The effects of the first and second wave of COVID-19 pandemic on public health

Coccia, Mario

doi:10.21203/rs.3.rs-110013/v1

Cited by 3 publications

(3 citation statements)

References 23 publications

(26 reference statements)

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“…As of 23 February 2022, over 420 million people have been diagnosed with COVID-19, with nearly 5.89 million associated deaths [ 3 ]. The consecutive waves of COVID-19 affected many societies, as well as scientific foundations and organisations [ 4 , 5 , 6 , 7 ]. On 30 January 2020, the World Health Organisation (WHO) issued a public health emergency of international concern (PHEIC) associated with COVID-19 and declared the state of a pandemic on 11 March 2020 [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Jemioło

Storman

Orzechowski

2022

JCM

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The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Jemioło

Storman

Orzechowski

2022

JCM

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“…[5][6][7][8] However, epidemiology and clinical presentation of this special population remain still largely unknown in the second wave, where overall COVID-19 incidence has increased, but the case mortality rate has dropped. 3,9 In a 3-week observation period of the first and second COVID-19 waves, we compared epidemiological features and clinical presentation of C19-CP referring to the emergency department (ED) of an Italian University Hospital located in northern Italy (Turin), which was heavily hit by the SARS-CoV-2 pandemic. 10…”

Section: Introductionmentioning

confidence: 99%

Descriptive Comparative Analysis of Patients With Cancer Referring to the Emergency Department of an Italian University Hospital Across the Severe Acute Respiratory Syndrome Coronavirus 2 Waves

Mariniello

Bironzo

Pisano

et al. 2021

JCO Oncology Practice

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PURPOSE: COVID-19 cancer patients (C19-CP) represent a population at high risk for mortality, whose clinical characteristics are still unknown in the second SARS-CoV-2 wave. The aim of this retrospective study was to compare epidemiology and clinical presentation of C19-CP referring to the emergency department (ED) of our institution (San Luigi Gonzaga University Hospital, Orbassano, Turin, Italy), in a 3-week observation period of the first and second COVID-19 waves, starting from the introduction of the corresponding national lockdowns. METHODS: We retrieved ED admissions from March 9 to 29, 2020, for the first wave, and from October 24 to November 13, 2020, for the second wave. We collected clinical characteristics of consecutive patients with molecularly confirmed SARS-CoV-2 infection. We also considered untested or SARS-CoV-2–negative cancer patients referring to the ED in the reference time frames. RESULTS: C19-CP in the second wave exceeded those in the first wave despite the nonsignificant difference (39 of 576 v 8 of 163; P = .5). Compared with nononcological patients, C19-CP were older (median age 70 years [interquartile range 61-77] v 60 years [interquartile range 45-73]; P = .02) and presented more often with ≥ 2 comorbidities (40.4% v 24.3%; P = .02). Compared with nononcological patients, in C19-CP, respiratory failure (29 of 47 v 321 of 692; P = .049) and hospitalization (37 of 47 v 363 of 692; P = .0004) were higher, with comparable frequencies across the waves. Five of 24 and 10 of 27 hospitalized cancer patients in the first and second waves developed SARS-CoV-2 infection during hospitalization. CONCLUSION: C19-CP were a vulnerable population, irrespective of the COVID-19 waves. This highlights the need to prioritize vaccinations in oncological patients to safeguard and guarantee optimal anticancer care.

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“…As of May 3, 2021, over 152 million people have been diagnosed with COVID-19, with nearly 3.2 million associated deaths 1 . Two first waves of COVID-19 affected many societies, as well as scientific organisations [4–7]. On January 30, 2020, the World Health Organisation (WHO) issued a public health emergency of international concern (PHEIC) associated with COVID-19, and declared the state of a pandemic on March 11, 2020 [8].…”

Section: Introductionmentioning

confidence: 99%

A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review

Jemioło

Storman

Orzechowski

2021

Preprint

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Objective: In this umbrella systematic review, we screen existing reviews on using artificial intelligence(AI) techniques to diagnose COVID-19 in patients of any age and sex (both hospitalised and ambulatory) using medical images and assess their methodological quality. Methods: We searched seven databases (MEDLINE, EMBASE, Web of Science, Scopus, dblp, CochraneLibrary, IEEE Xplore) and two preprint services (arXiv, OSF Preprints) up to September 1, 2020. Eligible studies were identified as reviews or surveys where any metric of classification of detection of COVID-19 using AI was provided. Two independent reviewers did all steps of identification of records (titles and abstracts screening, full texts assessment, essential data extraction, and quality assessment). Any discrepancies were resolved by discussion. We qualitatively analyse methodological credibility ofthe reviews using AMSTAR 2 and evaluate reporting using PRISMA-DTA tools, leaving quantitative analysis for further publications. Results: We included 22 reviews out of 725 records covering 165 primary studies. This review covers 416,254 participants in total, including 50,022 diagnosed with COVID-19. The methodological quality of all eligible studies was rated as critically low. 91% of papers had significant flaws in reporting quality. More than half of the reviews did not comment on the results of previously published reviews at all. Almost three fourth of the studies included less than 10% of available studies. Discussion: In this umbrella review, we focus on the descriptive summary of included papers. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Due to the low credibility of evidence and flawed reporting, any recommendation about automated COVID-19 clinical diagnosis from medical images using AI at this point cannot be provided. Funding: PO was supported by NIH grant AI116794 (the funding body had no role in the design, in any stage of the review, or in writing the manuscript); PJ and DS did not receive any funding. Registration:The protocol of this review was registered on the OSF platform at osf.io/kxrmh

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The effects of the first and second wave of COVID-19 pandemic on public health

Cited by 3 publications

References 23 publications

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Descriptive Comparative Analysis of Patients With Cancer Referring to the Emergency Department of an Italian University Hospital Across the Severe Acute Respiratory Syndrome Coronavirus 2 Waves

A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review

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