research paper covid 19

Research article
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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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Hebatullah Mohamed Abdulazeem

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

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

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1 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
2 Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan. Electronic address: [email protected].
3 Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
4 Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
5 School of Medicine, The University of Western Australia, Perth, Australia. Electronic address: [email protected].
6 Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia. Electronic address: [email protected].
7 Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia; Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA. Electronic address: [email protected].
8 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
9 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA. Electronic address: [email protected].
10 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
PMID: 32340833
PMCID: PMC7142680
DOI: 10.1016/j.jiph.2020.03.019

In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.

Keywords: 2019-nCoV; COVID-19; Novel coronavirus; Outbreak; SARS-CoV-2.

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COVID-19 pandemic and Internal Medicine Units in Italy: a precious effort on the front line. Montagnani A, Pieralli F, Gnerre P, Vertulli C, Manfellotto D; FADOI COVID-19 Observatory Group. Montagnani A, et al. Intern Emerg Med. 2020 Nov;15(8):1595-1597. doi: 10.1007/s11739-020-02454-5. Epub 2020 Jul 31. Intern Emerg Med. 2020. PMID: 32737837 Free PMC article. No abstract available.

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Communication of COVID-19 Misinformation on Social Media by Physicians in the US

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Correction Errors in Quotes and Dates JAMA Network Open

Question What types of COVID-19 misinformation have been propagated online by US physicians and through what channels?

Findings In this mixed-methods study of high-use social media platforms, physicians from across the US and representing a range of medical specialties were found to propagate COVID-19 misinformation about vaccines, treatments, and masks on large social media and other online platforms and that many had a wide reach based on number of followers.

Meaning This study’s findings suggest a need for rigorous evaluation of harm that may be caused by physicians, who hold a uniquely trusted position in society, propagating misinformation; ethical and legal guidelines for propagation of misinformation are needed.

Importance Approximately one-third of the more than 1 100 000 confirmed COVID-19–related deaths as of January 18, 2023, were considered preventable if public health recommendations had been followed. Physicians’ propagation of misinformation about COVID-19 on social media and other internet-based platforms has raised professional, public health, and ethical concerns.

Objective To characterize (1) the types of COVID-19 misinformation propagated by US physicians after vaccines became available, (2) the online platforms used, and (3) the characteristics of the physicians spreading misinformation.

Design, Setting, and Participants Using US Centers for Disease Control and Prevention guidelines for the prevention and treatment of COVID-19 infection during the study window to define misinformation, structured searches of high-use social media platforms (Twitter, Facebook, Instagram, Parler, and YouTube) and news sources ( The New York Times , National Public Radio) were conducted to identify COVID-19 misinformation communicated by US-based physicians between January 2021 and December 2022. Physicians’ state of licensure and medical specialty were identified. The number of followers for each physician on 4 major platforms was extracted to estimate reach and qualitative content analysis of the messages was performed.

Main Outcomes and Measures Outcome measures included categories of COVID-19 misinformation propagated, the number and traits of physicians engaged in misinformation propagation, and the type of online media channels used to propagate misinformation and potential reach.

Results The propagation of COVID-19 misinformation was attributed to 52 physicians in 28 different specialties across all regions of the country. General misinformation categories included vaccines, medication, masks, and other (ie, conspiracy theories). Forty-two physicians (80.8%) posted vaccine misinformation, 40 (76.9%) propagated information in more than 1 category, and 20 (38.5%) posted misinformation on 5 or more platforms. Major themes identified included (1) disputing vaccine safety and effectiveness, (2) promoting medical treatments lacking scientific evidence and/or US Food and Drug Administration approval, (3) disputing mask-wearing effectiveness, and (4) other (unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories).

Conclusions and Relevance In this mixed-methods study of US physician propagation of COVID-19 misinformation on social media, results suggest widespread, inaccurate, and potentially harmful assertions made by physicians across the country who represented a range of subspecialties. Further research is needed to assess the extent of the potential harms associated with physician propagation of misinformation, the motivations for these behaviors, and potential legal and professional recourse to improve accountability for misinformation propagation.

As of May 11, 2023, an estimated 1 128 000 COVID-19 deaths had occurred in the US, 1 and nearly 14% of people infected by the COVID-19 virus have experienced the post–COVID-19 condition. 2 , 3 As of December 2022, estimated death rates for unvaccinated persons in the US were 271 per 100 000 compared with 82 per 100 000 for those fully vaccinated, yet only 69.2% of eligible people had received the full primary vaccine series, and 15.5% had received the bivalent booster. 1 Vaccination rates have varied by region throughout the pandemic despite widespread availability, with southeastern states having lower full primary series rates (52%) compared with northeastern states (80%). 1 Other preventive behaviors, such as mask wearing and social distancing, have varied similarly by geographic region. 4 , 5

Individual health behaviors related to COVID-19 have been attributed to complex social phenomena, including inconsistent recommendations by government entities early in the pandemic, mistrust of the scientific community, political polarization, and unclear or incorrect guidance from other sources. 6 - 8 COVID-19 misinformation, defined as false, inaccurate, or misleading information according to the best evidence available at the time, and disinformation, defined as having an intentionally malicious purpose, have been ubiquitous on social media, despite major platforms’ COVID-19 misinformation policies. 9 Medical misinformation was propagated long before the COVID-19 pandemic, 10 but the internet increases reach and speed of dissemination, potentially exacerbating misinformation consequences during an unparalleled public health threat that has killed more than 7 million people across the globe. 11 - 13

COVID-19 misinformation has been spread by many people on social medial platforms, 14 but misinformation spread by physicians may be particularly pernicious. 15 Physicians are often considered credible sources of medical and public health information, increasing the potential negative impact of physician-initiated misinformation. The US Food and Drug Administration (FDA) and others have called for action to limit the potential harm of physician-propagated COVID-19 misinformation. 15 , 16 Despite the rising concerns voiced in news articles and opinion pieces, physician-propagated COVID-19 misinformation and its associated outcomes remain understudied.

This study aimed to address this gap in knowledge by examining COVID-19 misinformation communicated on social media platforms and other online sources by US physicians after vaccines were made available. Understanding the extent of this phenomenon, its potential impact, and associated professional, ethical, and legal ramifications may help to better understand the role that physician-propagated COVID-19 misinformation may have played in preventable COVID-19 deaths and mistrust in institutions.

This mixed-methods study sought to characterize the (1) type of COVID-19 misinformation physicians communicated online between January 1, 2021, and May 1, 2022; (2) social media and other online platforms where misinformation appeared; and (3) characteristics of the physicians. Physician age, sex, and race and ethnicity were not available on social media or other online postings. A decision was made to not infer these data from pictures or other means to avoid potential bias and misclassification. We defined COVID-19 misinformation as assertions unsupported by or contradicting US Centers for Disease Control and Prevention (CDC) guidance on COVID-19 prevention and treatment during the period assessed or contradicting the existing state of scientific evidence for any topics not covered by the CDC (eTable in Supplement 1 ). We conservatively classified inaccurate information as misinformation rather than disinformation because the intent of the propagator cannot be objectively assessed. The University of Massachusetts Institutional Review Board determined that this study did not meet criteria for human participant research. This study followed the Standards for Reporting Qualitative Research ( SRQR ) reporting guidelines.

First, we conducted structured searches of social media platforms and general web searches in late spring of 2022 to identify media containing COVID-19 misinformation attributed to US-based physicians, defined as using doctor of medicine (MD) or doctor of osteopathic medicine (DO) after their name and being licensed to practice medicine in the US at some time or never licensed but working in the US. The start date was selected in relation to the availability of the COVID-19 vaccines. Search terms included the following: “COVID,” “vaccine,” “doctor” or “physician,” “ineffective,” “pharmaceutical,” “medication,” “ivermectin,” “hydroxychloroquine,” and “purchase.” Search terms were refined based on initial searches to include “COVID misinformation,” “doctor” or “physician,” and/or “conspiracy theory.” Conspiracy theories were defined as communicating skepticism of all information that does not fit the theory, overinterpreting evidence that fits the theory, and/or evidence of internal inconsistency. 17 The platforms searched were selected based on the volume of news articles, popularity, and searchability (Instagram, Twitter, YouTube, Facebook, Parler, TikTok, The New York Times , National Public Radio) 18 ; if the findings on one platform indicated that another platform could have additional new data, it was added to the search list. Due to the large volume and repetitiveness of Tweets, Twitter searches focused initially on America’s Frontline Doctors’ Twitter profile because of the volume of COVID-19 misinformation in its Tweets, 19 its large following, and the potential for physicians propagating misinformation to follow the page. Followers of the America’s Frontline Doctors’ page with an MD or DO in their header were traced on Twitter and other platforms as well. General internet searches using Google’s search engine were conducted to identify misinformation attributed to physicians in third party platforms, such as local news articles.

The following information was collected from each source: physician’s name, medical specialty, the state(s) in which they were currently or had been licensed, whether their license to practice was active, had lapsed, or been revoked based on state medical board site searches, when the misinformation was posted (if available), from what source it was found, and the number of followers the physician had (if the source was a social media platform). Misinformation was classified into the following categories: medication, vaccine, mask/distancing, and other unsubstantiated or false claims. After the initial searches were completed, the physicians’ names were searched on the social media platforms and through general online searches to identify misinformation they posted that may have been missed in the initial searches and extended through December 2022.

Descriptive statistics were used to quantify the types of misinformation, the frequency in which they appeared, the platforms on which they were found, and characteristics of the physicians identified (eg, specialty and state[s] in which the physician was licensed). We calculated the total, median, and IQR for the number of followers on platforms with the highest volume of users (Twitter, Facebook, YouTube, Instagram) using Stata software, version 17 (StataCorp).

We performed directed qualitative content analysis 20 of the misinformation using a validated rapid qualitative analysis approach. 21 The analytic team (S.S. and M.D.) populated a templated summary table with misinformation text extracted from each media platform. The team divided the physician list and generated a summary of the misinformation associated with each of the physicians. In the second step of this analytic process, each team member individually identified pertinent and common themes, subthemes, and supporting quotes for each. After this was done individually, the team met to discuss their findings and combine the findings into a final list of themes and subthemes. Considerations regarding reflexivity included that S.G. is a public health professor and physician, and M.D. and S.S. are aspiring physicians, which may have increased sensitivity to potential harms.

A total of 52 US physicians were identified as having communicated COVID-19 misinformation in the period assessed. All but 2 were or had been licensed to practice medicine in the US; the others were researchers. The 50 physicians who currently were or had been licensed represented 28 distinct medical specialties (3 of 50 had 2 different specialties; primary care was the most common overall [18 (36.0%)]) and they were licensed or working in 29 states across the US ( Figure and Table 1 ). Forty-four of the 50 physicians (88.0%) held an active license in at least 1 state; 3 (6.0%) did not have an active license, 4 (8.0%) had had a license suspended or revoked, and 1 (2.0%) had active licenses in 2 states and revoked/suspended licenses in 2 other states. Nearly one-third (16 of 52) were affiliated with groups with a history of propagating medical misinformation, such as America’s Frontline Doctors. Specific types of misinformation included the following: (1) vaccines were unsafe and/or ineffective, (2) masks and/or social distancing did not decrease risk for contracting COVID-19, (3) medications for prevention or treatment were effective despite not having completed clinical trials or having been FDA approved, and (4) other (eg, conspiracy theories).

Most of the 52 physicians (40 [76.9%]) who posted misinformation did so in more than 1 of the 4 categories identified. Vaccine misinformation was posted by the majority (42 [80.8%]), followed by other misinformation (28 [53.8%]; eg, government and public health officials deliberately falsified COVID-19 statistics) and medication misinformation (27 [51.9%]).

Of these 52 physicians, 20 (38.5%) posted COVID-19 misinformation on 5 or more different social media platforms and 40 (76.9%) appeared on 5 or more third-party online platforms such as news outlets. Twitter was the most used platform, with 37 of the 52 physicians (71.2%) posting misinformation and a median of 67 400 followers (IQR, 12 900-204 000). Additional details of physicians’ reach by platforms and followers are in Table 2 and Table 3 .

Major themes identified included the following: (1) claiming vaccines were unsafe and/or ineffective, (2) promoting unapproved medications for prevention or treatment, (3) disputing mask-wearing effectiveness, and (4) other misinformation, including unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories. Supportive quotes are listed in Table 4 .

The most common theme identified was physicians discouraging the public from receiving COVID-19 vaccines. Promoting fear and distrust of the vaccine and reliance on “natural” immunity were common subthemes.

Some of the misinformation propagated by physicians claimed that COVID-19 vaccines were ineffective at preventing COVID-19 spread. A common approach included circulating counts of positive case rates by vaccination status, claiming that most positive cases were among vaccinated individuals. This claim is technically true but misleading, as many more people are vaccinated, and the proportion of unvaccinated people who are infected is much higher. 22 Some stated that the significant increase in case rates after the initial vaccine rollout was evidence for ineffectiveness.

Assertions that COVID-19 vaccines were harmful was not supported by scientific evidence at the time. Unfounded claims included that the vaccines caused infertility, irreparable damage to one’s immune system, increased risk of developing a chronic illness for children, and a higher risk of cancer and death. Claims that myocarditis was common in children who received the vaccine and that the risks of myocarditis outweighed the risk of vaccination were also unfounded. 23 Several physicians redistributed news articles with stories of individuals suddenly or mysteriously dying from the vaccine, despite evidence from the CDC confirming that deaths caused by a COVID vaccine were extremely rare (9 deaths for over 600 million doses administered in the US as of January 2023) and could be attributed only to the Johnson and Johnson COVID-19 vaccine, which was used much less frequently than other manufacturers’ vaccines in many countries. 24

Many of the identified physicians promoted the use of treatments that had not been tested or FDA approved for use in relation to COVID-19. The 2 most prominent medications promoted were ivermectin and hydroxychloroquine, which have been found to not be effective at treating COVID-19 infections in randomized clinical trials. 25 , 26

Anecdotal personal experiences of successfully treating patients with untested medications were commonly used to support claims about safety and effectiveness, such as patients’ conditions were not improving before receiving the untested medication, but the patient recovered after starting the treatment.

Many physicians posted links or screenshots to articles claiming that ivermectin decreased mortality and hospitalization and increased time to recovery and viral clearance. Although some of the articles appeared to be peer-reviewed, none were in high-quality peer-reviewed biomedical journals, and the FDA had not approved the use of these medications for treating COVID-19. At least 1 of the cited articles has been retracted due to misinterpretation of the data. 27

Many of the physicians propagating misinformation about masking effectiveness portrayed masks in a negative light. Claims centered on ineffectiveness, harm, or both.

Most of the misinformation propagated about wearing protective masks asserted that studies conducted before the pandemic definitively concluded that masks do not prevent the spread of respiratory viral infections. Additionally, data showing rising cases in areas enforcing mask mandates were interpreted to mean that the mandates did nothing to slow the spread of infection.

Allegations of consequences of mask wearing included medical and social or developmental effects, all of which were unfounded. 28 Alleged medical consequences included claims that wearing a face mask restricts one’s oxygen, increases the amount of carbon dioxide being inhaled, and causes mask wearers to inhale bacteria that gets trapped. Many physicians focused on negative consequences related to children and mask mandates in schools, claiming that masks interfered with social development despite lack of evidence and that requiring children to wear masks was a form of child abuse.

This misinformation category included conspiracy theories related to domestic and foreign governments and pharmaceutical companies. Theories related to the government included the following: (1) the COVID-19 pandemic was planned by government officials—the “plandemic”; (2) government and public health officials withheld key information regarding COVID-19 from the public, such as hydroxychloroquine effectiveness, falsified statistics to make the virus appear more severe, and censored information that challenged government messaging; (3) the virus originated in a laboratory in China, which contradicted scientific evidence at the time; and (4) the virus was part of a National Institutes of Health–funded study, was leaked, and that the leak was covered up by government and public health officials. Theories related to pharmaceutical companies included that they played a role in discouraging the use of ivermectin and hydroxychloroquine because these medications were inexpensive and easily accessible, and pharmaceutical companies benefited from the promotion of more novel and expensive treatments.

This study was the first, to our knowledge, to identify the types of COVID-19 misinformation propagated by US physicians on social media and the platforms they used, as well as characterize the physicians who spread the misinformation. The content of misinformation physicians spread was similar to the misinformation spread by others; this study contributes new information about the range of specialties and regions of the country the physicians represented. The widely varying number of followers on social media for each physician suggested that the impact of any individual physician’s social media postings also may vary.

Some of the physicians identified belonged to organizations that have been propagating medical misinformation for decades, 10 but these organizations became more vocal and visible in the context of the pandemic’s public health crisis, political divisiveness, and social isolation. Understanding the motivation for misinformation propagation is beyond the scope of this study, but it has become an increasingly profitable industry within and outside of medicine. For example, America’s Frontline Doctors implemented a telemedicine service that charged $90 per consult, primarily to prescribe hydroxychloroquine and ivermectin for COVID-19 to patients across the country, profiting at least $15 million from the endeavor. 29 Twitter’s elimination of safeguards against misinformation 30 and the absence of federal laws regulating medical misinformation on social media platforms suggest that misinformation about COVID-19 and other medical misinformation is likely to persist and may increase. Deregulation of COVID-19 misinformation on social media platforms may have far-reaching implications because consumers may struggle to evaluate the accuracy of the assertions made. 31

National physicians’ organizations, such as the American Medical Association, have called for disciplinary action for physicians propagating COVID-19 misinformation, 32 but stopping physicians from propagating COVID-19 misinformation outside of the patient encounter may be challenging. 33 Although professional speech may be regulated by courts 34 and the FDA has been called on to address medical misinformation, 16 few physicians appear to have faced disciplinary action. Factors such as licensing boards’ lack of resources available to dedicate toward monitoring the internet 35 and state government officials’ challenges to medical boards’ authority to discipline physicians propagating misinformation 36 may limit action.

Scientific evidence depends on a body of accumulated research to inform practice and guidelines and the evidence depends on the best quality research available at any given time. A recent Cochrane Review has been misinterpreted to have definitively shown that wearing masks does not reduce transmission of respiratory viruses and has been used to support assertions that masks definitively “do not work.” 37 Although the Federal Bureau of Investigation and Department of Energy presented a theory to Congress that the COVID-19 virus was the result of a laboratory leak, 38 scientific evidence and a more recent report from the Office of the Director of National Intelligence demonstrate lack of evidence for a laboratory leak and favor a zoonotic origin of the virus. 39 , 40 These recent challenges to prior understandings illuminate the importance of transparency and reproducibility of the process by which conclusions are drawn.

This study had some limitations. We conducted the study in the spring of 2022, after many major social media platforms had begun to establish policies to combat the propagation of COVID-19 misinformation, which means that the current study may underrepresent the extent of misinformation present before these policies were put in place. On some platforms (eg, Twitter), we were unable to analyze all posts by individuals due to the high volume of Tweets and degree of repetition. This study focused on online platforms whose content was readily accessible to the public; different approaches to identifying misinformation and searches of less used platforms might identify other physicians and include other topics. Misinformation disseminated in other ways, such as during clinical care, was not captured. Vaccines had been approved at the start of the period studied, but accessibility may have varied in the early days of the initial rollout. Finally, the state of scientific evidence for COVID-19 guidelines has evolved rapidly over the course of the pandemic, and this study represents a cross-section of time. The current evidence base for preventive and treatment practices, such as duration of vaccine effectiveness, may differ from the evidence base during the study time frame.

Results of this mixed-methods study of the propagation of COVID-19 misinformation by US physicians on social media suggest that physician-propagated misinformation has reached many people during the pandemic and that physicians from a range of specialties and geographic regions have contributed to the “infodemic.” High-quality, ethical health care depends on inviolable trust between health care professionals, their patients, and society. Understanding the degree to which the misinformation about vaccines, medications, masks, and conspiracy theories spread by physicians on social media influences behaviors that put patients at risk for preventable harm, such as illness or death, will help to guide actions to regulate content or discipline physicians who participate in misinformation propagation related to COVID-19 or other conditions. A coordinated response by federal and state governments and the profession that takes free speech carefully into account is needed.

Accepted for Publication: July 6, 2023.

Published: August 15, 2023. doi:10.1001/jamanetworkopen.2023.28928

Correction: This article was corrected on October 25, 2023, to fix dates in several of the quotes in Table 4 due to coding errors and to correct minor wording inaccuracies in several of the quotes. In addition, the date range of the initial social media searches was clarified in the Methods.

Corresponding Author: Sarah L. Goff, MD, PhD, Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts, 715 N Pleasant St, Amherst, MA 01002 ( [email protected] ).

Author Contributions: Dr Goff had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Mss Sule and DaCosta are considered co–first authors.

Concept and design: Sule, Gilson, Goff.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sule, DaCosta.

Statistical analysis: Sule, DaCosta, Gilson.

Administrative, technical, or material support: DaCosta, DeCou, Gilson, Goff.

Supervision: DaCosta, Goff.

Conflict of Interest Disclosures: Dr Wallace reported contributing to this work while she was a student at University of Massachusetts Amherst, before and outside of her official capacity as a government employee. No other disclosures were reported.

Funding/Support: The study was funded via internal support by the University of Massachusetts (Dr Goff).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed here are those of the authors and do not represent the official policy or position of the US Department of Veteran Affairs or the US government.

Data Sharing Statement: See Supplement 2 .

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Public Trust in Government: 1958-2024

Public trust in the federal government, which has been low for decades, has increased modestly since 2023 . As of April 2024, 22% of Americans say they trust the government in Washington to do what is right “just about always” (2%) or “most of the time” (21%). Last year, 16% said they trusted the government just about always or most of the time, which was among the lowest measures in nearly seven decades of polling.

Date	.	Individual polls	Moving average
5/19/2024	PEW	22	22
6/11/2023	PEW	16	19
5/01/2022	PEW	20	20
4/11/2021	PEW	24	21
8/2/2020	PEW	20	24
4/12/2020	PEW	27	21
3/25/2019	PEW	17	17
12/04/2017	PEW	18	18
4/11/2017	PEW	20	19
10/04/2015	PEW	19	18
7/20/2014	CNN	14	19
2/26/2014	PEW	24	18
11/15/2013	CBS/NYT	17	20
10/13/2013	PEW	19	19
5/31/2013	CBS/NYT	20	20
2/06/2013	CBS/NYT	20	22
1/13/2013	PEW	26	23
10/31/2012	NES	22	19
10/19/2011	CBS/NYT	10	17
10/04/2011	PEW	20	15
9/23/2011	CNN	15	18
8/21/2011	PEW	19	21
2/28/2011	PEW	29	23
10/21/2010	CBS/NYT	22	23
10/01/2010	CBS/NYT	18	21
9/06/2010	PEW	24	23
9/01/2010	CNN	25	23
4/05/2010	CBS/NYT	20	23
4/05/2010	PEW	25	22
3/21/2010	PEW	22	24
2/12/2010	CNN	26	22
2/05/2010	CBS/NYT	19	21
1/10/2010	GALLUP	19	20
12/20/2009	CNN	20	21
8/31/2009	CBS/NYT	24	22
6/12/2009	CBS/NYT	20	23
12/21/2008	CNN	26	25
10/15/2008	NES	31	24
10/13/2008	CBS/NYT	17	24
7/09/2007	CBS/NYT	24	24
1/09/2007	PEW	31	28
10/08/2006	CBS/NYT	29	29
9/15/2006	CBS/NYT	28	30
2/05/2006	PEW	34	31
1/20/2006	CBS/NYT	32	33
1/06/2006	GALLUP	32	32
12/02/2005	CBS/NYT	32	32
9/11/2005	PEW	31	31
9/09/2005	CBS/NYT	29	30
6/19/2005	GALLUP	30	35
10/15/2004	NES	46	39
7/15/2004	CBS/NYT	40	41
3/21/2004	PEW	36	38
10/26/2003	GALLUP	37	36
7/27/2003	CBS/NYT	36	43
10/15/2002	NES	55	46
9/04/2002	GALLUP	46	46
9/02/2002	CBS/NYT	38	40
7/13/2002	CBS/NYT	38	40
6/17/2002	GALLUP	44	43
1/24/2002	CBS/NYT	46	46
12/07/2001	CBS/NYT	48	49
10/25/2001	CBS/NYT	55	54
10/06/2001	GALLUP	60	49
1/17/2001	CBS/NYT	31	44
10/31/2000	CBS/NYT	40	38
10/15/2000	NES	44	42
7/09/2000	GALLUP	42	39
4/02/2000	ABC/POST	31	38
2/14/2000	PEW	40	34
10/03/1999	CBS/NYT	30	36
9/14/1999	CBS/NYT	38	33
5/16/1999	PEW	31	33
2/21/1999	PEW	31	31
2/12/1999	ABC/POST	32	32
2/04/1999	GALLUP	33	34
1/10/1999	CBS/NYT	37	34
1/03/1999	CBS/NYT	33	37
12/01/1998	NES	40	33
11/15/1998	PEW	26	30
11/01/1998	CBS/NYT	24	26
10/26/1998	CBS/NYT	26	28
8/10/1998	ABC/POST	34	31
2/22/1998	PEW	34	35
2/01/1998	GALLUP	39	33
1/25/1998	CBS/NYT	26	32
1/19/1998	ABC/POST	31	32
10/31/1997	PEW	39	31
8/27/1997	ABC/POST	22	31
6/01/1997	GALLUP	32	26
1/14/1997	CBS/NYT	23	27
11/02/1996	CBS/NYT	25	27
10/15/1996	NES	33	28
5/12/1996	GALLUP	27	31
5/06/1996	ABC/POST	34	29
11/19/1995	ABC/POST	25	27
8/07/1995	GALLUP	22	22
8/05/1995	CBS/NYT	20	21
3/19/1995	ABC/POST	22	20
2/22/1995	CBS/NYT	18	21
12/01/1994	NES	22	21
10/29/1994	CBS/NYT	22	22
10/23/1994	ABC/POST	22	20
6/06/1994	GALLUP	17	19
1/30/1994	GALLUP	19	20
1/20/1994	ABC/POST	24	22
3/24/1993	GALLUP	22	25
1/17/1993	ABC/POST	28	25
1/14/1993	CBS/NYT	24	25
10/23/1992	CBS/NYT	22	25
10/15/1992	NES	29	25
6/08/1992	GALLUP	23	29
10/20/1991	ABC/POST	35	35
3/06/1991	CBS/NYT	47	42
3/01/1991	ABC/POST	45	46
1/27/1991	ABC/POST	46	40
12/01/1990	NES	28	33
10/28/1990	CBS/NYT	25	32
9/06/1990	ABC/POST	42	35
1/16/1990	ABC/POST	38	38
6/29/1989	CBS/NYT	35	39
1/15/1989	CBS/NYT	44	41
11/10/1988	CBS/NYT	44	43
10/15/1988	NES	41	41
1/23/1988	ABC/POST	39	40
10/18/1987	CBS/NYT	41	43
6/01/1987	ABC/POST	47	43
3/01/1987	CBS/NYT	42	44
1/21/1987	CBS/NYT	43	43
1/19/1987	ABC/POST	44	42
12/01/1986	NES	39	44
11/30/1986	CBS/NYT	49	43
9/09/1986	ABC/POST	40	44
1/19/1986	CBS/NYT	42	44
11/06/1985	CBS/NYT	49	43
7/29/1985	ABC/POST	38	42
3/21/1985	ABC/POST	37	40
2/27/1985	CBS/NYT	46	42
2/22/1985	ABC/POST	43	45
11/14/1984	CBS/NYT	46	44
10/15/1984	NES	44	41
12/01/1982	NES	33	39
11/07/1980	CBS/NYT	39	32
10/15/1980	NES	25	30
3/12/1980	CBS/NYT	26	27
11/03/1979	CBS/NYT	30	28
12/01/1978	NES	29	31
10/23/1977	CBS/NYT	33	32
4/25/1977	CBS/NYT	35	34
10/15/1976	NES	33	36
9/05/1976	CBS/NYT	40	35
6/15/1976	CBS/NYT	33	35
3/01/1976	GALLUP	33	34
2/08/1976	CBS/NYT	36	35
12/01/1974	NES	36	36
10/15/1972	NES	53	53
12/01/1970	NES	54	54
10/15/1968	NES	62	62
12/01/1966	NES	65	65
10/15/1964	NES	77	77
12/01/1958	NES	73	73

When the National Election Study began asking about trust in government in 1958, about three-quarters of Americans trusted the federal government to do the right thing almost always or most of the time.

Trust in government began eroding during the 1960s, amid the escalation of the Vietnam War, and the decline continued in the 1970s with the Watergate scandal and worsening economic struggles.

Confidence in government recovered in the mid-1980s before falling again in the mid-’90s. But as the economy grew in the late 1990s, so too did trust in government. Public trust reached a three-decade high shortly after the 9/11 terrorist attacks but declined quickly after. Since 2007, the shares saying they can trust the government always or most of the time have not been higher than 30%.

Today, 35% of Democrats and Democratic-leaning independents say they trust the federal government just about always or most of the time, compared with 11% of Republicans and Republican leaners.

Democrats report slightly more trust in the federal government today than a year ago. Republicans’ views have been relatively unchanged over this period.

Since the 1970s, trust in government has been consistently higher among members of the party that controls the White House than among the opposition party.

Republicans have often been more reactive than Democrats to changes in political leadership, with Republicans expressing much lower levels of trust during Democratic presidencies. Democrats’ attitudes have tended to be somewhat more consistent, regardless of which party controls the White House.

However, Republican and Democratic shifts in attitudes from the end of Donald Trump’s presidency to the start of Joe Biden’s were roughly the same magnitude.

Date	.	Democrat/Lean Dem	Republican/Lean Rep
5/19/2024	PEW	35	11
6/11/2023	PEW	25	8
5/1/2022	PEW	29	9
4/11/2021	PEW	36	9
8/2/2020	PEW	12	28
4/12/2020	PEW	18	36
3/25/2019	PEW	14	21
12/04/2017	PEW	15	22
4/11/2017	PEW	15	28
10/04/2015	PEW	26	11
7/20/2014	CNN	17	11
2/26/2014	PEW	32	16
11/15/2013	CBS/NYT	31	8
10/13/2013	PEW	27	10
5/31/2013	CBS/NYT	30	8
2/06/2013	CBS/NYT	34	8
1/13/2013	PEW	37	15
10/31/2012	NES	29	16
10/19/2011	CBS/NYT	13	8
10/04/2011	PEW	27	12
9/23/2011	CNN	20	11
8/21/2011	PEW	25	13
3/01/2011	PEW	34	24
10/21/2010	CBS/NYT	36	7
10/01/2010	CBS/NYT	27	13
9/06/2010	PEW	35	13
9/01/2010	CNN	31	18
4/05/2010	CBS/NYT	27	14
3/21/2010	PEW	32	13
2/12/2010	CNN	34	18
2/05/2010	CBS/NYT	31	9
1/10/2010	GALLUP	23	16
12/20/2009	CNN	25	16
8/31/2009	CBS/NYT	34	12
6/12/2009	CBS/NYT	35	10
12/21/2008	CNN	30	22
10/15/2008	NES	34	31
10/13/2008	CBS/NYT	12	19
7/09/2007	CBS/NYT	18	31
1/09/2007	PEW	22	43
10/08/2006	CBS/NYT	20	50
9/15/2006	CBS/NYT	20	44
2/05/2006	PEW	20	53
1/20/2006	CBS/NYT	23	51
1/06/2006	GALLUP	20	44
12/02/2005	CBS/NYT	19	52
9/11/2005	PEW	19	49
9/09/2005	CBS/NYT	21	42
6/19/2005	GALLUP	24	36
10/15/2004	NES	35	61
3/21/2004	PEW	24	55
10/26/2003	GALLUP	35	42
7/27/2003	CBS/NYT	25	51
10/15/2002	NES	52	63
9/04/2002	GALLUP	38	55
9/02/2002	CBS/NYT	32	52
7/13/2002	CBS/NYT	34	45
6/17/2002	GALLUP	33	55
1/24/2002	CBS/NYT	39	56
12/07/2001	CBS/NYT	39	60
10/25/2001	CBS/NYT	47	70
10/06/2001	GALLUP	52	68
1/17/2001	CBS/NYT	26	38
10/15/2000	NES	48	43
7/09/2000	GALLUP	42	41
4/02/2000	ABC/POST	38	24
2/14/2000	PEW	46	37
10/03/1999	CBS/NYT	31	27
9/14/1999	CBS/NYT	42	35
5/16/1999	PEW	36	30
2/21/1999	PEW	35	25
2/12/1999	ABC/POST	41	21
2/04/1999	GALLUP	38	29
1/10/1999	CBS/NYT	42	33
1/03/1999	CBS/NYT	37	29
12/01/1998	NES	45	35
11/19/1998	PEW	31	23
11/01/1998	CBS/NYT	28	22
10/26/1998	CBS/NYT	28	25
8/10/1998	ABC/POST	40	30
2/22/1998	PEW	42	28
2/01/1998	GALLUP	52	26
1/25/1998	CBS/NYT	31	22
10/31/1997	PEW	46	32
6/01/1997	GALLUP	39	25
1/14/1997	CBS/NYT	29	20
11/02/1996	CBS/NYT	31	20
10/15/1996	NES	40	27
5/12/1996	GALLUP	32	20
5/06/1996	ABC/POST	41	35
11/19/1995	ABC/POST	27	26
8/07/1995	GALLUP	24	21
8/05/1995	CBS/NYT	20	20
3/19/1995	ABC/POST	27	20
2/22/1995	CBS/NYT	18	19
12/01/1994	NES	26	18
10/29/1994	CBS/NYT	26	19
10/23/1994	ABC/POST	27	16
6/06/1994	GALLUP	23	11
1/30/1994	GALLUP	25	14
1/20/1994	ABC/POST	30	18
3/24/1993	GALLUP	32	11
1/17/1993	ABC/POST	32	25
1/14/1993	CBS/NYT	26	21
10/23/1992	CBS/NYT	17	31
10/15/1992	NES	31	34
6/08/1992	GALLUP	17	31
10/20/1991	ABC/POST	31	41
3/06/1991	CBS/NYT	40	56
3/01/1991	ABC/POST	41	52
12/01/1990	NES	26	32
10/28/1990	CBS/NYT	21	31
9/06/1990	ABC/POST	37	48
1/16/1990	ABC/POST	32	46
6/29/1989	CBS/NYT	27	45
1/15/1989	CBS/NYT	37	54
11/10/1988	CBS/NYT	36	58
10/15/1988	NES	35	51
1/23/1988	ABC/POST	31	51
10/18/1987	CBS/NYT	36	47
6/01/1987	ABC/POST	38	59
3/01/1987	CBS/NYT	34	54
1/21/1987	CBS/NYT	36	51
1/19/1987	ABC/POST	39	51
12/01/1986	NES	31	53
11/30/1986	CBS/NYT	37	63
9/09/1986	ABC/POST	30	51
1/19/1986	CBS/NYT	36	51
11/06/1985	CBS/NYT	42	59
7/29/1985	ABC/POST	30	48
3/21/1985	ABC/POST	29	49
2/22/1985	ABC/POST	30	62
11/14/1984	CBS/NYT	36	59
10/15/1984	NES	41	50
12/01/1982	NES	32	41
11/07/1980	CBS/NYT	40	42
10/15/1980	NES	31	23
3/12/1980	CBS/NYT	30	22
11/03/1979	CBS/NYT	32	28
12/01/1978	NES	33	26
10/23/1977	CBS/NYT	40	25
4/25/1977	CBS/NYT	37	34
10/15/1976	NES	30	42
9/05/1976	CBS/NYT	38	45
6/15/1976	CBS/NYT	36	36
3/01/1976	GALLUP	31	40
12/01/1974	NES	36	38
10/15/1972	NES	48	62
12/01/1970	NES	52	61
10/15/1968	NES	66	60
12/01/1966	NES	71	54
10/15/1964	NES	80	73
12/01/1958	NES	71	79

Date	.	Liberal Dem/Lean Dem	Cons-Moderate Dem/Lean Dem	Moderate-Lib Rep/Lean Rep	Conservative Rep/Lean Rep
5/19/2024	PEW	33	36	17	7
6/11/2023	PEW	23	27	14	4
5/1/2022	PEW	26	32	13	7
4/11/2021	PEW	31	40	16	5
8/2/2020	PEW	8	16	31	27
4/12/2020	PEW	12	22	37	37
3/25/2019	PEW	13	15	21	20
12/04/2017	PEW	15	16	26	20
4/11/2017	PEW	15	16	32	26
10/04/2015	PEW	28	25	14	9
7/20/2014	CNN	19	16	15	7
2/26/2014	PEW	31	33	21	13
11/15/2013	CBS/NYT	38	25	13	5
10/13/2013	PEW	25	27	16	7
5/31/2013	CBS/NYT	30	30	16	4
2/06/2013	CBS/NYT	35	34	9	7
1/13/2013	PEW	34	37	17	14
10/31/2012	NES	26	32	18	15
10/19/2011	CBS/NYT	9	13	11	7
10/04/2011	PEW	30	25	14	9
9/23/2011	CNN	30	16	11	11
8/21/2011	PEW	26	24	18	10
3/01/2011	PEW	36	33	32	18
10/21/2010	CBS/NYT	37	35	12	4
10/01/2010	CBS/NYT	34	22	10	16
9/06/2010	PEW	39	31	19	10
9/01/2010	CNN	36	30	28	11
4/05/2010	CBS/NYT	37	21	23	7
3/21/2010	PEW	36	31	19	11
2/12/2010	CNN	36	34	25	9
2/05/2010	CBS/NYT	31	32	13	7
1/10/2010	GALLUP	29	22	20	12
12/20/2009	CNN	31	23	18	13
8/31/2009	CBS/NYT	38	30	14	10
6/12/2009	CBS/NYT	42	34	13	8
12/21/2008	CNN	36	28	28	17
10/15/2008	NES	37	34	48	28
10/13/2008	CBS/NYT	16	12	26	12
7/09/2007	CBS/NYT	14	21	38	28
1/09/2007	PEW	15	25	41	45
10/08/2006	CBS/NYT	14	22	50	51
9/15/2006	CBS/NYT	11	23	44	44
2/05/2006	PEW	13	23	52	54
1/20/2006	CBS/NYT	27	21	52	50
1/06/2006	GALLUP	10	26	33	56
12/02/2005	CBS/NYT	16	21	60	47
9/11/2005	PEW	13	22	39	54
9/09/2005	CBS/NYT	12	26	46	41
6/19/2005	GALLUP	25	24	31	41
10/15/2004	NES	24	39	63	59
3/21/2004	PEW	23	24	53	56
10/26/2003	GALLUP	23	39	31	52
7/27/2003	CBS/NYT	21	27	55	47
10/15/2002	NES	53	56	66	61
9/04/2002	GALLUP	31	40	50	60
9/02/2002	CBS/NYT	32	32	55	53
7/13/2002	CBS/NYT	37	33	50	42
6/17/2002	GALLUP	30	36	59	55
1/24/2002	CBS/NYT	38	39	58	54
12/07/2001	CBS/NYT	34	43	61	58
10/06/2001	GALLUP	46	55	66	69
1/17/2001	CBS/NYT	33	24	41	33
10/15/2000	NES	58	52	54	44
7/09/2000	GALLUP	41	42	50	35
4/02/2000	ABC/POST	38	39	28	20
10/03/1999	CBS/NYT	26	33	29	24
9/14/1999	CBS/NYT	38	45	42	27
2/12/1999	ABC/POST	40	43	26	16
2/04/1999	GALLUP	36	40	33	27
1/10/1999	CBS/NYT	39	44	40	28
1/03/1999	CBS/NYT	34	39	31	26
12/01/1998	NES	45	46	39	34
11/01/1998	CBS/NYT	28	28	23	22
10/26/1998	CBS/NYT	30	28	22	26
8/10/1998	ABC/POST	38	35	24	27
2/01/1998	GALLUP	55	52	33	23
1/25/1998	CBS/NYT	24	31	24	19
6/01/1997	GALLUP	41	38	31	21
1/14/1997	CBS/NYT	30	28	25	14
11/02/1996	CBS/NYT	30	32	21	19
10/15/1996	NES	38	39	30	25
5/12/1996	GALLUP	25	35	25	18
5/06/1996	ABC/POST	41	41	39	33
11/19/1995	ABC/POST	26	27	26	28
8/07/1995	GALLUP	16	27	17	25
8/05/1995	CBS/NYT	21	19	19	23
3/19/1995	ABC/POST	24	28	22	17
2/22/1995	CBS/NYT	20	18	22	17
12/01/1994	NES	22	28	21	16
10/29/1994	CBS/NYT	26	27	23	15
10/23/1994	ABC/POST	32	25	22	11
6/06/1994	GALLUP	16	26	15	9
1/30/1994	GALLUP	20	27	18	12
1/20/1994	ABC/POST	26	31	25	10
1/17/1993	ABC/POST	30	33	28	22
1/14/1993	CBS/NYT	17	30	20	20
10/23/1992	CBS/NYT	20	15	30	32
10/15/1992	NES	26	33	37	31
6/08/1992	GALLUP	13	19	31	30
10/20/1991	ABC/POST	25	33	42	39
3/06/1991	CBS/NYT	46	39	57	56
3/01/1991	ABC/POST	39	41	54	50
12/01/1990	NES	27	26	31	33
9/06/1990	ABC/POST	34	39	49	45
1/16/1990	ABC/POST	28	34	50	39
6/29/1989	CBS/NYT	27	27	38	55
1/15/1989	CBS/NYT	33	38	56	54
11/10/1988	CBS/NYT	24	40	65	52
10/15/1988	NES	34	35	52	51
1/23/1988	ABC/POST	30	31	54	49
10/18/1987	CBS/NYT	34	37	47	49
6/01/1987	ABC/POST	34	41	60	55
1/21/1987	CBS/NYT	34	37	54	48
1/19/1987	ABC/POST	37	38	52	51
12/01/1986	NES	25	36	53	53
9/09/1986	ABC/POST	25	34	55	44
1/19/1986	CBS/NYT	34	38	51	52
11/06/1985	CBS/NYT	42	43	60	56
7/29/1985	ABC/POST	26	33	53	41
3/21/1985	ABC/POST	27	29	52	48
2/22/1985	ABC/POST	28	33	62	63
10/15/1984	NES	34	47	52	46
12/01/1982	NES	29	35	48	38
11/07/1980	CBS/NYT	38	42	44	41
10/15/1980	NES	34	28	28	18
3/12/1980	CBS/NYT	31	29	25	18
11/03/1979	CBS/NYT	34	31	28	26
12/01/1978	NES	38	33	24	24
10/23/1977	CBS/NYT	41	41	32	16
4/25/1977	CBS/NYT	41	38	33	36
10/15/1976	NES	27	34	49	41
9/05/1976	CBS/NYT	33	42	45	45
6/15/1976	CBS/NYT	35	35	39	34
12/01/1974	NES	36	40	39	40
10/15/1972	NES	44	53	62	66

Among Asian, Hispanic and Black adults, 36%, 30% and 27% respectively say they trust the federal government “most of the time” or “just about always” – higher levels of trust than among White adults (19%).

During the last Democratic administration, Black and Hispanic adults similarly expressed more trust in government than White adults. Throughout most recent Republican administrations, White Americans were substantially more likely than Black Americans to express trust in the federal government to do the right thing.

Date	.	Hispanic	Black	White	Asian
5/19/2024	PEW	30	27	19	36
6/11/2023	PEW	23	21	13	23
5/1/2022	PEW	29	24	16	37
4/11/2021	PEW	36	37	18	29
8/2/2020	PEW	28	15	18	27
4/12/2020	PEW	29	27	26
3/25/2019	PEW	28	9	17
12/04/2017	PEW	23	15	17
4/11/2017	PEW	24	13	20
10/04/2015	PEW	28	23	15
7/20/2014	CNN			9
2/26/2014	PEW	33	26	22
11/15/2013	CBS/NYT			12
10/13/2013	PEW	21	24	17
5/31/2013	CBS/NYT			15
2/06/2013	CBS/NYT		39	15
1/13/2013	PEW	44	38	20
10/31/2012	NES	38	38	16
10/19/2011	CBS/NYT	15	15	8
10/04/2011	PEW	29	25	17
9/23/2011	CNN			10
8/21/2011	PEW	28	35	15
3/01/2011	PEW	28	25	30
10/21/2010	CBS/NYT		40	15
10/01/2010	CBS/NYT			17
9/06/2010	PEW	37	37	20
9/01/2010	CNN			21
4/05/2010	CBS/NYT			18
3/21/2010	PEW	26	37	20
2/12/2010	CNN			22
2/05/2010	CBS/NYT			16
1/10/2010	GALLUP			16
12/20/2009	CNN		21	18
8/31/2009	CBS/NYT			21
6/12/2009	CBS/NYT			16
12/21/2008	CNN			22
10/15/2008	NES	34	28	30
10/13/2008	CBS/NYT			18
7/09/2007	CBS/NYT		11	25
1/09/2007	PEW	35	20	32
10/08/2006	CBS/NYT			31
9/15/2006	CBS/NYT			31
2/05/2006	PEW		26	36
1/20/2006	CBS/NYT		19	34
1/06/2006	GALLUP			33
12/02/2005	CBS/NYT			35
9/11/2005	PEW		12	32
9/09/2005	CBS/NYT		12	29
6/19/2005	GALLUP			32
10/15/2004	NES		34	50
3/21/2004	PEW		17	41
10/26/2003	GALLUP			39
7/27/2003	CBS/NYT		19	37
10/15/2002	NES		41	58
9/04/2002	GALLUP			46
9/02/2002	CBS/NYT			39
7/13/2002	CBS/NYT			39
6/17/2002	GALLUP			48
1/24/2002	CBS/NYT			48
12/07/2001	CBS/NYT			51
10/25/2001	CBS/NYT			60
10/06/2001	GALLUP			61
1/17/2001	CBS/NYT			33
10/15/2000	NES		32	46
7/09/2000	GALLUP			41
4/02/2000	ABC/POST			28
2/14/2000	PEW		36	40
10/03/1999	CBS/NYT			28
9/14/1999	CBS/NYT		30	39
5/16/1999	PEW		28	31
2/21/1999	PEW		32	31
2/12/1999	ABC/POST			32
2/04/1999	GALLUP			33
1/10/1999	CBS/NYT		37	35
1/03/1999	CBS/NYT		39	31
12/01/1998	NES	57	36	38
11/19/1998	PEW		27	26
11/01/1998	CBS/NYT		29	22
10/26/1998	CBS/NYT		26	25
8/10/1998	ABC/POST			33
2/22/1998	PEW		42	33
2/01/1998	GALLUP			36
1/25/1998	CBS/NYT			25
10/31/1997	PEW		39	38
6/01/1997	GALLUP		31	32
1/14/1997	CBS/NYT		15	24
11/02/1996	CBS/NYT	31	30	24
10/15/1996	NES		35	32
5/12/1996	GALLUP			24
5/06/1996	ABC/POST			34
11/19/1995	ABC/POST			26
8/07/1995	GALLUP			22
8/05/1995	CBS/NYT		24	19
3/19/1995	ABC/POST		27	21
2/22/1995	CBS/NYT		20	17
12/01/1994	NES		22	20
10/29/1994	CBS/NYT		16	22
10/23/1994	ABC/POST			21
6/06/1994	GALLUP			15
1/30/1994	GALLUP			17
1/20/1994	ABC/POST		34	21
3/24/1993	GALLUP			20
1/17/1993	ABC/POST		45	25
1/14/1993	CBS/NYT		22	24
10/23/1992	CBS/NYT		21	23
10/15/1992	NES	37	27	28
6/08/1992	GALLUP			23
10/20/1991	ABC/POST		29	36
3/06/1991	CBS/NYT		30	49
3/01/1991	ABC/POST		35	46
12/01/1990	NES	39	22	27
10/28/1990	CBS/NYT		26	25
9/06/1990	ABC/POST		39	43
1/16/1990	ABC/POST		35	38
6/29/1989	CBS/NYT		26	36
1/15/1989	CBS/NYT		33	46
11/10/1988	CBS/NYT		33	45
10/15/1988	NES		25	43
1/23/1988	ABC/POST		29	41
10/18/1987	CBS/NYT		32	41
6/01/1987	ABC/POST		34	49
3/01/1987	CBS/NYT		20	45
1/21/1987	CBS/NYT		27	46
1/19/1987	ABC/POST		31	47
12/01/1986	NES		21	42
11/30/1986	CBS/NYT		23	52
9/09/1986	ABC/POST		26	42
1/19/1986	CBS/NYT		22	45
11/06/1985	CBS/NYT		34	52
7/29/1985	ABC/POST		22	40
3/21/1985	ABC/POST		29	40
2/22/1985	ABC/POST		24	46
10/15/1984	NES		33	46
12/01/1982	NES		26	34
11/07/1980	CBS/NYT		30	40
10/15/1980	NES		26	25
3/12/1980	CBS/NYT		35	24
11/03/1979	CBS/NYT		36	29
12/01/1978	NES		29	29
10/23/1977	CBS/NYT		28	34
4/25/1977	CBS/NYT		34	35
10/15/1976	NES		22	35
6/15/1976	CBS/NYT		35	34
3/01/1976	GALLUP		23	34
12/01/1974	NES		19	38
10/15/1972	NES		32	56
12/01/1970	NES		40	55
10/15/1968	NES		62	61
12/01/1966	NES		65	65
10/15/1964	NES		77	77
12/01/1958	NES		62	74

Note: For full question wording, refer to the topline . White, Black and Asian American adults include those who report being one race and are not Hispanic. Hispanics are of any race. Estimates for Asian adults are representative of English speakers only.

Sources: Pew Research Center, National Election Studies, Gallup, ABC/Washington Post, CBS/New York Times, and CNN Polls. Data from 2020 and later comes from Pew Research Center’s online American Trends Panel; prior data is from telephone surveys. Details about changes in survey mode can be found in this 2020 report . Read more about the Center’s polling methodology . For analysis by party and race/ethnicity, selected datasets were obtained from searches of the iPOLL Databank provided by the Roper Center for Public Opinion Research .

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a Department of Pediatric Urology and Pediatric Surgery, Hopital Pellegrin-Enfants, CHU Bordeaux, France

b Service de chirurgie et urologie pédiatrique, hôpital Lapeyronie, CHU de Montpellier et Université de Montpellier, France

G.M.A. Beckers

c Department of Urology, Section of Pediatric Urology, AmsterdamUMC, Location VUmc, Amsterdam, the Netherlands

d Indiana University, 702 Barnhill Drive, Suite 4230, Indianapolis, IN, USA

A.J. Nieuwhof-Leppink

e Department of Medical Psychology and Social Work, Urology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, PO box 85090, 3508 AB, Utrecht, the Netherlands

Magdalena Fossum

f Department of Pediatric Surgery, Copenhagen University Hospital Rigshospitalet, DK-2100, Denmark

g Department of Women's and Children's Health, Bioclinicum, Floor 10, Karolinska Institutet, SE-171 76, Stockholm, Sweden

K.W. Herbst

h Division of Urology, Department of Research, Connecticut Children's Medical Center, Hartford, CT, USA

i Hospital for Sick Chidlren, Univeristy of Toronto, Canada

Coronavirus disease 2019 (COVID-19) has swept across the globe causing hundreds of thousands of deaths, shutting down economies, closing borders and wreaking havoc on an unprecedented scale. It has strained healthcare services and personnel to the brink in many regions and will certainly deeply mark medical research both in the short and long-term.

Prior to the COVID pandemic, virology research (including influenza) represented less than 2% of all biomedical research. However, the number of laboratories and investigators that have pivoted to address COVID related research questions is astonishing, likely comprising 10–20% of current biomedical investigation, showing the incredible adaptability of the research community [ 1 ]. The multinational support rapidly infused for COVID-19 research is in the billions of euros [ 2 ]. The sharing of research findings and research data has never been as rapid and efficient [ 3 ]. The crisis has also brought disease, health, and healthcare back to the forefront of societal issues, and will have a lasting impact on public spending. However, with all this optimism and focus, there is a downside.

To begin, the COVID-19 crisis has led to a massive influx of publications. Not only are specialty journals being flooded with submissions by authors being unwittingly granted much needed writing time, but publications on COVID have literally inundated us. More than 20,000 papers have been published since December 2019, many in prestigious journals. There are also an increasing number of studies being uploaded to preprint servers, such as BioRxiv, for rapid dissemination prior to any peer review. However, we cannot assume that the time and quality available for peer review is able to keep pace with the explosion of publication. There is need for increased caution in the wake of this massive influx of submissions, especially since we are increasingly seeing these results being picked up by the media and diffused to a less attuned audience. In recent weeks, several prestigious journals, including the Lancet and the New England Journal of Medicine, have published retractions of earlier and potentially major COVID-related findings [ 4 , 5 ]. On June 15, 2020, The New York Times highlighted potential lapses in the peer review process affecting major scientific journals [ 6 ].

We must strive to improve scientific quality always. The current debate over the use of hydroxychloroquine further illustrates the undermining of the scientific process when faced with global desperation for ready-made truths and solutions [ 4 , 7 , 8 ]. Science needs time, and good science needs a lot of it for data to grow and knowledge to evolve, but this process is ill-prepared to handle the rush for solutions to the COVID crises.

Moreover, just as COVID-19 has shown social, racial, and economic health disparities, the pandemic seems also to have accentuated existing gender inequalities within the field of research [ 9 ]. Indeed, early analyses suggest that female academics are publishing less and starting fewer research projects than their male peers. This might be an effect of the lockdown and the fact that more women than are men are juggling caring for families and children despite both “working” from home [ 10 , 11 ].

Travel, social, and funding restrictions will also take a serious toll on scientific research worldwide. Research staff and resources have been purposely and purposefully prioritized to COVID-19 activities above all else. Distancing and transmission issues have caused most non-COVID clinical research to be suspended, causing a reduction in recruitment of research subjects and a delay in data entry into clinical trial databases [ 12 ]. Research-related hiring has been suspended because of travel restrictions and young researchers might soon find themselves out of a job if their subject is not the pandemic. Indeed, though government-funded medical research bodies worldwide say they are committed to maintaining the continuity and breadth of biomedical research, how the economic downfall will influence government spending remains to be seen. Furthermore, research funding that relies on public fundraising is expected to drop substantially and many researchers will see a significant decrease in funding opportunities [ 13 ]. The global impact the crisis will have on the economy makes it hard to imagine that future research funding will not be substantially affected.

During this crisis, many resources were understandably redirected toward preparing for and caring for COVID-19 patients, but the collateral damage to so many patients with non-COVID-19 medical conditions that did not receive, or failed to seek, treatment will surely emerge [ 14 ]. Finally, children have also paid a high price for the redirecting of medical resources, with delays in their medical and surgical management, as well as vaccinations [ 15 , 16 ]. This may be especially problematic when many aspects of pediatric care is based on their developmental clock, which even the pandemic cannot stop. Whether this was the best option will certainly be analyzed in retrospect. Congenital anomalies alone account for over 400,000 deaths worldwide every year, and inflict a considerable burden both on children, families, and healthcare systems [ 17 ]. Thus, it is essential that funding for medical research does not follow the same pattern with a disproportionate decrease in funding for non-COVID research including pediatric and developmental urology.

COVID-19 has already changed the world, not only because of the disease itself, but because of the long-term effects of the world's reaction to the pandemic. While the pandemic may have brought with it some silver linings, it is crucial that the scientific community conduct current and future research broadly and openly, lest future pandemic preparedness in research repeat the hard-fought lessons of today.

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Published: 24 June 2024

Re-thinking all-cause COVID-19 hospitalizations as a surrogate measure for severe illness in observational surveillance studies

J. Daniel Kelly 1 , 2 , 3 , 4 ,
Samuel Leonard 1 ,
W. John Boscardin 3 ,
Katherine J. Hoggatt 1 , 2 ,
Emily N. Lum 1 ,
Charles C. Austin 5 ,
Amy L. Byers 1 , 2 , 6 ,
Phyllis C. Tien 1 , 2 ,
Dawn M. Bravata 5 , 7 , 8 &
Salomeh Keyhani 1 , 2

Scientific Reports volume 14 , Article number: 14555 ( 2024 ) Cite this article

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Infectious diseases

All-cause COVID-19 hospitalization ≤ 30 days of infection is a common outcome for severe illness in observational/surveillance studies. Milder COVID-19 disease and COVID-19-specific measurements calls for an evaluation of this endpoint. This was a descriptive, retrospective cohort study of adults ≥ 18 who were established in primary care at Veteran Health Administration (VHA) facilities. The outcome was hospitalization within 30 days of a laboratory-confirmed, symptomatic SARS-CoV-2 infection. Between December 15, 2021 and May 1, 2022, a simple random sample of all VA facilities, excluding Puerto Rico or Philippines, was drawn to identify these hospitalized cases and determine whether hospitalization was due to COVID-19-specific causes. A chart review was conducted to record the inpatient clinical team’s diagnosis and whether the inpatient team classified the diagnosis as COVID-19 related or not. These data were used to classify hospitalizations as either due to COVID-19-specific causes (direct manifestations of SARS-CoV-2 infection) or non-COVID-19-specific hospitalizations (incidental SARS-CoV-2 infection), A simple random sample of 9966 (12.3%) all-cause hospitalizations (95% CI: 12.1%, 12.5%) was used to select 300 representative patients. Of these, 226/300 (75.3%) were determined to be COVID-19-specific. COVID-19 pneumonia was most common (147/226, 65.0%). The highest proportion of COVID-19-specific hospitalizations occurred among unvaccinated (85.0%), followed by vaccinated but not boosted (73.7%) and boosted (59.4%) ( p < 0.001). The proportion of non-COVID-19-specific hospitalizations was higher in the later period (15–30 days: 55.0%) than the early (0–15 days: 22.5%) ( p = 0.003). This study supports the outcome of COVID-19-specific hospitalization instead of all-cause hospitalization in observational studies. The earlier outcome period (0–15 days) was less susceptible to potential measurement bias.

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Introduction.

Observational, surveillance studies rely on readily available data from electronic health records, insurance databases, or other sources to assess the effectiveness of COVID-19 vaccines or therapeutics through a target trial emulation approach 1 , 2 , 3 , 4 , 5 . This approach uses all-cause hospitalization after symptomatic COVID-19 as a proxy for COVID-19-related hospitalization with the intent to measure severe illness. In contrast, clinical trials of COVID-19 vaccines and therapeutics have the intensive resources to directly measure COVID-19-related hospitalization 6 , 6 , 8 . The U.S. Veterans Health Administration (VHA) has access to detailed, patient-level data so that chart reviews can be conducted, offering the ability to evaluate all-cause COVID-19 hospitalization for mismeasurement biases.

In an era of vaccines, increasing natural immunity, and viral evolution, it is important to re-evaluate all-cause COVID-19 hospitalization as a useful surrogate outcome in observational studies because COVID-19 disease has been associated with less severe illness and complications, including pneumonia, acute myocardial infarction, and ischemic stroke after infection 9 . Without the late occurrence of cytokine storms, progression to severe illness typically occurs over a shorter period, suggesting that measurement of all-cause COVID-19 hospitalization over a 30-day period may be more likely to detect hospitalizations unrelated than related to COVID-19. At the same time, COVID-19 remains a relatively common occurrence 10 and infected, symptomatic individuals can be hospitalized for many reasons that may be unrelated to COVID-19 disease (incidental SARS-CoV-2 infections) 10 .

During a period with a predominance of Omicron SARS-CoV-2 variants, this study drew a random sample of all-cause COVID-19 hospitalizations among a national cohort of U.S. Veterans. Reasons for hospitalization documented by the inpatient care team in medical charts were abstracted and used to determine whether the hospitalization was due to a COVID-19-specific cause.

Ethics statement

The institutional review board of the University of California, San Francisco, approved this study and waived requirement for patient consent, due to the retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Study design, data sources, and participants

We conducted a retrospective cohort study using chart review to describe the proportion of all-cause hospitalizations due to COVID-19-specific or non-COVID-19-specific causes among hospitalized patients with laboratory-confirmed, symptomatic SARS-CoV-2 infection. The cohort included adults aged 18 or older who were established at the U.S. VHA facilities. Exclusion criteria included (1) use of nursing home or hospice care services within the 2 years prior to a COVID-19 diagnosis, and (2) hospitalization in Puerto Rico or the Philippines. We used VA Corporate Data Warehouse (CDW) 11 and COVID-19 shared data resource 12 to construct the cohort and identify patients who had laboratory-confirmed, symptomatic SARS-CoV-2 infection and were hospitalized within 30 days of infection. COVID-19 shared data resource was established to document symptomatic infections and hospitalizations occurring outside of VA facilities and integrate these data back into the VA health records. A random sample of hospitalizations was drawn for chart review between December 15, 2021 and May 1, 2022. Thirty percent of these charts were reviewed in duplicate.

Measurements

When reviewing charts, study staff recorded the inpatient clinical team’s diagnosis and whether the inpatient team classified the diagnosis as COVID-19 related or not. Diagnoses related to COVID-19 were defined as COVID-19-specific (direct manifestations of SARS-CoV-2 infection), and those unrelated to COVID-19 were defined as non-COVID-19-specific causes (incidental SARS-CoV-2 infection).

Any hospitalization with unclear labeling and/or disagreement amongst the chart abstractors about the diagnosis being related to COVID-19 was flagged for review by three clinicians (JDK, SK, DMB) and consensus was reached via discussion. An example of unclear labeling in a person with symptomatic SARS-CoV-2 infection was pneumonia, either due to bacterial and viral pathogen, so unless these were witnessed aspiration events, the pneumonia was classified as COVID-19-related since bacterial and viral infections were radiologically indistinguishable. The study team did not overrule any diagnoses or etiological statements made by the clinical team.

Covariates used to describe the cohort were extracted from the VA Corporate Data Warehouse. Those with high-risk comorbid and immunocompromising conditions were classified using U.S. Centers for Disease Prevention and Control definitions 10 , 13 .

Statistical analysis

The cumulative incidence of all-cause hospitalization was described in the overall cohort and sub-groups stratified by vaccination status (unvaccinated [no vaccine dose], vaccinated but not boosted [received primary series], boosted [received all recommended doses]) and time since infection (0–15 days, 16–30 days). In the random sample, reasons for hospitalization as determined by the abstraction team’s chart review were categorized as COVID-19-specific causes, and non-COVID-19-specific causes. Descriptive statistics with Chi-squared tests were used to compare the proportion of all-cause and cause-specific hospitalizations among those related and not related to COVID-19, stratified by vaccination status and by time since COVID-19 infection. A 2-tailed p < 0.05 was considered significant. Analyses were conducted in R version 1.2.5019.

Among 6,634,897 patients receiving care at VHA facilities, 80,761 had laboratory-confirmed, symptomatic SARS-CoV-2 infection (121.7 events per 10,000 persons; 95% CI: 120.9, 122.6). Among these infected patients, there were 9966 (12.3%) all-cause hospitalizations (95% CI: 12.1%, 12.5%). A simple random sample of this hospitalized population was used to select 300 patients who were representative of the national cohort (Fig. 1 ). Among these 300 hospitalized patients, 196 (65.3%) were aged 65 or older and 17 (5.7%) were female; 198 (66.0%) had high-risk comorbid conditions and 67 (22.3%) had an immunocompromising condition. See Table 1 for additional characteristics.

Cohort selection in U.S. cohort of patients receiving care at Veterans Health Administration (VHA) facilities during a period of Omicron variant predominance and were hospitalized within 30 days of a symptomatic SARS-CoV-2 infection.

Among all-cause hospitalizations, 226/300 (75.3%) were determined to be COVID-19-specific. The most common causes of the 226 COVID-19-specific hospitalizations were as follows: COVID-19 pneumonia, 147 (65.0%); cardiovascular events (e.g., myocarditis, new onset atrial fibrillation), 23 (10.2%); weakness/falls, 12 (5.3%); COPD/asthma exacerbation, 8 (3.5%); neurocognitive disorders (e.g., encephalopathy), 8 (3.5%); and gastrointestinal (GI) illness (e.g., diarrhea), 10 (4.0%). The remaining hospitalizations (74/300, 24.7%) were non-COVID-19-specific. The most common causes of the 74 non-COVID-19-specific hospitalizations were as follows: GI illness (e.g., recurrent GI bleeding, abdominal pain from constipation), 17 (23.0%); mental illness or substance abuse (e.g., alcohol withdrawal), 16 (21.6%); non-COVID infectious disease (e.g., urinary tract infection), 15 (20.3%); cardiovascular events (e.g., hypertensive emergency off blood pressure medications), 6 (8.1%); and pain syndrome, 6 (8.1%). See Table 2 for other diagnoses classified as COVID-specific or non-COVID-19-specific hospitalizations. See Supplement for descriptions of these diagnoses during the hospitalization and whether the case was classified as COVID-19-specific or non-COVID-19-specific.

The proportion of COVID-19-specific hospitalizations among all-cause hospitalizations varied by vaccination status. The highest proportion of COVID-19-specific hospitalizations occurred among the unvaccinated group (108/127, 85.0%), followed by the vaccinated but not boosted group (73/99, 73.7%) and boosted group (41/69, 59.4%) ( p < 0.001). Stratified by time since infection (0–15 days, 15–30 days), the majority of all-cause hospitalizations (280, 93.3%) occurred within the first 15 days of infection. The proportion of non-COVID-19-specific hospitalizations was higher in the later period (15–30 days: 11/20, 55.0%) than the early period (0–15 days: 63/280, 22.5%) ( p = 0.003) (Table 2 ).

In this cohort of patients hospitalized at VHA facilities 30 days after symptomatic SARS-CoV-2 infection, we evaluated the extent to which all-cause hospitalization within 30 days of symptomatic SARS-CoV-2 infection remains a useful outcome for severe COVID-19 illness in observational, surveillance studies. Consistent with the literature 14 , 15 , about one-quarter of all-cause hospitalizations were non-COVID-19-specific (incidental SARS-CoV-2 infection). A higher proportion of non-COVID-19-specific hospitalizations occurred among those who were boosted than unvaccinated and among those hospitalized 16–30 days instead of 0–15 days after infection. As the U.S. population has become highly immunized to COVID-19 either through vaccination or infection and as the clinical spectrum of illness has moderated 9 , continued use of all-cause hospitalization within 30 days of symptomatic SARS-CoV-2 infection as a study outcome should be approached with caution because of its trade-off between sensitivity and specificity.

Hospitalization within 15 days after symptomatic SARS-CoV-2 infection was a more specific measurement of COVID-19 hospitalization than within 30 days and may be less prone to measurement bias. Over time, the likelihood that SARS-CoV-2 infection results in a hospitalization due to COVID-19-specific causes decreases; thus we expect to observe more incidental SARS-CoV-2 infections during this later period (16–30 days). In addition, summary measures of COVID-19-specific hospitalization have yet to be validated. This study found hospitalization due to COVID-19 pneumonia accounted for most COVID-19-specific causes and can be identified with an ICD-10 code and chart review. To this end, most studies attempting to use a specific outcome have focused on the most common, cause-specific diagnoses such as pneumonia 10 , and to a lesser extent, arterial and venous thrombotic events 16 . A tradeoff of these more focused analyses has been the loss of sensitivity, which could slow down research on time-sensitive questions.

There are limitations of this study. First, we relied on the inpatient medical team’s assessment of the cause for hospitalization. In some cases, the team’s indicated diagnosis was clearly not related to COVID-19 (e.g., fracture, fall, pain, substance use) but in other cases distinguishing etiology of symptoms was more challenging (e.g., progression of kidney disease). Second, the study population was predominantly White men and included community-dwelling individuals living in the U.S. Third, it is likely that the proportion of all-cause hospitalization (12.3%) was an overestimate given the timeframe under investigation (large Omicron wave). Additional limitations of generalizability extend to Omicron variants beyond BA.1 and BA.2 and after receipt of the bivalent Omicron booster vaccine. Finally, during the observational study period, rapid antigen test became available, so the total number of cases was an underestimate.

Evidence from this study supports use of COVID-19-specific hospitalization in many scenarios instead of all-cause hospitalization in observational surveillance studies. Further, the earlier period (0–15 days) of the outcome was less susceptible to potential measurement bias in contrast to the later period (16–30 days). This report highlights the importance of re-evaluating clinical endpoints in an evolving landscape of viral variants and booster vaccine.

Data availability

The data that support the findings of this study are available from Veterans Health Administration, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Veterans Health Administration. Please contact Dr. Dan Kelly ([email protected]) to request information regarding access for data from this study. More information is available at https://www.virec.research.va.gov . Source data are provided with this paper.

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Acknowledgements

This study was supported by Veterans Health Administration Clinical Sciences Research and Development (VA CSR&D) grant I01 CX002417 (Kelly, Keyhani). Dr. Keyhani is also supported by VA Health Sciences Research and Development (VA HSR&D) grants I01 HX002737 and I01 HX003522.

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J. Daniel Kelly, Samuel Leonard, Katherine J. Hoggatt, Emily N. Lum, Amy L. Byers, Phyllis C. Tien & Salomeh Keyhani

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J. D. K., S. K. conceived and designed the study. J. D. K., S. L., C. C. A., D. M. B. and S. K. contributed to data collection and curation. J. D. K., S. L., C. C. A., D. M. B. and S. K. accessed and verified all data, did data analysis, drafted the first version of the manuscript. All authors assisted in revisions and approval of the final version.

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Kelly, J.D., Leonard, S., Boscardin, W.J. et al. Re-thinking all-cause COVID-19 hospitalizations as a surrogate measure for severe illness in observational surveillance studies. Sci Rep 14 , 14555 (2024). https://doi.org/10.1038/s41598-024-61244-7

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The staff of the Division of Corporation Finance is aware of logistical difficulties submitting certain forms (other than Forms 144) in paper given the spread of coronavirus disease 2019 (COVID-19). In light of ongoing health and safety concerns related to COVID-19, the staff is providing the following statement to those affected by COVID-19 regarding the forms listed below.
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Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

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

Conclusions

Methodology

Study design

Eligibility criteria

Information sources

Study selection

Data collection process

Quality assessment in individual reviews

Synthesis of results

Managing overlapping systematic reviews

Characteristics of included reviews

Population and study designs

Systematic review findings

Clinical symptoms

Diagnostic aspects

Therapeutic possibilities

Laboratory and radiological findings

Quality of evidence in individual systematic reviews

Availability of data and materials

Acknowledgments

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Competing interests

Additional information

Supplementary Information

Additional file 2: Appendix 2.

Additional file 3: Appendix 3.

Additional file 4: Appendix 4.

Additional file 5: Appendix 5.

Additional file 6: Appendix 6.

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