HIV and Vulnerability

Stuart GillespieInternational Food Policy Research Institute

Regional Network on AIDS, Livelihoods and Food Security

Cape Town, 10 November 2010

HIV AIDS

upstream downstream

Food insecurity Malnutrition

mid-stream

Three stages of vulnerability

The world of income

© Copyright 2006 SASI Group (University of Sheffield) and Mark Newman (University of Michigan).

The world of HIV

© Copyright 2006 SASI Group (University of Sheffield) and Mark Newman (University of Michigan).

“Is Poverty or Wealth Driving HIV Transmission?”

Gillespie, Kadiyala, Greener (2007)AIDS, Vol. 21, Suppl. 7, S5-16

www.AIDSonline.com

HIV

Food insecurity Malnutrition

Upstream vulnerability

Risk in southern Africa

• Unprotected sex • Multiple, concurrent sexual partnerships• Coexisting STIs• Non-circumcision• Early sexual debut

……but what underpins and drives these risk factors and behaviors?

HIV and Poverty in Africa

0%

5%

10%

15%

20%

25%

0 10 20 30 40 50 60 70 80Percentage below $1 per day

HIV

Pre

vale

nce

BotswanaLesotho

NamibiaZimbabwe

Zambia

Malawi

Mozambique

Sierra Leone

Tanzania

Central African Republic

Ethiopia

Côte d'Ivoire Uganda

Kenya

Rwanda

South Africa

Mali

NigeriaCameroon

NigerMadagascar

GambiaBurundi

Ghana

Burkina FasoSenegalMauritania

Southern AfricaR squared = 0.0996not significant

E&W AfricaR squared = 0.0307not significant

HIV and Income Inequality in Africa

R2 = 0.4881p=0.005%

0%

5%

10%

15%

20%

25%

30%

35%

0.25 0.35 0.45 0.55 0.65 0.75GINI Coefficient

HIV

Pre

vale

nce

Botswana

Lesotho

NamibiaZimbabwe

Zambia

Malawi

Mozambique

Tanzania

Central African Republic

Ethiopia

Côte d'IvoireUgandaKenya

Rwanda

South Africa

Mali

NigeriaCameroon

Niger

BurundiGhana

Senegal

Swaziland

Recent evidence (2005 -2008) from Africa

Data– Cross-sectional cross country analyses (DHS)– Longitudinal seroconversion studies– Longitudinal household surveys – Studies linking other interacting factors (mobility,

gender, malnutrition, comorbidities) with HIV risk

Outcomes– High risk behaviors– HIV prevalence (% of population estimated to be HIV +)

– HIV incidence (number of new infections/year)

– Prime age adult mortality (15-59 years of age)

Economic status and HIV prevalence

• Limitations:– Simultaneous causality (Economic status HIV)– Wealthier more likely to live longer ( HIV prev. among wealthy)

Cross-sectional data from 8 countries (Mishra et al 2007)

Lowest, 4.8

Lowest, 5.9

Second, 5.1

Second, 8.2

Middle, 6.9

Middle, 9.1

Fourth, 7.3

Fourth, 10.5

Highest, 7.6

Highest, 11.9

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Men Women

HIV

Prev

alen

ce

Asset quintiles

Factors predisposing wealthier groups to…• Greater risk:

– More money– Greater mobility– More leisure time– Earlier sexual debut– More lifetime concurrent partners– More likely to be urban-resident– Greater alcohol consumption – Better nourished (live longer)– Better access to health care and ARV drugs

• Less risk– Better nourished (less biological susceptibility?)– Better access to health care (e.g. STI treatment)– Better communications– Better education– Men more likely to be circumcised– More likely to use a condom

Economic status, HIV incidence and adult mortality

• 3 prospective seroconversion studies– Lowest male HIV incidence among wealthiest asset

tertile (Lopman et al, Manicaland)

– Lowest incidence in middle tertile (Barnighausen et al, KZN)

– No association (Hargreaves et al, Limpopo)

– Limitation: High attrition rates

• Rural household panel data (MSU and Kadiyala)– In Kenya and Zambia, asset non-poor men more likely

to die in prime age– In Ethiopia, poor men more likely to die in prime age

Role of other socioeconomic factors

• Education increasingly associated with less risky behaviors and lower HIV incidence (Hargreaves et al 2008)

• Gender, age and economic asymmetries • Food insecurity (among women)

• Low social cohesion (e.g. slums)

• Mobility (“Rhodes not roads”)

• Women engaged in some form of self-employment less likely to die in prime age (MSU and Kadiyala)

Positively associated with HIV +ve status

ConclusionsPathways and interactions are complex.Relationships are dynamic and may change over time

Upstream• “Poverty” is not the predominant driver of HIV transmission in most

contexts in southern Africa• Inequalities (gender, economic, age) are important• “Food insecure” women are also particularly vulnerable• Social cohesion and individual hope are under-researchedMidstream• Malnutrition and coexisting STIsDownstream• AIDS impoverishes households, but depends on configuration of assets

and capabilities• Women and children particularly affected