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dc.contributor.authorChami, Goyletteen
dc.contributor.authorAhnert, Sebastianen
dc.contributor.authorVoors, Maartenen
dc.contributor.authorKontoleon, Andreasen
dc.date.accessioned2014-11-12T14:45:49Z
dc.date.available2014-11-12T14:45:49Z
dc.date.issued2014-07-29en
dc.identifier.citationPLoS ONE 9(7): e103500. DOI:10.1371/journal.pone.0103500en
dc.identifier.issn1932-6203
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/246358
dc.description.abstractThe provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.
dc.description.sponsorshipThis work was supported by N.W.O. grant #W07.68.116 to Professor Erwin Bulte.
dc.languageEnglishen
dc.language.isoenen
dc.publisherPLOS
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.titleSocial network analysis predicts health behaviours and self-reported health in African villagesen
dc.typeArticle
dc.description.versionThis is the final version, originally published by PLOS at http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0103500.en
prism.publicationDate2014en
prism.publicationNamePLOS ONEen
prism.volume9en
dcterms.dateAccepted2014-07-02en
rioxxterms.versionofrecord10.1371/journal.pone.0103500en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-07-29en
dc.contributor.orcidKontoleon, Andreas [0000-0003-4769-898X]
dc.identifier.eissn1932-6203
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEconomic and Social Research Council (ES/J017620/1)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales