Evidence map›Paper›PMID 33682801›Full record

ArticleActa bio-medica : Atenei Parmensis2021

Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection in an Italian cohort in Marche Region, Italy.

Mauro De Santi, Aurora Diotallevi, Giorgio Brandi

Abstract read
In one paragraph

Article in Acta bio-medica : Atenei Parmensis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Trial
  2. Article
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  4. Article
  5. Seroprevalence of SARS-CoV-2 in a Large Cohort of Italian Police Officers.International journal of environmental research and public health · 2021
    Article
  6. Observational
  7. Article
  8. Article
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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.

Mauro De SantiArray. mauro.desanti@uniurb.it.
Aurora DiotalleviDepartment of Biomolecular Sciences, Biochemistry and Biotechnology Unit, University of Urbino Carlo Bo, Urbino, Italy. aurora.diotallevi@uniurb.it.
Giorgio BrandiDepartment of Biomolecular Sciences, Pharmacology and Public Health Unit, University of Urbino Carlo Bo, Urbino, Italy. giorgio.brandi@uniurb.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimThe COVID-19 is an infectious disease caused by the novel coronavirus SARS-CoV-2, declared a public health emergency by the World Health Organization. In this study, we evaluated the seroconversion of SARS-CoV-2 antibodies to find predictors of infection in terms of symptoms, health status, and professions.

methodsSerological samples of 341 volunteers in a cohort in Marche Region, Italy, were analyzed for the presence of IgM and/or IgG immunoglobulins specific for the SARS-CoV-2. Contextually, an anamnestic questionnaire was administered. The binary logistic regression analysis was used to find the predictors of seroconversion.

resultsForty-nine subjects (14.4 %) were found positive, without significant differences between gender and age groups. The predictors identified inside the variable categories "symptoms," "risk factors" (smoking habit and established pathologies), and "professions" were the loss of taste and smell (OR, 8.563), cardiovascular diseases (OR, 2.912), and policeman profession (OR, 3.875), respectively.

conclusionsAlthough the limited number of subjects recruited in this study, our results could give important findings to be considered for planning preventive strategies in the view of the next COVID-19 waves.

Indexed as

AdultAgedAged, 80 and overAntibodies, ViralCohort StudiesCOVID-19FemaleHumansImmunoglobulin GImmunoglobulin MItalyMaleMiddle AgedSARS-CoV-2Seroepidemiologic StudiesYoung AdultAntibodies, ViralImmunoglobulin GImmunoglobulin M

Identifiers

PMID33682801
PMCPMC7975922

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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.