Evidence map›Paper›PMID 33102879›Full record

ArticleHeliyon2020

Time-related changes in sex distribution of COVID-19 incidence proportion in Italy.

Vanessa Bianconi, Massimo R Mannarino, Paola Bronzo, Ettore Marini, Matteo Pirro

Abstract read
In one paragraph

Article in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. 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

5 authors.

Vanessa BianconiUnit of Internal Medicine, Department of Medicine, University of Perugia, Perugia, Italy.
Massimo R MannarinoUnit of Internal Medicine, Department of Medicine, University of Perugia, Perugia, Italy.
Paola BronzoUnit of Internal Medicine, Department of Medicine, University of Perugia, Perugia, Italy.
Ettore MariniUnit of Internal Medicine, Department of Medicine, University of Perugia, Perugia, Italy.
Matteo PirroUnit of Internal Medicine, Department of Medicine, University of Perugia, Perugia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVariable sex-disaggregated data on Coronavirus disease 2019 (COVID-19) incidence proportion (IP) have been reported in different datasets and studies. Factors explaining the inconsistent distribution of COVID-19 among sexes are still unclear.

objectivesThis study aimed to analyse time-related variation of sex-disaggregated COVID-19 IP in Italy since March 9 STUDY

designSex-disaggregated data on COVID-19 cases were collected from Italian publicly accessible databases along with undisaggregated data on the number of reverse transcriptase-polymerase chain reaction (RT-PCR) SARS-CoV-2 tests. Crude and adjusted associations between the frequency of RT-PCR SARS-CoV-2 testing and male-to-female (M/F) ratio of COVID-19 IP were performed.

resultsCOVID-19 IP increased progressively in both sexes. Sex prevalence of COVID-19 IP reversed over time, with the M/F ratio of COVID-19 IP having passed from 1,73 to 0,91. The mean number of daily swabs for RT-PCR SARS-CoV-2 test increased progressively until reaching a plateau in the last three weeks of the study period. The M/F ratio of COVID-19 IP inversely correlated with the number of daily swabs for RT-PCR SARS-CoV-2 test (r = -0,87, p < 0.001), even after adjusting for the median age of COVID-19 cases (β = -0,66, p < 0,001).

conclusionsTime-related changes of sex distribution of COVID-19 IP in Italy are strongly influenced by the number of swabs testing for SARS-CoV-2. Whether gender-related disparities in the access to the diagnosis of COVID-19 may explain such a result need to be explored.

Indexed as

CoronavirusCOVID-19EpidemiologyGenderIncidencePublic healthSARS-CoV-2SwabVirology

Identifiers

PMID33102879
PMCPMC7568205

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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.