Evidence map›Paper›PMID 34083555›Full record

ArticleScientific reports2021

Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19.

Carlo Vittorio Cannistraci, Maria Grazia Valsecchi, Ilaria Capua

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  17. Using data from a private provider of telemedicine to assess the severity of the early 2021 Covid-19 wave in Brazil.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Carlo Vittorio CannistraciCenter for Complex Network Intelligence (CCNI) at the Tsinghua Laboratory of Brain and Intelligence (THBI), Department of Biomedical Engineering, Tsinghua University, 160 Chengfu Rd., SanCaiTang Building, Haidian District, Beijing, 100084, China. kalokagathos.agon@gmail.com.ORCID 0000-0003-0100-8410
Maria Grazia ValsecchiBicocca Center of Bioinformatics, Biostatistics and Bioimaging, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Ilaria CapuaOne Health Center of Excellence, University of Florida, Gainesville, FL, USA. icapua@ufl.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Governments continue to update social intervention strategies to contain COVID-19 infections. However, investigation of COVID-19 severity indicators across the population might help to design more precise strategies, balancing the need to keep people safe and to reduce the socio-economic burden of generalized restriction precedures. Here, we propose a method for age-sex population-adjusted analysis of disease severity in epidemics that has the advantage to use simple and repeatable variables, which are daily or weekly available. This allows to monitor the effect of public health policies in short term, and to repeat these calculations over time to surveille epidemic dynamics and impact. Our method can help to define a risk-categorization of likeliness to develop a severe COVID-19 disease which requires intensive care or is indicative of a higher risk of dying. Indeed, analysis of suitable open-access COVID-19 data in three European countries indicates that individuals in the 0-40 age interval and females under 60 are significantly less likely to develop a severe condition and die, whereas males equal or above 60 are more likely at risk of severe disease and death. Hence, a combination of age-adaptive and sex-balanced guidelines for social interventions could represent key public health management tools for policymakers.

Indexed as

Health PolicyPublic PolicyAdultAgedAged, 80 and overCOVID-19COVID-19 VaccinesFemaleGermanyHumansInfection ControlItalyMaleMiddle AgedPublic HealthSeverity of Illness IndexCOVID-19 Vaccines

Identifiers

PMID34083555
PMCPMC8175671

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.