Social Network Analysis Predicts Health Behaviours and Self-Reported Health in African Villages
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{"title"=>"Social network analysis predicts health behaviours and self-reported health in African villages", "type"=>"journal", "authors"=>[{"first_name"=>"Goylette F.", "last_name"=>"Chami", "scopus_author_id"=>"56049050700"}, {"first_name"=>"Sebastian E.", "last_name"=>"Ahnert", "scopus_author_id"=>"14028134000"}, {"first_name"=>"Maarten J.", "last_name"=>"Voors", "scopus_author_id"=>"36638692400"}, {"first_name"=>"Andreas A.", "last_name"=>"Kontoleon", "scopus_author_id"=>"6507535776"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84904997823", "doi"=>"10.1371/journal.pone.0103500", "pui"=>"373644969", "pmid"=>"25072820", "scopus"=>"2-s2.0-84904997823", "issn"=>"19326203"}, "id"=>"8a7dbc6f-01f7-364e-b957-e1d3f815b5a6", "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 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.", "link"=>"http://www.mendeley.com/research/social-network-analysis-predicts-health-behaviours-selfreported-health-african-villages-4", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>4, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>4, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>1, "Nursing and Health Professions"=>1, "Mathematics"=>3, "Agricultural and Biological Sciences"=>2, "Medicine and Dentistry"=>3, "Business, Management and Accounting"=>1, "Psychology"=>3, "Chemistry"=>1, "Social Sciences"=>9, "Computer Science"=>3, "Economics, Econometrics and Finance"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>9}, "Psychology"=>{"Psychology"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Japan"=>1, "Brazil"=>1, "United Kingdom"=>1, "Spain"=>1}, "group_count"=>5}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1613441"], "description"=>"<p>Each village is labelled with an uppercase letter. <b>A)</b> Village 26007 with 6 nodes and 10 edges. <b>B)</b> Village 13233 with 12 nodes and 11 edges. <b>C)</b> Village 26036 with 12 nodes and 16 edges. <b>D)</b> Village 26016 with 25 nodes and 37 edges. <b>E)</b> Village 13247 with 10 nodes and 23 edges. <b>F)</b> Village 13111 with 9 nodes and 11 edges. <b>G)</b> Village 13245 with 9 nodes and 16 edges. There are a total of 83 nodes and 124 edges. The nodes represent households. The size of the node represents in-degree, which is the number of other households in the village that named the household of interest. The number of nodes and their in-degree in parentheses are: 23(0), 26(1), 19(2), 5(3), 9(4), and 1(5). The colour of the node represents betweenness centrality. In all networks, and particularly in <b>B)</b> and <b>C)</b>, not all nodes with high in-degree have high betweenness centrality. The directed edges (arcs) represent pairwise friendship connections between any two members of the households. An outgoing edge indicates that the sending household named the receiving household as a friend and vice versa. The curved edges in the graph show two households where there is reciprocity in friendship connections. If there were multiple edges between the same pair of households we treated them as one edge. The number of households and their betweenness range in parentheses are: 49(0), 4(<0.01 and > = 0), 7(<0.02 and > = 0.01), 10(<0.08 and > = 0.02), 7(<0.20 and > = 0.08), and 6(> = 0.20). See Table S3 in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103500#pone.0103500.s001\" target=\"_blank\">File S1</a> for network construction statistics.</p>", "links"=>[], "tags"=>["Network Analysis", "social networks", "health care", "Health care policy", "Public and occupational health", "Economics", "Microeconomics", "networks", "liberian"], "article_id"=>1121091, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Goylette F. Chami", "Sebastian E. Ahnert", "Maarten J. Voors", "Andreas A. Kontoleon"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103500.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Friendship_networks_of_7_Liberian_villages_/1121091", "title"=>"Friendship networks of 7 Liberian villages.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-29 03:04:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1613443"], "description"=>"<p>Blue dots are raw values and connected red dots are predicted values from general linear models. The geometric mean is plotted (Gmean), as multiple households have the same in-degree or betweenness value. All unique values for in-degree and betweenness are plotted. All regressions (N = 83) controlled for village-level fixed effects, not shown. For full regression results, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103500#pone-0103500-t001\" target=\"_blank\">Table 1</a>. <b>A)</b> In-degree is positively related to the self-reported morbidity indicator of total people sick in the household. In-degree coeff. 0.308 (p<0.05); Betweenness coeff. −0.581 (p>0.10). Negative binomial model. <b>B)</b> Betweenness is a measure of preventative health and negatively related to poor health behaviours. In-degree coeff. 0.034 (p>0.10); Betweenness coeff. −5.107 (p<0.01). Poission model. <b>C)</b> Betweeness positively predicts medical care expenditure. In-degree coeff. 398.84 Liberian dollars (LBD) (p>0.10); Betweenness coeff. 23,084.77 LBD (∼296.34 USD) (p<0.05). Gaussian model. <b>D)</b> Betweenness is an indicator of household head susceptibility to health influences from other villagers. In-degree coeff. −0.106 (p>0.10); Betweenness coeff. −7.117 (p<0.05). Binomial probit model.</p>", "links"=>[], "tags"=>["Network Analysis", "social networks", "health care", "Health care policy", "Public and occupational health", "Economics", "Microeconomics", "centrality", "predictor", "regressions"], "article_id"=>1121093, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Goylette F. Chami", "Sebastian E. Ahnert", "Maarten J. Voors", "Andreas A. Kontoleon"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103500.g002", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_centrality_predictor_in_regressions_on_health_outcomes_/1121093", "title"=>"Significant centrality predictor in regressions on health outcomes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-29 03:04:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1613444"], "description"=>"<p>*p<0.05.</p><p>**p<0.01.</p><p>***<0.001.</p>a<p>Incident rate ratio.</p><p>Panels (1A–B) Negative binomial, Panel (2) Poisson, Panel (3) Gaussian, and Panel (4) Binomial probit.</p>", "links"=>[], "tags"=>["Network Analysis", "social networks", "health care", "Health care policy", "Public and occupational health", "Economics", "Microeconomics", "in-degree", "aspects"], "article_id"=>1121094, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Goylette F. Chami", "Sebastian E. Ahnert", "Maarten J. Voors", "Andreas A. Kontoleon"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103500.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Betweenness_and_in_degree_explain_different_aspects_of_health_/1121094", "title"=>"Betweenness and in-degree explain different aspects of health.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-29 03:04:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1613445"], "description"=>"<p>*p<0.05.</p><p>**p<0.01.</p><p>***p<0.001.</p>a<p>Incident rate ratio.</p><p>Panel (1A–B) Negative binomial, Panel (2) Poisson, Panel (3) Gaussian, and Panel (4) Binomial probit.</p>", "links"=>[], "tags"=>["Network Analysis", "social networks", "health care", "Health care policy", "Public and occupational health", "Economics", "Microeconomics", "in-degree", "betweenness"], "article_id"=>1121095, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Goylette F. Chami", "Sebastian E. Ahnert", "Maarten J. Voors", "Andreas A. Kontoleon"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103500.t003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Extended_models_of_in_degree_and_betweenness_for_health_/1121095", "title"=>"Extended models of in-degree and betweenness for health.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-29 03:04:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1613446"], "description"=>"<p>*p<0.05.</p><p>** p<0.01.</p><p>***<0.001.</p><p>GLM model was used with the Gaussian family.</p>", "links"=>[], "tags"=>["Network Analysis", "social networks", "health care", "Health care policy", "Public and occupational health", "Economics", "Microeconomics", "betweenness", "self-reported"], "article_id"=>1121096, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Goylette F. Chami", "Sebastian E. Ahnert", "Maarten J. Voors", "Andreas A. Kontoleon"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103500.t002", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Medical_care_and_betweenness_relation_with_self_reported_health_covariate_/1121096", "title"=>"Medical care and betweenness relation with self-reported health covariate.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-29 03:04:32"}
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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[291]}, {"subject_area"=>"/Biology and life sciences/Behavior", "average_usage"=>[333]}, {"subject_area"=>"/People and places", "average_usage"=>[302]}, {"subject_area"=>"/Social sciences", "average_usage"=>[318]}]}
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