Evidence map›Paper›PMID 34003761›Full record

ArticleJMIR public health and surveillance2021

The Effect of Test Timing on the Probability of Positive SARS-CoV-2 Swab Test Results: Mixed Model Approach.

Roberto Benoni, Silvia Panunzi, Irene Campagna, Francesca Moretti, Giuliana Lo Cascio, Gianluca Spiteri, Stefano Porru, Stefano Tardivo

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  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

8 authors at 1 institution in 1 country.

Roberto Benoni *Postgraduate School of Hygiene and Preventive Medicine, University of Verona, Verona, Italy.ORCID 0000-0002-1144-8471
Silvia PanunziDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.ORCID 0000-0003-4100-414X
Irene CampagnaPostgraduate School of Hygiene and Preventive Medicine, University of Verona, Verona, Italy.ORCID 0000-0003-3851-7753
Francesca MorettiDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.ORCID 0000-0003-1235-1054
Giuliana Lo CascioDepartment of Pathology, University Hospital of Verona, Verona, Italy.ORCID 0000-0003-3971-2008
Gianluca SpiteriDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.ORCID 0000-0001-8536-7095
Stefano PorruDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.ORCID 0000-0003-3564-7202
Stefano TardivoDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.ORCID 0000-0002-9161-2189
University of Verona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, swab tests proved to be effective in containing the infection and served as a means for early diagnosis and contact tracing. However, little evidence exists regarding the correct timing for the execution of the swab test, especially for asymptomatic individuals and health care workers.

objectiveThe objective of this study was to analyze changes in the positive findings over time in individual SARS-CoV-2 swab tests during a health surveillance program.

methodsThe study was conducted with 2071 health care workers at the University Hospital of Verona, with a known date of close contact with a patient with COVID-19, between February 29 and April 17, 2020. The health care workers underwent a health surveillance program with repeated swab tests to track their virological status. A generalized additive mixed model was used to investigate how the probability of a positive test result changes over time since the last known date of close contact, in an overall sample of individuals who tested positive for COVID-19 and in a subset of individuals with an initial negative swab test finding before being proven positive, to assess different surveillance time intervals.

resultsAmong the 2071 health care workers in this study, 191 (9.2%) tested positive for COVID-19, and 103 (54%) were asymptomatic with no differences based on sex or age. Among 49 (25.7%) cases, the initial swab test yielded negative findings after close contact with a patient with COVID-19. Sex, age, symptoms, and the time of sampling were not different between individuals with an initial negative swab test finding and those who initially tested positive after close contact. In the overall sample, the estimated probability of testing positive was 0.74 on day 1 after close contact, which increased to 0.77 between days 5 and 8. In the 3 different scenarios for scheduled repeated testing intervals (3, 5, and 7 days) in the subgroup of individuals with an initially negative swab test finding, the probability peaked on the sixth, ninth and tenth, and 13th and 14th days, respectively.

conclusionsSwab tests can initially yield false-negative outcomes. The probability of testing positive increases from day 1, peaking between days 5 and 8 after close contact with a patient with COVID-19. Early testing, especially in this final time window, is recommended together with a health surveillance program scheduled in close intervals.

Indexed as

AdultContact TracingCOVID-19COVID-19 Nucleic Acid TestingFalse Negative ReactionsFemaleHealth PersonnelHumansInfectious Disease Transmission, Patient-to-ProfessionalItalyMaleMiddle AgedPandemicsSARS-CoV-2Time Factorsclose contactCOVID-19health care workershealth surveillanceswab test timing

Identifiers

PMID34003761
PMCPMC8176944
OpenAlexW3157467849

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.