conceptual framework in research about covid 19

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

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

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

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

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

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

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

Methodology

Research question.

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

Study design

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

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

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

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

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

Eligibility criteria

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

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

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

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

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

Information sources

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

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

Study selection

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

Data collection process

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

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

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

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

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

Quality assessment in individual reviews

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

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

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

Synthesis of results

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

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

Managing overlapping systematic reviews

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

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

PRISMA flow diagram

Characteristics of included reviews

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

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

Population and study designs

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

Systematic review findings

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

A meta-analysis of the prevalence of mortality

Clinical symptoms

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

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

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

Therapeutic possibilities

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

Laboratory and radiological findings

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

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

Quality of evidence in individual systematic reviews

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Availability of data and materials

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

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Acknowledgments

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

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Israel Júnior Borges do Nascimento

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

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

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

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

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

Additional file 5: Appendix 5.

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

Additional file 6: Appendix 6.

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

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

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• Published: 15 September 2021

Differences and similarities in the conceptualization of COVID-19 and other diseases in the first Italian lockdown

• Claudia Mazzuca   ORCID: orcid.org/0000-0002-1568-2425 1 ,
• Ilenia Falcinelli 1   na1 ,
• Arthur-Henri Michalland 2   na1 ,
• Luca Tummolini 3 &
• Anna M. Borghi 1 , 3  

Scientific Reports volume  11 , Article number:  18303 ( 2021 ) Cite this article

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• Human behaviour

Several studies have highlighted the flexible character of our conceptual system. However, less is known about the construction of meaning and the impact of novel concepts on the structuring of our conceptual space. We addressed these questions by collecting free listing data from Italian participants on a newly–and yet nowadays critical–introduced concept, i.e., COVID-19, during the first Italian lockdown. We also collected data for other five illness-related concepts. Our results show that COVID-19’s representation is mostly couched in the emotional sphere, predominantly evoking fear —linked to both possible health-related concerns and social-emotional ones. In contrast with initial public debates we found that participants did not assimilate COVID-19 neither completely to severe illnesses (e.g., tumor) nor completely to mild illnesses (e.g., flu). Moreover, we also found that COVID-19 has shaped conceptual relations of other concepts in the illness domain, making certain features and associations more salient (e.g., flu-fear; disease-mask). Overall, our results show for the first time how a novel, real concept molds existing conceptual relations, testifying the malleability of our conceptual system.

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Introduction.

What comes to our mind when we read the word “COVID-19”? Does the way in which we think of COVID-19 resemble how we think of other diseases, such as tumor and flu? And if so, to what extent?

The term COVID-19 has been coined by the World Health Organization (WHO) on February 11, 2020 1 as an acronym for the COronaVIrus Disease 19, a respiratory syndrome caused by the new coronavirus SARS-CoV-2, first discovered in Wuhan, China, in December 2019. Initially, the fact that this new viral disease shared many symptoms with seasonal diseases (e.g., cold, flu) gave rise to a heated public debate. In this context, COVID-19 has been frequently assimilated to these more familiar diseases, thus creating a long-lasting confusion in the population. Understanding how COVID-19 is represented is therefore important for practical reasons: knowing how laypeople conceptually represent COVID-19 can support practitioners, scientists, and politicians to deal with invisible barriers that may facilitate the spread of this pandemic 2 . At the same time, it is important for scientific reasons, given that it is rare to observe the emergence of a new concept quickly spreading on a global scale, affecting people’s everyday life, and that might also impact the representation of well-established conceptual domains (e.g., diseases).

Other concepts, such as those related to technology (e.g., smart-phone, social network), have emerged and rapidly spread in recent years. However, previous work on novel concepts often concentrated on artificial categories created by the experimenters. For instance, Granito, Scorolli and Borghi 3 presented participants with novel concrete and abstract categories formed by Lego bricks and asked them to freely categorize them after having received either a sensorimotor or a linguistic training. Afterwards, they performed a categorical recognition task. The results show that abstract categories were more difficult to form, and benefited more from the linguistic training compared to concrete categories, suggesting a critical role of sociality and language in the acquisition of more abstract entities. We chose to focus on COVID-19, aside from its intrinsic interest, not because it is representative of novel concepts, but because of its peculiar character. It is peculiar for at least two reasons: because it is easy to determine when it was introduced, and because no other concept has spread so quickly across different countries and populations. In addition, in Italy as in many other countries, it extensively attracted the media's attention. So, the first reason motivating our study was the desire to understand how people in the first wave of the pandemics represented COVID-19. Which kinds of conceptual relations did it evoke? Did people think more of its symptoms, of its social consequences, of the emotions it evokes? Did they represent it more like a severe disease, such as a tumor, or maybe like a disease easier to deal with, such as a flu? Accessing the general public’s conceptual representation of this new, complex, and yet extremely salient concept is therefore the first aim of this study.

The second reason to investigate COVID-19 representation has implications more directly relevant to the literature on concepts. Concepts can be considered aggregates of experiences in semantic memory, characterized by a certain degree of stability 4 . They connect our past with current and future experiences, gluing them together 5 . Furthermore, concepts are flexible entities, continuously updated in light of novel experiences. Indeed, many authors recognized the importance of investigating not only stable but also variable aspects of concepts (e.g. 6 , 7 , 8 ). In the case of COVID-19, we have witnessed a new phenomenon: the emergence of a new concept, the use of which spread very rapidly. Initially known only in laboratories and in specialist settings, this concept rapidly entered our houses and became commonly used. Importantly, the concept of COVID-19 is continuously updated, discussed, and refined by an entire community of knowledge composed of scientists, practitioners, and politicians 9 . In addition, given the initial parallelisms that have been drawn between COVID-19 and other diseases, we might assist to a bidirectional influence on the semantic space of diseases. For instance, on the one hand the newly gained information on COVID-19 might impact previous knowledge related to other diseases, while on the other hand established knowledge of other diseases might serve as a scaffolding for reinforcing new conceptual associations needed for the concept of COVID-19. So, the second aim of this study is to assess whether–and to what extent–the introduction of COVID-19 has affected more established concepts, and what is the impact of these concepts on the conceptual representation of COVID-19.

Concepts are flexible entities, receptively adapting to new situations. Not only different extensive experiences (e.g., practicing sports, playing instruments) modulate our conceptual system (e.g. 10 , 11 ), but even engaging in varied sensorimotor training for three weeks can impact objects’ conceptual representation (e.g. 12 ). Conceptual activation is further affected by task conditions. For instance, the modality in which stimuli are presented to participants (e.g., auditory, visual) affects response times in property-verification tasks. Van Dantzig, Pecher, Zeelenberg and Barsalou 13 , for example, found slower response times for trials preceded by a perceptual trial presented in an incongruent modality with respect to those presented in congruent modalities. So, concepts can flexibly re-enact relevant information related to a given category depending on the specific situation ( 14 for reviews, see 15 , 16 , 17 ).

Language is a further source of conceptual flexibility. Cross-linguistic and cross-cultural investigations demonstrated that conceptual representations are carved up by languages very differently across cultures 18 . Several domains including mental states, events, time, and spatial relations (see 19 , 20 ), but also seemingly naturally-bounded entities such as body parts 21 dramatically vary in their conceptual representation depending on the language investigated. In sum, language contributes to our reality’s co-construction in many different ways 22 . Among these, the information we gain from public discussions, experts, and media greatly contribute to shaping and refining our conceptual system 23 , 24 , 25 . This might be especially true for relatively new concepts, or concepts initially mainly used in specialist settings, such as “COVID-19”.

Remarkably, in the present study we are able to timely investigate how a real concept, recently introduced and often the object of public discussions, is represented by laypersons. This constitutes the first important novelty of our work: compared to previous studies addressing the consolidation of meanings, often focused on artificial categories, our study directly tackles the emergence and representation of a new concept. In addition, here, we sought to assess whether and how the introduction of a new concept further carved the conceptual relations of semantically related concepts. In fact, while studies dealing with conceptual flexibility provided evidence that task conditions, points of view, and cultural and linguistic environment shape conceptual representations, the impact of the introduction of a new concept on such a large-scale on the semantic space is clearly understudied. This unique opportunity to address conceptual flexibility represents the second focal novelty of our work.

The current study: how is COVID-19 conceptualized, and what is its semantic relation with other disease concepts?

In the present study, our aim was to investigate how people conceptualize a relatively new concept, i.e., COVID-19, and whether and how the introduction of this concept shaped the semantic associations of other concepts in the disease domain. Importantly, we recruited participants from Italy, a country severely hit by COVID-19, and the data were collected in the first wave of the pandemic, during lockdown—the first national lockdown in the world. So, the timing of the data collection allowed us to capture the emerging meaning of COVID-19 in its initial phase.

To tackle these questions, here we apply a method typically used to investigate conceptual representations, i.e., a semantic fluency task. The main assumption underlying semantic fluency tasks is that when participants are presented with a target word, concepts that are semantically related to it will be immediately activated and produced. Unlike explicit questions concerning attitudes or definitions, semantic fluency tasks are thought to be an indirect measure of psychological proximity of concepts, hence providing access to semantic memory. This family of methods is widely employed in neuropsychology, to measure the semantic integrity of certain domains in patients with brain damage 26 , or to assess memory organization in schizophrenic individuals 27 . Anthropologists and linguists make a consistent use of semantic fluency tests too, to build folk taxonomies of how specific cultural domains are conceptualized by a particular cultural group 28 . Different varieties of semantic fluency tasks exist. Participants might be required to produce features that are typically true of a given concept (i.e., feature generation task; e.g. 20 , 29 ), or they can be asked to produce all the words that come to their mind (i.e., free-listing task; e.g. 30 , 31 , 32 ). Semantic property norms collected through these methods have been extensively employed 33 to show that words with more semantic associates tended to be responded to faster and/or more accurately in various semantic tasks 34 . Here, we chose to use a free-listing task because we were dealing with a novel concept, and we were especially interested in the dynamic aspects that characterized the pattern of produced relations. We wanted to capture the whole pattern of elicited conceptual relations, avoiding constraining participants to produce only properties that are true of the concept. Importantly, free-listing tasks have also been used to understand how people represent concepts 35 , 36 . In free-listing tasks, concepts that are mentioned earlier and more frequently in a given list are thought to be more psychologically salient for the target concept. So, this method allows us to address how we represent the concept of COVID-19, and how and to what extent its representation is similar to that of five further semantically related concepts, i.e., disease, virus, flu, fever, tumor.

Furthermore, we were interested in understanding whether and to what extent the introduction of this new concept has changed an entire semantic field, i.e., modifying the relationship between more and less severe diseases, from “flu” to “tumor”. Finally, we intended to capture whether the associations to other similar concepts, i.e., diseases, have been restructured in light of the spread of COVID-19—for example, whether terms specifically related to COVID-19 appear also among the features listed with other concepts.

Correlation between demographic information, perception of risk, and level of information

Before moving to the analysis of free-listing data, we addressed whether participants had a different perception of COVID-19 related risks (tested with a continuous scale from 1 to 7) depending on three main parameters: their regional provenience (North of Italy; Center of Italy; South of Italy), the frequency with which they received information and news about COVID-19 (never; rarely; sometimes; often; very often), and the number of cases positively tested for COVID-19 within regions at the time of participation—as reported by the National website https://github.com/pcm-dpc/COVID-19 . Data were analyzed using R (version 3.6.3 37 ) and RStudio (version 1.4.1100 38 ). All data, scripts, and analyses are available at https://osf.io/dsvm3/ .

We found no difference in risk perception depending on regional provenience, F (2, 71) = 0.17, p  = 0.843, while participants differed in their perception of risk depending on the frequency with which they received information about COVID-19, F (3, 70) = 3.26, p  = 0.026. Specifically, Turkey’s post-hoc tests showed that participants who indicated that they received news about COVID-19 “very often” perceived significantly more risk ( M  = 4.09; SD  = 1.57) than participants who received the news “often” ( M  = 3; SD  = 1.19), p  = 0.017. We found no correlation between the number of cases and risk perception, r (72) = -0.021, p  = 0.853, but found that the scores in the GAD-7 scale (measuring the general anxiety of participants 39 ), were positively correlated to the perception of risks linked to COVID-19, r (72) = 0.33, p  = 0.003. There was also a tendency of a positive correlation between GAD-7 scores and the frequency with which participants followed news about the COVID-19 pandemic r (72) = 0.20, p  = 0.07.

Free-listing data descriptive statistics

Free-listing data were pre-processed as follows: all punctuation characters (periods, commas, semicolons, etc.) used by participants to separate the generated words were deleted and all the words were put in separate cells. All upper-case letters were changed to lower case to allow comparison of strings. Obvious spelling mistakes and typos were corrected. Alternative spellings of the same word were unified, as well as singular and plural forms of the same word.

Participants produced a total of 169 single occurrences for COVID-19 ( M  = 4.81; SD  = 0.80), 73% of which were produced only once by one participant. Participants produced 167 single occurrences for DISEASE ( M  = 4.81; SD  = 0.83), with 69% of associates produced only once; 182 single occurrences for VIRUS ( M  = 4.85; SD = 0.67), with a percentage of 73% of associates produced only once; 153 single occurrences for TUMOR ( M  = 4.79; SD  = 0.90), with a percentage of 74% of associates produced only once; 125 single occurrences for FEVER ( M  = 4.77; SD  = 0.83), with a percentage of 74% of associates produced only once, and 144 single occurrences ( M  = 4.74; SD  = 0.92) for FLU, with 71% of associates produced only once. Frequency distribution followed Zipf’s law 40 typically observed in free-listing data (see also 31 , 41 ), with fewer items produced by most participants and a long tail of less frequently produced items. As a first step, for each target concept, we identify the most frequently produced associates (listed by at least 10% of participants) and calculate their index of cognitive salience 41 . Cognitive salience is defined as the combination of two pivotal parameters in free-listing data, i.e., term frequency and its mean position, and varies between 1 and 0. Terms that are most salient for a given target concept have an index of 1, while terms that are not mentioned at all have an index of 0. Cognitive salience is thus calculated as follows: CS = F/(NmP) 41 , 42 , where F = term frequency, N = number of participants, and mP = mean position of the term. Once the most salient concepts associated with a target concept have been identified, we turn to a broader analysis of its conceptual representation by relying on co-occurrences—as represented by semantic networks (see e.g. 43 ). Semantic networks show how salient features of each target concept are organized in the semantic space. For each target concept, we created undirected weighted semantic networks using “igraph” 44 , “ggraph” 45 , and “tidygraph” 46 R packages. Counts of co-occurrences of bigrams (i.e., couples of words that were listed in succession) were used as direct input for constructing the networks.

Table 1 shows the most frequently produced terms for COVID-19, the percentage of participants producing each term, and the cognitive salience index for each produced term.

The first term both in terms of frequency and cognitive salience is virus , i.e., technically a non-superordinate concept but possibly perceived as such by laypeople. The second one for frequency (third for cognitive salience) is an emotional term, i.e., fear . Besides these cases, participants seem to focus mainly on the outcomes of COVID-19, both at an individual and social level (e.g., disease , quarantine ). One of these possible outcomes is death (5th in cognitive salience).

To better visualize how participants represented COVID-19 overall, we created an undirected weighted semantic network. Words that were not listed in succession more than once were excluded from the analysis to avoid idiosyncrasies. The resulting network comprises 15 nodes (i.e., associates) and 15 edges (i.e., links). Before applying any clustering algorithms, we calculated the modularity of the network. Modularity values greater than 0 indicate non-random clustering. The modularity of COVID-19 was 0.38. We used Louvain’s algorithm for community detection to detect representative clusters of associates 47 . Communities are groups of nodes in a network that are more densely connected to one another than to other nodes. We found five different communities in our dataset. To visualize the network, we used the Fruchterman-Reingold force-directed layout algorithm.

Figure  1 shows the semantic network for COVID-19; associates that were listed in succession (i.e., bigrams) are connected by links. Thicker links represent bigrams that were most frequently linked together. Different communities are indicated by different colors.

Network of words (translated in English) produced by participants in relation to COVID-19 . Thicker links represent words that were most frequently produced together.

The term virus is central and associated with several other words, especially with the term pandemic . A clearly delimited sub-group pertains to symptoms ( cough , fever ), another to the spreading of COVID-19 ( contagion , pandemic ). The red sub-group includes more general terms ( home , virus , China , disease ), one of which is emotionally connoted ( danger ). Interestingly, the emotional term fear is not linked to death , but rather to solitude , quarantine , and pain , while death is linked to vaccine . While most terms are commonly used in other contexts not necessarily related to COVID-19, the terms pandemic , China , and quarantine are unequivocally linked to the novel COVID-19 emergency situation.

Disease related concepts

The same procedure for analyzing free-listing data of COVID-19 was applied to the remaining five target concepts, all related to the semantic domain of “disease”: DISEASE , VIRUS , TUMOR , FEVER , FLU. We present these results together, focusing on differences and similarities across target concepts.

Table 2 shows the most frequently produced features (produced by at least 10% of participants) for each concept, as well as their frequency, and cognitive salience index.

The emotional term fear ranked third in cognitive salience not only for COVID-19 but also for DISEASE, VIRUS, and TUMOR, while it is fifth for FEVER and it is not produced in association with FLU. The term death , which is fifth in terms of frequency for COVID-19, appears in a similar position with DISEASE (5th) and VIRUS (4rth), but has a more prominent role for TUMOR (1rst). These results suggest that the concept of “COVID-19”is emotionally more similar to serious diseases and illnesses than to simple flu since it generates fear; at the same time, however, it evokes the spectrum of death less than the concept of “tumor”. In addition, VIRUS seems to be the concept semantically closer to COVID-19, with three terms in common among those most frequently produced ( fear , death , and contagion ).

To better visualize how participants represented the five disease-related concepts overall, we created undirected weighted semantic networks following the same procedures and algorithms we used for analyzing COVID-19 . For the target concept DISEASE , the network is composed of 10 nodes and 10 edges (modularity = 0.36); for the target concept VIRUS the resulting network is composed of 11 nodes and 13 edges (modularity = 0.5); for TUMOR , the resulting network is composed of 11 nodes and 14 edges (modularity = 0.34); for FEVER, the resulting network is composed of 18 nodes and 18 edges; finally, for the target concept FLU , the resulting network is composed of 16 nodes and 18 edges (modularity = 0.39).

Figure  2 shows the semantic networks for DISEASE (panel A), VIRUS (panel B) , TUMOR (panel C) , FEVER (panel D) , and FLU (panel E); associates that were listed in succession (i.e., bigrams) are connected by links. Thicker links represent bigrams that were most frequently linked together. Different communities are indicated by different colors.

Networks of words (translated in English) produced by participants in relation to disease ( A ), virus ( B ), tumor ( C ), fever ( D ), and flu ( E ). Thicker links represent bigrams that co-occurred most frequently. Colors of communities are randomly assigned across networks, so they do not indicate similarities across concepts.

This analysis allows us to understand to what extent the current spread of COVID-19 influences the features listed for other concepts, i.e., whether the introduction of a new concept has led to a restructuring of the semantic field of other associated concepts. Although we have no data on how this semantic field was previously organized (large word association norms in Italian are not available), the production of concepts that are self-evidently COVID-19-related suggests that a change has occurred. We will therefore consider words that, within the semantic domain of each concept, refer explicitly, or unambiguously, to COVID-19.

For DISEASE, the only term unequivocally associated with the spread of COVID-19, i.e., mask , is linked to an emotional term, fear , and to hospital . All the terms produced in association to VIRUS are likely automatically associated with the concept of “COVID-19”. However, we can identify three interrelated words that unambiguously refer to it, i.e., corona , pandemic , and quarantine . Neither with the concept TUMOR nor with the concept FLU, we find terms that can be unequivocally associated with COVID-19—although we cannot exclude a possible influence. As to the concept FEVER, the two associated words avian – pneumonia , even if not unambiguously referred to COVID-19, are very likely influenced by its spread. Below we will briefly discuss highlights from the networks resulting from participants’ responses to the disease-related target concepts.

In the network resulting from the free listing of DISEASE, we found three main communities. One mainly related to the role of doctors, and overall negatively connotated ( suffering, pain, doctors, medicines ); the second one contains possible outcomes of being ill ( cure, healing, death ). The last community is the one more probably affected by COVID-19-related experiences ( hospital, mask, fear ).

The network composed of associates to VIRUS resulted in five communities. Two among these stand alone: one explicitly related to COVID-19 ( quarantine, pandemic, corona ), and the second one composed only of fever and bed.

The network of TUMOR appears as the most emotionally loaded. In fact, out of four communities, only one does not contain explicit emotional terms ( doctors, hospital , which is an isolated community in the network).

In the FEVER network, communities refer mostly to symptoms; interestingly, the influence of the current situation related to COVID-19 might be reflected by the presence in the network of the term fear .

The last network, FLU, is composed of five communities, with one of these that stands alone ( avian, pneumonia , possibly an association with other virus outbreaks triggered by the COVID-19 emergency).

COVID-19 and disease-related concepts shared semantic space

Among all the associates produced for all five disease-related concepts and COVID-19 , we found seven common words: fear, danger, anxiety, cure, doctors, medicines, and hospital . Table 3 shows the frequencies of production of each of the seven common words across the six concepts.

Looking at the percentages, COVID-19 is the only concept, together with VIRUS, for which the term fear alone is highly frequent, followed by two further emotional terms, danger and anxiety , which are in any case much less frequent. Compared to other concepts, COVID-19 evokes fewer possibilities of cure and healing, probably because it is less known. It has to be borne in mind that these data were collected during the first COVID-19 outbreak, when the possibility of a vaccine was still far from its current development. For example, with the concept TUMOR, the word fear is also the most frequent word, but also cure and hospital are highly frequent. With the concept DISEASE, and even more with FEVER and FLU, the focus is not only on fear but on the concrete possibilities to deal with the illness: in fact, hospital , medicines , doctors are highly frequent words.

To further investigate commonalities across concepts, we inspected the ANEW database 48 searching for the seven terms shared across the six target concepts. ANEW provides affective norms for over 1000 Italian words, measuring on 9-point Likert scales Valence (1 = very unpleasant; 9 = very pleasant); Arousal (1 = very calm; 9 = very aroused); Dominance (1 = very submissive; 9 = very dominant) and Concreteness (1 = abstract; 9 = concrete), among other psycholinguistic variables. We found 4 of the seven words in the database, i.e., fear, valence M  = 2.21 ( SD  = 1.45); arousal M  = 6.94 ( SD  = 2.25); dominance M  = 3.52 ( SD  = 2.08); concreteness M  = 5.00 ( SD  = 2.96), medicine, valence M  = 4.93 ( SD  = 2.47); arousal M  = 5.59 ( SD  = 2.09); dominance M  = 4.41 ( SD  = 2.07); concreteness M  = 6.30 ( SD  = 1.87), hospital, valence M  = 2.83 ( SD  = 1.84); arousal M  = 6.71 ( SD  = 1.82); dominance M  = 4.06 ( SD  = 2.07); concreteness M  = 8.45 ( SD  = 0.76) and danger , valence = 2.28 ( SD  = 1.59); arousal = 7.25 ( SD  = 2.18); dominance = 4.16 ( SD  = 2.23); concreteness M  = 5.35 ( SD  = 2.32). We also found anxious (instead of anxiety), valence M  = 2.16 ( SD  = 1.22); arousal M  = 7.25 ( SD  = 1.77); dominance M  = 3.16 ( SD  = 2.01); concreteness M  = 4.90 ( SD  = 2.38). Overall, it seems the six target concepts share an intense emotional load, mainly related to sensation of instability and arousal (for similar findings on Italian emotional responses to COVID-19 emergency see e.g. 49 ).

Correspondence analysis

To further assess the semantic similarity and diversity across the six concepts, we conducted a correspondence analysis, implemented through the “FactoMineR” and “factoextra” R’s packages 50 , 51 . Correspondence Analysis is a data reduction technique allowing to extract the main dimensions along which semantic information is grouped ( 52 , see also 53 , 54 ). The input for the following correspondence analysis is a matrix constructed relying on words that were produced by at least 10% of participants for COVID-19 (rows) and their frequencies across all six concepts (columns). The identified words were differentially distributed across the six concepts, \({\rm X}^{2}\) (30) = 230.65, p  = 0.002. We found the first two dimensions explained 76% of the variance, with Dimension 1 explaining most of the variance (50.38%), followed by Dimension 2 (25.87%); this can be seen in Fig.  3 .

Percentage of variance explained by dimensions extracted by correspondence analysis.

The results of the correspondence analysis allow us to clearly understand the particularity of the pattern of features elicited by COVID-19 with respect to those evoked by the other target concepts. Figure  4 shows the results of the correspondence analysis. Target-concepts are represented in red, words that were produced in association with COVID-19 are represented in blue. Lighter blue indicates the word has a stronger contribution to the Dimensions.

Plot of dimensions 1 and 2 of words that were most frequently produced for COVID-19, and their relation with the target concepts.

We discuss only concepts and features weighing more than 10% of the variance in each dimension. On the first Dimension (50.4% of the overall variance) COVID-19 and FLU (even if FLU insists more on Dimension 2) are opposed to TUMOR. COVID-19 and FLU are associated with virus and pandemic (even if the term pandemic has a higher weight on Dimension 2), while TUMOR is associated with death . On the second Dimension (25.9% of the overall variance) FLU and FEVER are associated with the term disease and opposed to COVID-19 (even if the concept insists more on Dimension 1) and to the words pandemic , quarantine , and death (even if the term death has higher weight on Dimension 1). The interpretation of the correspondence analysis evidences that participants represent ‘COVID-19’ as differing from a severe disease like ‘tumor’, because less mortal and more contagious. At the same time, they do not seem to assimilate it to a normal ‘flu’ or to reduce it to the symptom of ‘fever’, since it is more contagious ( pandemic ), it has practical and impacting consequences ( quarantine ), and it can eventually be lethal ( death ).

In the present study, we were interested in assessing the conceptual structure of a newly introduced–and yet extremely salient–concept and its possible impact on similar semantic domains. To this end, we tested how Italian participants during the first lockdown represented the concept COVID-19, which features it elicited, and its similarity and differences with less novel concepts referring to the same semantic domain—that of (more or less severe) diseases, using a free-listing task.

Overall, the results offer a very clear pattern. First, our sample’s representation of COVID-19 is strongly emotionally connoted. Fear is the most frequently produced term, after the term virus . Compared to other disease-related concepts, COVID-19 is the only concept that is highly associated with a single term, an emotional one, i.e., fear . Fear (27%) is produced far more frequently than all other features. Interestingly, it is followed at a distance by two further emotional terms, danger and anxiety . This is in line with preliminary results 49 that showed through an analysis of linguistic networks of Italian hashtags performed during the initial phase of lockdown an intense and complex emotional pattern related to COVID-19 pandemics. We can better qualify this result by noticing that, differently from all other diseases, COVID-19 hardly evokes terms related to hospitals, cures, doctors, medicines. Aside from fear and death , the features produced are either superordinate terms ( virus , illness ), or terms related to the new phenomenon ( pandemic , contagion , quarantine ). TUMOR also evokes fear , but it also yields the words hospitals and cures . This is not the case for COVID-19, where fear is likely associated with its mysterious nature, and especially to the fact that little is known as to how to deal with it. Interestingly, the network analysis suggests that fear is also related to scary scenarios owing to changes in social relations ( solitude , quarantine ). The emotional activation is likely linked mostly to its vagary and indeterminacy, to the feeling of not knowing how to deal with it, and to its social consequences, rather than to its strong association with death —differently from what happens with TUMOR.

Second, we found evidence suggesting a possible modification of the semantic field of diseases, as a consequence of the introduction of a novel concept such as COVID-19. Although we have no data collected before the spread of Covid-19 to perform a comparison, the correspondence analysis shows that COVID-19 has an important weight on both Dimension 1 and 2. The introduction of COVID-19 seems to modify the distinction between severe diseases, such as TUMOR, and mild ones, such as FLU. COVID-19 is conceived as less mortal than TUMOR but as more mortal than FLU. Compared to TUMOR, but also to FEVER and FLU, people represent COVID-19 as more contagious and as endowed with significant social consequences, such as quarantine .

Third, the emergence of the concept of COVID-19, led not only to change the semantic space of diseases but also to the restructuring of patterns of conceptual relations activated by associated concepts, such as those of DISEASE, VIRUS, and FEVER. Specifically, when asked to list their features, participants produced features explicitly and unequivocally elicited by COVID-19, such as mask and pneumonia . Such modifications are less present, or less clearly apparent, for concepts characterized by a more established and stable representation, like TUMOR and FLU, even if the association with COVID-19 is likely to have triggered the feature fear , produced with FLU.

Even if the generalizability of our results is limited by the small-scale focus on the Italian sample in a specific timeframe, these findings provide an initial and illuminating picture of the perception of COVID-19 during lockdown. Our study clearly indicates the need for large-scale cross-cultural and longitudinal studies. In fact, another way to approach this topic would be analyzing natural language use (e.g., from social media 49 , or 55 ), as it has been done for other countries 56 . Other studies concerned with the perception of COVID-19 in the Italian lockdown have focused on large corpora derived from social media discourses (e.g. 49 , 57 ). However, to date our study is the first exploring this issue using a semantic fluency task, that while not detecting natural language use has the advantage of explicitly tapping into participants’ perceptions.

Conclusions

Overall, our results have implications for policies on COVID-19. Knowing how people represented it during the first wave can help politicians and scientists to operate during the possible following waves. Furthermore, they have wide implications for studies on concepts, and more specifically for research on conceptual flexibility. The pattern of associated features shows that we mainly represent the concept of COVID-19 in terms of one dominant emotion, i.e., fear . The correspondence analysis on COVID-19 and other associated concepts suggests that the introduction of this new concept led to restructuring the semantic field of diseases, as it is represented as contrasting both to mild and severe diseases. Finally, the pattern of features produced with the associated concepts reflects the influence of the pandemic situation in which participants were.

To conclude, we showed how people during the first Italian lockdown represented COVID-19, and how they understood it compared to other concepts in the ‘disease’ domain. The use of a free-listing method allowed us to tackle people’s perceptions directly. Our results highlight how rich a novel concept can be and even suggest that introducing a novel concept might rapidly modify previous knowledge, allowing us to appreciate the exquisite flexibility of our concepts.

Participants

Ethics permission was granted by the Ethics Committee of the Department of Dynamic and Clinical Psychology, Sapienza University of Rome (Prot. no. 000275—23/03/2020). All methods conformed to the Declaration of Helsinki. Before completing the survey, participants were informed of the general purpose of the study and provided informed consent.

A total of 74 Italian participants took part in the study in a window of time between April, 2nd and May, 14th 2020—i.e., in the initial phase of the first Italian lockdown. The questionnaire was implemented in Qualtrics. Participants were contacted via anonymous link either by posting the questionnaire on social networks (Facebook, Twitter) or spreading the questionnaire through the research team’s extended network of acquaintances. Originally we contacted 166 people, but 90 of them did not complete the questionnaire, likely because it required a long time (see below) and we allowed participants to interrupt and continue it later within a 3-days-time ( n  = 88). A small percentage of participants ( n  = 2, 1.2%) instead completed the task, but typed answers not congruent with what we were asking (e.g., swear words, symbols), so their responses were not considered for the analyses. From the remaining 76 participants, we excluded data from participants who indicated that their nationality was other than Italian ( n  = 2, 2.63% of the sample), as we were specifically interested in testing people sharing a common cultural milieu. The final sample is therefore composed of 74 participants (50 females, M age = 37.46; SD  = 12.46; 24 males, M age = 42.20; SD  = 14.64). All socio-demographic information collected is reported in Appendix A (see Supplementary Materials ).

Design and procedure

Participants took part in an online survey, divided into three sections. They completed the three sections in a fixed order. In the first part of the survey, participants completed the free-listing task. In the second part of the survey, participants were asked to complete four scales: the Interpersonal Reactivity Index (IRI) 58 , 59 , 60 , testing their general empathy; the Multidimensional Assessment of Interoceptive Awareness (MAIA) 61 investigating their interoceptive awareness; the Stereotype Content Model (SCM) 62 , that investigates the content of the stereotypes endorsed by individuals towards specific social groups; and the Generalised Anxiety Disorder-7 (GAD-7) 39 aimed at measuring the severity of Generalized Anxiety Disorder. Here we will focus only on GAD-7, because it is directly relevant to the purposes of this study. In the third part of the survey, we collected socio-demographic information. We asked participants to report their age, birth sex, level of education, profession, birth nation, city and region of provenience, current health condition, current way of living (confined or not confined to the house), frequency with which participants received information and news about COVID-19, and personal perceived risk to contract COVID-19 (see Appendix A, Supplementary Materials ).

In the first part of the survey, containing the free-listing task, participants were asked to list the first five words that came to their mind in relation to the words presented. We encouraged them to respond as quickly as possible, without spending too much time thinking about every single word. Participants typed their responses into separate text boxes for each target word.

The free-listing section was designed as follows: participants responded to a total of 96 target words, divided in 11 categories (e.g., emotional words, words referring to the body, to the family, institutional words) (see Appendix B, Supplementary Materials ), and randomly presented. To better distinguish target concepts and associated features, throughout the paper we will refer to the first ones with upper case letters, while the second ones will be given in italics. For the present study and analyses, we took into account only the six words of the “disease” category: COVID-19 , DISEASE , VIRUS , TUMOR , FEVER , and FLU. To select words of the “disease” domain we used the word COVID-19, with two superordinate terms of different level of generality (VIRUS, DISEASE), two coordinate concepts referring to more or less severe diseases (FLU, TUMOR) and a term referring to a symptom that characterizes a variety of diseases (FEVER).

Data availability

All data and scripts are available at https://osf.io/dsvm3/ .

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Acknowledgements

The authors would like to thank all the members of the BallaB, and in particular Chiara Fini and Federico Da Rold for insightful comments, and Giulia Andrighetto for providing access to the Qualtrics platform.

This study was funded by H2020-TRAINCREASE Project “From social interaction to abstract concepts and words: towards human centered technology development” CSA, Proposal no. 952324, P.I. Anna Borghi, and co-financed by the European Regional Development Fund (ERDF) and the Occitanie Region, convention number 182117SR.

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BallaB (Body, Action, Language Lab), Department of Dynamic and Clinical Psychology, and Health Studies, Sapienza University of Rome, Rome, Italy

Claudia Mazzuca, Ilenia Falcinelli & Anna M. Borghi

University of Montpellier-LIFAM, Montpellier, France

Arthur-Henri Michalland

Institute of Cognitive Sciences and Technologies, Italian National Research Council, Rome, Italy

Luca Tummolini & Anna M. Borghi

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All the authors contributed to develop and implement the idea of study. I.F. and A.H.M. prepared the questionnaires and collected the data. C.M., I.F., and A.H.M. pre-processed and analyzed the data. C.M. and A.M.B. drafted the paper, and all the other authors provided critical feedback. L.T. revised the paper, and provided constructive comments. A.H.M. provided helpful remarks on the final version of the text. All the authors have approved the final version of this manuscript.

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Mazzuca, C., Falcinelli, I., Michalland, AH. et al. Differences and similarities in the conceptualization of COVID-19 and other diseases in the first Italian lockdown. Sci Rep 11 , 18303 (2021). https://doi.org/10.1038/s41598-021-97805-3

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Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients

• Nina Van Goethem   ORCID: orcid.org/0000-0001-7316-6990 1 , 2   na1 ,
• Ben Serrien 1   na1 ,
• Mathil Vandromme 1 ,
• Chloé Wyndham-Thomas 1 ,
• Lucy Catteau 1 ,
• Ruben Brondeel 1 ,
• Sofieke Klamer 1 ,
• Marjan Meurisse 1 ,
• Lize Cuypers 3 ,
• Emmanuel André 3 , 4 ,
• Koen Blot 1 &
• Herman Van Oyen 1  

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SARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients.

A causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants.

A well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries.

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Each individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29 ). OSF project created on 18 May 2021.

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The pathogenesis of SARS-CoV-2 infection ranges from mild symptoms to severe respiratory failure [ 1 ]. Host factors play an important role in COVID-19 disease severity. Many studies have already identified older age and certain comorbidities, such as chronic immunocompromised conditions, chronic kidney disease, cardiovascular disease, diabetes mellitus, and obesity, as risk factors for hospitalization and mortality [ 2 , 3 , 4 , 5 ]. In addition, genetic association studies have identified several host genetic risk factors to become severely ill when infected by SARS-CoV-2 [ 6 ], including genetic variants in genes related to the immune system, such as the Human Leukocyte Antigens (HLA) gene complex [ 7 ] and cytokine genes, or in genes encoding human receptors of SARS-CoV-2 [ 8 ], such as ACE2 [ 9 ] and TMPRSS2 [ 10 , 11 , 12 ]. COVID-19 vaccines have proven to be highly effective against laboratory-confirmed SARS-CoV-2 infections and COVID-19 hospitalizations, severe disease, and deaths [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Whilst the vaccine effectiveness is shown at the population level, individual responses to vaccines will differ as a result of host factors and external factors [ 20 , 21 ]. Next to vaccination and other factors related to the host, severity of outcome can be influenced by aspects related to the healthcare organization and patient management [ 5 , 22 , 23 ].

Finally, severity of a SARS-CoV-2 infection could depend on the viral genetic characteristics. For other viruses such as influenza, it is well documented that viral genetic variation plays an important role in pathogenicity [ 24 , 25 , 26 , 27 , 28 ]. SARS-CoV-2, as other RNA viruses, evolves continuously via point mutations, deletions, insertions and possibly re-assortments resulting in an expanding phylogenetic diversity. This genetic diversity can lead to the emergence of new variants with specific characteristics. Most emerging mutations will not provide a selective advantage to the virus, however some circulating variants may have increased viral fitness and are consequently labeled as ‘Variant of Concern (VOC)’ [ 29 ]. When the emerging variant possesses a selective advantage, it may dominate other circulating variants as time goes by [ 30 ]. Whole-genome sequencing (WGS) of the SARS-CoV-2 genome has been extensively applied during the COVID-19 pandemic [ 31 , 32 ] and rapid public sharing of sequences allowed researchers to look at associations between SARS-CoV-2 genomic variants and disease severity [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. However, these analyses have often remained inconclusive due to small sample sizes, sampling biases, limited availability of detailed patient data and the inability to appropriately adjust for potential confounding factors. Given these encountered limitations that are inherent to observational studies based on real-life data, making causal claims is challenging and requires transparency of assumptions and corrections through study design and statistical analyses. All factors listed above should be studied within one framework as they all influence disease outcomes among COVID-19 patients.

In this manuscript, we present a conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. First, we construct a causal model in a general hospital setting and identify important confounders, assess potential selection bias and state the underlying assumptions. Next, the causal model is translated into data needs and we describe the structure of the data collection that is specific to the surveillance of COVID-19 in Belgium, as well as the corresponding architecture linking the different data sources which allows to combine information on the viral genome, host characteristics, vaccination status, clinical outcomes, and other factors such as healthcare organization.

Definition of exposure and outcome

SARS-CoV-2 genetic variation can be defined on different levels, being SARS-CoV-2 lineages, clades, protein-level mutations, or single nucleotide polymorphisms (SNPs). WGS, or at least Sanger sequencing of selected parts of the viral genome, should be performed to confirm infection with a specific variant. Alternative methods, such as diagnostic PCR-based assays, can also be used as an indicator or screening method for particular VOCs. However, results from the latter should not be over-interpreted as they only identify specific mutations, and fail in definitive confirmation of a VOC or non-VOC variant [ 42 ]. The genetic variants of interest will change over the course of the epidemic; yet, the study protocol can and should stipulate a priori hypotheses to avoid fishing expeditions. For simplicity, we will denote the exposure of interest as ‘variant’. Similarly, the outcome ‘severity’ is a broad denominator that should be clearly defined before starting any analysis. Examples of severity indicators among hospitalized patients are admission to an intensive care unit, the use of invasive ventilation or extracorporeal life support (ECLS), acute respiratory distress syndrome (ARDS), length of stay in the hospital, or mortality.

Description of the causal model

We use a Directed Acyclic Graph (DAG) to explicitly state the underlying assumptions that are made to estimate the causal effect of infection with a SARS-CoV-2 variant on COVID-19 disease severity among hospitalized patients [ 43 ]. The DAG represents both observed and unobserved random variables. Arrows in the DAG denote direct causal effect, while the absence of an arrow between two variables represents the assumption of no causal effect. More details on causal inference and basic DAG concepts can be found elsewhere [ 43 , 44 , 45 ]. The DAG in Fig.  1 comprises our qualitative causal assumptions using background knowledge based on the literature and expert opinion.

Conceptual framework to assess the causal relationship between SARS-CoV-2 variants and COVID-19 disease severity among hospitalized patients presented in a Directed Acyclic Graph (DAG). VARIANT = infection with a particular SARS-CoV-2 variant. SEVERITY = developing severe complications following SARS-CoV-2 infection. SES = socio-economic status

The probability of infection with a SARS-CoV-2 variant depends among others on the age, gender, ethnicity, host genetics, immune status, comorbidities, and socio-economic status (SES) of the patient [ 38 , 46 , 47 ]. These factors may also influence the severity of COVID-19 disease and can therefore be considered as common causes of the exposure-outcome relationship. Adjusting for these confounders is necessary in order to obtain an unbiased estimate of the effect of variant on severity. For example, if we assume that host genetics influence both the chance of being infected with a particular SARS-CoV-2 variant and the chance to develop severe complications, omission of this variable could potentially invalidate the conclusions. In the reality of an observational design, we will be faced with unmeasured factors and we will not be able to adjust for all variables that we consider as potential confounders.

Further, we assume that the increased circulation of a SARS-CoV-2 variant within the general population might be associated with a peak in cases [ 47 ] and a subsequent surge in hospital admissions due to increased transmissibility and/or severity, or due to coincidence [ 40 ]. Consequently, the circulation of a SARS-CoV-2 variant and a high occupancy rate of hospital beds reserved for COVID-19 patients are possibly linked to the stage of the epidemic (time) within a given geographical region (place of residence) [ 48 ]. We learned from previous analyses that overflow of recognized intensive care unit (ICU) beds negatively impacts the outcome of the patient [ 5 ]. In order to estimate the causal effect of a SARS-CoV-2 variant on severity, we therefore need to adjust the analysis for bed occupancy to avoid a spurious association between variant and severity that flows from variant via time or place of residence to bed occupancy and finally to severity.

In addition, the circulation of a variant can coincide in time with changes in treatment guidelines, protocols and experience. A spurious association between variant and severity can be prevented by adjusting for time, which could for example be defined by “wave”. This will block the backdoor pathway from variant to treatments/protocols/experience through time to severity. Previous analyses also showed that patient outcomes significantly vary between hospitals [ 5 , 49 ]. Patients are more likely to be admitted to a hospital in close proximity to their place of residence. The increased circulation of a variant could coincide in place with the location of a hospital. Therefore, a spurious association between variant and severity should be prevented by adjusting for place or adjusting for the hospital where the patient is treated.

Similarly as for time and place of residence, the circulation of SARS-CoV-2 variants may differ between settings that are relatively disconnected from each other. The risk to be infected with a particular variant within a healthcare setting, such as a hospital or nursing home, is potentially different from the risk within the community. Healthcare-associated COVID-19 infections have been shown to be associated with higher risk of clinical deterioration (requirement of ventilatory support, critical care or death), likely because hospitalized patients represent a frailer population with decompensated comorbidities compared to the community [ 23 ]. Consequently, the analysis should be adjusted for infections contracted within the hospital or within a nursing home.

Blocking non-causal associations

After identifying the sufficient set of variables from the DAG, we can remove the bias resulting from non-directed open paths by covariate adjustment in a regression analysis and subsequently identify the targeted causal path between variant and severity. Alternatively, we can consider balanced matching to force the distribution of the matching factors to be identical across the exposed and unexposed groups. Figure  2 represents a DAG of a possible matched cohort design within the conceptual framework where the exposed and non-exposed are matched based on similar levels of care, i.e. hospital and bed occupancy. The variable S indicates whether an individual in the source population is selected for the matched study. Most cohort matching starts with exposed individuals, i.e. those infected with an emerging SARS-CoV-2 variants, and subsequent selection of unexposed individuals with the same values for the matching values. As a results, the exposed are more likely to be selected into the matched sub-cohort as represented by the arrow from variant to S. Unexposed individuals that are not matched to the exposed are usually discarded from the analysis (i.e. analysis is restricted to the matched subset) and hence the study sample has proportionally more patients exposed to the emerging variant than in the source population. The arrow from the bed occupancy and hospital to S indicate that among the unexposed, individuals will be selected based on their values for bed occupancy and hospital. Matching (i.e. conditioning on S represented by a square box) induces an association via the path occupancy/hospital to S to variant that is of equal magnitude, but opposite direction, to the association via the path occupancy/hospital to variant (through time/place), ensuring that bed occupancy and hospital are both independent from variant in the matched sub-cohort. Because of the matching, the joint distribution of the matched data does not follow the causal structure for the source population as presented in the DAG, in the sense that the paths between variant and hospital and between variant and occupancy through time and place are no longer present in the matched sub-population (i.e. unfaithfulness [ 50 ]).

Blocking non-causal associations between SARS-CoV-2 variants and COVID-19 disease severity among hospitalized patients by the use of a matched cohort design presented in a Directed Acyclic Graph (DAG). S = selection into the study

Identification of selection bias

Even in the scenario in which it would be possible to adjust the analysis for all important confounders as depicted in the DAG (Fig. 1 ), there can still arise a spurious association between variant and severity due to selection bias (Fig.  3 ). WGS is not performed on the samples of all COVID-19 patients, but on a selection of them. Subsequently, only patients with known exposure, i.e. information on the SARS-CoV-2 variant that they are infected with, will be included in the study. Figure  3 A represents the situation in which the selection of samples for WGS depends on the severity of the patient, i.e. conditioning on the outcome, which is potentially problematic and is often denoted as sample truncation bias [ 51 ]. Severity is affected by the variant, as well as by other factors which are subsumed in an error term E. Indeed, severity is a collider variable (i.e. a common effect) on the path between variant and the error term E, and conditioning on severity induces a spurious association between variant and E in the selected sample, even if they are independent in the general population. As such, restricting the sample to severe patients results in a newly induced non-causal path that flows from variant to E to severity. Figure 3 B represents the situation when the total causal effect cannot be estimated in the case of conditioning (i.e. selection) on an intermediate variable. Due to analytic quality reasons it is recommended to only select samples for sequencing with a sufficient high viral load (≥10 3 –10 4 RNA copies/mL) [ 52 ]. Conditioning on samples with a high viral load blocks the causal path of variant on severity that is mediated by the viral load [ 53 ] leading to overcontrol bias [ 51 ]. In addition, conditioning on viral load may induce a spurious association between variant and severity when there exists an unmeasured confounder (U) of the relationship between viral load (i.e. the mediator) and severity (i.e. the outcome).

Conceptual framework to assess the causal relationship between SARS-CoV-2 variants and COVID-19 disease severity among hospitalized patients presented in a Directed Acyclic Graph (DAG) in the scenario of selection bias. CONFOUNDERS = all other confounders as listed in the DAG in Fig. 1 . E = error term. U = unmeasured confounders

Translation into data requirements

The causal model is translated into data requirements in order to meet the assumptions as depicted in the DAG (Fig. 1 ). In the context of the COVID-19 pandemic, Sciensano, the Belgian institute for health, has been mandated to describe the evolution of the epidemic and assess its consequences on the health of the Belgian population. Sciensano collects data on laboratory-confirmed COVID-19 cases, testing, hospitalized COVID-19 patients, COVID-19 deaths and COVID-19 vaccinations. These surveillance systems have in common that they channel data flows from one or more sources that reach Sciensano either directly or via an intermediate step. Amongst others, Sciensano has launched the LINK-VACC project, which aims at linking selected variables from existing registries for COVID-19 vaccine surveillance. The IT architecture of the LINK-VACC platform, hosted by the healthdata.be service of Sciensano, is used to organize data transfers to store and to link the different data sources based on the national registry number within a pseudonymized environment. The present framework takes place within the architecture of the LINK-VACC project which allows to meet the assumptions of the causal model by combining selected variables from multiple data sources merged on the individual patient level (Fig.  4 ). The protocol of the LINK-VACC project was approved by the medical ethics committee University Hospital Brussels – Vrije Universiteit Brussel (VUB) on 03/02/2021 (reference number 2020/523) and obtained authorization from the Information Security Committee (ISC) Social Security and Health (reference number IVC/KSZG/21/034).

Data linkage of existing COVID-19 surveillance registries within the context of the LINK-VACC project, Belgium

Data on hospitalized COVID-19 patients

Data on hospitalized COVID-19 patients is collected through two complementary surveillance systems [ 54 ]. The Surge Capacity Survey (SCS) exhaustively collects data on the number of COVID-19 patients at an aggregated level per hospital and enables calculation of the daily occupancy rate of beds reserved for COVID-19 patients per hospital accreditation number. The Clinical Hospital Survey (CHS) collects individual data of patients hospitalized with confirmed COVID-19 through an admission, discharge and detailed ICU form. The CHS is not exhaustive as participation is voluntary and covers approximately 65% of all hospitalized COVID-19 patients in Belgium. However, the CHS is considered to be representative as it covers all provinces in Belgium and includes public and private, academic and non-academic hospitals.

Data on COVID-19 test results

The COVID-19 TestResult Database, hosted by the Sciensano service Healthdata.be [ 55 ], collects the RT-PCR and antigen test results from clinical microbiology laboratories (CML) and physicians since the 5th of May 2020, including retrospective data collection since February 2020 [ 56 ]. The test results are accompanied by the date of sampling, the date of test result, the test result, the type of diagnostic test, a sample identification number, the laboratory identification number and patient demographic variables. Daily reporting of all test results of RT-PCR and antigenic diagnostic tests is mandatory for reimbursement.

Data on COVID-19 sequencing results

The National Reference Center (NRC) of respiratory diseases has put in place genomic surveillance at the national level since the first introduction of the virus, together with other university centers. The genomic surveillance of SARS-CoV-2 has scaled-up from December 2020 onwards resulting in a federal sequencing consortium currently including a total of 17 laboratories. Given the emergence of VOCs in December 2020, the samples selected for sequencing at that time resulted mainly from active surveillance focusing on returning travelers, atypical PCR results and large outbreaks. Subsequently, baseline surveillance was set up in January 2021 to obtain a representative sample of the positive cases. This is obtained through collaboration with a sentinel network of laboratories that send a proportion of their positive samples to one of the laboratories that are part of the sequencing consortium to ensure an optimal geographical coverage and a diversity of clinical severity patterns. The aim is to cover approximately 10% of all positive cases in Belgium. The baseline surveillance is complemented by targeted active surveillance that focuses on the systematic screening of patients experiencing re-infection, vaccine breakthrough cases, immunocompromised patients, and a selection of samples linked to outbreaks, returning travelers from red zones and atypical PCR results [ 52 ]. In addition, hospitals and laboratories are allowed to perform additional sequencing outside reimbursement indications on own initiative: some hospitals took the initiative to systematically sequence all available positive samples from patients admitted to ICU, while other hospitals aim to sequence exhaustively all available SARS-CoV-2 positive samples from their hospitalized patients.

The reporting of variant information from sequencing laboratories to Sciensano through h-Healthdata.be has been put in place since March 2021. For this purpose, an additional message (“LaboratoryTestResultVariants”) is to be sent to the Central COVID-19 Database [ 57 ]. After sequencing, the SARS-CoV-2 variants are assigned a Pangolin lineage, the nomenclature system for SARS-CoV-2 that has been put in place by Rambaut et al [ 58 ], and are subsequently designated as confirmed SARS-CoV-2 variants. Besides information on confirmed variants obtained through sequencing, the laboratories also report test results from diagnostic SNP assays to detect samples compatible with known VOCs. The collected information consists of the indication of the reason for which the sample was selected for sequencing (baseline or active surveillance), the mutations tested in case of an RT-PCR SNP assay, the mutations and deletions detected in the S gene, and in case of sequencing: the Pangolin lineage and the GISAID accession number.

Data on COVID-19 vaccines

As defined by the Belgian law, all COVID-19 vaccines administered in Belgium are recorded in Vaccinnet+, the national vaccine registry. Vaccination data are subsequently sent to Healthdata.be at Sciensano. Researchers at Sciensano have access to a pseudonymized version of these data including demographic data of the vaccinated person (age, gender and place of residence) and information on the administered vaccine (brand, lot number, administration date and registration date).

Other data sources

Within the LINK-VACC environment, data from existing registries outside Sciensano will also be linked on the individual patient level using the national registry number. Statbel, the Belgian statistical office, collects and shares data on the Belgian economy, society and territory. Individually-linked data will allow to retrieve socio-economic information (e.g. civil status, employment status and income decile), identify residents of collectivities, and obtain information on long-term survival and cause of death. The Intermutualistic Agency (IMA) collects data on reimbursed care and medications of citizens insured in Belgium and will enable to provide information on comorbidities and pregnancy status, but also give insights on long-term healthcare and medication consumption (e.g. after COVID-19 hospitalization). The Common Base Registry for HealthCare Actor (CoBRHA) enables to identify Belgian healthcare workers by profession type based on their license to practice.

Study population

The study population consists of hospitalized COVID-19 patients with an available admission form registered in the CHS and with information available on the SARS-CoV-2 variant (exposure) of their infection as obtained through the linkage of the CHS database with the COVID-19 TestResult Database (Fig.  5 ). The choice to study severity among hospitalized patients is supported by the detailed information on patient characteristics and the well-defined severity indicators available in the CHS data collection. Patient information related to positive cases is limited to demographics (age, gender, and residence) and is not suitable to study severity of disease. In addition, defining severity based on hospitalization itself is hampered by the fact that information on hospital admission is derived from the non-exhaustive registration of hospitalized COVID-19 patients in the CHS. As a consequence, absence of registration of a patient in the CHS does not imply that this patient was not admitted to the hospital.

Flow chart of the study population selection to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. CHS = Clinical Hospital Survey

Study design

The study is an observational multi-center cohort study where COVID-19 hospitalized patients are followed-up from hospital admission until death or hospital discharge and for whom information is obtained by merging different national surveillance systems based on the national registry number. Within the described framework, several research questions, study designs and analyses are possible and should be defined on a case-by-case basis depending on the requirements at a specific time of the epidemic.

Data analysis plan

The definition of the outcome will define the statistical model being used. A logistic regression or log-binomial model may be used for binary outcomes and a survival analysis for time-to-event data. Selection of main effects in the model will be based on the minimal sufficient adjustment set of covariates as identified from the DAG. The model with all main effects will be compared to models including interaction terms between the exposure and covariates or including non-linear terms for continuous covariates. Regression standardization may be used to estimate the causal effects of interest. Alternative to standardization, we may use inverse-probability weighting and doubly-robust methods [ 43 ]. The modeling approach should account for the clustering effects within hospitals, for example by matching on hospital or by using generalized estimating equation (GEE) models. Sensitivity analyses can be used to assess robustness to unmeasured confounding, selection bias or measurement error [ 59 ]. If a matching procedure is used, the matching factors do not require adjustment in the model as they are already accounted for by the design. Multiple imputation will be used for missing data on the outcome or potential confounders. As the emergence of VOCs in Belgium occurred from December 2020 onwards, patients admitted before 1/12/2020 are classified as being exposed to “previously circulating SARS-CoV-2 variants”. Vaccination rollout began in January 2021 and initially focused on nursing home residents and healthcare workers. From March 2021 onwards, people were vaccinated according to their age group. Therefore, patients admitted in the year 2020 are classified as non-vaccinated. A detailed data analysis plan will be anticipated for each specific research question addressing a particular exposure-outcome relationship. Each individual research project will be registered in Open Science Framework ( https://osf.io/zg3dj )

This manuscript presents a conceptual framework to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. A DAG was used to explicitly state the underlying assumptions of the causal model. Next, the DAG was translated into the data requirements, i.e. the necessary information on important confounders, that would allow to obtain an unbiased estimate of the causal effect of SARS-CoV-2 variants on severity of COVID-19 disease. The current framework takes advantage of the LINK-VACC architecture to combine information from existing surveillance registries on the viral genome, host characteristics, vaccination status, clinical outcomes, and other factors such as healthcare organization. As such, it allows to perform a cohort study among hospitalized COVID-19 patients, for whom information on the exposure, outcome and confounders is obtained by merging existing national surveillance systems based on the national registry number, to study the clinical impact of SARS-CoV-2 variants. Within the described framework, several research questions, study designs and analyses are possible and should be defined on a case-by-case basis depending on the requirements at a specific time of the epidemic.

The conceptual framework underlines important limitations of the current data architecture. First, not all confounders are correctly measured or even measured at all and the resulting bias should be quantified to assess robustness of the conclusions [ 59 ]. Host genetic variants may partially account for the residual variability in disease severity between patients after adjusting for other risk factors such as age and comorbidities [ 12 , 60 ]. Further, it is not unlikely that host genetic variants influence the probability to be infected with a particular SARS-CoV-2 variant given the importance of host-pathogen interactions [ 61 ]. Collecting information on the ethnicity of the patient may partially account for this bias [ 62 , 63 , 64 ], but residual confounding is likely to exist. Genetic profiling to discover patients’ increased susceptibility to life-threatening conditions caused by an infectious disease such as COVID-19 has been put forward as one of the priorities in the 1+ Million Genomes Roadmap. However, integration of omics data in public health remains limited given the challenges related to the generation, analysis and interpretation of high-dimensional data [ 65 ] and potential privacy and security concerns. Next to the absence of information on confounders that should be adjusted for, the analysis can also suffer from inaccurate measurements or partial information on certain confounders. For example, the occupancy rate of ICU beds is currently calculated based on the number of COVID-19 patients in the hospital and the number of recognized ICU beds available in the hospital reserved for COVID-19 patients. However, the latter also depends on the load of non-COVID-19 patients which may vary between hospitals and the different waves according to the directives in force. Information on the number of non-COVID-19 patients and their impact on total hospital or ICU capacity is less well documented. In addition, quality of care not only depends on occupancy rate, but also on the staff-to-patient ratio and the professional experience of healthcare workers.

Second, a potential threat for the current conceptual framework is the selection bias that arises when there is an increased focus on selecting samples for WGS from patients that have developed severe complications. As a consequence, the selection bias will have a strong effect on the representativeness as the study population will not represent the full spectrum of hospitalized COVID-19 patients. In addition, selection bias can induce collider bias (which occurs after conditioning on a common effect) which can lead to substantially biased estimates of associations [ 66 ]. This underlines the importance of obtaining a representative and unbiased SARS-CoV-2 sequence collection. Both the European Centre for Disease Prevention and Control (ECDC) [ 67 ] and the World Health Organization (WHO) [ 68 ] have provided guidance on representative sampling and sequencing of SARS-CoV-2 cases from routine surveillance. In Belgium, genomic surveillance consists of a passive component that aims to obtain a representative set of sequences based on a network of sentinel laboratories, and an active component, including targeting sequencing of vaccine breakthrough cases and a selection of samples from outbreaks and returning travelers. In addition, there has been an increased focus on sequencing samples from COVID-19 patients admitted to an intensive care unit. The data collection tool contains a variable “indication for WGS” which allows to differentiate between samples obtained from the active and passive surveillance arm and can hence be used to eliminate the selection bias. Even if there is no selection bias among the hospitalized patients, the current framework only allows to estimate the risk associated with a particular SARS-CoV-2 variant on severe clinical evolution once hospitalized. Estimating the effect of SARS-CoV-2 variants on disease severity among the general population of COVID-19 patients requires linkage with an exhaustive data source of hospital admission data based on the national registry number.

Here, we have established a framework to study the exposure-outcome relationship by merging data from routine national surveillance systems. Observational studies that aim to estimate a causal effect are often faced with an imbalance of baseline characteristics of patients between groups. Adjustment for confounding variables can be accomplished by including them in a regression model or by conducting a matched cohort study [ 69 ]. Matching aims to balance the groups with respect to factors which may influence the outcome. However, if the matching ratio is low, the matching design may suffer from loss of efficiency as the analysis is restricted to a subset of patients. Also, if interaction effects between the exposure and covariates are of interest and the objective of the study, these covariates cannot be used in the matching criterion.

The study population consists of a cohort of hospitalized COVID-19 patients that are registered in the CHS and with available SARS-CoV-2 variant information. The sample size is therefore limited by the presence of the patient in the two independent data sources. The advantage of this strategy is that re-using existing data sources avoids investing resources in setting up additional data collection systems. However, the ability to provide an answer to our research questions heavily depends on the features of the existing data sources. Given that the data collection system was not specifically designed for the requirements of the current analysis, it may result in potential threats to the causal model of interest, such as unmeasured confounding and selection bias. However, the anticipation of future research questions is a challenging task as research interests typically change over the course of a pandemic. This underlines the importance of the establishment of a versatile framework that allows researchers to efficiently combine data from different sources and which is flexible to be adjusted for different purposes. An alternative approach for the selection of samples for sequencing that would circumvent the selection bias and the relatively small sample size related to linkage of existing data sources is to conduct a nested case-control study within the cohort of hospitalized patients registered in the CHS. Several methods to sample controls can be applied. For example, one can randomly select a control each time a case is diagnosed. Another possibility is to sample the control group at the beginning of the follow-up period resulting in a sample that is representative of the full cohort from which all future cases will develop. This type of nested case-control study is usually referred to as a case-cohort study. If the controls are indeed a representative sample of the study base, the exposure odds ratio is a valid estimate of the incidence rate ratio one would obtain from a cohort study. In addition, a case-cohort design has the advantage that a single control group can be applied for multiple outcomes. A careful selection of cases and controls and the subsequent sequencing of the samples of these patients allows an efficient use of resources. Indeed, a nested case-control study design is particularly appealing when the assessment of the exposure is expensive. However, a retrospective selection for WGS requires the preservation of samples, i.e. by bio-banking or mid-term storage of the samples of all hospitalized patients.

As a final remark, statistical analyses under the proposed causal model require considerable sample sizes which can take a while to accumulate while a new variant emerges. After the arrival of a new variant, it takes several weeks before it becomes dominant and/or before it reaches the more vulnerable population that has a high risk of hospitalization. In addition, we should take into account the length of stay and reporting delay before the hospital discharge data is recorded in the surveillance system. As a consequence, rapid assessments of the clinical impact of new emerging SARS-CoV-2 variants among hospitalized patients is challenging. The uncertainty related to small sample sizes in the early phases of a new emerging variant should be acknowledged by researchers and policy makers in their communication to the public.

A well-established framework that brings together information from different domains and thereby provides a complete view on the factors that influence COVID-19 disease severity will enable to assess the impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. This conceptual framework is important as a theoretical foundation for the development of routine clinical epidemiological research and may serve as a basis for future pandemics. An evaluation and update of the framework should be conducted regularly in terms of emerging new viral, social or clinical trends or when a new data architecture allows for improved analyses.

Availability of data and materials

The data obtained through the surveillance systems are available from the corresponding author on reasonable request according to Sciensano scientific policy and after approval by the Belgian data protection authority.

Abbreviations

Angiotensin converting enzyme 2

Acute respiratory distress syndrome

Clinical hospital survey

cliNical microbiology laboratories

Common Base Registry for HealthCare Actor

Coronavirus disease 2019

Directed acyclic graph

European Centre for Disease Prevention and Control

Extracorporeal life support

General Data Protection Regulation

Generalized estimating equation

Global initiative on sharing all influenza data

Human Leukocyte Antigens

Intensive Care Unit

Intermutualistic Agency

Information Security Committee

Information Technology

Linking of registries for COVID-19 vaccine surveillance

National Reference Center

Ribonucleic Acid

Reverse Transcriptase–Polymerase Chain Reaction

severe acute respiratory syndrome coronavirus 2

Surge Capacity Survey

Single Nucleotide Polymorphism

Transmembrane Serine Protease 2

Variant of Concern

Whole-Genome Sequencing

World Health Organization

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Acknowledgements

We would like to sincerely thank all hospitals taking part in the surveillance and providing valuable information about hospitalized COVID-19 patients, greatly contributing to the management of COVID-19 in Belgium. We would like to thank all people in charge of the clinical microbiology laboratories for their collaboration and transfer of data. We would like to thank Johan Van Bussel, Kurt Vanbrabant, Andreas Gryncewicz, and other colleagues of Healthdata.be to set up the data infrastructure. Finally, we would like to thank Dieter Van Cauteren, Freek Haarhuis, and other colleagues at Sciensano for their valuable insights and feedback.

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Nina Van Goethem and Ben Serrien contributed equally to this work.

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Scientific Directorate of Epidemiology and public health, Sciensano, J. Wytsmanstraat 14, 1050, Brussels, Belgium

Nina Van Goethem, Ben Serrien, Mathil Vandromme, Chloé Wyndham-Thomas, Lucy Catteau, Ruben Brondeel, Sofieke Klamer, Marjan Meurisse, Koen Blot & Herman Van Oyen

Department of Epidemiology and Biostatistics, Institut de recherche expérimentale et clinique, Faculty of Public Health, Université catholique de Louvain, Clos Chapelle-aux-champs 30, 1200, Woluwe-Saint-Lambert, Belgium

Nina Van Goethem

Department of Laboratory Medicine, National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Herestraat 49, BE-3000, Leuven, Belgium

Lize Cuypers & Emmanuel André

KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory Clinical Bacteriology and Mycology, Herestraat 49, box 1040, BE-3000, Leuven, Belgium

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Conceptualization: N.V.G., B.S., M.V., K.B.; Project Administration: L.C., C.W.T.; Writing – Original Draft Preparation: N.V.G., B.S.; Writing – Review & Editing: all authors; Supervision: H.V.O. The author(s) read and approved the final manuscript.

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Van Goethem, N., Serrien, B., Vandromme, M. et al. Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. Arch Public Health 79 , 185 (2021). https://doi.org/10.1186/s13690-021-00709-x

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CONCEPTUAL ANALYSIS article

Covid-19, economic impact, mental health, and coping behaviors: a conceptual framework and future research directions.

• 1 School of Business Administration, Jimei University, Xiamen, China
• 2 Durham University Business School, Durham University, Durham, United Kingdom

The COVID-19 pandemic has caused serious economic and social consequences. Recent research shows that the pandemic has not only caused a physical health crisis but also caused many psychological and mental crises. Based on the contemporary cognitive-behavioral models, this article offers a conceptual analysis of how the pandemic affects individual mental health and coping behaviors from the perspective of individual economic status, individual context, and social context. The analysis shows that (1) the pandemic has led to increased economic uncertainty, increased unemployment and underemployment pressure, increased income uncertainty, and different degrees of employment pressure and economic difficulties; (2) these difficulties have stimulated different levels of mental health problems, ranging from perceived insecurity (environmental, food safety, etc.), worry, fear, to stress, anxiety, depression, etc., and the mental health deterioration varies across different groups, with the symptoms of psychological distress are more obvious among disadvantageous groups; and (3) mental health problems have caused behavior changes, and various stress behaviors such as protective behaviors and resistive behaviors. Future research directions are suggested.

Introduction

The current COVID-19 pandemic is still ongoing, and it is concerning that we still do not know how long it will last and what long-term effects it will have. Despite the successful development of vaccines, the medical capacity to completely treat this disease is still limited. Non-pharmaceutical interventions (NPIs), such as increasing handwashing, reducing physical contact, wearing masks in public places, maintaining social distance, quarantine, and isolation, are still the main strategies for handling this pandemic ( Van Bavel et al., 2020 ; Gössling et al., 2021 ). The social and economic consequences of the pandemic are devastating: almost half of the global workforce is at risk of losing their livelihoods, tens of millions are at risk of falling into extreme poverty, and millions of companies are facing existential threat ( Alauddin et al., 2021 ). In addition to the pandemic itself, the economic impact of the crisis brings heavy psychological stress to individuals, causing mental health problems, and may trigger long-lasting behavior changes. Other pandemic-related factors may also cause psychological distress, including mandatory use of face masks ( Wang et al., 2020a ), lockdowns ( Le et al., 2020 ), lack of access to medical services ( Hao et al., 2020 ; Tee et al., 2021 ), dissatisfaction with health information ( Tee et al., 2021 ), perceived discrimination ( Wang et al., 2021 ), and stress about returning to work ( Tan et al., 2020 ).

Prior behavioral science research focuses on perceived threats, stress, and coping ( Van Bavel et al., 2020 ). In the early stages of the pandemic, the physical health risks associated with the COVID-19 pandemic have received extensive attention from the academic community ( Mehta et al., 2020 ; Odayar et al., 2020 ), and there is growing research attention on the risks of mental health associated with the spread of the pandemic ( Auerbach and Miller, 2020 ; Xiong et al., 2020 ; Wang et al., 2020a ). The focal attention since the outbreak of the pandemic has been the psychological distress as a result of the pandemic itself ( Jungmann and Witthöft, 2020 ) or the adverse economic impact of the pandemic ( Bierman et al., 2021 ). However, it is still unclear how the pandemic control measures cause mental health problems through economic impact ( Murakami et al., 2021 ). Many scholars believe that the measures taken during the pandemic may cause people to suffer more economic losses and fall into economic difficulties, thereby causing serious mental health problems ( Timming et al., 2021 ), while some scholars believe that although the pandemic may cause huge economic losses, people’s mental health status has not decreased ( Murakami et al., 2021 ). Therefore, it is necessary to conduct a conceptual analysis of the economic impact of the pandemic and mental health by synthesizing the relevant findings in existing literature ( Ali et al., 2021 ).

This study aims to develop a conceptual framework linking the pandemic to individual economic problems, unemployment, mental health, and behavior change. The main research questions are (1) what kind of individual economic stress has the pandemic caused? 2) what mental health problems have this individual economic stress caused, and to what extent? 3) does the mental health problem vary by different groups or individuals? 4) what kind of behaviors may be caused by the deterioration of mental health?

Theoretical Background

According to the World Health Organization, mental health includes subjective well-being, self-efficacy, autonomy, ability, intergenerational dependence, intellectual or emotional potential. When there is a problem with mental health, there will be a decline in subjective well-being and various negative emotions (such as fear, nervousness, loneliness, and despair), and symptoms such as mental distress (such as anxiety, depression, and stress) will appear ( Hossain et al., 2020 ). Mental health issues are considered as public health problems that are often affected by factors related to occupation, employment opportunities, and economic stress ( Ali et al., 2021 ). Many scholars have examined the impact of economic poverty and unemployment on mental health ( Jin et al., 1997 ). Disaster mental health research also shows that people generally suffer emotional or psychological distress following a disaster ( Pfefferbaum and North, 2020 ).

Mental Health Amid the Pandemic

The World Health Organization (2020) proposes mental health indicators for the COVID-19 pandemic: painful symptoms and perceived danger. Mental distress is a short-term state of emotional distress, often driven by limited resources to manage stressors and daily life needs ( Patel and Rietveld, 2020 ). The pandemic can become a major source of stress, especially in chronic anxiety and financial stress ( Van Bavel et al., 2020 ). Mental distress has become the focus of research on mental health problems amid a large-scale crisis ( Cheng et al., 2004 ; Wang et al., 2020b ). Preliminary evidence suggests that symptoms of anxiety, depression, and self-reported stress are common psychological responses to the pandemic ( Rajkumar, 2020 ). Salari et al. (2020) reported that the prevalence of stress was between 29.6 and 33.7%. In addition to mental distress, the pandemic and corresponding interventions or preventive measures may make people feel insecure, fearful, uncertain, lonely, or isolated ( Auerbach and Miller, 2020 ), which exacerbates the psychological distress ( Pfefferbaum and North, 2020 ).

Public Health Interventions

Non-medical interventions or control measures during the pandemic may weaken social relationships that can help people to regulate emotions, cope with stress, and maintain adaptability ( Rimé, 2009 ; Jetten et al., 2017 ; Williams et al., 2018 ), exacerbate feelings of loneliness and isolation ( Hawkley and Cacioppo, 2010 ; Holmes et al., 2020 ), and become a risk factor for more serious mental health disorders ( Cacioppo et al., 2006 ). The stresses experienced during the pandemic, especially the economic stress, may cause difficulties in interpersonal relationships, destroy psychological resources, and make normal interactions difficult ( Karney, 2020 ). The impact of the pandemic interventions on mental health vary across different (employment) groups.

Contemporary Cognitive-Behavioral Models and Mental Health

The contemporary cognitive-behavioral models ( Taylor and Asmundson, 2004 ; Asmundson et al., 2010 ) explore the key role of traits, triggering events, cognition, and behaviors in the development and maintenance of health anxiety, which can be used to analyze mental health problems during the pandemic period. Jungmann and Witthöft (2020) believe that during the pandemic, idiosyncratic health anxiety regulates the relationship between excessive online information search and viral anxiety, and adaptive emotions serve as a buffer between the two. The “Role Tension” model explores mental health issues from the perspective of role conflicts. It believes that individuals with multiple social roles may experience role conflicts, resulting in stress and adverse mental health ( Oomens et al., 2007 ). The broader behavioral immune system theory ( McKay et al., 2020 ) explores the specific path of disease anxiety, and believes that disgust tendency and sensitivity, and emotional response are all part of the behavioral immune system.

Conceptual Framework

Figure 1 summarizes the themes from recent research findings in a conceptual framework.

Figure 1 . Conceptual framework.

The Mechanism of COVID-19’s Impact on Mental Health

In addition to the pandemic itself, the lockdown, quarantine, or self-isolation policies that aim at fighting the pandemic, the involuntary underemployment or unemployment have led to individuals’ economic difficulties and mental health problems of varying degrees for many people. The economic impact on individuals seems to further exuberate the suffering from the pandemic ( Bierman et al., 2021 ).

Employment Uncertainty

Employment problems caused by the pandemic include involuntary unemployment ( Piltch-Loeb et al., 2021 ), involuntary underemployment ( Pierce et al., 2020 ; Ferry et al., 2021 ), employment uncertainty and insecurity ( Wilson et al., 2020 ), job instability or inability to work ( Sirviö et al., 2012 ), and others. Studies have shown that involuntary underemployment and/or unemployment are related to poor mental health ( Dooley et al., 2000 ; Pharr et al., 2012 ), especially those who are unemployed during economic crises or recessions ( Uutela, 2010 ; Drydakis, 2015 ; Fiori et al., 2016 ). It is reported that crisis-related unemployment has led to a sharp rise in psychological disorders in low- and middle-income countries ( Uutela, 2010 ). Despite the government measures to limit the economic impact, the involuntary underemployment or unemployment caused by the epidemic is prominent.

The impact of the pandemic on mental health varies ( Pierce et al., 2020 ). Long-term unemployed people are most vulnerable to adverse mental health effects ( Pierce et al., 2020 ), and those who were employed and retired in the months before the pandemic experience worse than expected mental health conditions ( Ferry et al., 2021 ). Reduced work has different effects on the mental health of different groups. People who are in a poor health condition or self-isolated, and those who have their work reduced due to care responsibilities, have a higher degree of psychological distress ( Ferry et al., 2021 ). The higher the work insecurity caused by the pandemic, the more severe the symptoms of depression ( Wilson et al., 2020 ). As the pandemic continues, the fear of the pandemic itself has not increased mental health problems, but the deterioration of the labor market and the increase in the unemployment rate may intensify people’s fear of unemployment, thereby increasing the degree of mental distress ( Timming et al., 2021 ). In addition, due to the lockdown, people’s work routines can be broken. Remote work, interruption of work activities due to lockdown measures, or increased workload due to the needs of the pandemic may also become factors affecting mental health ( Rossi et al., 2020 ).

Economic Uncertainty

The analysis of individual economic distress during the pandemic usually focus on short-term economic distress or economic stress, such as personal income uncertainty, personal financial difficulties, salary reduction and other economic (income) losses ( Piltch-Loeb et al., 2021 ), as well as the expected long-term financial impact, such as depletion of savings and/or retirement funds ( Piltch-Loeb et al., 2021 ).

There are two possible ways in which economic distress mediates the impact of the pandemic on psychological distress. One is the economic hardship or economic threat triggered by the pandemic itself. Individual economic loss, economic hardship, or economic threat was significantly associated with mental health ( Ali et al., 2021 ). The pandemic has led to increased risks of depression, anxiety, stress, despair ( Pettinicchio et al., 2021 ), insomnia ( Hossain and Ali, 2021 ), and other common mental health problems. The negative relationship between economic distress and mental health may be a cumulative process. As exposure to distress extends, the average level of individual sufferings increases ( Bierman et al., 2021 ). At the later stage in the pandemic, economic-related anxiety may be a major predictor of psychological distress ( Timming et al., 2021 ).

Second, the unemployment and employment transition triggered by the pandemic affects the financial situation, which in turn affects psychological distress ( Thomas et al., 2007 ). The economic recession triggered by the pandemic and the increase in economic uncertainty has led to business bankruptcy or downsizing, increased involuntary underemployment or unemployment, increased uncertainty in personal income, and increased likelihood of individuals or families experiencing financial difficulties and economic pressure, consequently triggering large scale mental health problems ( Kimhi et al., 2020 ).

Coping Behaviors

The direct consequence of the pandemic’s impact on mental health is the change of personal behavior and habits. Studies on past epidemics and pandemics have shown that negative emotions such as anxiety and stress during the epidemic may lead to different behavior patterns.

Positive Defensive Behavior

Humans are born with a set of defense systems against ecological threats ( Mobbs et al., 2015 ). The main emotional response during a pandemic is fear. When people feel capable of responding to the threat of the pandemic, fear can cause individual behavior changes, but if people feel powerless, a defensive response occurs. Positive defensive behavior includes protective, defensive (avoidance), and substitution behaviors.

Protective Behavior

Mental health problems, such as high anxiety, during the epidemic may produce protective behaviors or compensatory behaviors ( Wheaton et al., 2012 ), including washing hands frequently, wearing masks, increasing cleaning of items, social distancing, and other restrictions. Protective behavior can be voluntary ( Rubin et al., 2009 ) or compliant with government regulation ( Fragkaki et al., 2021 ). In addition, people actively engage in physical activities to cope with stress and anxiety ( Ai et al., 2021 ).

Defensive (Avoidance) Behavior

Such behavior includes avoiding touching public goods, strangers, keeping a distance from “patients,” avoiding densely populated places and public transport ( Rubin et al., 2009 ), or even resigning from jobs that are perceived to be dangerous ( Yin and Ni, 2021 ).

Substitution Behavior (E.g. Using Technologies)

Service provision based on digital and artificial intelligence technology has become a possible solution to replace human service provision ( Nayal et al., 2021 ), triggering changes in consumer behavior by using technology-mediated services (such as robots) to replace manual services ( Kim et al., 2021 ).

Negative Resistance or Disruptive Behaviors

Resisting behavior.

People with low economic status are more likely to be vigilant about the public health information they receive are less willing to take recommended safety measures and may be more susceptible to “fake news” ( Van Bavel et al., 2020 ). Misunderstandings and worries about the pandemic may also cause the public to refuse to comply with preventive measures ( Prati et al., 2011 ). When people are less worried about the pandemic, they are less likely to engage in hygiene behaviors (such as washing hands), comply with social distance regulations, or be vaccinated if vaccines are available ( Taylor, 2019 ). People also resist or refuse to participate in protective actions proposed by the government when they maintain an optimistic bias about the consequences of the outbreak ( Fragkaki et al., 2021 ).

Panic Consumption Behavior

During the pandemic, a large number of customers stocked up on daily necessities to avoid the expected future threat due to uncertainty and panic arising from perceived scarcity, resulting in panic buying ( Omar et al., 2021 ). People flooded hospitals and clinics unnecessarily when they misunderstood their minor illness as a sign of a serious infection ( Asmundson and Taylor, 2020a , 2020b ).

Negative Idleness or Sabotage Behavior

Anxiety is an important driving force of behavior ( Taylor, 2019 ). Overly anxious individuals may engage in socially disruptive behaviors, especially for frontline service workers who are directly exposed to the outbreak (e.g., hotel staff), and may result in negative idleness (e.g., tardiness and absenteeism) or even disruptive behaviors or sabotage ( Karatepe et al., 2021 ).

Excessive Stress Behavior

Anxiety and depression caused by the economic difficulties and employment difficulties caused by the crisis may result in various excessive stress behaviors, such as alcoholism ( Ahmed et al., 2020 ) drug abuse ( Nagelhout et al., 2017 ), even suicide ( Milner et al., 2013 ), etc.

The Boundary Conditions

Sociodemographic factors.

The impact of economic or employment difficulties caused by the pandemic on mental health may be related to socio-demographic factors, including age, gender, ethnicity, family size, occupation, and income ( Ferry et al., 2021 ). Age is one factor. Young people are more likely to have a higher level of anxiety and stress due to the pandemic and corresponding intervention measures than the elderly ( Mann et al., 2020 ; Salameh et al., 2020 ; Hu and Qian, 2021 ; Ribeiro et al., 2021 ). Young people with mental health problems are especially likely to experience adverse health, well-being, and employment outcomes with long-term consequences ( Bauer et al., 2021 ). However, there are also arguments that the elderly may have greater financial difficulties due to the increase in medical expenses during the epidemic, which may trigger mental health problems ( Van Bavel et al., 2020 ), and the elderly’s negative health consequences have been long-term ones ( Van Bavel et al., 2020 ).

Gender is another one. Studies have shown that women are more likely to have higher levels of anxiety and stress when faced with possible physical health problems ( Salameh et al., 2020 ; Ferry et al., 2021 ; Ribeiro et al., 2021 ). However, when there is the fear of losing their job and the economic anxiety surrounding this possibility, the psychological distress level is more serious for male than female employees ( Timming et al., 2021 ). The third factor is ethnicity. Black and ethnic minority respondents have a higher level of economic anxiety ( Mann et al., 2020 ). The study by Timming et al. (2021) shows that, compared with non-Hispanic respondents, Hispanic respondents are significantly more anxious about losing their jobs. The fourth factor is family size and the number of children. Respondents from families with no children have lower levels of economic anxiety ( Mann et al., 2020 ). People living non-marital life have higher levels of psychological distress ( Ferry et al., 2021 ).

Occupation is the fifth factor. People working at the emergency and customer-facing services, such as doctors, medical staff, police forces, frontline volunteer organizations, and bankers, have a higher risk of infection and subsequent mental stress ( Shammi et al., 2020 ). The mental health of the unemployed, self-employed, and private professionals is worse than that of government professionals ( Ali et al., 2020 ) for increased income (or economic) uncertainty caused by the pandemic ( Patel and Rietveld, 2020 ) or for self-isolation or social distancing measures ( Auerbach and Miller, 2020 ).

The sixth factor is income status. Most studies show that economic hardship resulting from the pandemic may make those disadvantaged groups (e.g., those living in poverty, low-income families, homeless, and refugees) the most vulnerable to experience the corresponding negative consequences ( Van Bavel et al., 2020 ; Długosz, 2021 ; Hu and Qian, 2021 ). The mental health of people with disabilities and chronic diseases ( Pettinicchio et al., 2021 ), living alone, and socially marginalized people is even more hostile ( Kwong et al., 2020 ). However, some studies have suggested that the pandemic has a greater impact on the mental health of employees from high-income families ( Ferry et al., 2021 ).

Personality Traits and Psychological Conditions

Personality traits and psychological conditions play an important role in the formation of mental health. Fisher et al. (2021) suggested that depressed and anxious psychological states during the epidemic were associated with diminished energy, functional efficiency, optimism, creativity, engagement, and the ability to focus and solve problems, all of which are necessary for social and economic participation. During the pandemic, those with low collective self-esteem, low responsibility, and low openness to experience have higher levels of economic anxiety, as do those with high levels of neuroticism, perceived vulnerability to illness, and attribution from large group activities ( Mann et al., 2020 ). People with mental and physical health conditions may have higher levels of depression and anxiety because they are more likely to be unemployed and are prone to have higher levels of depression and anxiety ( Hao et al., 2020 ; Kwong et al., 2020 ; Jung et al., 2021 ). Extreme loneliness is the main cause of psychological distress ( Mikocka-Walus et al., 2021 ).

Emotional responses are part of the behavioral immune system. McKay et al. (2020) suggested that emotional reactions such as aversive tendencies and sensitivities are moderators of people’s disease sensitivity and anxiety. High perceived risks, especially economic risks, are significantly associated with less positive emotions and more negative emotions, leading to more severe mental health problems ( Han et al., 2021 ). The “optimism bias” may help individuals to avoid negative emotions ( Van Bavel et al., 2020 ); however, it may not be conducive for people to engage in behavior change in response to non-pharmacological interventions while individuals with high levels of anxiety and high perceived severity are more likely to be involved in behavior change ( Fragkaki et al., 2021 ).

External Environment

The complex factors of population density, health care capacity, limited resources and existing poverty, environmental factors, social structure, cultural norms, the number of people already infected, and the rapidly occurring community transmission of COVID-19 virus in a country or region can all contribute to public fears, which may lead to higher levels of mental health problems ( Shammi et al., 2020 ).

Level of Economic Development or Socio-Economic Crisis

People in low- and middle-income countries may have higher levels of stress, anxiety, and depression than those in high- and middle-income countries ( Tee et al., 2021 ). In lower-middle-income countries with socio-economic crises, political instability, dense population and limited resources, the stress and anxiety during the pandemic are high ( Salameh et al., 2020 ). Even in high-income countries such as Canada and the United Kingdom, deterioration in mental health has been reported, and are increasing along with the extension of the pandemic period ( Zajacova et al., 2020 ).

Government Economic Intervention Policies or Welfare Policies

Policies that reduce economic stress (e.g., economic interventions such as emergency response benefits) may alleviate the level of mental health deterioration in the early stages of a pandemic by reducing economic hardship and making people less worried about their economic situation ( Zajacova et al., 2020 ). Vaccine-based interventions help to mitigate the economic impact of the outbreak ( Meltzer et al., 1999 ).

Future Research Directions

Mental health management, monitoring and preventive measures.

For policymakers, health authorities and health care professionals, it is very important to understand the impact of health anxiety on behavior. Future research should investigate the monitoring and preventive measures for different industries or different groups so as to help the government, service providers and employers understand the groups that should be given priority in mental health support ( Ferry et al., 2021 ) and better conduct mental health rehabilitation. More studies are needed to examine the risk assessment of the pandemic, reliable risk communication with risk groups, the establishment of a cross-departmental management task force, and other measures.

Social Protection Measures and Relief Programs

Social protection measures include daily demand provision and social support ( Jung et al., 2021 ) and cash transfer programs ( Bauer et al., 2021 ). Future research should examine how to effectively use social protection measures (or relief plans) to solve the short-term and long-term effects of economic uncertainty caused by large-scale epidemics or economic crises on mental health. First, it is necessary to study how to support individual and family cash transfer programs to support young people’s future life opportunities and break the vicious circle between mental illness and poverty that puts many young people at a disadvantage in socio-economic and mental health ( Bauer et al., 2021 ). Second, it is essential to study the physical and mental health of the most economically disadvantaged during economic downturns ( Holmes et al., 2020 ; Bierman et al., 2021 ), and specialized relief measures that target low-income populations ( Shammi et al., 2020 ). Third, future research should consider both material and social supports in the examination of social protection measures (or relief programs). Fourth, future research attention needs to be paid to employee assistance programs, with a particular focus on mental health support for male employees.

Intervention and Rehabilitation Measures

Interventions to reduce economic uncertainty and economic risks should be a focus of future research from two aspects. Future research can be conducted around three aspects: First, to examine how to cultivate an individual’s adaptive mentality to epidemics. Second, to explore individual resilience and psychological rehabilitation during and after a pandemic crisis ( Hjemdal et al., 2011 ). Third, to explore the use of online interaction for social and mental health support. During the pandemic, providing remote mental health services is very important ( Salameh et al., 2020 ). Future studies should examine online interactions to cultivate empathy and a sense of connection to enhance mental health ( Schroeder et al., 2017 ; Waytz and Gray, 2018 ).

Consequences of Mental Health

There are currently few studies on the behavioral consequences of mental health, and more research is needed to understand the behavioral consequences of mental health caused by the epidemic. For example, the current research mainly focuses on panic buying behavior, and other compensatory behaviors can be added in the future, such as increasing the number of purchased goods, increasing specific food consumption, online shopping, and so on. Another example is to understand how individual factors (including health anxiety) specifically affect people’s behavior in response to the pandemic ( Asmundson and Taylor, 2020b ). In addition, more research is required to examine the impact of the economic impact of the epidemic on the long-term behavior of individuals, especially stressful behaviors such as alcohol abuse, drug abuse, and suicide.

Impact of Macro-Environmental Factors

In different cultural contexts (e.g., collectivism vs. individualism), economic distress and non-interventional measures such as social distancing may have different effects on mental health. From an evolutionary psychology perspective, when a group encounters a collective threat, strict rules may help the collective to coordinate and survive ( Roos et al., 2015 ). In the face of a pandemic, a culture that is accustomed to putting freedom above safety can make community coordination difficult. However, currently there is little comparative research on mental health and behavior changes specifically for different cultures, and it is worthy of further thinking in the future.

Ethnic Group

People of different ethnic groups may have different attitudes and behaviors toward the epidemic. Further research is needed to examine the different responses of different ethnic groups to the epidemic ( Rubin et al., 2009 ). Moreover, ethnic groups may have different degrees of xenophobia due to fear of coronavirus, and more research is needed to understand the relationship between coronavirus phobia and coronavirus-related xenophobia, and the possible role of individual difference variables (e.g., susceptibility to disease) within an ethnic group ( Taylor, 2019 ).

Economic Development

Future research may examine the relationships between economic development and the impact of the pandemic on mental health based on the economic status of different countries, and explore solutions to the severe psychosocial health phenomena that may be caused by socio-economic crises in economically underdeveloped countries amid a large scale crisis.

Relatively little research is focused on how psychological distress caused by the pandemic varies across countries. Future studies can compare and analyze the differences in the level of psychological distress in different countries with different economic conditions. As countries have achieved varying degrees of success in controlling the spread of the COVID-19 virus ( Patel and Rietveld, 2020 ). Future research based on international data can further explore the level of psychological distress in countries where government interventions are relatively successful, in comparison with those countries that are not so successful.

Long-Term Effects

As the COVID-19 pandemic continues to evolve, the sources of psychological distress surrounding the pandemic and the degree of psychological distress may change ( Piltch-Loeb et al., 2021 ). The extant research mainly focuses on the early or short-term psychological impact of the pandemic. Long-term longitudinal research should be added in the future to investigate the sources of psychological and mental distress at different time points ( Magnavita et al., 2020 ). Although a large number of studies have found a positive relationship between the economic uncertainty (or difficulties) and mental health problems, other studies do not degree with the relationship between deteriorating mental health and the level of job insecurity and financial impact ( Kwong et al., 2020 ). Further empirical research is needed to understand the interrelationships among various antecedents and how different factors mediate or moderate the relationship between the pandemic and mental health.

This conceptual analysis article explores two mechanisms (i.e., economic distress and employment distress) that lead to the deterioration of individuals’ mental health. The proposed conceptual framework explains how the COVID-19 pandemic and public health interventions affect people’s mental health, the responding coping behaviors. The extant literature provides evidence supporting the hypothesis that the COVID-19 pandemic and its associated measures increase individual economic uncertainty and employment uncertainty, thereby triggering mental health problems and coping behaviors. The findings of most studies support this mechanism from the onset of the pandemic to the emergence of economic distress and employment distress, to the deterioration of mental health, and then to changes in people’s behaviors. Supportive evidence was found in different countries (e.g., the United States, China, Bangladesh, Italy, etc.) and in different groups (elderly, young, disabled, mentally ill, etc.).

Author Contributions

XL: conceptualization, methodology, and writing – original draft preparation. ZL: conceptualization and writing – reviewing and editing. All authors contributed to the article and approved the submitted version.

This research was supported by the Educational Commission of Fujian Province of China (grant no. JAS20129) and the Science Foundation of Jimei University, China.

Conflict of Interest

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

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Keywords: COVID-19 pandemic, economic difficulty, employment difficulty, mental health, coping behavior

Citation: Lu X and Lin Z (2021) COVID-19, Economic Impact, Mental Health, and Coping Behaviors: A Conceptual Framework and Future Research Directions. Front. Psychol . 12:759974. doi: 10.3389/fpsyg.2021.759974

Received: 17 August 2021; Accepted: 22 October 2021; Published: 11 November 2021.

Reviewed by:

Copyright © 2021 Lu and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhibin Lin, [email protected]

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The Societal Value of Vaccines: Expert-Based Conceptual Framework and Methods Using COVID-19 Vaccines as a Case Study

Affiliations.

• 1 Health Economics and Outcomes Research, Pfizer Inc., New York, NY 10017, USA.
• 2 Health & Value, Pfizer Co., Ltd., Tadworth KT20 7NS, UK.
• 3 Office of Health Economics, London SW1E 6QT, UK.
• 4 Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
• 5 Centre for Health Economics, Alcuin A Block, University of York, Heslington, York YO10 5DD, UK.
• 6 Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, London W2 1PG, UK.
• 7 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK.
• 8 Health Analytics, Lane Clark & Peacock, London W1U 1DQ, UK.
• 9 The Patients Association, PO Box 935, Harrow HA1 3YJ, UK.
• 10 Programme for Global Health, Royal Institute of International Affairs, Chatham House, London SW1Y 4LE, UK.
• 11 Evidence, Value and Access by PPD, Evidera, London W6 8BJ, UK.
• 12 Evidence, Value and Access by PPD, Evidera, H-1113 Budapest, Hungary.
• 13 Institute for Social and Economic Research and Policy, Graduate School of Arts and Science, Columbia University, New York, NY 10027, USA.
• PMID: 36851112
• PMCID: PMC9961127
• DOI: 10.3390/vaccines11020234

Health technology assessments (HTAs) of vaccines typically focus on the direct health benefits to individuals and healthcare systems. COVID-19 highlighted the widespread societal impact of infectious diseases and the value of vaccines in averting adverse clinical consequences and in maintaining or resuming social and economic activities. Using COVID-19 as a case study, this research work aimed to set forth a conceptual framework capturing the broader value elements of vaccines and to identify appropriate methods to quantify value elements not routinely considered in HTAs. A two-step approach was adopted, combining a targeted literature review and three rounds of expert elicitation based on a modified Delphi method, leading to a conceptual framework of 30 value elements related to broader health effects, societal and economic impact, public finances, and uncertainty value. When applying the framework to COVID-19 vaccines in post-pandemic settings, 13 value elements were consensually rated highly important by the experts for consideration in HTAs. The experts reviewed over 10 methods that could be leveraged to quantify broader value elements and provided technical forward-looking recommendations. Limitations of the framework and the identified methods were discussed. This study supplements ongoing efforts aimed towards a broader recognition of the full societal value of vaccines.

Keywords: COVID-19; COVID-19 vaccination; Delphi; expert consensus; health technology assessment; societal impact; vaccine value; vaccines.

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Conflict of interest statement

M.D.F., D.M., J.Y. are employees of Pfizer and may hold stock or stock options of Pfizer. K.M., J.R. and G.S. are employees of Evidera, which received financial support from Pfizer, Inc. in connection with the study and the development of this manuscript. Shailja Vaghela is an employee of HealthEcon Consulting, Inc. and an external consultant for Pfizer who received consulting fees from Pfizer in connection with the development of this manuscript.

Expert elicitation using a modified…

Expert elicitation using a modified Delphi method. HTA, health technology assessment; TLR, targeted…

Inclusion of value elements in…

Inclusion of value elements in key value frameworks. ACIP, Advisory Committee on Immunization…

Visualised Vaccine Value Framework.

Results of expert elicitation rounds.…

Results of expert elicitation rounds. * Experts were asked to choose the top…

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Mental Health Interventions during the COVID-19 Pandemic: A Conceptual Framework by Early Career Psychiatrists

Ramdas ransing.

a Department of Psychiatry, BKL Walawalkar Rural Medical College, Ratnagiri-415606, Maharashtra, India

Frances Adiukwu

b Department of Neuropsychiatry, University of Port Harcourt Teaching Hospital. East West Road, Alakahia, PMB 6173, Port Harcourt, Nigeria

Victor Pereira-Sanchez

c Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA

Rodrigo Ramalho

d School of Population Health, University of Auckland. Auckland-1142, New Zealand

Laura Orsolini

e Department of Clinical Neurosciences/DIMSC, School of Medicine, Section of Psychiatry, Polytechnic University of Marche, Ancona 60126, Italy

f Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Herts AL109AB, UK

André Luiz Schuh Teixeira

g Department of Childhood and Adolescent Psychiatry, Universidade Federal do Rio Grande do Sul. Porto Alegre 2350, Brazil

Jairo M. Gonzalez-Diaz

h CERSAME School of Medicine and Health Sciences, Universidad del Rosario – Clinica Nuestra Senora de la Paz. Calle 12C No. 6-25 - Bogotá D.C. Colombia

Mariana Pinto da Costa

i Unit for Social and Community Psychiatry, WHO Collaborating Centre for Mental Health Services Development, Queen Mary University of London, London E138SP, UK

j Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto, Portugal

k Hospital de Magalhães Lemos, Porto, Portugal

Joan Soler-Vidal

l Fidmag Research Foundation, Hermanas Hospitalarias, Barcelona, 08830, Spain

m Hospital Benito Menni CASM, Hermanas Hospitalarias, Sant Boi de Llobregat, Barcelona, 08035, Spain

n Medicine and Traslational Research Doctorate Programme, University of Barcelona, Barcelona, Spain

Drita Gashi Bytyçi

o Hospital and University Clinical Service of Kosovo, Community Based Mental Health Center and House for Integration, Prizren 20000, Kosovo

Samer El Hayek

p Department of Psychiatry, American University of Beirut, Bliss Street, PO Box: 11-0236. Riad El Solh, Beirut 1107 2020, Lebanon

Amine Larnaout

q Razi Hospital, Faculty of Medicine of Tunis, Tunis El Manar University, Tunis 2010, Tunisia

Mohammadreza Shalbafan

r Mental Health Research Center, Iran University of Medical Sciences, Tehran1449614535, Iran

Zulvia Syarif

s Department of Psychiatry, Tarakan General Hospital, Jakarta, 10150, Indonesia

Marwa Nofal

t Helwan Mental Health Hospital, Extension of Mansour St., behind Kbretaj Helwan Club, Helwan, 25562198 Cairo, Egypt

Ganesh Kudva Kundadak

u Early Psychosis Intervention Programme, Institute of Mental Health, Singapore 539747, Singapore

The emergence of mental health (MH) problems during a pandemic is extremely common, though difficult to address due to the complexities of pandemics and the scarcity of evidence about the epidemiology of pandemic-related MH problems and the potential interventions to tackle them. Little attention has been devoted so far to this topic from policymakers, stakeholders and researchers, resulting in a lack of replicable, scalable and applicable frameworks to help plan, develop and deliver MH care during pandemics. As a response, we have attempted to develop a conceptual framework (CF) that could guide the development, implementation, and evaluation of MH interventions during the ongoing COVID-19 pandemic. This CF was developed by early career psychiatrists from 16 countries that cover all the WHO regions. Their opinions were elicited via a semi-structured questionnaire. They were asked to provide their views about the current MH situation in their countries and to elaborate on existing 'myths' and misinformation. They were also asked to name the resources available and to propose solutions and approaches to provide accessible and affordable care. The CF was prepared based on the extant literature and the views discussed in this group; it illustrates the epidemiology of MH problems, preparedness plans, stage-specific plans or innovative solutions, opportunities to integrate those plans and possible outcomes at policy level. This CF can serve as a technical guide for future research regarding pandemics. It can be used to monitor trends and to optimize efforts, and to develop evidence based MH interventions. Still, further research focusing on the individual components of this framework is needed.

1. Introduction

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov2) virus and its associated illness, termed COVID-19, have led to a global health crisis of unparalleled proportions ( Wang et al., 2020b ). Deemed by the World Health Organization (WHO) as a pandemic on March 11th, 2020, COVID-19 has led to a plethora of follow-on effects, with one country after another coming under lockdown so as to bring the spread under control ( Iacobucci, 2020 ; Lau et al., 2020 ; Pulla, 2020 ). The outbreak and the attendant measures to limit its spread have resulted in many countries reporting increased mortality and morbidity rates whilst facing huge economic losses, social disruption and physical distancing. Importantly, it has also raised concerns about the potential for a widespread increase in mental health issues ( Dong and Bouey, 2020 ; Ho et al., 2020 ; Torales et al., 2020 ).

So far, there is a lack of evidence based guidelines and clear intervention plans, which may have limited the effectiveness and accessibility of interventions in this pandemic ( Jung and Jun, 2020 ; Lima et al., 2020 ). Many mental health professionals have attempted to provide primary mental health care (advisories, psychotherapy or counseling and pharmacological treatment) through their professional organization or as volunteers ( Dong and Bouey, 2020 ; Jung and Jun, 2020 ). These efforts have thus far not been well organized, stage-specific or directed towards the specific needs of the general population and health care workers (HCW) ( Dong and Bouey, 2020 ; Lima et al., 2020 ; Xiang et al., 2020 ).

A pandemic, as a form of a disaster is a complex and unpredictable situation that, may leave limited opportunities to conduct well planned, organized and controlled clinical trials ( Benight and McFarlane, 2007 ). Most of the published studies are cross-sectional in nature, phase-specific (i.e. either first phase of disaster or post-disaster phase) and limited to certain populations (e.g. health care workers) ( Brooks et al., 2020 ; Lai et al., 2020 ; Makwana, 2019 ; Math et al., 2015 ; Wang et al., 2020a ), which has limited value to develop comprehensive strategies to address the mental health issues during a pandemic. This could be due to the current theoretical framework or recommendation that fails to address the mental health needs of the affected population ( Math et al., 2015 ; Ornell et al., 2020 ; Purgato et al., 2018 ). The Young's theoretical framework is one of the commonly used frameworks for disaster research. It includes the heroic, honeymoon, disillusionment and restoration phases of a disaster, with preventive interventions, curative interventions and preparedness as treatment strategies ( Math et al., 2015 , 2008 ). However, it is not disaster-specific (e.g. earthquake, terrorist attack, infectious disease), so it may have limited value in the current pandemic. In addition, most national and international recommendations for mental health care during disasters are vertical in nature. These recommendations or frameworks do not consider the use of recently available digital technology and global plans for mental health ( Math et al., 2015 ). There is thus a need to reconsider the current theoretical frameworks and provide alternative approaches ( Benight and McFarlane, 2007 ; Dong and Bouey, 2020 ). However, thus far, limited attempts have been made to provide new or alternative frameworks for global and national disaster preparedness.

To address the limitations of previous theoretical or conceptual frameworks and to develop global evidence-based measures, we attempted to develop a conceptual framework to address mental health care during pandemics from an early career psychiatrists' perspective.

2. Material and methods

For this study, we invited early career psychiatrists (at least one) from each WHO region to share information related to their country and the COVID-19 situation in each of their nations. This team, connected through the Early Career Psychiatrist (ECP) Section of the World Psychiatric Association (WPA) ( Pinto da Costa, 2020 ), held group discussions via online messaging and conferencing platforms (email, WhatsApp and Zoom).

2.1. Phase-1: Preliminary assessment

This phase was directed towards assessing the current scenario of mental health and approaches (preventive and therapeutic, if any) adopted. The ECPs were requested to share information about their country (economic status, number of cases and deaths from COVID-19 at the time of writing), preparatory plans, measures (e.g. innovative, replicable or scalable) adopted or available for the future, as well as to discuss pre-existing infrastructure, ongoing training, and funding.

2.2. Phase-2: Development of an a priori conceptual framework

The lead (RR) and co-lead (FA) started developing a preliminary conceptual framework (epidemiology, interventions and outcomes) based on a comprehensive review of existing literature and the responses submitted by all the co-authors (acting as country representatives). The literature review was focused on identifying relevant existing information related to epidemiology, interventions, opinions, and recommendations. This information formed the basis of a preliminary conceptual model which we attempted to categorize as per the WHO’s pandemic preparedness plan ( Droogers et al., 2019 ).

2.3. Phase -3: Mental health preparedness and action framework (MHPAF)

An a Priori Conceptual Framework was shared with the country representatives for comments, suggestions and modifications and was modified iteratively based on consensus and feedback. The modified delphi method (i.e. consensus decision making (>70% representative agreeable)) was adopted for final inclusion.

2.4. Ethics

As there was no direct involvement of human participants or utilization of identifiable data, ethical approval from local institutional review boards was not necessary.

3.1. Baseline characteristics

All WHO regions were represented in this sample of early career psychiatrists. This sample included colleagues from five lower-middle-income countries (India, Nigeria, Indonesia, Egypt and Tunisia), four upper-middle-income countries (Colombia, Brazil, Iran and Lebanon), and six high-income countries (Italy, Spain, United States of America, Portugal, Singapore and New Zealand); one came from a country not recognized by the United Nations (Kosovo). All countries were in stage 5–6 as per WHO- global influenza preparedness plan (WHO-GIPP) and were reporting local transmission, except Nigeria (mainly imported) and Italy (mixed) ( WHO, 2020 ). The countries differed from each other in terms of the occurrence of the first case, number of confirmed cases, rates of mortality and measures adopted ( WHO, 2020 ).

3.2. A Conceptual Model of the Emotional Epidemic Curve

We attempted to construct a conceptual model (Emotional Epidemic Curve - EEC) to depict the possible change in emotional behaviour in different phases ( Fig. 1 ). To understand the emotional epidemiology of the pandemic, we should understand the transmission dynamics of COVID-19 (serial interval-3.96 days, doubling time and extent to control the epidemic) .( Svensson, 2007 ).

Emotional Epidemic Curve of the COVID-19 Pandemic in acountrywithoutadequatemeasures (mitigation) (A double peak phenomenon) .

Footnote: Illustrative simulations of an Emotional Epidemic Curve of the COVID-19 (red), Number of confirmed cases of COVID-19 (blue) & Number of deaths due to COVID-19 (black dotted curve), both curves may be the potential predictor of peak [i.e first peak (a) and second peak (c)] This is a qualitative illustration only, not a quantitative estimation.

3.2.1. Components of the Emotional Epidemic Curve

The EEC may have two-peaks as described below.

• a) First peak: It may be associated with inadequate communication, misinformation, myths and fake news. Rapid or exponential growth of COVID-19 cases may cause fear, distress, anxiety, depression, sleep disorders, panic attacks, adjustment disorders, and suicidal ideation and/or behavior ( Ho et al., 2020 ; Li et al., 2020 ; Lima et al., 2020 ).
• b) Dipping point: Some countries may show a dipping point, indicative of community resilience, with a rapid reduction of distress after the first peak.
• c) Second peak: This can be unpredicted and complex, occurring due to the death of loved ones, economic damage and marked social disruption. The predominant effects may be post-traumatic stress disorder, grief, depression and relapse of pre-existing mental health conditions ( Liu et al., 2020 ; Neria et al., 2008 ).

3.3. Mental Health Preparedness and Action Framework (MHPAF)

The WHO-Global Influenza Preparedness Plan (WHO-GIPP) neither mentions nor recommends the inclusion of a mental health component in the management of pandemics despite evidence that the mental health of the general population and health care workers is at risk ( WHO, 2005 ). The recently published report on pandemic preparedness in WHO Member States has not taken into account the need for a mental health response ( WHO, 2020 ). Therefore, we considered the opinions of different country representatives to develop stage-specific measures ( Fig. 2 ) pertaining to the emotional epidemic curve mentioned above. The framework was prepared in line with the WHO-GIPP ( WHO, 2005 ).

Mental Health Preparedness and Action Framework (MHPAF).

Foot n ote : a) Color of text box is similar to color of Phases on time line, b) Text color is similar to component (legend 2)Abbreviation: MHSS-Mental health surveillance system, HCW-health care worker (modified and developed as per WHO-GIPP Plan) .

3.3.1. Brief summary of the WHO-GIPP

This includes the description of the stages of the pandemic, the role of the WHO and the recommendations for before and during a pandemic ( WHO, 2005 , 2009 ). It classifies the pandemic in six phases and includes a post-peak and post-pandemic period. The phase 1–3 measures are directed towards increasing the health care capacity through training, development of infrastructure and a surveillance system, while phase 4–6 includes the coordination, support, response and community mitigation efforts. Community mitigation efforts are vital public response measures aimed to halt transmission of the influenza virus. They include social distancing (e.g. travel restriction, working from home, closing of school, city or country lockdown), masks, respiratory and hand washing hygiene ( WHO, 2005 ).

3.3.2. Components of MHPAF

The MHPAF is mainly focused on the development of a Mental Health Surveillance System (MHSS) to understand the epidemiology of mental disorders during the pandemic, as well as to develop possible measures directed towards reducing the burden of mental disorders and the use of appropriate technology. We have discussed these components in depth subsequently.

3.4. Probable Effect of MHPAF on the Emotional Epidemic Curve

In our opinion, MHPAF can address mental health issues more effectively than existing approaches. Real time data obtained through MHSS can help to prioritize interventions (e.g. population at risk, HCW), with dynamic modifications being made based on the changing needs of the population.

The effects can be categorized as follows:

COVID-19 Epidemic Curve: MHPAF along with WHO-GIPP may have an additive effect on flattening the epidemic curve, allowing more time to the first response and increasing the capacity or performance of the healthcare system ( Fig. 3 ).

Effect of MHPAF + WHO-GIPP on Epidemic Curve and Health Care System.

Footnote: Illustrative simulations of Number of confirmed cases of COVID-19 (blue)

Effect of inclusion of MHPAF + Measures to reduce transmission on Epidemic Curve of the COVID-19 (green), Effect of inclusion of only Measures to reduce transmission on Epidemic Curve of the COVID-19 (red) and Incease in overall capacity of health care system (green arrow).

( This is a qualitative illustration only, not a quantitative estimation. ) .

Emotional Epidemic Curve (EEC): MHPAF integrated with global influenza preparedness may reduce the height of the first and second peak of EEC, and may help in flattening of the epidemic cure, and thus may reduce the prevalence of post-peak or post-pandemic disorders ( Fig. 4 ). Together, the performance or capacity of the mental health system can be well preserved.

Effect of MHPAF + WHO-GIPP on Emotional epidemic curve.

Footnote: Illustrative simulations of an Emotional Epidemic Curve of the COVID-19 (blue), Number of confirmed cases of COVID-19 (dotted black curve), Additive effect of Mental health measures on both COVID-19 epidemic curve and Mental health curve. Health care system and mental health system can cope with volume of both group patients i.e. COVID-19 and mental health problems due to COVID-19.This is a qualitative illustration only, not a quantitative estimation .

4. Discussion

The paucity of organization, advanced preparation, and a lack of cross-disciplinary collaboration and integration are major hurdles that need to be overcome in creating a comprehensive and effective pandemic related mental health response ( Dong and Bouey, 2020 ; Ornell et al., 2020 ). In addition, there needs to be a greater consideration of crucial parameters such as the aforementioned emotional epidemic curve. The MHPAF was focused to address most of these – it may thus provide a dedicated, sustainable, accessible, scalable and affordable mental health intervention.

4.1. Conceptual model of emotional epidemic curve of pandemic

It is noted that in the midst of a pandemic, people may experience an ‘emotional contagion’ (i.e., the spread of mood and affect through populations by the direct induction of emotions) ( Hatfield et al., 1993 ). The fear and distress experienced by one person may be unconsciously mirrored by others, thus leading to a spread of these emotions throughout society. In our deeply interconnected and digitalized world, these emotions may spread far more easily, as dire news from one corner of the world can influence people in another distant country by a myriad of news outlets, social media networks, videos and chat rooms ( Gao et al., 2020 ). As a consequence of this, we may expect first peak of EEC in the community. There may also be an increase in the rates of relapse and recurrence of mental disorders in those already living with mental illness ( Torales et al., 2020 ). Furthermore, the stigma against mental health disorders, and inadequate mental health-related infrastructure, serve as additional barriers for those who are distressed to obtain help. This conceptual model is well supported by the existing literature ( Ho et al., 2020 ; Li et al., 2020 ; Lima et al., 2020 ; Liu et al., 2020 ; Neria et al., 2008 ). The time interval between the two peaks will be dependent on country-specific measures and mitigation efforts. We note however, that in this model, we have not considered the appearance of the second wave of COVID-19 re-infection. In such case, a second wave of EEC may have a more heterogeneous mixture of cases. It is thus imperative that this emotional contagion is prepared for by stakeholders at both national and international levels.

4.2. Conceptual framework for Mental Health Preparedness and Action Framework (MHPAF)

The five components of the preparedness and action framework are closely interlinked with each other; therefore, inadequate preparation of one component can affect the success of mental health interventions before, during and after a pandemic.

4.2.1. Preparation and coordination

In the early phase of a pandemic, this component should be directed towards preparing infrastructure (e.g. Mental Health Surveillance System (MHSS) and telepsychiatry), training of volunteers and health care workers, and creating materials to disseminate during each phase, for example psychological first aid (PFA) materials, which can act as a psychological personal protective equipment (PPE).

4.2.1.1. Mental Health Surveillance System (MHSS)

A MHSS enables systematic data collection, analysis, interpretation and the timely dissemination of the data to those responsible for prevention and control of the epidemic ( Nsubuga et al., 2020 ). Unfortunately, at present, there is no active MHSS in the sampled countries, except in Iran, Italy and Tunisia. This MHSS must be strengthened to increase the likelihood of early detection and the tracking of mental health issues. Continued virtual support and other innovative surveillance strategies (e.g., mobile app trackers and other mobile apps, screening tools) will benefit the public during a pandemic (considering the varying degrees of movement restriction and social distancing enforced) ( Keesara et al., 2020 ; Li et al., 2020a ; Xiang et al., 2020 ).

In the absence of a MHSS, countries will limit their opportunities to integrate and collaborate for global response and assistance, making it difficult to manage mental illnesses in the community. We recommend the establishment of a MHSS with teams (psychiatrist, psychologist and other mental health professionals) at different levels of service administration. The teams should coordinate public health, medical and emergency responses to enable the effective detection and management of common mental illnesses ( Banerjee, 2020 ).

4.2.1.2. Psychological first aid (PFA)

The PFA (developed by WHO) has been used in many countries as the primary psychological intervention of choice ( WHO, 2011 ). However, most HCW are inadequately trained in the intervention. In addition, the WHO-PFA does not address the primary mental health needs of the people under quarantine, those facing COVID-19 related social stigma, and those affected by national mitigation measures (social distancing, lockdowns, etc.). Furthermore, the WHO-PFA provides little attention to managing myths, misinformation and fake news, the impact of the digital world on pandemic-related distress and the effects of the epidemic curve on mental health. Thus, it does not address the first peak of the EEC nor the second. It may also have poor face- and content-validity, all of which limits its use in practice. As such, we suggest the following modifications in the WHO-PFA (Ref. Box 1 ) to address core mental health issues. Further tailoring of the PFA to the socio-cultural aspects of various nations may be required.

Psychological first aid for COVID-19 Pandemic.

• 1 COVID-19 Pandemic: Brief overview, etiology, and mode of transmission.
• 2 Self and family care: Identifying safe areas and constructive behaviors to protect.
• 3 Primary information about COVID-19, preventive mental health, mental health promotion, and mental health surveillance.
• 4 Information regarding official websites or mobile apps, radio, television, or printed material.
• 5 Learning the calming skills and maintenance of biological functions (e.g., nutrition, sleep, rest, exercise).
• 6 Maximizing and facilitating connectedness to family and other social support systems to the extent possible (through electronic media rather than physical presence).
• 7 Fostering hope and optimism.
• 8 Identification of stage specific red flag signs of deteriorating mental health.

Alt-text: Box 1

4.2.1.3. Special cells or clinics for mental health

Major alterations to existing mental health care services have been reported in all countries during this pandemic. We noted that most countries, except Iran, had yet to create COVID 19-specific mental health services. Special clinics should be created for COVID-19 patients. The possibility of telepsychiatry or a remote clinic can provide an opportunity to limit the attendance in outpatient services.

4.2.1.4. Training

The ECPs noted that primary health care workers could do with more training in conducting PFA. We recommend that frontline health workers should be trained in PFA and they must assume a leadership position in the dissemination of updated knowledge. This can be done digitally through webinars and video-conferencing.

4.2.2. Monitoring and Assessment

4.2.2.1. identification and monitoring of the population at risk population.

The MHSS team should prepare a well-circulated and written pandemic mental health emergency plan with special attention to the population at risk (HCW, pre-existing mental illness and quarantined or isolated population). In Italy, Brazil, Lebanon and Colombia such efforts have been undertaken.

4.2.2.2. Health care technology for assessment and monitoring

Telepsychiatry, digital platforms, dedicated hotlines and mental health apps have the potential to reduce the treatment gap ( Xiang et al., 2020 ; Yao et al., 2020 ). These tools were used in Italy, which were aimed at assessing, evaluating and providing psychological and psychiatric support to vulnerable people. MHSS should be equipped with such tools for monitoring and assessment in all phases of pandemic.

4.2.3. Reducing the mental distress due to misinformation and ‘myths’

In many countries, there is no well-defined approach to address the circulation of misinformation, fake news and myths related to the pandemic. Most of these myths seem religious in nature (e.g. COVID-19 can be treated by religious practices or faith healers), particularly in low-income countries. The myths, fakes and misinformation were similar to those usually referring to psychiatric disorders. Some myths can be easily predicted based on previous experiences and these should be addressed at an early phase of the pandemic.

4.2.3.1. Interventions

Social media, television and radio are the most frequently used approaches by the government to address misinformation. The MHSS team, public health system and technical team (social media, media) should be well equipped for continuous monitoring and should address myths promptly. We note the innovative concept of Corona Warriors (a team of volunteers to monitor social media sites to check for rumors) and the creation of a chatbot for this purpose ( Standard, 2020 ). The success and sustainability of Corona Warriors will be limited if not integrated as part of a MHSS team. However, mental health preparedness and response were not included in these chatbots. The preventive tips about mental health, nearby surveillance centers and other vital information regarding mental health can be provided through these tools. In addition, global efforts should be attempted to develop technology based interventions with local translations or adaptations. These will bring the uniform approach to address the mental distress and myths.

4.2.4. Sustainability of mental health care services

4.2.4.1. funding.

Funds are critical for strengthening the country’s preparedness and response to a pandemic. Inadequate funding for mental health resources will affect mental health preparedness and the ability to mitigate against a surge of mental health related problems. We thus recommend that substantial funds should be allotted to mental health. It may help to reduce the burden of mental health disorders in both the first and second peak of EEC.

4.2.4.2. Policy

In many countries, early career psychiatrists have been redeployed to provide medical services to COVID-19 patients. This can lead to serious consequences with inadequate preparation for the second wave of EEC, a lack of mental health workers, and a lack of mental health training and support to HCW.

4.2.4.3. Coordination and collaboration

A lack of coordination and collaboration within the healthcare system can affect the delivery of mental health services during a pandemic ( Banerjee, 2020 ). In most of the countries, there were few efforts taken to improve coordination amongst healthcare service providers. A broad, multi-disciplinary approach that sees relationships being established both within, and outside the medical fraternity (e.g. with law enforcement) is crucial to enhancing interventive strategies.

4.2.5. Communication

Communication is a key component in the mental health response during the pandemic. Previous epidemiological reports suggest that the public demands up-to-date information on an ongoing basis throughout the period ( Van Bavel et al., 2020 ; Zandifar and Badrfam, 2020 ). Factual information presented by trusted public health officials and websites often assist in minimizing fear and hysteria. We opine that one of the key facets of MHSS is to communicate important and accurate information in a timely manner. We note the ongoing efforts by some governments, professional bodies and the WHO to do this – this is a commendable endeavor and we believe that these efforts need to be scaled up and continued.

4.2.5.1. Media

Traditional media (e.g. television), social media (e.g. Facebook), phone calls and official websites are commonly used to disseminate the information about COVID-19. However, this information may not be accessible to those who are illiterate, remote or underprivileged. In such a scenario, caller tunes or recorded phone calls can be used to ensure the information is more widely accessible.

4.2.5.2. Digital support groups

Few countries (e.g. Iran, Colombia and Italy) attempted to provide digital remote support through Whatsapp, Telegram, Skype, Facebook and others. Integrating telepsychiatry with digital support groups may improve the accessibility and affordability of mental health services ( Yao et al., 2020 ).

4.3. Probable effect of MHPAF on emotional epidemiology of pandemic

We expect MHPAF to reduce the impact of the epidemic in mental health, if implemented and suitably resourced. It will help mental health professionals to prepare for the expected surge of serious mental disorders (e.g. suicide, depression). Some countries (e.g. Spain, Italy, Iran) have already initiated the redeployment of early career psychiatrists to units managing COVID-19 patients, and this move may stymie preparations to manage the anticipated mental health surge. We thus recommend that redeployment efforts should be done with caution.

4.4. Strengths and limitations

In this study, early career psychiatrists from a broad swathe of nations were interviewed, which is a strength as these are commonly those who are heavily involved, at ground-level, in the clinical mental health work and can offer key perspectives on the mental health challenges encountered. This is especially important as national level planning may, at times, not percolate down to service providers conducting front-line work. The heterogeneity between the nations from which the early career psychiatrists came, further strengthens the broad applicability of our framework across nations with varying socio-economic situations.

We are mindful, however, that further validation will be needed. Admittedly, it is difficult to conduct well planned studies or controlled trials during pandemics ( Benight and McFarlane, 2007 ). Nonetheless, our conceptual framework can be useful for researchers to develop future studies about this topic. Indeed, MHPAF is a semi-structured framework which provides the opportunity to incorporate evidence-based interventions and the qualitative collection of data related to culture, health systems and other systems (legal, administrative and political).

This framework considers the time frame of interest (starting point of disaster to recovery trajectory). Thus, it can be used to frame various research and policy questions in advance. However, there is a need to define key terms and construct analytic strategies. In subsequent studies, we are planning to explore various components of this framework to evaluate process indicators, challenges and barriers as well as outcome evaluation in selected countries.

5. Conclusions

This conceptual framework is not a “one-size-fits-all” approach to mental health needs during a pandemic but provides a means by which national health care stakeholders can prepare for a potential rise in mental health issues. Further explorative studies with contemporary literature reviews and analytic strategies are needed to develop an effective preparedness and response plan to strengthen the response to pandemics at both national and global level.

The authors declare that there was no funding for this work.

Declaration of Competing Interest

The authors declare that they have no conflict of interest.

Acknowledgment

The authors wish to thank the Early Career Psychiatrists Section of the World Psychiatric Association (WPA) for being a supportive network that allowed to connect early career psychiatrists from different countries to work together on this initiative.

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