Evidence map›Paper›PMID 35231023›Full record

ArticlePLoS medicine2022

Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study.

Amy Gimma, James D Munday, Kerry L M Wong, Pietro Coletti, Kevin van Zandvoort, Kiesha Prem, CMMID COVID-19 working group, Petra Klepac, G James Rubin, Sebastian Funk and 2 more

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 1 pooled it
22.6field-weighted citation impact, top 1% 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

90 citing papers in PubMed, 1 synthesis or guideline pooled it, 172 citations in OpenAlex.

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30 more citing papers are in PubMed but not listed here.

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

12 authors at 3 institutions in 2 countries.

Amy GimmaCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0003-2645-7212
James D MundayCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-6206-7134
Kerry L M WongCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-4400-2257
Pietro ColettiUHasselt, Data Science Institute and I-BioStat, Hasselt, Belgium.ORCID 0000-0001-9935-1692
Kevin van ZandvoortCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-7770-0537
Kiesha PremCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0003-0528-798X
CMMID COVID-19 working group
Petra KlepacCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0003-4132-3933
G James RubinDepartment of Psychological Medicine, King's College London, Denmark Hill, London, United Kingdom.ORCID 0000-0002-4440-0570
Sebastian FunkCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-2842-3406
W John EdmundsCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-9179-2917
Christopher I JarvisCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-0812-2446
London School of Hygiene & Tropical Medicine · GBKing's College London · GBHasselt University · BE

Funding

Does repeated influenza vaccination constrain influenza immune responses and protection?R01AI141534 · NIAID · UNIVERSITY OF MELBOURNE · PI FOX, ANNETTE, KUCHARSKI, ADAM JAMES · 2019 to 2023
$4.9M
CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain StimulationR01NS110424 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RICHARDSON, ROBERT MARK, TURNER, ROBERT STERLING · 2018 to 2020
$673k
Gates Foundation INV-001754Gates Foundation INV-003174Gates Foundation INV-016832Medical Research Council MC_PC_19065NIAID NIH HHS R01 AI141534NINDS NIH HHS R01 NS110424Wellcome Trust 210758/Z/18/ZWellcome Trust 221303/Z/20/Z
6 · The paper itself

Abstract

backgroundDuring the Coronavirus Disease 2019 (COVID-19) pandemic, the United Kingdom government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We conducted a repeated cross-sectional study to measure contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering 3 national lockdowns interspersed by periods of less restrictive policies. METHODS AND

findingsThe repeated cross-sectional survey data were collected using online surveys of representative samples of the UK population by age and gender. Survey participants were recruited by the online market research company Ipsos MORI through internet-based banner and social media ads and email campaigns. The participant data used for this analysis are restricted to those who reported living in England. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. To put the findings in perspective, we discuss contact rates recorded throughout the year in terms of previously recorded rates from the POLYMOD study social contact study. The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. We observed changes in social contact patterns in England over time and by participants' age, personal risk factors, and perception of risk. The mean reported contacts for adults 18 to 59 years old ranged between 2.39 (95% confidence interval [CI] 2.20 to 2.60) contacts and 4.93 (95% CI 4.65 to 5.19) contacts during the study period. The mean contacts for school-age children (5 to 17 years old) ranged from 3.07 (95% CI 2.89 to 3.27) to 15.11 (95% CI 13.87 to 16.41). This demonstrates a sustained decrease in social contacts compared to a mean of 11.08 (95% CI 10.54 to 11.57) contacts per participant in all age groups combined as measured by the POLYMOD social contact study in 2005 to 2006. Contacts measured during periods of lockdowns were lower than in periods of eased social restrictions. The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. The main limitations of this analysis are the potential for selection bias, as participants are recruited through internet-based campaigns, and recall bias, in which participants may under- or overreport the number of contacts they have made.

conclusionsIn this study, we observed that recorded contacts reduced dramatically compared to prepandemic levels (as measured in the POLYMOD study), with changes in reported contacts correlated with government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, the mean number of reported contacts only returned to about half of that observed prepandemic at its highest recorded level. The CoMix survey provides a unique repeated cross-sectional data set for a full year in England, from the first day of the first lockdown, for use in statistical analyses and mathematical modelling of COVID-19 and other diseases.

Indexed as

Social InteractionAdolescentAdultAgedAttitude to HealthCOVID-19Cross-Sectional StudiesEnglandFemaleHumansMaleMiddle AgedModels, PsychologicalPandemicsSurveys and QuestionnairesYoung Adult

Identifiers

PMID35231023
PMCPMC8887739
OpenAlexW4214484419

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.