Social and Spatial Clustering of
Personal Relationships
Understanding the Impact of
Behavioural Risks on Sexually
Transmitted Disease Transmission
James Tompkins
GEOG 596A
Adviser: Justine Blanford
Gaetan Dugas
World map shows flight routes from the 40 largest U.S. airports.1
Image: Christos Nicolaides, Juanes Research Group
1 http://web.mit.edu/newsoffice/2012/spread-of-disease-in-airports-0723.html
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
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 http://www.who.int/mediacentre/factsheets/fs110/en/
STIs
3 http://www.infographicsarchive.com/health-and-safety/std-statistics-worldwide
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 http://www.phac-aspc.gc.ca/sti-its-surv-epi/surveillance-eng.php
5 http://www.phac-aspc.gc.ca/std-mts/report/sti-its2008/index-eng.php
6 http://www.phac-aspc.gc.ca/sti-its-surv-epi/hepc/surv-eng.php
7 http://www.phac-aspc.gc.ca/aids-sida/publication/survreport/estimat2011-eng.php
Groups at Risk
8 http://www.cdc.gov/std/health-disparities/age.htm
Transmission Methods
• 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:
Chlamydia
35% per unprotected act of sexual
intercourse10
Gonorrhea
30 – 50% per unprotected act of sexual
intercourse10
Infectious Syphilis
20% per contact10
Hepatitis C
3% per exposure of contaminated needle
point (likelihood increases with HIV coinfection)11
HIV
0.1 to 3.4% per unprotected act of sexual
intercourse7 / 0.3% per exposure of
contaminated needle point12
9 Hethcote and Yorke (1984)
10 http://www.phac-aspc.gc.ca/aids-sida/publication/hivtr-rtvih-eng.php
11 Anderson and May (1995)
12 Mastro and de Vincenzi (1996)
Infection
Method of
Curable (or
Transmission
not) [How?]
Chlamydia Sexual
Yes; Antibiotics
intercourse
Groups at Risk
Rate for women almost twice as
high as males; 86% reported cases
in women younger than 30.
Under 30 years of age:
Females from 15 to 24
Males from 20 to 24
Males aged 25 to 29
Transmitted From
Mother?
During childbirth (60%
efficiency) [3]
Gonorrhea Sexual
intercourse
Yes; Antibiotics
Syphilis
Direct contact
with syphilis
sores
Through contact
with blood of
infected person;
unlikely and rare
but possible to
transmit during
sex.
Yes; Antibiotics
Treatment with
combinations of
antivirals over 6
to 12 months.
“Baby boomers”: People born from
1945 to 1965 (risk of contaminated
blood products prior to 1992);
Injected Drug Users (Demog.
Associated with 61% of infections);
Majority in males 30+ and
increasing rates in younger females
Possible during
development but not
definite; not conclusive
that transmission could
not result during
childbirth.
Sexual
intercourse
(varying risk
levels); contact
with blood of
infected person
No; treatment
with antiviral
medication
Men who have sex with men
(MSM) made up 46.7% of those
living with HIV in Canada during
2011
During development,
childbirth, and feeding
(treatment reduces risk)
HCV
HIV
During childbirth
During development
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.
Social Network Theory
Social Network:
A set of people, objects, events or places and the
relationships that connect them all.
Social Network Theory
Adolescent romantic and sexual networks of “Jefferson” High11
13 Bearman, Moody and Stovel (2004)
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.
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”
The Core Population
18 Potterat et al (1984)
19 Becker et al (1998)
A Small World After All
Centrality
Any of various measures that determine
the relative importance of a node within
a graph
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.
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)
Social to Spatial
Objectives
1. To identify the social networks of at-risk
individuals in Winnipeg, MB and Ottawa, ON
with consideration of their activity space
2. To determine whether mapping specific
behaviours is analogous to mapping specific
‘hangouts’
3. To determine the nature of the relationship
between social clusters and spatial clusters
among at-risk individuals
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:
1. Social Network Analysis: Use social networking
software (ie. Gephi) to identify linkages and
communities of individuals within CIHR survey.
2. Identify measures of degree centrality for all
members in the survey network. These metrics will
be used as variables in spatial operations.
Methodology
To understand the role of place and space, use ArcGIS
to plot and analyse intersections identifying key
‘hangouts’
1. Kernel Density Estimation (KDE) on degree
centrality values: Interpolate the strength and
concentration of communities at risk.
2. KDE on counts of ‘hangout’ mentions for each
documented site.
3. 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
Methodology
Integrate SNA with geography to identify the
topology of social connections
1. Explore the geography of relationships and
infection simultaneously.
2. Examine links between spatial (distance
between individuals, hangouts) and social
(individual’s centrality) ties.
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
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
References
1 http://web.mit.edu/newsoffice/2012/spread-of-disease-in-airports-0723.html
2 http://www.who.int/mediacentre/factsheets/fs110/en/
3 http://www.infographicsarchive.com/health-and-safety/std-statistics-worldwide
4 http://www.phac-aspc.gc.ca/sti-its-surv-epi/surveillance-eng.php
5 http://www.phac-aspc.gc.ca/std-mts/report/sti-its2008/index-eng.php
6 http://www.phac-aspc.gc.ca/sti-its-surv-epi/hepc/surv-eng.php
7 http://www.phac-aspc.gc.ca/aids-sida/publication/survreport/estimat2011-eng.php
8 http://www.cdc.gov/std/health-disparities/age.htm
9 Hethcote, H. and J.A. Yorke (1984). Gonorrhea: transmission dynamics and control. New York, Springer.
10 http://www.phac-aspc.gc.ca/aids-sida/publication/hivtr-rtvih-eng.php
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 Pilon et 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 Potterat et 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”.
Acknowledgements
Ann Jolly
Justine Blanford