21
Network Analysis of the local Network Analysis of the local Public Health Sector: Public Health Sector: Translating evidence into practice Translating evidence into practice Helen McAneney Helen McAneney School of Medicine, Dentistry and Biomedical School of Medicine, Dentistry and Biomedical Sciences, Sciences, Queen’s University Belfast Queen’s University Belfast

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

  • Upload
    xenia

  • View
    30

  • Download
    0

Embed Size (px)

DESCRIPTION

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 - PowerPoint PPT Presentation

Citation preview

Page 1: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

Network Analysis of the local Network Analysis of the local Public Health Sector: Public Health Sector:

Translating evidence into practiceTranslating evidence into practice

Helen McAneneyHelen McAneney

School of Medicine, Dentistry and Biomedical Sciences,School of Medicine, Dentistry and Biomedical Sciences,Queen’s University BelfastQueen’s University Belfast

Page 2: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

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)

Page 3: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

Applications

• Knowledge transfer

• Disease transfer

– STDs

– Avian flu (hub airports)

• Drugs/smoking/obesity

• Web, Google

• Citations of articles

• Neighbourhood effects

Page 4: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

The shape of the US purely from the flight paths.

Page 5: Network Analysis of the local  Public Health Sector:  Translating evidence into practice
Page 6: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

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

n

jiji Ak

1

maxkki

Page 7: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

SNA Theory

• Bonacich Eigenvector Centrality – Edges weighted by influence of node connected to

– 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

n

jjiji xAx

1

1

Page 8: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

A few examples: Star network

• Star network

• Adjacency matrix of

0000001000000100000010000001000000100000011111110

STARA

Page 9: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

A few examples: Star network

• Centrality measures

– Freeman Degree

– Bonacich Eigenvector

– Betweenness

• Centralisation 100%, node1 dominates

Node Degree Eigenvector Betweenness 1 6 0.707 15 2 1 0.29 0 3 1 0.29 0 4 1 0.29 0 5 1 0.29 0 6 1 0.29 0 7 1 0.29 0

Page 10: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

A few examples: Circle network

• Circle network

• Adjacency matrix of

0100001101000001010000010100000101000001011000010

CIRCLEA

Page 11: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

A few examples: Circle network

• Centrality measures

– Freeman Degree

– Bonacich Eigenvector

– Betweenness

• Centralisation 0%, all nodes equal

Node Degree Eigenvector Betweenness 1 2 0.38 3 2 2 0.38 3 3 2 0.38 3 4 2 0.38 3 5 2 0.38 3 6 2 0.38 3 7 2 0.38 3

Page 12: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

A few examples: Line network

• Line network (‘broken circle’)

• Adjacency matrix of

0010000000100010001000100010001000100010010000110

LINEA

Page 13: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

A few examples: Line network

• Centrality measures

• Centralisation

– 6.67% (degree)

– 39% (e-vector)

– 31% (betweenness)

Node Degree Eigenvector Betweenness 1 2 0.50 9 2 2 0.46 8 3 2 0.46 8 4 2 0.35 5 5 2 0.35 5 6 1 0.19 0 7 1 0.19 0

Page 14: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

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

Page 15: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

How personal goals related to those of CoE

Page 16: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

CoE Network in Public Health

193 organisations and research clusters

Page 17: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

• Centrality measures

• Centralisation

– 16% (out-degree) & 5% (in-degree)

– 51% (eigenvector)

– 4% (betweenness)

Out-Degree In-Degree Eigenvector Betweenness 1. QUB_CCPS DHSSPS BHSCT DHSSPS 2. EHSSB BHSCT DHSSPS BHSCT 3. NICR IPH QUB_CCPS QUB_NM 4. DHSSPS HSCT UU UU 5. QUB_NM QUB EHSSB IPH 6. BHSCT UU RDO RDO

Page 18: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

Block-model of Network

Page 19: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

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)

Page 20: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

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

Page 21: Network Analysis of the local  Public Health Sector:  Translating evidence into practice

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