Evidence map›Paper›PMID 35075070›Full record

ArticleActa bio-medica : Atenei Parmensis2022

The temporal correlation between positive testing and death in Italy: from the first phase to the later evolution of the COVID-19 pandemic.

Vincenzo D'Alessandro, Nicole Balasco, Pietro Ferrara, Luigi Vitagliano

Open access · greenAbstract read
In one paragraph

Article in Acta bio-medica : Atenei Parmensis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 62% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

4 authors at 4 institutions in 3 countries.

Vincenzo D'Alessandro. vindales@unina.it.
Nicole Balasco. n_balasco@hotmail.it.
Pietro Ferraraa:1:{s:5:"en_US";s:29:"University of Milan - Bicocca";}. p_ferrara@alice.it.
Luigi Vitagliano. luigi.vitagliano@unina.it.
Luigi Einaudi Foundation · ITUniversidad Latina · MXUniversity of Kerbala · IQUniversity of Milano-Bicocca · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimAfter the global spread of the novel coronavirus disease 2019 (COVID-19), research has concentrated its efforts on several aspects of the epidemiological burden of pandemic. In this frame, the presented study follows a previous analysis of the temporal link between cases and deaths during the first epidemic wave (Phase 1) in Italy (March-June 2020).

methodsWe here analyze the COVID-19 epidemic in the time span from March 2020 to June 2021.

resultsThe elaboration of the curves of cases and deaths allows identifying the temporal shift between the positive testing and the fatal event, which corresponds to one week from W2 to W33, two weeks from W34 to W41, and three weeks from W42 to W67. Based on this finding, we calculate the Weekly Lethality Rate (WLR). The WLR was grossly overestimated (~13.5%) in Phase 1, while a mean value of 2.6% was observed in most of Phase 2 (starting from October 2020), with a drop to 1.4% in the last investigated weeks.

conclusionsOverall, these findings offer an interesting insight into the magnitude and time evolution of the lethality burden attributable to COVID-19 during the entire pandemic period in Italy. In particular, the analysis highlighted the impact of the effectiveness of public health and social measures, of changes in disease management, and of preventive strategies over time.  (www.actabiomedica.it).

Indexed as

COVID-19HumansItalyPandemicsPublic HealthSARS-CoV-2

Identifiers

PMID35075070
PMCPMC8823554
OpenAlexW4225581289

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

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.