Evidence map›Paper›PMID 33598880›Full record

ArticleIrish journal of medical science2022

Trauma workload during COVID19 lockdown: an analysis of incidence in 4 million people.

Alessandro Aprato, Nathalie Bini, Silvia Ferro, Lucia Favella, Luigi Conforti, Alessandro Massè

Erratum issuedAbstract read
In one paragraph

Article in Irish journal of medical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Alessandro ApratoSchool of Medicine, University of Turin, Turin, Italy. ale_aprato@hotmail.com.ORCID http://orcid.org/0000-0002-2514-4719
Nathalie BiniSchool of Medicine, University of Turin, Turin, Italy.
Silvia FerroPiedmont Regional Health Center, Turin, Italy.
Lucia FavellaPiedmont Regional Health Center, Turin, Italy.
Luigi ConfortiSchool of Medicine, University of Turin, Turin, Italy.
Alessandro MassèSchool of Medicine, University of Turin, Turin, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAim of this study is to report the trauma workload during COVID19 lockdown in a region of four million people and to compare it with the same period in 2019.

methodsThe regional register for A&E admissions and hospitalizations has been reviewed in order to compare the number of A&D admission, the triage colour codes rates, aetiology of trauma, number of patients hospitalized for trauma, number of fractures that required surgery, type of fractures and injuries and mean patients' age.

resultsDuring lockdown 7314 patients were admitted in A&E, while 22,508 patients were admitted in 2019. In 2020 and 2019 triage codes were respectively distributed as follows: red code 0.1% vs 0.2%, yellow code 8.9% vs 6.3%, green code 84% vs 84.7% and white code 6% vs 8.8%. (p = 0.042). The number of hospitalized patients for trauma was 670 in 2020, while in 2019 was 1774 (p = 0.02). The most common fracture that required surgery was femur fracture (409 in 2020 vs 635 in 2019); fracture subtype distribution and mean age of the patients were significantly different in the two groups (respectively p < 0.01 and p = 0.02).

conclusionsOne month of lockdown showed a 68% decrease in the number of A&E visits and a 74% decrease of fractures that required surgery. Femur fracture showed the lowest decrease moving from 635 to 409 units but increasing their incidence rate (42 to 61%).

Indexed as

COVID-19Communicable Disease ControlHumansIncidenceRetrospective StudiesSARS-CoV-2Trauma CentersWorkloadCOVID19FractureInjuryLockdownTrauma

Identifiers

PMID33598880
PMCPMC7888695

What OpenQuestion holds

Textmetadata
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.