Social network analysis predicts health behaviours and self-reported health in African villages.

ABSTRACT: The 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 na

SUBMITTER: Chami GF 

PROVIDER: S-EPMC4114748 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature