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Network Analysis of the local Public Health Sector: Translating evidence into practice

Network Analysis of the local Public Health Sector: Translating evidence into practice. Helen McAneney. School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast. Early beginnings for Social Network Analysis. Stanley Milgram and six degrees of separation

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Network Analysis of the local Public Health Sector: Translating evidence into practice

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  1. Network Analysis of the local Public Health Sector: Translating evidence into practice Helen McAneney School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast

  2. Early beginnings for Social Network Analysis • Stanley Milgram and six degrees of separation • the Erdös number and the Kevin Bacon game • Granovetter (1973): • “The strength of weak ties” • Watts and Strogatz (1998): • “Collective dynamics of small-world networks” Euler’s Konigsberg's Bridges Problem (1736)

  3. Applications • Knowledge transfer • Disease transfer • STDs • Avian flu (hub airports) • Drugs/smoking/obesity • Web, Google • Citations of articles • Neighbourhood effects

  4. The shape of the US purely from the flight paths.

  5. SNA Theory • Nodes (actors) and edges (ties) • Adjacency matrix A • SNA measures • Centrality, centralisation, block-modelling • Freeman Degree Centrality • No. of edges attached to it • Normalised Degree

  6. SNA Theory • Bonacich Eigenvector Centrality • Edges weighted by influence of node connected to • l is largest e-value, x is e-vector of A • Betweenness Centrality • Fraction of geodesic paths that a given node lies on • Control a node has over flow of information

  7. A few examples: Star network • Star network • Adjacency matrix of

  8. A few examples: Star network • Centrality measures • Freeman Degree • Bonacich Eigenvector • Betweenness • Centralisation 100%, node1 dominates

  9. A few examples: Circle network • Circle network • Adjacency matrix of

  10. A few examples: Circle network • Centrality measures • Freeman Degree • Bonacich Eigenvector • Betweenness • Centralisation 0%, all nodes equal

  11. A few examples: Line network • Line network (‘broken circle’) • Adjacency matrix of

  12. A few examples: Line network • Centrality measures • Centralisation • 6.67% (degree) • 39% (e-vector) • 31% (betweenness)

  13. CoE Network in Public Health • Launch of UKCRC CoE in Public Health (NI) June 2008 • Questionnaire to provide baseline data • Create a map of PH community in NI • 98 participants from 44 organisations & research clusters • 193 nodes (organisations) nominated

  14. How personal goals related to those of CoE

  15. CoE Network in Public Health 193 organisations and research clusters

  16. Centrality measures • Centralisation • 16% (out-degree) & 5% (in-degree) • 51% (eigenvector) • 4% (betweenness)

  17. Block-model of Network

  18. Block-model of Network Root mean square of impact and strength Values of 1 (high) – 3 (low) Strongest if 2 (1+1), weakest if 6 (3+3)

  19. Questions for the future • Identified difference in attitudes/goals of academics & non-academics. • Sectors with little or no interaction • Influential organisation • good or bad? • ‘Value’ of trans-disciplinary interaction • CoE’s translational message, • improving cross collaboration • improving effectiveness for clinical or PH outcomes

  20. Acknowledgement • Dr Jim McCann • School of Mathematics and Physics • Prof. Lindsay Prior • School of Sociology, Social Policy and Social Work, • Jane Wilde CBE • The Institute of Public Health in Ireland • Prof. Frank Kee • Director UKCRC Centre of Excellence for Public Health • www.qub.ac.uk/coe

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