Social Network Analysis in Public Health

1. Social Network Analysis in Public Health Reza YousefiNooraie SAPHIR webinar, Nov 2012

2. 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’!

3. 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

4. Networks • Networks consist of actors connected to one another by relations • Social Network Analysis: a perspective to analyze social relationships • relations • informal • advice, trust, respect, • information exchange • formal • exchange of money, • information exchange • multiplex • actors • persons • groups • organisations • countries

5. 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

6. Georg Simmel (1858-1918) • Precursor of structuralism in social sciences • Introduced dyads, triads, distance, and network size

7. Jacob Moreno (1889-1974) • The founder of sociogram and sociometry

8. 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 positionin 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

9. MA NE Small World phenomenon Milgram (1969) Six degrees of separation

10. The strength of weak ties (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

11. Network theories • Steve Borgatti (2011) Resources flow through network the pattern of interconnection generates outcomes

12. Network theories How people benefit by connectivity Why some people are more similar • Steve Borgatti (2011)

13. Network theories • Steve Borgatti (2011) • Connectivity leads in more access to resources: • social relationships and health outcomes • social capital and social support

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

15. Network theories • Steve Borgatti (2011) • transmission of traits: • the patterns of disease flow (HIV, STD, obesity) • diffusion of knowledge and innovation

16. 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.

17. Theoretical framework • Social influence/induction • Social selection/homophily • Common context

18. Christakis & Fowler, 2007

19. Network theories • Steve Borgatti (2011) Location is power: Transactional knowledge, inter-organizational partnership

20. Saskatchewan Agency D Prince Edward Island Agency B Manitoba Agency C Alberta Agency A British Columbia Ontario Partnerships among Canadian Agencies Serving Women with Substance Abuse (Niccoles, Yousefi-Nooraie, et al.) responsiveness and trustworthiness: sending referrals friendliness: joint programming and consultation.

21. Network theories • Steve Borgatti (2011) Position shapes attitudes and behaviors: organizational isomorphism, etc

22. Cool question 2 • In adolescents, who is more likely to be influenced to smoke, if their friends become smoker? • a) girls • b) boys

23. 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.

24. Design and analysis of SNA studies

25. 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

27. 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

28. 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

29. Association between co-authorship network and scientific productivity (YousefiNooraie, 2008) DDRC EMRC Density: 41% 37% Centralization: 16% 28%

30. Degree centrality • the number of connections any actor has. • in-degree: the number of connections from other to him/her A B C D In-degree of actor A: 1

31. Information seeking for making evidence-informed decisions (YousefiNooraie, 2012) Division 1 The Office of MOH Division 2 Division 2 Division 5 Division 4 Nodes are sized by indegrees

32. Betweenness centrality • the extent that an actor appears between the other actors’ connections in the network A B C D betweenness of actor A: 2

33. Information seeking for making evidence-informed decisions(YousefiNooraie, 2012) Division 1 The Office of MOH Division 2 11 Division 2 Division 5 Division 4 Nodes are sized by betweenness

34. Sub-graphs • Clusters based on attributes • Cliques • Blocks

35. 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

36. Stochastic models • Dynamic actor-based modeling • How the outcome variableco-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

37. 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.

38. 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