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Social and Spatial Clustering of Personal Relationships

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  1. Social and Spatial Clustering of Personal Relationships Understanding the Impact of Behavioural Risks on Sexually Transmitted Disease Transmission James Tompkins GEOG 596AAdviser: Justine Blanford

  2. GaetanDugas World map shows flight routes from the 40 largest U.S. airports.1Image: Christos Nicolaides, Juanes Research Group 1

  3. Introduction • STIs on the Rise: Rates, Transmission and Risk • Social Network Analysis / Graph Theory • Social Networks • Sexual Networks • Spatial Epidemiology of STIs • Social to Spatial Links • Objectives • Methodology • Timeline • References • Acknowledgements

  4. STIs • Each year 448 million new cases of curable STIs (syphilis, gonorrhea, chlamydia) occur throughout the world in adults aged 15-49 years (WHO) • More than 60 million men, women and children have been affected by HIV. The spread of HIV continues, causing more than 14,000 new infections every day throughout the world (WHO)2 2

  5. STIs 3

  6. STIs on the Rise In Canada: Over 100,000 cases annually Over 10 years: Syphilis: Increased 568.2% Gonorrhea: Increased 116.5% Chlamydia: Increased 80.2% 4 5 6 7

  7. Groups at Risk 8

  8. Require far fewer average contacts to spread than other contagious diseases9 • Due to nature of infection, infection is more prevalent in some populations • due to risk-based behaviours BUT … • Efficiency: Transmission Methods 9 Hethcote and Yorke (1984) 10 11 Anderson and May (1995) 12 Mastro and de Vincenzi (1996)

  9. Groups at Risk • “The behavioural, social and cultural factors affecting the epidemiology of sexually transmitted and bloodborne pathogens in high-risk populations: Determining risk space in Canada’s vulnerable populations” CIHR (2007) • Respondent-Driven Sampling • Findings supported statistics that highlight behaviours of greatest risk • Injected Drug Users (IDU) account for 61% of total prevalent HCV cases in Canada. • Men who have sex with men (MSM) account for 46.7% of individuals living with HIV.

  10. Social Network Theory Social Network: A set of people, objects, events or places and the relationships that connect them all.

  11. Social Network Theory Adolescent romantic and sexual networks of “Jefferson” High11 13 Bearman, Moody and Stovel (2004)

  12. Social Network Theory • Aitken et al (2004) demonstrated that infection paths amongst IDU and sexual partners were difficult to trace based on genetic markers identifying the infections. • Pilon et al (2011) found that few HIV and HCV infections coincided with the recruitment networks they had achieved. • De Rubeis et al (2007) indicated that despite widely diverse social networks with sexual links and repeat infections, disease clusters often varied with respect to the infecting agent.

  13. Socializing in a Spatial World “The Core Population” • Rothenberg (1983) “The geography of gonorrhea” • Potterat et al (1984) “Gonorrhea as a social disease”6 locations made up over half of the locational references for social contacts studied who identified a ‘specific locus for socializing’. • Becker et al (1998) “Geographic Epidemiology of Gonorrhea in Baltimore”

  14. The Core Population 18 Potterat et al (1984) 19 Becker et al (1998)

  15. A Small World After All Centrality Any of various measures that determine the relative importance of a node within a graph

  16. Small World Theory • Homophily: tendency of people to associate with similar people • Heterogeneity: a minority of people will associate with dissimilar people • Social Aggregation: many people will congregate in a few places to socialize 21 Jolly AM, Wylie JL (2013). Sexually Networks and Sexually Transmitted Infections; “The Strength of Weak (Long Distance) Ties”, The New Public Health and STI/HIV Prevention.

  17. Social to Spatial Tobler’s First Law of Geography “Everything is related to everything else, but near things are more related than distant things.” • 52% of all pairs separated by a distance of 4 km or less • - Rothenberg et al (2005) “Social and geographic distance in HIV risk” • Geographic proximity associated with adoption of high risk behaviors (e.g. needle sharing) • - Shane (2011) “Defining Intervention Location from social network geographic data” 22 Rothenberg et al (2005)

  18. Social to Spatial

  19. Objectives • To identify the social networks of at-risk individuals in Winnipeg, MB and Ottawa, ON with consideration of their activity space • To determine whether mapping specific behaviours is analogous to mapping specific ‘hangouts’ • To determine the nature of the relationship between social clusters and spatial clusters among at-risk individuals

  20. Methodology Data from Jolly and Wylie “Vulnerable Peoples Study” – Survey administered by health care workers during 2009 in Winnipeg and Ottawa To understand social connections and roles of participants in the network: • Social Network Analysis: Use social networking software (ie. Gephi) to identify linkages and communities of individuals within CIHR survey. • Identify measures of degree centrality for all members in the survey network. These metrics will be used as variables in spatial operations.

  21. Methodology To understand the role of place and space, use ArcGIS to plot and analyse intersections identifying key ‘hangouts’ • Kernel Density Estimation (KDE) on degree centrality values: Interpolate the strength and concentration of communities at risk. • KDE on counts of ‘hangout’ mentions for each documented site. • Perform a Local Indicators of Spatial Association (LISA) analysis on instances of infected individuals against location of ‘hangouts’ to identify statistically significant locations of social activity

  22. Methodology Integrate SNA with geography to identify the topology of social connections • Explore the geography of relationships and infection simultaneously. • Examine links between spatial (distance between individuals, hangouts) and social (individual’s centrality) ties.

  23. Expected Results Understand “Socio-Sexual Networks” Are they contained? What is the role and potential for bridges? What do centrality and the type of network (homophily, heterogeneity, social congregation) tell us? What infections exist in the network and at what incidence rate? Understand “social hangouts” What role do they play in the network? Understand the role geography plays in clustering of hangouts and transmission of STIs

  24. Timeline June 2013: Analyse survey result data Process data in Gephi, determine shape of social networks, calculate centrality measures August 2013: Geocode intersections and place names for hangouts and residences for each record September – December 2013: Interpolations, LISA analysis of data Overlays of spatial and social interpolations Analysis of spatial and social connections January – March 2014: Final analysis, synthesizing results and writing up

  25. References 1 2 3 4 5 6 7 8 9 Hethcote, H. and J.A. Yorke (1984). Gonorrhea: transmission dynamics and control. New York, Springer. 10 11 Anderson RA and R.A. May (1995). Infectious diseases in humans. London, Oxford University Press. 12 Mastro, T.D. and I. de Vincenzi (1996). Probabilities of sexual HIV-1 transmission.AIDS1996;10(Suppl A):S75–82. 13 Bearman, Moody and Stovel (2004). “Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks”. 14 Aitken et al (2004). “Change in hepatitis C virus genotype in injecting drug users”. 15 Pilonet al (2011). “Transmission Patterns of HIV and Hepatitis C Virus among Networks of People Who Inject Drugs”. 16 De Rubeis et al (2007). “Combining social network analysis and cluster analysis to identify sexual network types”.18 Potteratet al (1984). “Gonorrhea as a social disease”. 19 Becker et al (1997). “Geographic epidemiology of Gonorrhea in Baltimore”. 20 De et al (2004). “Sexual network analysis of a gonorrhea outbreak”. 21 Jolly AM, Wylie JL (2013). Sexually Networks and Sexually Transmitted Infections; “The Strength of Weak (Long Distance) Ties”, The New Public Health and STI/HIV Prevention. 22 Rothenberg et al (2005). “Social and Geographic Distance in HIV Risk”. 23 Shane (2011). “Defining intervention location from social network geographic data of people who inject drugs in Winnipeg, Canada.” Issues of Substance Conference, Vancouver. 24 Auerbach et al (1984). “Cluster of cases of the Acquired Immune Deficiency Syndrome; patients linked by sexual contact”. 25 De et al (2004). “Sexual network analysis of a gonorrhea outbreak”. Sex Transm Infect 2004; 80:280-285. 26 Wylie et al (2000). “Patterns of Chlamydia and Gonorrhea Infection in Sexual Networks in Manitoba, Canada”.

  26. Acknowledgements Ann Jolly Justine Blanford