The role of Networks within The role of Networks within Public HealthPublic Health

Helen McAneneyHelen McAneney

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

OutlineOutline

• Background– Historical setting and recent research

• Some theory– Centrality, centralisation, block-modelling,

• A few simple examples– Star, circle and line networks

• Networks within Public Health– Results and discussion

• Questions for the future

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)

Source: art-sciencefactory.com

The shape of the US purely from the flight paths.

Collectively a pattern emerges.

It’s a small world: New Scientist 20 April 2009

Time to travel to nearest city of 50K+ by land or water

Less than 10% of the world's land is more than 48 hours of ground-based travel from the nearest city.

It’s a small world: New Scientist 20 April 2009

The planet's navigable rivers

The network of rivers produced by nature.

It’s a small world: New Scientist 20 April 2009

Keeping track of trains

Railway lines are mainly confined to the richer nations. The railway networks in India, Argentina and parts of Africa give clues to their colonial heritage.

Applications

• Knowledge transfer

• Disease transfer

– STDs

– Avian flu (hub airports)

• Drugs/smoking/obesity

• Web, Google

• Citations of articles

• Neighbourhood effects

• Friendship sites

Friendship as a Health Factor

Science 23 January 2009:Vol. 323. no. 5913, pp. 454 - 457

How your friends' friends can affect your mood

New Scientist, 30 December 2008 by Michael Bond

• The Spread of Obesity in a Large Social Network Over 32 Years

N. Christakis, J. Fowler N Engl J Med (2007): 357: 370-9

• The Collective Dynamics of Smoking in a Large Social Network

N. Christakis, J. Fowler N Engl J Med (2008): 358: 2249-58

• Dynamic Spread of Happiness in a Large Social Network: Longitudinal

Analysis Over 20 Years in the Framingham Heart Study

J. Fowler, N. Christakis BMJ (2008) 337: a2338

• Model of Genetic Variation in Human Social Networks

J. Fowler, C. Dawes, N. Christakis PNAS (2009) 106: 1720-1724

Networks

• Nodes (actors) and edges (ties)

• Mark Newman, The physics of networks. Physics Today, November 2008, 33-38.

– “In its simplest form, a network is a collection of points, or nodes, joined by lines, or edges.”

– “Statistical analysis of interconnected groups—of computers, animals, or people—yields important clues about how they function and even offers predictions of their future behaviour.”

SNA Theory

• Adjacency matrix A, (nxn)

• SNA measures

– Centrality, centralisation, block-modelling

• Freeman Degree Centrality

– No. of edges attached to it

– Normalised Degree

– Popularity, advice

n

jiji Ak

1

1max nkkk ii

SNA Theory

• The degree distribution is

the probability distribution of

these degrees over the whole

network

• The distribution of the degrees

of nodes on the internet. As

indicated, the distribution

roughly follows a straight line

on a logarithmic plot; that is, it

obeys a power law.

MEJ Newman. The physics of networks. Physics Today, November 2008, 33-38

SNA Theory

• Bonacich Eigenvector Centrality

– Edges weighted by influence of node connected to

– is largest e-value, x is e-vector of A

– By Perron-Frobenius Theorem, e-vector of dominant e-value has non-negative entries.

• 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

A few examples: Star network

• Star network

• Adjacency matrix of

0000001

0000001

0000001

0000001

0000001

0000001

1111110

STARA

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

A few examples: Circle network

• Circle network

• Adjacency matrix of

0100001

1010000

0101000

0010100

0001010

0000101

1000010

CIRCLEA

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

A few examples: Line network

• Line network (‘broken circle’)

• Adjacency matrix of

0010000

0001000

1000100

0100010

0010001

0001001

0000110

LINEA

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

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

How personal goals related to those of CoE

CoE Network in Public Health

193 organisations and research clusters

• 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

Block-model of Network

Block-model of Network

Root mean sum of square of impact (x) and strength (y),

Scale of 1 (high) – 3 (low)Strongest if 2 (12+12), weakest if 18 (32+32)Entry (i; j) from row i and column j, gives the RMSS from block i to block j.

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

• Health reforms in NI - new PH Agency, HSCB

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