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Social Network Analysis in Public Health

Reza Yousefi Nooraie

SAPHIR webinar, Nov 2012

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Cool question 1

• If your close friend becomes obese, your chance of becoming obese…

• a) will increase by 20%• b) will increase by 70%• c) will increase by 170%• d) doesn’t matter. I know what I’m eatin’!

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• If your close friend becomes obese, your chance of becoming obese…

• a) will increase by 20%• b) will increase by 70%• c) will increase by 170%• d) doesn’t matter. I know what I’m eatin’!

Christakis N, Fowler J. The Spread of Obesity in a Large Social Network over 32 Years. N Engl J Med 2007;357:370-9

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Networks• Networks consist of actors connected to one another

by relations • Social Network Analysis: a perspective to analyze

social relationships

• actors persons

groupsorganisationscountries

• relationsinformal

advice, trust, respect,information exchange

formalexchange of money,information exchange

multiplex

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Social Network Analysis

• A ‘relational’ thinking in social sciences

• All social entities and concepts, e.g. power, freedom, and society, are redefined as the functions of the dynamic relationships

• Relations as the units of analysis

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Georg Simmel (1858-1918)

• Precursor of structuralism in social sciences• Introduced dyads, triads, distance, and

network size

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Jacob Moreno (1889-1974)

• The founder of sociogram and sociometry

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Social Capital Coleman, Katz, Menzel (1957)

• The time to adoption of a newly developed tetracycline by physicians

• to whom they turned to for professional advice, with whom they discussed, and with whom they socialized

• the position in the network predicted early adoption more than personal characteristics.

• Physicians who were considered by more peers as advisors, discussion partners and friends were more likely to use the new drug earlier

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Milgram (1969)Small World phenomenon

NE

MA

Six degrees of separation

(Granovetter, 1973)

• the weak ties which bridge unconnected clusters are especially important

– provide access to novel and heterogeneous resources– more likely to adopt innovations/ less bound to the

group norms

• You are more likely to hear about a job from an acquaintance than a close friend

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The strength of weak ties

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Network theories

• Steve Borgatti (2011) perspectives

models Social capitalSocial homogeneity

Network flow capitalization contagion

Network architecture coordination adaptation

Resources flow through network

the pattern of interconnection

generates outcomes

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Network theories

• Steve Borgatti (2011) perspectives

models Social capitalSocial homogeneity

Network flow capitalization contagion

Network architecture coordination adaptation

How people benefit by connectivity

Why some people are more similar

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Network theories

• Steve Borgatti (2011) perspectives

models Social capitalSocial homogeneity

Network flow capitalization contagion

Network architecture coordination adaptation

Connectivity leads in more access to resources: social relationships and health outcomessocial capital and social support

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Social Capital, Income Inequality,and Mortality (Kawachi et al., 1997)

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Network theories

• Steve Borgatti (2011) perspectives

models Social capitalSocial homogeneity

Network flow capitalization contagion

Network architecture coordination adaptation

transmission of traits: the patterns of disease flow (HIV, STD, obesity)diffusion of knowledge and innovation

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The Spread of Obesity in a Large Social Network over 32 Years (Christakis & Fowler, 2007)

• social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study.

• longitudinal GEE model

• whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors.

Theoretical framework

•Social influence/induction

• Social selection/homophily

• Common context

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Christakis & Fowler, 2007

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Network theories

• Steve Borgatti (2011) perspectives

models Social capitalSocial homogeneity

Network flow capitalization contagion

Network architecture coordination adaptation

Location is power: Transactional knowledge, inter-organizational partnership

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Partnerships among Canadian Agencies Serving Women with Substance Abuse (Niccoles, Yousefi-Nooraie, et al.)

OntarioBritish Columbia

Alberta

Prince Edward Island

Saskatchewan

Manitoba

Agency A

Agency BAgency C

Agency D

responsiveness and trustworthiness: sending referralsfriendliness: joint programming and consultation.

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Network theories

• Steve Borgatti (2011) perspectives

models Social capitalSocial homogeneity

Network flow capitalization contagion

Network architecture coordination adaptation

Position shapes attitudes and behaviors: organizational isomorphism, etc

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Cool question 2

• In adolescents, who is more likely to be influenced to smoke, if their friends become smoker?

• a) girls

• b) boys

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• In adolescents, who is more likely to be influenced to smoke, if their friends become smoker?

• a) girls

• b) boys

Mercken, L., et al. (2010). Smoking based selection and influence in gender segregated friendship networks: a ‐ ‐social network analysis of adolescent smoking. Addiction, 105(7), 1280-1289.

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Design and analysis of SNA studies

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Types of networks

• egocentric or personal networks– relations defined from focal individuals

• compare relational structures of actors

• sociocentric or whole networks– relations linking members of a single,

bounded population

• examine internal structures and positioning of actors within one network

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Data collection• Questionnaires– Name generators

• Roster / Choose from a list• Free recall

– Name interpreters• Rate the frequency, quality, … of the connection

• Interviews• Observation• Recordings– Documents– Electronic logs

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Basic measures–Overall shape•Density: proportion of available ties to all possible•Centralization: resembling a star network

–Central actors•Degree: the number of ties•Betweenness: the mediatory role•Closeness: accessibility and distance

–Subgroups•Cliques: all connected to each other•Blocks: more connected with each other than outside

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DDRC EMRCDensity: 41% 37%Centralization: 16% 28%

Association between co-authorship network and scientific productivity (Yousefi Nooraie, 2008)

Degree centrality• the number of connections any actor has.

• in-degree: the number of connections from other to him/her

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A B

C

DIn-degree of actor A: 1

Information seeking for making evidence-informed decisions (Yousefi Nooraie, 2012)

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Division 1

Division 2

Division 5

Division 2

Division 4

The Office of MOH

Nodes are sized by indegrees

Betweenness centrality

• the extent that an actor appears between the other actors’ connections in the network

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A B

C

Dbetweenness of actor A: 2

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Division 1

Division 5

Division 2

Division 4

The Office of MOH

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Division 2

Information seeking for making evidence-informed decisions (Yousefi Nooraie, 2012)

Nodes are sized by betweenness

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Sub-graphs

• Clusters based on attributes

• Cliques

• Blocks

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Stochastic models

• The problem of the dependence of observations

• Exponential random graph modeling (ERGM)

• the effect of different structural, node-level, and dyadic factors on the formation of ties

Stochastic models

• Dynamic actor-based modeling

• How the outcome variable co-evolves with the longitudinal evolution of structural, node-level, and dyadic variables

• for any point in time, the current state of the network determines probabilistically its further evolution

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Smoking-based selection and influence in gender-segregated friendship networks (Mercken, et al., 2010)

• Longitudinal design with four measurements.

• A total of 1163 adolescents in 9 junior high schools in Finland.

• Smoking behaviour of adolescents, parents, siblings and friendship ties.

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Mercken, et al., 2010

• Smoking-based selection of friends was found in males and females

• Social influence only in females

• Implication: prevention campaigns targeting resisting peer pressure may be more effective in girls than boys