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• Published: 04 June 2021

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

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

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

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

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

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

Methodology

Research question.

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

Study design

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

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

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

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

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

Eligibility criteria

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

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

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

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

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

Information sources

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

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

Study selection

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

Data collection process

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

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

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

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

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

Quality assessment in individual reviews

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

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

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

Synthesis of results

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

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

Managing overlapping systematic reviews

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

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

PRISMA flow diagram

Characteristics of included reviews

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

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

Population and study designs

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

Systematic review findings

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

A meta-analysis of the prevalence of mortality

Clinical symptoms

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

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

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

Therapeutic possibilities

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

Laboratory and radiological findings

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

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

Quality of evidence in individual systematic reviews

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Availability of data and materials

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

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Acknowledgments

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

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

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

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Thilo Caspar von Groote

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Livia Puljak

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

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

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

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

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

Additional file 5: Appendix 5.

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

Additional file 6: Appendix 6.

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

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

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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National Institutes of Health. NIH clinical trials shows remdesivir accelerates recovery from advanced COVID-19. NIH New Releases . https://www.nih.gov/news-events/news-releases/nih-clinical-trial-shows-remdesivir-accelerates-recovery-advanced-covid-19#.XrIX75ZmQeQ.email (2020).

Radcliffe, S. Here’s exactly where we are with vaccines and treatments for COVID-19. Health News . https://www.healthline.com/health-news/heres-exactly-where-were-at-with-vaccines-and-treatments-for-covid-19 (2020).

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Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

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Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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Greater Good Science Center • Magazine • In Action • In Education

11 Questions to Ask About COVID-19 Research

Debates have raged on social media, around dinner tables, on TV, and in Congress about the science of COVID-19. Is it really worse than the flu? How necessary are lockdowns? Do masks work to prevent infection? What kinds of masks work best? Is the new vaccine safe?

You might see friends, relatives, and coworkers offer competing answers, often brandishing studies or citing individual doctors and scientists to support their positions. With so much disagreement—and with such high stakes—how can we use science to make the best decisions?

Here at Greater Good , we cover research into social and emotional well-being, and we try to help people apply findings to their personal and professional lives. We are well aware that our business is a tricky one.

Summarizing scientific studies and distilling the key insights that people can apply to their lives isn’t just difficult for the obvious reasons, like understanding and then explaining formal science terms or rigorous empirical and analytic methods to non-specialists. It’s also the case that context gets lost when we translate findings into stories, tips, and tools, especially when we push it all through the nuance-squashing machine of the Internet. Many people rarely read past the headlines, which intrinsically aim to be relatable and provoke interest in as many people as possible. Because our articles can never be as comprehensive as the original studies, they almost always omit some crucial caveats, such as limitations acknowledged by the researchers. To get those, you need access to the studies themselves.

And it’s very common for findings and scientists to seem to contradict each other. For example, there were many contradictory findings and recommendations about the use of masks, especially at the beginning of the pandemic—though as we’ll discuss, it’s important to understand that a scientific consensus did emerge.

Given the complexities and ambiguities of the scientific endeavor, is it possible for a non-scientist to strike a balance between wholesale dismissal and uncritical belief? Are there red flags to look for when you read about a study on a site like Greater Good or hear about one on a Fox News program? If you do read an original source study, how should you, as a non-scientist, gauge its credibility?

Here are 11 questions you might ask when you read about the latest scientific findings about the pandemic, based on our own work here at Greater Good.

1. Did the study appear in a peer-reviewed journal?

In peer review, submitted articles are sent to other experts for detailed critical input that often must be addressed in a revision prior to being accepted and published. This remains one of the best ways we have for ascertaining the rigor of the study and rationale for its conclusions. Many scientists describe peer review as a truly humbling crucible. If a study didn’t go through this process, for whatever reason, it should be taken with a much bigger grain of salt. 

“When thinking about the coronavirus studies, it is important to note that things were happening so fast that in the beginning people were releasing non-peer reviewed, observational studies,” says Dr. Leif Hass, a family medicine doctor and hospitalist at Sutter Health’s Alta Bates Summit Medical Center in Oakland, California. “This is what we typically do as hypothesis-generating but given the crisis, we started acting on them.”

In a confusing, time-pressed, fluid situation like the one COVID-19 presented, people without medical training have often been forced to simply defer to expertise in making individual and collective decisions, turning to culturally vetted institutions like the Centers for Disease Control (CDC). Is that wise? Read on.

2. Who conducted the study, and where did it appear?

“I try to listen to the opinion of people who are deep in the field being addressed and assess their response to the study at hand,” says Hass. “With the MRNA coronavirus vaccines, I heard Paul Offit from UPenn at a UCSF Grand Rounds talk about it. He literally wrote the book on vaccines. He reviewed what we know and gave the vaccine a big thumbs up. I was sold.”

From a scientific perspective, individual expertise and accomplishment matters—but so does institutional affiliation.

Why? Because institutions provide a framework for individual accountability as well as safety guidelines. At UC Berkeley, for example , research involving human subjects during COVID-19 must submit a Human Subjects Proposal Supplement Form , and follow a standard protocol and rigorous guidelines . Is this process perfect? No. It’s run by humans and humans are imperfect. However, the conclusions are far more reliable than opinions offered by someone’s favorite YouTuber .

Recommendations coming from institutions like the CDC should not be accepted uncritically. At the same time, however, all of us—including individuals sporting a “Ph.D.” or “M.D.” after their names—must be humble in the face of them. The CDC represents a formidable concentration of scientific talent and knowledge that dwarfs the perspective of any one individual. In a crisis like COVID-19, we need to defer to that expertise, at least conditionally.

“If we look at social media, things could look frightening,” says Hass. When hundreds of millions of people are vaccinated, millions of them will be afflicted anyway, in the course of life, by conditions like strokes, anaphylaxis, and Bell’s palsy. “We have to have faith that people collecting the data will let us know if we are seeing those things above the baseline rate.”

3. Who was studied, and where?

Animal experiments tell scientists a lot, but their applicability to our daily human lives will be limited. Similarly, if researchers only studied men, the conclusions might not be relevant to women, and vice versa.

Many psychology studies rely on WEIRD (Western, educated, industrialized, rich and democratic) participants, mainly college students, which creates an in-built bias in the discipline’s conclusions. Historically, biomedical studies also bias toward gathering measures from white male study participants, which again, limits generalizability of findings. Does that mean you should dismiss Western science? Of course not. It’s just the equivalent of a “Caution,” “Yield,” or “Roadwork Ahead” sign on the road to understanding.

This applies to the coronavirus vaccines now being distributed and administered around the world. The vaccines will have side effects; all medicines do. Those side effects will be worse for some people than others, depending on their genetic inheritance, medical status, age, upbringing, current living conditions, and other factors.

For Hass, it amounts to this question: Will those side effects be worse, on balance, than COVID-19, for most people?

“When I hear that four in 100,000 [of people in the vaccine trials] had Bell’s palsy, I know that it would have been a heck of a lot worse if 100,000 people had COVID. Three hundred people would have died and many others been stuck with chronic health problems.”

4. How big was the sample?

In general, the more participants in a study, the more valid its results. That said, a large sample is sometimes impossible or even undesirable for certain kinds of studies. During COVID-19, limited time has constrained the sample sizes.

However, that acknowledged, it’s still the case that some studies have been much larger than others—and the sample sizes of the vaccine trials can still provide us with enough information to make informed decisions. Doctors and nurses on the front lines of COVID-19—who are now the very first people being injected with the vaccine—think in terms of “biological plausibility,” as Hass says.

Did the admittedly rushed FDA approval of the Pfizer-BioNTech vaccine make sense, given what we already know? Tens of thousands of doctors who have been grappling with COVID-19 are voting with their arms, in effect volunteering to be a sample for their patients. If they didn’t think the vaccine was safe, you can bet they’d resist it. When the vaccine becomes available to ordinary people, we’ll know a lot more about its effects than we do today, thanks to health care providers paving the way.

5. Did the researchers control for key differences, and do those differences apply to you?

Diversity or gender balance aren’t necessarily virtues in experimental research, though ideally a study sample is as representative of the overall population as possible. However, many studies use intentionally homogenous groups, because this allows the researchers to limit the number of different factors that might affect the result.

While good researchers try to compare apples to apples, and control for as many differences as possible in their analyses, running a study always involves trade-offs between what can be accomplished as a function of study design, and how generalizable the findings can be.

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You also need to ask if the specific population studied even applies to you. For example, when one study found that cloth masks didn’t work in “high-risk situations,” it was sometimes used as evidence against mask mandates.

However, a look beyond the headlines revealed that the study was of health care workers treating COVID-19 patients, which is a vastly more dangerous situation than, say, going to the grocery store. Doctors who must intubate patients can end up being splattered with saliva. In that circumstance, one cloth mask won’t cut it. They also need an N95, a face shield, two layers of gloves, and two layers of gown. For the rest of us in ordinary life, masks do greatly reduce community spread, if as many people as possible are wearing them.

6. Was there a control group?

One of the first things to look for in methodology is whether the population tested was randomly selected, whether there was a control group, and whether people were randomly assigned to either group without knowing which one they were in. This is especially important if a study aims to suggest that a certain experience or treatment might actually cause a specific outcome, rather than just reporting a correlation between two variables (see next point).

For example, were some people randomly assigned a specific meditation practice while others engaged in a comparable activity or exercise? If the sample is large enough, randomized trials can produce solid conclusions. But, sometimes, a study will not have a control group because it’s ethically impossible. We can’t, for example, let sick people go untreated just to see what would happen. Biomedical research often makes use of standard “treatment as usual” or placebos in control groups. They also follow careful ethical guidelines to protect patients from both maltreatment and being deprived necessary treatment. When you’re reading about studies of masks, social distancing, and treatments during the COVID-19, you can partially gauge the reliability and validity of the study by first checking if it had a control group. If it didn’t, the findings should be taken as preliminary.

7. Did the researchers establish causality, correlation, dependence, or some other kind of relationship?

We often hear “Correlation is not causation” shouted as a kind of battle cry, to try to discredit a study. But correlation—the degree to which two or more measurements seem connected—is important, and can be a step toward eventually finding causation—that is, establishing a change in one variable directly triggers a change in another. Until then, however, there is no way to ascertain the direction of a correlational relationship (does A change B, or does B change A), or to eliminate the possibility that a third, unmeasured factor is behind the pattern of both variables without further analysis.

In the end, the important thing is to accurately identify the relationship. This has been crucial in understanding steps to counter the spread of COVID-19 like shelter-in-place orders. Just showing that greater compliance with shelter-in-place mandates was associated with lower hospitalization rates is not as conclusive as showing that one community that enacted shelter-in-place mandates had lower hospitalization rates than a different community of similar size and population density that elected not to do so.

We are not the first people to face an infection without understanding the relationships between factors that would lead to more of it. During the bubonic plague, cities would order rodents killed to control infection. They were onto something: Fleas that lived on rodents were indeed responsible. But then human cases would skyrocket.

Why? Because the fleas would migrate off the rodent corpses onto humans, which would worsen infection. Rodent control only reduces bubonic plague if it’s done proactively; once the outbreak starts, killing rats can actually make it worse. Similarly, we can’t jump to conclusions during the COVID-19 pandemic when we see correlations.

8. Are journalists and politicians, or even scientists, overstating the result?

Language that suggests a fact is “proven” by one study or which promotes one solution for all people is most likely overstating the case. Sweeping generalizations of any kind often indicate a lack of humility that should be a red flag to readers. A study may very well “suggest” a certain conclusion but it rarely, if ever, “proves” it.

This is why we use a lot of cautious, hedging language in Greater Good , like “might” or “implies.” This applies to COVID-19 as well. In fact, this understanding could save your life.

When President Trump touted the advantages of hydroxychloroquine as a way to prevent and treat COVID-19, he was dramatically overstating the results of one observational study. Later studies with control groups showed that it did not work—and, in fact, it didn’t work as a preventative for President Trump and others in the White House who contracted COVID-19. Most survived that outbreak, but hydroxychloroquine was not one of the treatments that saved their lives. This example demonstrates how misleading and even harmful overstated results can be, in a global pandemic.

9. Is there any conflict of interest suggested by the funding or the researchers’ affiliations?

A 2015 study found that you could drink lots of sugary beverages without fear of getting fat, as long as you exercised. The funder? Coca Cola, which eagerly promoted the results. This doesn’t mean the results are wrong. But it does suggest you should seek a second opinion : Has anyone else studied the effects of sugary drinks on obesity? What did they find?

It’s possible to take this insight too far. Conspiracy theorists have suggested that “Big Pharma” invented COVID-19 for the purpose of selling vaccines. Thus, we should not trust their own trials showing that the vaccine is safe and effective.

But, in addition to the fact that there is no compelling investigative evidence that pharmaceutical companies created the virus, we need to bear in mind that their trials didn’t unfold in a vacuum. Clinical trials were rigorously monitored and independently reviewed by third-party entities like the World Health Organization and government organizations around the world, like the FDA in the United States.

Does that completely eliminate any risk? Absolutely not. It does mean, however, that conflicts of interest are being very closely monitored by many, many expert eyes. This greatly reduces the probability and potential corruptive influence of conflicts of interest.

10. Do the authors reference preceding findings and original sources?

The scientific method is based on iterative progress, and grounded in coordinating discoveries over time. Researchers study what others have done and use prior findings to guide their own study approaches; every study builds on generations of precedent, and every scientist expects their own discoveries to be usurped by more sophisticated future work. In the study you are reading, do the researchers adequately describe and acknowledge earlier findings, or other key contributions from other fields or disciplines that inform aspects of the research, or the way that they interpret their results?

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This was crucial for the debates that have raged around mask mandates and social distancing. We already knew quite a bit about the efficacy of both in preventing infections, informed by centuries of practical experience and research.

When COVID-19 hit American shores, researchers and doctors did not question the necessity of masks in clinical settings. Here’s what we didn’t know: What kinds of masks would work best for the general public, who should wear them, when should we wear them, were there enough masks to go around, and could we get enough people to adopt best mask practices to make a difference in the specific context of COVID-19 ?

Over time, after a period of confusion and contradictory evidence, those questions have been answered . The very few studies that have suggested masks don’t work in stopping COVID-19 have almost all failed to account for other work on preventing the disease, and had results that simply didn’t hold up. Some were even retracted .

So, when someone shares a coronavirus study with you, it’s important to check the date. The implications of studies published early in the pandemic might be more limited and less conclusive than those published later, because the later studies could lean on and learn from previously published work. Which leads us to the next question you should ask in hearing about coronavirus research…

11. Do researchers, journalists, and politicians acknowledge limitations and entertain alternative explanations?

Is the study focused on only one side of the story or one interpretation of the data? Has it failed to consider or refute alternative explanations? Do they demonstrate awareness of which questions are answered and which aren’t by their methods? Do the journalists and politicians communicating the study know and understand these limitations?

When the Annals of Internal Medicine published a Danish study last month on the efficacy of cloth masks, some suggested that it showed masks “make no difference” against COVID-19.

The study was a good one by the standards spelled out in this article. The researchers and the journal were both credible, the study was randomized and controlled, and the sample size (4,862 people) was fairly large. Even better, the scientists went out of their way to acknowledge the limits of their work: “Inconclusive results, missing data, variable adherence, patient-reported findings on home tests, no blinding, and no assessment of whether masks could decrease disease transmission from mask wearers to others.”

Unfortunately, their scientific integrity was not reflected in the ways the study was used by some journalists, politicians, and people on social media. The study did not show that masks were useless. What it did show—and what it was designed to find out—was how much protection masks offered to the wearer under the conditions at the time in Denmark. In fact, the amount of protection for the wearer was not large, but that’s not the whole picture: We don’t wear masks mainly to protect ourselves, but to protect others from infection. Public-health recommendations have stressed that everyone needs to wear a mask to slow the spread of infection.

“We get vaccinated for the greater good, not just to protect ourselves ”

As the authors write in the paper, we need to look to other research to understand the context for their narrow results. In an editorial accompanying the paper in Annals of Internal Medicine , the editors argue that the results, together with existing data in support of masks, “should motivate widespread mask wearing to protect our communities and thereby ourselves.”

Something similar can be said of the new vaccine. “We get vaccinated for the greater good, not just to protect ourselves,” says Hass. “Being vaccinated prevents other people from getting sick. We get vaccinated for the more vulnerable in our community in addition for ourselves.”

Ultimately, the approach we should take to all new studies is a curious but skeptical one. We should take it all seriously and we should take it all with a grain of salt. You can judge a study against your experience, but you need to remember that your experience creates bias. You should try to cultivate humility, doubt, and patience. You might not always succeed; when you fail, try to admit fault and forgive yourself.

Above all, we need to try to remember that science is a process, and that conclusions always raise more questions for us to answer. That doesn’t mean we never have answers; we do. As the pandemic rages and the scientific process unfolds, we as individuals need to make the best decisions we can, with the information we have.

This article was revised and updated from a piece published by Greater Good in 2015, “ 10 Questions to Ask About Scientific Studies .”

About the Authors

Jeremy Adam Smith

Uc berkeley.

Jeremy Adam Smith edits the GGSC’s online magazine, Greater Good . He is also the author or coeditor of five books, including The Daddy Shift , Are We Born Racist? , and (most recently) The Gratitude Project: How the Science of Thankfulness Can Rewire Our Brains for Resilience, Optimism, and the Greater Good . Before joining the GGSC, Jeremy was a John S. Knight Journalism Fellow at Stanford University.

Emiliana R. Simon-Thomas

Emiliana R. Simon-Thomas, Ph.D. , is the science director of the Greater Good Science Center, where she directs the GGSC’s research fellowship program and serves as a co-instructor of its Science of Happiness and Science of Happiness at Work online courses.

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The COVID-19 pandemic significantly increased interest in wearable health-monitoring devices among low-income Hispanic and Latine adults living in the U.S., a new Northwestern University study has found.

While the pandemic highlighted the need for regular health monitoring, these groups often lack access to affordable health care and sometimes distrust existing health systems . Wearables, therefore, could provide a reliable, at-home alternative to traditional in-clinic health monitoring.

But, although interest has increased, several barriers remain that prevent these groups from adopting wearable technologies . According to the researchers, tech companies historically have designed current wearable devices with affluent, predominantly white users in mind.

"Current designers do not consider the needs of low-income people of color regarding usability, accessibility and affordability," said Northwestern's Stefany Cruz, who led the study.

"If this trend continues, it will worsen digital and health inequities. In this study, we want to bring attention to existing health disparities and how wearable devices expand that gap. Wearables have the potential to fill the gap eventually, but we're not there yet. We need to build devices that are more inclusive, and the design process should consider the context and culture of individuals from marginalized communities."

The study, "Perceptions of wearable health tools post-COVID-19 in low-income Latine communities," was published May 8 in the Journal of Medical Internet Research .

A personal connection

Cruz is a Ph.D. candidate in computer engineering at Northwestern's McCormick School of Engineering. In her engineering work, Cruz is particularly interested in building equitable, efficient and intelligent wearable systems for groups historically excluded from the design process.

Cruz's own experiences as a child of Salvadoran immigrants inspired her to embark on this new study. Growing up in East Los Angeles, Cruz was often sick, and her family did not have health insurance. After suffering a bout of strep throat, she watched her family struggle to pay the medical bills—an experience that sparked her interest in developing new technologies with a focus on health.

"That set up the whole trajectory of what I want to pursue in the computer engineering field," Cruz said. "Because I witnessed the severe lack of access to health care, I want to build technologies from the ground up that can help support and uplift my community."

Assembling participants

Although Cruz planned the study before pandemic hit, she noticed that COVID-19 changed the role of wearables in society. Once used mostly for counting steps and motivating people to move through the day, wearable devices now began playing a bigger role in health monitoring. These devices could track vital physiological signals, including blood oxygen levels .

Low blood oxygen levels often have no symptoms until organs are irreparably damaged. But wearables could detect early warning signs, prompting a person to head to the hospital sooner—before it's too late.

It was easy for Cruz to see how this technology could help her community. But why weren't people taking advantage of these devices?

To understand perceptions of wearables and identify the barriers to adoption, Cruz assembled a small group of low-income Hispanic and Latine adults in Chicago and Los Angeles. Participants met the low-income criteria if their income levels fell at or below the low-income threshold according to their county's Department of Housing and Community Development.

After establishing a focus group, Cruz held two rounds of in-depth interviews between December 2021 and March 2022. In the first interviews, Cruz noticed that multiple participants made connections between COVID-19 and wearable devices.

So, then she conducted a second round of interviews with more emphasis on using wearables for health monitoring. In these conversations, Cruz explored the participants' opinions regarding wearable technology for health, their community's perception of wearables and the features they would like to see in future wearables. She also asked participants about their access to Wi-Fi and other resource constraints.

Uncovering an overwhelming interest

Throughout the interviews, Cruz consistently found that the COVID-19 pandemic strongly influenced perceptions of wearable electronics. Participants who felt apathetic before the pandemic expressed a significantly increased interest in wearables for personal health monitoring and management.

About two-thirds of the participants in the study lost a close family member to COVID-19. Several of the participants also contracted COVID-19 before the vaccine and other treatments became available. These experiences made them realize how useful wearable health-monitoring tools can be.

"I guess the one thing that scares me that I never even thought of until I got COVID were my oxygen levels," one participant said. "Like, am I at normal levels? Is that an issue that I need to kind of think about?"

"One thing I noticed, especially with COVID right now which is…the timing of getting all your vitals measured can actually save somebody's life," another participant said. "So, I think that's a very important thing. Like oxygen levels to be measured."

Alternative to in-clinic care

Participants also discussed difficulties when trying to access health care and how wearables could potentially compensate for the lack of local resources. Specifically, some participants shared how their neighborhood hospitals had closed, forcing community members to seek care at small, overcrowded clinics.

"It's overly populated. Even if you make an appointment, you're there all day," one Los Angeles-based participant said. "Whatever time you go, whatever day you go, it's always crowded, because it's one of the very few [clinics] that accepts Medi-Cal. So low-income communities, they don't have the resources; it's always crowded."

One participant highlighted that community members' lack of trust in doctors, coupled with high medical expenses, posed barriers to seeking medical treatment.

"Hispanic people don't go to the doctor because they don't believe in the doctor," the participant said. "They think the doctors are gonna kill them and then they're poor, so they can't pay for the doctor. So, like if [a wearable] could do basic [vital] tests that would be great."

Community-driven design

As a part of the interview process, Cruz asked participants what features and functions they desired in wearable devices. Cruz noted that oftentimes technologies designed for low-income groups do not take the intended users' needs into account.

"If we are the ones that are supposed to wear the devices, then it makes sense to ask our opinions of how they can be incorporated into our daily lives," she said.

In addition to wanting health monitoring capabilities (for heart rate, oxygen levels, blood pressure and more), the participants also desired enhanced affordability, control over the captured health data and increased durability. For wearables to be most effective, users must wear them continuously to capture consistent health data. This is where durability becomes a critical factor.

"I do think that it has to be very durable because the purpose is [for] low-income communities," one participant said. "They don't have money to replace it. We just don't have comfy jobs. A lot of us work more physically demanding jobs. Some of us are plumbers, some are construction workers, some of us are gardeners. Some of us run a business and like that business involves pots and pans like we're restaurant workers. If [the device] breaks, they're just gonna say 'oops' and throw it away…If it is more durable that's one of the biggest keys to wearing it."

'My community suffered a lot'

Although many people have moved on from the pandemic and resumed normal lives, Cruz said her community is still reeling. Cruz lost several family members to COVID-19 and hopes that designing more inclusive technologies can prevent future suffering.

"During COVID-19, my community suffered a lot," Cruz said. "Some people have been able to brush it off and move on, but some of us are still scarred. We lost family members that probably would still be alive if they weren't infected. Many people have long-COVID symptoms, which wearables also could help monitor. As these technologies get better at sensing vital signals, they also should become more inclusive."

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Air Cleaners, HVAC Filters, and Coronavirus (COVID-19)

Portable air cleaners (also known as air purifiers) may be particularly helpful when additional ventilation with outdoor air is not possible without compromising indoor comfort (temperature or humidity), or when outdoor air pollution is high.

Caution: The use of air cleaners alone cannot ensure adequate indoor air quality, particularly where significant pollutant sources are present and ventilation is insufficient. Read EPA’s “Guide to air cleaners in the home" (PDF).

When used properly, air cleaners and HVAC filters can help reduce airborne contaminants including viruses in a building or small space. By itself, air cleaning or filtration is not enough to protect people from COVID-19. When used along with other best practices recommended by CDC and other public health agencies, including social distancing and mask wearing, filtration can be part of a plan to reduce the potential for airborne transmission of COVID-19 indoors.

Air cleaners and HVAC filters are designed to filter pollutants or contaminants out of the air that passes thru them. Air cleaning and filtration can help reduce airborne contaminants, including particles containing viruses. 

In order for an air cleaner to be effective in removing viruses from the air, it must be able to remove small airborne particles (in the size range of 0.1-1 um). Manufacturers report this capability in several ways. In some cases, they may indicate particle removal efficiency for specific particle sizes (e.g. “removes 99.9% of particles as small as 0.3 um”). Many manufacturers use the Clean Air Delivery Rate (CADR) rating system to rate air cleaner performance. Others indicate they use High Efficiency Particulate Air (HEPA) filters. In order to select an air cleaner that effectively filters viruses from the air, choose: 1) a unit that is the right size for the space you will be using it in (this is typically indicated by the manufacturer in square feet), 2) a unit that has a high CADR for smoke (vs. pollen or dust), is designated a HEPA unit, or specifically indicates that it filters particles in the 0.1-1 um size range.

Air Cleaners and HVAC Filters in Homes

Choose a portable air cleaner that is intended for the room size in which it will be used and be sure it meets at least one of the following criteria:

• it is designated as High-Efficiency Particulate Air (HEPA),
• it is CADR rated for smoke, or
• the manufacturer states that the device will remove most particles in the size range below 1 um.

Most manufacturers provide this information on the air cleaner packaging, label or website description.

Do not use air cleaners that intentionally generate ozone in occupied spaces or that do not meet state regulations or industry standards for ozone generation.

Where to place a portable air cleaner in your home

Choosing where in your home to place a portable air cleaner to help protect from airborne infections depends on the situation. Put the air cleaner in the room where most people spend most of their time (e.g., a living room or bedroom) unless: 

• Someone in a household is especially vulnerable to the risks from infection, then, place the air cleaner where they spend most of their time or
• If someone is isolating because of an active infection, then, place the air cleaner where they are isolating.  See CDC Website 
• Read  EPA’s “Guide to air cleaners in the home”  for more information on HVAC filters and placing and operating a portable air cleaner.
• Use CDC's  Interactive Ventilation Tool  to learn how to decrease levels of virus particles during and after a guest visits a home.

Air Cleaners and HVAC Filters in Offices, Schools, and Commercial Buildings

The HVAC systems of large buildings typically filter air before it is distributed throughout a building, so consider upgrading HVAC filters as appropriate for your specific building and HVAC system (consult an HVAC professional). The variety and complexity of HVAC systems in large buildings requires professional interpretation of technical guidelines, such as those provided by ASHRAE and CDC . EPA, ASHRAE and CDC recommend upgrading air filters to the highest efficiency possible that is compatible with the system and checking the filter fit to minimize filter air bypass.

Consider using portable air cleaners to supplement increased HVAC system ventilation and filtration, especially in areas where adequate ventilation is difficult to achieve. Directing the airflow so that it does not blow directly from one person to another reduces the potential spread of droplets that may contain infectious viruses.

Air cleaning may be useful when used along with source control and ventilation, but it is not a substitute for either method. Source control involves removing or decreasing pollutants such as smoke, formaldehyde, or particles with viruses. The use of air cleaners alone cannot ensure adequate air quality, particularly where significant pollutant sources are present and ventilation is insufficient. See ASHRAE and CDC for more information on air cleaning and filtration and other important engineering controls. 

• See CDC's Interactive School Ventilation Tool to learn how to improve ventilation.

Air Cleaning Devices That Use Bipolar Ionization, Including Portable Air Cleaners and In-duct Air Cleaners Used in HVAC Systems

Some products sold as air cleaners intentionally generate ozone. These products are not safe to use when people are present because ozone can irritate the airways. Do not use ozone generators in occupied spaces . When used at concentrations that do not exceed public health standards, ozone applied to indoor air does not effectively remove viruses, bacteria, mold, or other biological pollutants.

Bipolar ionization (also called needlepoint bipolar ionization) is a technology that can be used in HVAC systems or portable air cleaners to generate positively and negatively charged particles. Provided manufacturers have data to demonstrate efficacy, manufacturers of these types of devices may market this technology to help remove viruses, including SARS-2-CoV, the virus that causes COVID-19, from the air, or to facilitate surface disinfection of surfaces within a treated area. This is an emerging technology, and little research is available that evaluates it outside of lab conditions. As typical of newer technologies, the evidence for safety and effectiveness is less documented than for more established ones, such as filtration. Bipolar ionization has the potential to generate ozone and other potentially harmful by-products indoors, unless specific precautions are taken in the product design and maintenance. If you decide to use a device that incorporates bipolar ionization technology, EPA recommends using a device that meets UL 2998 standard certification (Environmental Claim Validation Procedure (ECVP) for Zero Ozone Emissions from Air Cleaners).

Please note that there are many air cleaning devices that do not use bipolar ionization – the device packaging or marketing materials will typically indicate if bipolar ionization technology is being used.

DIY Air Cleaners

Do-it-yourself (DIY) air cleaners are indoor air cleaners that can be assembled from box fans and square HVAC (or furnace) filters. They are sometimes used during wildfire or other events when air quality is poor and other indoor air filtration options are unavailable.

Evidence from multiple studies indicates that well-built DIY air cleaners can be of comparable effectiveness to commercial air cleaners in reducing airborne particles (including viral particles). However, their performance does vary based on the design selected and the quality of materials and assembly. Each time a DIY air cleaner is re-assembled after changing a filter, its performance may be different. Commercial devices, on the other hand, are tested for performance, and this performance information can be used to match them to the size of a room.

Therefore, EPA does not recommend the routine use of DIY air cleaners as a permanent alternative to products of known performance (such as commercially available portable air cleaners). However, this recommendation should not be interpreted to discourage the use of DIY air cleaners in circumstances when commercially available portable air cleaners or other products of known performance are not available. Using a DIY air cleaner that was inadequately designed or assembled does not worsen indoor air quality and may still offer some benefits.

To address concerns that box fans in DIY air cleaners might be associated with increased risk of fire, EPA and Underwriter Laboratories evaluated the use of DIY air cleaners and the risk of fire. Fans that were built since 2012 and met UL standard 507 did not pose a fire hazard under the conditions tested in the study. (See Research on DIY Air Cleaners to Reduce Wildfire Smoke Indoors for more information.

Tips - If You Choose to Use a DIY Air Cleaner

• Initial costs for single filter designs can be lower than designs that use multiple filters, but operation costs for single filter designs may be higher, for the same performance.
• Multi-filter designs can be harder to put together, and it can be harder to replace their filters. They are also bulkier, and more difficult to move around than single filter designs. However, multi-filter designs generally have superior performance, and they can be more cost effective.
• Using multiple single-filter units in the same room is also worth considering, when balancing performance, costs, space, and ease of assembly for your specific needs.
• Spanish version (pdf)
• One filter flat against the fan (from the Washington Dept of Ecology)
• Two filters taped with cardboard to form a triangle against the fan (from the Confederated Tribes of the Colville Reservation)
• Four filters used to create an air filtration box, also known as the Corsi-Rosenthal box (pdf) (from the University of California, San Diego)
• Use a newer box fan (made since 2012) with a UL (Underwriters Laboratory) or ETL (Intertek) logo because they have verified safety features to reduce the risk of the fan overheating. EPA recommends not using DIY air cleaners built with older model box fans (built before 2012), because their fire hazard is unknown. If older fans are used, they should not be used unattended or while sleeping.
• Use filters of approximately the same shape and size as the box fan. Filters that only partially overlap the fan will result in reduced performance. Filters that are bigger than the fan may be unnecessarily more expensive.
• When assembling a DIY air cleaner, choose a high-efficiency filter, rated MERV 13 or higher, for better filtration. Align the arrows on the filter to be in the same direction of the air flow through the fan. Create a good seal between the fan and the filter.

Features That Can Improve DIY Air Cleaner Performance

• Increase the number of filters in the design. Some designs can have 2, 3, 4 or 5 filters. This feature generally improves performance the most. 
• Use a thick HVAC filter that is 2” or 4” thick instead of a 1” filter. Generally, thicker filters are more expensive than thinner filters, but need to be changed less often. Thicker filters generally provide a large improvement in performance. 
• Cover the outside corners of the front of the box fan  so that air flows only through the center part of the fan where the blades are visible. This approach generally provides a large improvement in performance. You can use cardboard, duct tape, or wood to make the cover – some DIY fan designers call these “shrouds”. This cover can also be made from the cardboard box in which the fan was packaged, at no additional cost. 
• Improve the seal where the filters are attached to the fan or each other. Seal the edges using duct tape, for example, instead of ties or clamps. This is less important for performance than the other features listed above. 

Tips For Operating a DIY Air Cleaner

• Run the device whenever the room is occupied.
• Make sure the device is free from obstructions and air can flow through it.
• Run the device at the highest speed setting acceptable to you.
• Check the filter(s) regularly and replace when dirty.  

Air Cleaner Operation

• Place DIY air cleaners in the rooms where people are spending the most time, in general. To protect especially vulnerable people, place the air cleaner where they spend most of their time.  If someone is isolating because they could be transmitting an infectious disease (such as COVID-19 or flu), place the air cleaner nearest them. 
• Make sure air can flow to the device and away from it, keeping it clear from obstructions. A central place in a room works best, but it is not essential as long as air flow is free. Do not operate an air cleaner inside a closet, as this would limit its effectiveness. 
• Consider running DIY air cleaners the entire time a space is occupied. The longer they run, the more particles they will likely remove.
• Consider running the fan at higher speed settings. Air cleaning performance improves at higher fan speeds, although noise and air movement in the room also increase.
• Change the filters periodically. Longer run times, higher fans speeds, and higher levels of air pollution will mean that the filter will be removing more particles from the air, but the filter will also get dirty more quickly. Change the filter when it appears dirty. When changing the filter(s), wear gloves, an N-95 respirator or similar, and goggles (without holes) for personal protection. Remove the filters gently - outdoors if possible. Avoid shaking or banging the filters to minimize the release of accumulated dust. Dispose of the filters in garbage bags.

Additional Information

• See EPA Air Cleaners and Air Filters in the Home for more information.
• Schools and universities (pdf)  (1.93 MB)
• Commercial buildings (pdf)  (1.32 MB)
• Multifamily owners/managers (pdf) (1.19 MB)
• Core Recommendations for Reducing Airborne Infectious Aerosol Exposure (pdf)  (152.72 KB) 
• Improving Ventilation in Your Home
• CDC Interactive Ventilation Tool (for Homes)

Return to Indoor Air and Coronavirus (COVID-19).

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• Learn about Indoor Air Quality
• IAQ by Building Type
• Network and Collaborate
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COVID-19 variants continue to emerge, no ties to biolabs in Ukraine | Fact check

The claim: There have been no new COVID-19 variants since Russia attacked US biolabs in Ukraine

A June 1 Instagram post ( direct link , archive link ) ties Russia’s invasion of Ukraine to a purported elimination of COVID-19 variants.

“Did you notice that the Covid variants stopped when Russia started going after the Biolabs?” the post reads. “Omicron was declared a variant of concern on 11/26/2021. Russia began neutralizing US Biolabs in Ukraine on 02/24/2022. No variants since.”

The post was liked more than 1,000 times in seven days.

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Our rating: False

New COVID-19 variants are routinely discovered around the world. There is no credible evidence to show the U.S. operated biolabs in Ukraine.

Many new variants, no evidence of of Russia destroying US biolabs

Publicly available data shows variants of the virus that causes COVID-19 have continually been discovered in the months since Russia invaded Ukraine in February 2022.

A database maintained by the Global Initiative on Sharing All Influenza Data, an international effort to make virus data quickly accessible, shows there have been new SARS-CoV-2 variants in at least 11 countries since the start of June and in 71 countries since the start of May.

The World Health Organization also tracks variants and lists XBB.1.5 and XBB.1.16 as variants of interest on its website as of June 5. Both were discovered far after the timeframe mentioned in the social media post.

The post claims omicron was the last variant of concern, but there have been numerous variants of the omicron lineage in recent months. A variant is defined by the Centers for Disease Control and Prevention as a viral genome with at least one mutation from a previous version.

WHO's variant tracker notes that all known variants currently circulating are part of the omicron lineage, while a University of Nebraska report notes the original variant in the omicron lineage “is gone now.”

Fact check : False claim UK approved plan to spray COVID-19 vaccines from airplanes

The social media post also claims Russia has been destroying U.S. biolabs in Ukraine, a claim that was previously debunked by USA TODAY and continues to be refuted by international authorities.

In June 2022, the Defense Department released a fact sheet outlining its involvement in a program to reduce “legacy threats from nuclear, chemical, and biological weapons” in states that formerly made up the Soviet Union. It notes the U.S. provided support to 46 “peaceful Ukrainian laboratories, health facilities, and disease diagnostic sites” that worked to promote human and animal health.

The document also says the facilities are owned and operated by Ukraine, with the U.S. acting as a partner that provides equipment and other support. It says both nations have agreed to not develop nuclear, biological or chemical weapons.   

“These facilities operate just like other state or local public health and research laboratories around the world,” the fact sheet says.

USA TODAY reached out to the user who shared the post for comment but did not immediately receive a response.

Our fact-check sources:

• GISAID, accessed June 11, Tracking of hCoV-19 Variants
• Department of Defense, June 9, 2022, Fact Sheet on WMD Threat Reduction Efforts with Ukraine, Russia and Other Former Soviet Union Countries
• WHO, June 8, COVID-19 Weekly Epidemiological Update
• CDC, updated March 20, SARS-CoV-2 Variant Classifications and Definitions
• CDC, accessed June 7, Variants & Genomic Surveillance
• University of Nebraska Medical Center, May 31, What COVID-19 variants are going around in May 2023?
• Yale Medicine, Feb. 10, Omicron XBB.1.5 'Kraken' Subvariant Is on the Rise: What To Know
• USA TODAY, April 14, 2023, Fact check: New COVID-19 variants continue to be discovered
• USA TODAY, Feb. 25, 2022, Fact check: False claim of US biolabs in Ukraine tied to Russian disinformation campaign
• NBC News, April26, What to know about XBB.1.16, the 'Arcturus' variant
• CBS News, Nov. 2, 2022, U.N. Security Council rejects Russia's call to probe debunked U.S.-Ukraine biological weapons claims

Thank you for supporting our journalism. You can subscribe to our print edition, ad-free app or electronic newspaper replica here .

Our fact-check work is supported in part by a grant from Facebook.

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Americans are less likely than others around the world to feel close to people in their country or community

Americans are less likely than people abroad to feel close to others in their country and community, according to a 2023 Pew Research Center survey of 24 nations . This is especially the case among certain groups of Americans, including younger adults, those with lower incomes and less education, those who identify with or lean toward the Democratic Party, and those who are religiously unaffiliated.

This Pew Research Center analysis focuses on Americans’ feelings of closeness to others in their community and their country. We compare data from the United States to data from 23 other countries in the Asia-Pacific region, Europe, Latin America, the Middle East, North America and sub-Saharan Africa.

In the U.S., we surveyed 3,576 adults from March 20 to 26, 2023. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

For non-U.S. data, this report draws on nationally representative surveys of 27,285 adults conducted from Feb. 20 to May 22, 2023. All surveys were conducted over the phone with adults in Canada, France, Germany, Greece, Italy, Japan, the Netherlands, South Korea, Spain, Sweden and the United Kingdom. Surveys were conducted face-to-face in Argentina, Brazil, Hungary, India, Indonesia, Israel, Kenya, Mexico, Nigeria, Poland and South Africa. In Australia, we used a mixed-mode probability-based online panel. Read more about our international survey methodology .

Across all 24 countries surveyed, a median of 83% of adults say they feel very or somewhat close to other people in their country. A majority of U.S. adults (66%) also hold this view, but Americans are the least likely among those in the countries surveyed to do so.

Even fewer Americans feel close to people in their local community : 54% feel a connection to others near them, compared with a median of 78% of adults across all 24 countries. South Korea is the only country with a lower share of adults who feel connected with others in their community (50%).

Feeling close to other Americans

Some Americans are less likely than others to feel a connection to people in their country. For example, only 46% of adults under 30 feel connected to other Americans, compared with 83% of those ages 65 and older.

There are also differences by party and ideology. Six-in-ten Democrats and Democratic-leaning independents – compared with three-quarters of Republicans and Republican leaners – feel close to other Americans. Liberal Democrats are the least likely to say they feel close to other Americans, while conservative Republicans are the most likely to do so.

A similar ideological gap exists in several other countries , with people on the political left less likely than those on the right to feel close to people in their country.

Religion also plays a role. Religiously unaffiliated Americans are far less likely than their affiliated counterparts to feel close to others in the U.S. (51% vs. 73%). This pattern is mirrored in other measures of religiosity. For example, Americans who say religion is not too or not at all important to them, or who never attend religious services, are generally less likely to feel close to other Americans.

Feeling close to others in their local community

When it comes to feeling close to other people in the same community , there are again large differences by age. Only 42% of U.S. adults under 30 feel close to people in their community, compared with larger shares of older Americans.

There are additional differences on this question by education, income and community type:

• 51% of Americans without a college degree feel close to other people in their local community, compared with 61% of those with a college degree. A similar educational gap is evident in several other countries.
• Lower-income Americans are less likely than those with upper incomes to feel this connection (50% vs. 63%).
• While urban residents may live physically closer to others, they’re less likely than suburban or rural residents to say they feel connected to people in their community.

Differences by religion also emerge. Religious ly  unaffiliated Americans are much less likely than those who are religiously affiliated to feel connected to others in their local community (43% vs. 60%).

This pattern aligns with previous research on interpersonal connectedness and philanthropy among religious people. Religious people tend to be more likely than nonreligious people to  volunteer and give to charity  – though they prefer these activities benefit others  within their own religious groups .

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Janell Fetterolf is a senior researcher focusing on global attitudes at Pew Research Center .

Stephanie Kramer is a senior researcher focusing on religion at Pew Research Center .

How People in 24 Countries Think Democracy Can Improve

What can improve democracy, representative democracy remains a popular ideal, but people around the world are critical of how it’s working, language and traditions are considered central to national identity, attitudes on an interconnected world, most popular.

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Pressure to be 'perfect' causing burnout for parents, mental health concerns for their children

New data finds stress, anxiety and depression spike for those feeling the weight of a 'culture of achievement'.

Is the status of "perfect parent" attainable?

Researchers leading a national dialogue about parental burnout from The Ohio State University College of Nursing and the university's Office of the Chief Wellness Officer say "no," and a new study finds that pressure to try to be "perfect" leads to unhealthy impacts on both parents and their children.

The survey of more than 700 parents nationwide from June 15 -- July 28, 2023 is summarized in the new report, " The Power of Positive Parenting: Evidence to Help Parents and Their Children Thrive. " The data shows that:

• Fifty-seven percent (57%) of parents self-reported burnout.
• Parental burnout is strongly associated with internal and external expectations, including whether one feels they are a good parent, perceived judgment from others, time to play with their children, the relationship with their spouse and keeping a clean house.
• The more free play time that parents spend with their children and the lighter the load of structured extracurricular activities, the fewer mental health issues in their children (i.e. anxiety, depression, OCD, ADHD, bipolar disorder).
• Parents' mental health and behaviors strongly impact their children's mental health. If their children have a mental health disorder, parents report a higher level of burnout and a greater likelihood for them to insult, criticize, scream at, curse at and/or physically harm their children (i.e. repeated spanking). Higher levels of self-reported parental burnout and harsh parenting practices are associated with more mental health problems in children.

Kate Gawlik, DNP, one of the lead researchers on the study who pursues this research based on her experience as a working mother of four, said the illusion and expectations of "perfect parenting" can be deflating.

"I think social media has just really tipped the scales," said Gawlik, an associate clinical professor at the Ohio State College of Nursing. "You can look at people on Instagram or you can even just see people walking around, and I always think, 'How do they do that? How do they seem to always have it all together when I don't?'

"We have high expectations for ourselves as parents; we have high expectations for what our kids should be doing. Then on the flipside, you're comparing yourself to other people, other families, and there's a lot of judgment that goes on. And whether it's intended or not, it's still there."

Data from the study shows that force of expectations from what Gawlik calls a "culture of achievement" leads to burnout (a state of physical and emotional exhaustion), which in turn leads to other, potentially debilitating issues.

"When parents are burned out, they have more depression, anxiety and stress, but their children also do behaviorally and emotionally worse," said Bernadette Melnyk, PhD, FAAN, vice president for health promotion and chief wellness officer at Ohio State. "So it's super important to face your true story if you're burning out as a parent and do something about it for better self-care."

Gawlik and Melnyk's new report brings critical updates to their initial study in 2022, which measured working parent burnout during the height of the COVID-19 pandemic. Gawlik and Melnyk created a first-of-its-kind Working Parent Burnout Scale, a 10-point survey that allows parents to measure their burnout in real time and use evidence-based solutions to help.

That scale is included in the new report, along with new guidance on positive parenting strategies, techniques and tips to form deeper connections with one's children.

"Positive parenting is when you give your children a lot of love and warmth, but you also provide structure and guidance in their life," Melnyk explained. "You gently teach them consequences of behaviors. So that is a much better goal to shoot for being a positive parent than a perfect parent."

Among the strategies:

• Connection and active listening
• Catching, checking and changing negative thoughts into positive ones
• Readjusting expectations for the parent and the child
• Reflecting and acting on priorities

"If maybe you're prioritizing making sure your house is spotless all the time, but then you don't feel like you have time to go for a walk every night with your children, maybe you need to reorganize or find a way to make both of those things work," Gawlik suggested.

Melnyk said these evidence-based approaches can help calm what she calls a "public health epidemic" of parental burnout.

"Parents do a great job caring for their children and everybody else, but they often don't prioritize their own self-care," Melnyk said. "As parents, we can't keep pouring from an empty cup. If children see their parents taking good self-care, the chances are they're going to grow up with that value as well. It has a ripple effect to the children and to the entire family."

"As one parent told me," Gawlik added, "'I would much rather have a happy kid than a perfect kid.'"

• Child Psychology
• Child Development
• Mental Health
• ADD and ADHD
• Infant and Preschool Learning
• Early childhood education
• Perfectionism (psychology)
• Microeconomics
• Child abuse
• Psycholinguistics
• Growth hormone treatment
• Developmental psychology

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Emerging coronaviruses have dramatically influenced human health. From the initial epidemic of Severe Acute Respiratory Syndrome (SARS), to sporadic outbreaks of Middle-East Respiratory Syndrome (MERS), to the pandemic of COVID-19, diseases caused by emerging coronaviruses have caused significant global ...

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