Evidence map›Paper›PMID 35691100›Full record

ArticleEpidemics2022

Social mixing patterns relevant to infectious diseases spread by close contact in urban Blantyre, Malawi.

Deus Thindwa, Kondwani C Jambo, John Ojal, Peter MacPherson, Mphatso Dennis Phiri, Amy Pinsent, McEwen Khundi, Lingstone Chiume, Katherine E Gallagher, Robert S Heyderman and 3 more

Open access · goldAbstract read
In one paragraph

Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.4field-weighted citation impact, top 4% 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

17 citing papers in PubMed, 33 citations in OpenAlex.

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  13. Infectious Disease Modelling · 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

13 authors at 7 institutions in 3 countries.

Deus ThindwaCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi. Electronic address: deus.thindwa@lshtm.ac.uk.
Kondwani C JamboMalawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
John OjalCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; KEMRI-Wellcome Research Programme, Geographic Medicine Centre, Kilifi, Kenya.
Peter MacPhersonMalawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK; Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK.
Mphatso Dennis PhiriMalawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
Amy PinsentAquarius Population Health, London, UK.
McEwen KhundiDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; Malawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi.
Lingstone ChiumeMalawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi.
Katherine E GallagherDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK; KEMRI-Wellcome Research Programme, Geographic Medicine Centre, Kilifi, Kenya.
Robert S HeydermanNIHR Global Health Research Unit on Mucosal Pathogens, Research Department of Infection, Division of Infection and Immunity, University College London, London, UK.
Elizabeth L CorbettDepartment of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK.
Neil FrenchMalawi-Liverpool-Wellcome Clinical Research Programme, Blantyre, Malawi; Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, UK.
Stefan FlascheCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
London School of Hygiene & Tropical Medicine · GBMalawi-Liverpool-Wellcome Trust Clinical Research Programme · MWUniversity of Liverpool · GBAquarius Population Health · GBKenya Medical Research Institute · KELiverpool School of Tropical Medicine · GBUniversity College London · GB

Funding

Department of HealthWellcome Trust 206575/Z/17/ZWellcome Trust 208 812/Z/17/ZWellcome Trust 208812/Z/17/Z
6 · The paper itself

Abstract

introductionUnderstanding human mixing patterns relevant to infectious diseases spread through close contact is vital for modelling transmission dynamics and optimisation of disease control strategies. Mixing patterns in low-income countries like Malawi are not well known. METHODOLOGY: We conducted a social mixing survey in urban Blantyre, Malawi between April and July 2021 (between the 2nd and 3rd wave of COVID-19 infections). Participants living in densely-populated neighbourhoods were randomly sampled and, if they consented, reported their physical and non-physical contacts within and outside homes lasting at least 5 min during the previous day. Age-specific mixing rates were calculated, and a negative binomial mixed effects model was used to estimate determinants of contact behaviour.

resultsOf 1201 individuals enroled, 702 (58.5%) were female, the median age was 15 years (interquartile range [IQR] 5-32) and 127 (10.6%) were HIV-positive. On average, participants reported 10.3 contacts per day (range: 1-25). Mixing patterns were highly age-assortative, particularly those within the community and with skin-to-skin contact. Adults aged 20-49 y reported the most contacts (median:11, IQR: 8-15) of all age groups; 38% (95%CI: 16-63) more than infants (median: 8, IQR: 5-10), who had the least contacts. Household contact frequency increased by 3% (95%CI: 2-5) per additional household member. Unemployed participants had 15% (95%CI: 9-21) fewer contacts than other adults. Among long range (>30 m away from home) contacts, secondary school children had the largest median contact distance from home (257 m, IQR 78-761). HIV-positive status in adults >=18 years-old was not associated with changed contact patterns (rate ratio: 1.01, 95%CI: (0.91-1.12)). During this period of relatively low COVID-19 incidence in Malawi, 301 (25.1%) individuals stated that they had limited their contact with others due to COVID-19 precautions; however, their reported contacts were 8% (95%CI: 1-13) higher.

conclusionIn urban Malawi, contact rates, are high and age-assortative, with little reported behavioural change due to either HIV-status or COVID-19 circulation. This highlights the limits of contact-restriction-based mitigation strategies in such settings and the need for pandemic preparedness to better understand how contact reductions can be enabled and motivated.

Indexed as

Communicable DiseasesCOVID-19HIV InfectionsAdolescentAdultChildFemaleHumansInfantMalawiMaleSchoolsAfricaInfectious diseaseMalawiMixing dataSocial contactsTransmission

Identifiers

PMID35691100
PMCPMC9176177
OpenAlexW4282938851

What OpenQuestion holds

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Registered trials

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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.