong Chen contributed equally to this work.
Authors and Affiliations
School of Physical Education, Shanghai University of Sport, Shanghai, 200438, China
Danqing Zhang & Yang Liu
Institute for Health and Sport, Victoria University, Melbourne, VIC, 8001, Australia
One Health Research Group, Universidad de Las Américas, Quito, 170124, Ecuador
José Francisco López-Gil
Shanghai Research Institute of Sports Science (Shanghai Anti-doping Agency), Shanghai, 200030, China
Kun Shan Lu Jia Senior High School, Jiangsu, 215331, China
Shanghai Research Centre for Physical Fitness and Health of Children and Adolescents, Shanghai University of Sport, Shanghai, 200438, China
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YL designed the study and drafted the framework of this study, DZ drafted the manuscript. DZ and FW extracted data and completed all figures and tables. JH revised figures, tables and manuscript. SC: designed the study, checked all data and edited the manuscript. JFLG edited the manuscript.
Correspondence to Yang Liu .
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Zhang, D., Chen, S., López-Gil, J.F. et al. 24-Hour movement behaviours research during the COVID-19 pandemic: a systematic scoping review. BMC Public Health 23 , 2188 (2023). https://doi.org/10.1186/s12889-023-17136-y
Received : 04 March 2023
Accepted : 02 November 2023
Published : 07 November 2023
DOI : https://doi.org/10.1186/s12889-023-17136-y
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- 24-hour movement guidelines
- The COVID-19
- Moderate to vigorous physical activity
- Evidence synthesis
BMC Public Health
This article is part of the research topic.
Emerging Research: Conspiracy Beliefs
Editorial for the research topic "Emerging research: Conspiracy beliefs"
- 1 College of the Holy Cross, United States
- 2 University of Basel, Switzerland
The final, formatted version of the article will be published soon.
People sometimes assert that a powerful group's secret, malign efforts are responsible for bad outcomes even with scant evidence to accept this belief over more plausible explanations. Psychologists' recent interest to understand the consequences of conspiracy beliefs and the antecedent factors that incline people to hold them has made this a rapidly growing research area, especially now as we grapple with the implications of conspiracy theories that are prevalent in public discourse about pressing concerns such as the COVID-19 pandemic (Pilch et al., 2023). One indicator of this area's explosive growth is that Pilch et al. (2023) In their review, Pilch et al. (2023) find six categories of antecedents of conspiracy beliefs in the literature and a diversity of consequences. The categories of antecedents identified are: cognitive (e.g., analytic vs. intuitive thinking style, relation to other cognitive biases), motivational (epistemic and existential -the two most studied -as well as social motives), personality (including temperament, personality traits, selfevaluations, as well as other dispositional traits), psychopathology (mostly subclinical manifestations of psychiatric disorders; Dark Triad and Dark Tetrad traits), political (ideological orientation, extremist ideology) and sociocultural factors (e.g., Hostede's cultural values, moral foundations, media habits or media consumption, trust in institutions, and religiousness). The authors also examine the consequences of conspiracy beliefs: "The endorsement of conspiracy theories may have a range of negative consequences for both individuals and the society at large" (Pilch et al., 2023, p. 9). For the individual, holding conspiracy beliefs can be associated with social stigma and fear of social exclusion, as well as less likelihood to engage in evidencebased prophylaxis (but not pseudo-scientific prevention) and to rely on biomedical treatment. As for social consequences, conspiracy beliefs are also associated with criminal intentions, support for violence, and dehumanization of others. The authors also note conspiracy beliefs might shape political preferences and nonconventional political participation. They note recent increases in the geographical diversity of where conspiracy research is conducted but that it is still heavily reliant on European and North American samples.Two articles in this Research Topic focus on understanding specific antecedents of conspiracy beliefs. In a sample of adults living in Iran, Nejat et al., (2023) find conspiracy beliefs regarding COVID-19 to be associated with religiosity and endorsement of moral foundations of authority and sanctity but not strongly related to Big 5 personality traits. Cosgrove & Murphy (2023) examine whether education moderates the association between conspiracy belief and narcissism. Their first study finds that variables related to narcissism (i.e., grandiosity, vulnerable narcissism, need for uniqueness, and need for supremacy) are positively related to conspiracy beliefs. The overall association between conspiracy beliefs and education, including STEM education, are negative, but there also is a surprising moderating relationship such that education predicts higher levels of conspiracy belief for narcissistic individuals. Interestingly, the positive association between need for uniqueness and conspiracy beliefs is only present in highly educated individuals. The authors originally predicted the opposite moderating relationship because education was postulated to be linked to increased critical thinking that would reduce the association of conspiracy beliefs and narcissism. In a second study relying on pre-existing data (N = 51,404), the authors examine similar hypotheses in the context of conspiracy belief related to the COVID-19 pandemic: They test the main effects of critical thinking (negatively associated) as well as narcissism and collective narcissism (positively associated) on conspiracy beliefs and further test whether critical thinking moderates the association of conspiracy beliefs with narcissism and collective narcissism. Contrary to the results of the first study and in line with their expectations, the authors find that critical thinking reduces the relationship between collective narcissism and conspiracy beliefs. It could follow that education that fails at increasing critical thinking is not a protective factor in relation to conspiracy belief in narcissistic individuals.Two other articles in the Research Topic demonstrate the consequences of holding conspiracy beliefs. Romer & Jamieson (2023) examine the association between conspiracy mindset and the perceived risk of vaccination of children against COVID-19, as such mindset is grounded in the distrust of governments. In a sample of 1,941 U.S. adults, the authors find strong direct links between conspiracy mindset and endorsement of COVID conspiracies (positive), vaccine misinformation and conspiracies (positive), trust in authorities (negative) and intention to vaccinate against MMR (negative). Perceived COVID risk is negatively associated with trust in authorities, and being vaccinated is negatively associated with vaccine misinformation and conspiracies. Lower risk for child COVID-19 vaccination is predicted by all these intermediary variables, which together explain 76 % of variance in that outcome. Smallpage et al. (2023) show that authoritarian tendencies such as social dominance orientation and right-wing authoritarianism are associated with conspiracy beliefs and with other epistemically unwarranted beliefs such as paranormal thinking and belief in pseudoscience. In addition, they posit that conspiracy beliefs may be part of a broader construct that shares common cognitive foundations with these other epistemically unwarranted beliefs.Conspiracy beliefs are pervasive, consequential, and often difficult to change. Although they have long been part of the human experience, psychologists have only recently turned their attention to understanding these beliefs and why people hold them. The work on this topic has so far been fruitful in identifying factors associated with conspiracy beliefs and the articles in this Special Topic illustrate some of the key lines of inquiry. As a whole, the articles from this special topic synthesize past literature on conspiracy beliefs, provide new scientific evidence of their antecedents as well as examples of their important societal consequences, and suggest directions for further research on conspiracy beliefs.
Keywords: Conspiracy beliefs, Conspiracy theories, conspiracy mindset, narcissism, Moral foundations theory, Vaccination hesitancy, Authoritarianism
Received: 20 Oct 2023; Accepted: 07 Nov 2023.
Copyright: © 2023 Hallahan and Mayor. 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) or licensor 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: Mx. Eric Mayor, University of Basel, Basel, Switzerland