HIV/AIDS AND SEXUAL NETWORS

Dimitri Fazito(CEDEPLAR/UFMG)

International Workshop on Demography of Lusophane African Countries 22nd - 24th of May, 2007

The global AIDS epidemic in 2006• An estimated 39.5 million people are living with

HIV/AIDS. The vast majority are aged 15-49 years. • 4.3 million people were newly infected with the virus in

2006.• 2.9 million people died of AIDS.• There are 11,000 new infections and nearly 8,000

deaths daily.• 2.3 million children (under 15 years) are living with HIV. • Nearly one-third of the world’s HIV-infected people – or

13 million – lives in countries classified by the World Bank as heavily burdened by debt. Of the 41 poorest and most indebted countries, 34 are in sub-Saharan Africa.

Vulnerable Groups

• Children: Globally, 2.3 million children are living with HIV;• Women: 2.5 times more vulnerable to HIV infection than men.

UNAIDS estimates that 60% of all people living with HIV in sub-Saharan Africa were women;

• Young People: More than one-third of all people living with HIV/AIDS are under the age of 25, accounting for 2 million infections each year. In sub-Saharan Africa, more than half of all new infections are among young people, with girls being particularly affected;

• Sex Workers: High rates of HIV infection have been found among sex workers. Higher proportion in Asia, especially among women;

• Injecting Drug Users: UNAIDS estimates that injecting drug use accounts for one-third of new infections outside sub-Saharan Africa, especially in Europe, North and Latin America and Asia;

• Prisioners: The prevalence of HIV infection in prisons is higher than that in the general population. In South Africa it is estimated that 41% of prisioners are HIV positive.

Estimated HIV/AIDS, 2003

Table 1: Maplecroft’s HIV/AIDS Index (HAI) Worldwide (2006)

CountryHAI Rank Category Adults

(%)Adults

(#)Women

(#)Children

(#)Deaths

(#)Orphans

(#)

Mozambique 0.132 147 extreme 16.1% 1,600,000 960,000 140,000 140,000 510,000

Angola 1.491 137 extreme 3.7% 280,000 170,000 35,000 30,000 160,000

South Africa 1.733 133 extreme 18.8% 5,300,000 3,100,000 240,000 320,000 1,200,000

Guinea-Bissau

2.613 120 high 3.8% 29,000 17,000 3,200 2,700 11,000

India 2.696 117 high 0.9% 5,600,000 1,600,000 No Data No Data No Data

Brazil 4.185 91 high 0.5% 610,000 220,000 No Data 14,000 No Data

USA 5.167 69 medium 0.6% 1,200,000 300,000 No Data 16,000 No Data

Portugal 5.547 51 medium 0.4% 32,000 1,300 No Data <1000 No Data

South Korea 7.714 20 low 0.1% 13,000 7,400 No Data <500 No Data

Finland 10.000 1 low 0.1% 1,900 <1000 No Data <100 No Data

HIV/AIDS Index (HAI): level of prevalence in adults (%) + total number of infected adults (year) + country’s capacity of disease contentionCountries Studied: 148Category Risk: extreme (0–2.5), high (2.5–5.0), medium (5.0–7.5) and low (7.5–10)

Source: Maplecroft & UNAIDS, 2007

Why Networks Matter

• Sexual behaviors are socially sanctioned in groups (eg. dyads, personal networks, cliques and cores) within the context of social norms (cultural values and social interactions);

Culture

Social position

Role expectation

Gender identities

Community values

Symbolic

representations

Individual Sexuality

Why Networks Matter

How social structure influences sexual behavior?

Network Analysis Collective Patters / Structure

of Sexual relations

Individual Attributes

Normative prescriptions

Dyadic Relationships Sexual Behavior

Network Properties

Local network involvementThe strength and qualities of particular network ties

(“direct embeddedness”)• Degree, tie strength, condom use, etc

One’s position in the overall network (“structural embeddedness”)

• Centrality, local-network density, transitivity, membership.

Global network structureThe global structure of the network affects how

goods can travel throughout the population. • Distance distribution • Connectivity structure

Among the most challenging tasks for modeling networks is building a robust link from the first to the second.

Why Networks Matter

Why Networks matter

• Disease transmission occurs through diffusion networks ( “one-by-one” personal contacts);

• Sexual risk is a function of relational and structural composition of networks (dyads and cliques);

• Network ties established within structuring environments do not occur at random – the network “clustering” effect;

A simplified multi-layered framework

Social units (y)

individuals

...

Ties among social units (x)

person-to-person

...

Settings (s)

geographical

sociocultural

...

For example:

Interactions between tie variables depend on node attributes

social selection effects

Interactions between ties depend on proximity through settings

context effects

The Network “Clustering” Effect

When different processes can lead to similar macro signatures:

For example: “clustering” typically observed in social nets

• Sociality – highly active persons create clusters (eg. Leaders, drug-dealers, brokers)

• Homophily – assortative mixing by attribute creates clusters (eg. Ethinic cliques, religious communities)

• Triad closure – triangles create clusters (eg. Work and schoolmates)

Friend of a friend, or birds of a feather?

1.Homophily:: People tend to chose friends who are like them, in grade, race, etc. (“birds of a feather”), triad closure is a by-product

2.Transitivity:: People who have friends in common tend to become friends (“friend of a friend”), closure is the key process

Why do Networks Matter? Local vision

Why do Networks Matter? Global vision

Networks are structurally cohesive if they remain connected even when nodes are removed

Node Connectivity

0 1 2 3

Disease Transmission and the Network Density

Variation in the Timing and Intensity of HIV Epidemic

• The rate of sexual partner acquisition• The impact of “core groups” activities• The presence of different sexually

transmitted diseases (infection amplification)

• Higher mobility (migration)• The rate of concurrent (simultaneous)

sexual partnerships and duration• The rate of partnership stability

Definition of Concurrency

Concurrent partnerships

Same contact rate (5/yr), but the timing and sequence of partnerships is different

From M. Morris (2006)

1

2

34

5

Serial monogamy1

2

3

45

time

Why concurrency matters

1. Less protection afforded by sequence

2. virus-eye view: Less time lost locked in partnership

3. Larger “connected component” in the network

2

1 13

2

3

monogamy concurrency

concurrencymonogamy

Connectivity in sparse networks

• High degree hubs • Low degree linking

Both have mean degree = 1.9

Connectivity in sparse networks and Concurrency

“Low degree”“High degree”

-Some individuals are highly connected (core

transmitters) -Perceived as “high risk”

-Potentially more likely to motivate prevention

behavior

-Most individuals are less connected

-Perceived as “lower risk”

-Potentially less likely to motivate prevention

behavior

Structural degree and cohesion gives rise automatically to a clear notion of embeddedness, since cohesive sets nest inside of each other supporting concurrency partnership and contagion

17

1819

20

222

23

8

11

10

14

12

9

15

16

13

4

1

75

6

3

2

Structural Properties: Concurrency and Speed of HIV/AIDS Transmission

Degree Networks, Cohesion, Concurrency and transmission core

In largest component:

In largest bicomponent:

2%

0

41%

5%

64%

15%

10%

1%

Mean: 1.74

Mean: 1.80

Mean: 1.86

Largestcomponents

Mean: 1.68

Number ofPartners

Bicomponentsin red

Source: Martina Morris, Univ. of Washingtion, used with permission from a presentation given at a meeting on concurrent sexual parnerships and sexually transmitted infections at Princeton University, 6 May 2006.

Worldwide, almost all studies show increased riskswith increased sexual partners

Partner reduction has been associated with declines in HIV at the

population level in both concentrated and

generalized epidemic settings

Multiple sexual partnerships

Morris et al. (2006)

Men Women

Concurrencies reported

Uganda US Thailand

Uganda

US

0 71.7 84.8 74.0 96.4 92.5

1 19.4 9.7 10.6 3.4 5.1

2 0.5 2.3 10.9 0.0 1.3

3 8.3 3.3 4.6 0.3 1.1

Total any concurrency 28.3 15.2 26.0 3.6 7.5

Uganda vs. US and Thailand

Thailand’s population has many more partners, but the network connections are extremely short duration.

Despite much higher contact rates, transmission dynamics are dampened, and prevalence will remain low

Uganda’s population has fewer partners, but the network is more continuously connected over time.

This long term concurrency amplifies transmission dynamics, allowing prevalence to rise much higher.

Empirical Findings: Rate of Concurrency and Duration

Concluding Remarks: the importance of networks

• Large populations exhibit network structure– Social, sexual, infrastructure, transportation

• Large epidemics need to be understood as many small epidemics linked by networks (clustering and overlapping effects)

• Incorporating “multi-scale” structure of the world in epidemic models can explain multi-modality and resurgence of HIV/AIDS

• “Rare events” (e.g. one person getting on a plane) can have big consequences. Such events can be modeled by Network Models (eg. Small World, Random Graphs, Free Scale Networks)

• Population structure itself can be used as control measure (e.g. intermediate connections)