Evidence map›Paper›PMID 35606803›Full record

ArticleBMC medicine2022

Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic.

Aurélie Godbout, Mélanie Drolet, Myrto Mondor, Marc Simard, Chantal Sauvageau, Gaston De Serres, Marc Brisson

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Contact behaviour before, during and after the COVID-19 pandemic in the Netherlands: evidence from contact surveys, 2016 to 2017 and 2020 to 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024
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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

7 authors at 2 institutions in 1 country.

Aurélie GodboutCentre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.
Mélanie DroletCentre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.
Myrto MondorCentre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.
Marc SimardInstitut National de Santé Publique du Québec, Québec, Canada.
Chantal SauvageauCentre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.
Gaston De SerresCentre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada.
Marc BrissonCentre de recherche du CHU de Québec-Université Laval, 1050 Chemin Sainte-Foy, Québec, G1S 4L8, Canada. marc.brisson@crchudequebec.ulaval.ca.
Université Laval · CAInstitut National de Santé Publique du Québec · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs we are confronted with more transmissible/severe variants with immune escape and the waning of vaccine efficacy, it is particularly relevant to understand how the social contacts of individuals at greater risk of COVID-19 complications evolved over time. We described time trends in social contacts of individuals according to comorbidity and vaccination status before and during the first three waves of the COVID-19 pandemic in Quebec, Canada.

methodsWe used data from CONNECT, a repeated cross-sectional population-based survey of social contacts conducted before (2018/2019) and during the pandemic (April 2020 to July 2021). We recruited non-institutionalized adults from Quebec, Canada, by random digit dialling. We used a self-administered web-based questionnaire to measure the number of social contacts of participants (two-way conversation at a distance ≤2 m or a physical contact, irrespective of masking). We compared the mean number of contacts/day according to the comorbidity status of participants (pre-existing medical conditions with symptoms/medication in the past 12 months) and 1-dose vaccination status during the third wave. All analyses were performed using weighted generalized linear models with a Poisson distribution and robust variance.

resultsA total of 1441 and 5185 participants with and without comorbidities, respectively, were included in the analyses. Contacts significantly decreased from a mean of 6.1 (95%CI 4.9-7.3) before the pandemic to 3.2 (95%CI 2.5-3.9) during the first wave among individuals with comorbidities and from 8.1 (95%CI 7.3-9.0) to 2.7 (95%CI 2.2-3.2) among individuals without comorbidities. Individuals with comorbidities maintained fewer contacts than those without comorbidities in the second wave, with a significant difference before the Christmas 2020/2021 holidays (2.9 (95%CI 2.5-3.2) vs 3.9 (95%CI 3.5-4.3); P<0.001). During the third wave, contacts were similar for individuals with (4.1, 95%CI 3.4-4.7) and without comorbidities (4.5, 95%CI 4.1-4.9; P=0.27). This could be partly explained by individuals with comorbidities vaccinated with their first dose who increased their contacts to the level of those without comorbidities.

conclusionsIt will be important to closely monitor COVID-19-related outcomes and social contacts by comorbidity and vaccination status to inform targeted or population-based interventions (e.g., booster doses of the vaccine).

Indexed as

Contact TracingCOVID-19Vaccination CoverageAdultComorbidityCross-Sectional StudiesHumansPandemicsSARS-CoV-2Social BehaviorTime FactorsVaccinationComorbiditiesCOVID-19COVID-19 vaccinationInfectious diseasesPublic healthSocial contacts

Identifiers

PMID35606803
PMCPMC9126104
OpenAlexW4281287297

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.