Do poverty and income inequality drive HIV transmission in subSaharan Africa

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Do poverty and income inequality drive HIV
transmission insub-Saharan Africa?

• Challenges in Defining the Economic Impact of the
  Global fight against HIV and AIDS
• AIDS2008, Mexico City
• August 3, 2008

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(No Transcript)
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Estimated number of people newly infected with
HIV in Sub-Saharan Africa, 19902007
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Does poverty fuel HIV transmission?

• Extreme poverty is the world's biggest killer and
  the greatest cause of ill health and suffering
  across the globe Thabo Mbeki , Opening session,
  13th International Aids Conference Durban, 9th
  July 2000
• Poverty, underdevelopment and illiteracy are
  principal factors contributing to the spread of
  HIV Statement of the Joint United Nations
  Programme on HIV/AIDS (UNAIDS) at the Fifth WTO
  Ministerial Conference Cancun, Mexico

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Prevalence and Impact the long waves
T.Barnett, A.Whiteside
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Upstream and Downstream
HIV Infection
Upstream
Downstream
Poverty and Social Deprivation
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HIV and GDP per capita - Global
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HIV and GDP per capita - Global
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HIV and GDP per capita - SSA
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HIV and Income Poverty
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HIV and Literacy
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HIV and Nutritional Status
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HIV and Income Inequality
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Measurement challenges

• HIV prevalence is not a measure of new cases
• The observed association confounds vulnerability
  to infection and impact
• HIV infection occurs 5-12 years before impact

Socio-economic conditions
HIV incidence
HIV prevalence
AIDS illness and death
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Measurement challenges

• HIV prevalence is not a measure of new cases
• The observed association confounds vulnerability
  to infection and impact
• HIV infection occurs 5-12 years before impact

Socio-economic conditions
HIV incidence
HIV prevalence
AIDS illness and death
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Measurement challenges

• HIV prevalence is not a measure of new cases
• The observed association confounds vulnerability
  to infection and impact
• HIV infection occurs 5-12 years before impact

Socio-economic conditions
HIV incidence
HIV prevalence
AIDS illness and death
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Household Level Evidence

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

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HIV prevalence by wealth status MEN
Mishra, Van Assche, Greener, Vaessen, Hong, Ghys,
Boerma, Van Assche, Khan, Rutstein, 2007
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HIV prevalence by wealth status WOMEN
Mishra, Van Assche, Greener, Vaessen, Hong, Ghys,
Boerma, Van Assche, Khan, Rutstein, 2007
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HIV prevalence by estimated income MEN
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HIV prevalence by estimated income WOMEN
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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
• More likely to live longer
• Better nourished, better access to health care
  and ARV drugs
• Greater alcohol consumption

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HIV Incidence and Wealth Status

• 3 prospective seroconversion studies
• Lowest male HIV incidence among wealthiest asset
  tercile (Lopman et al, Manicaland)
• Lowest incidence in middle tercile (Barnighausen
  et al, KZN)
• No association (Hargreaves et al, Limpopo)
• Limitation High attrition rates

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Role of other socioeconomic factors

• Education associated with less risky behaviors
  and lower HIV incidence
• Age and economic asymmetries
• Gender inequality
• Low social cohesion (e.g. slums)
• Mobility
• Women engaged in some form of self-employment
  less likely to die in prime age
• (MSU and Kadiyala)

Positively associated with HIV ve status
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Some Conclusions

• Economic status in itself is not a strong
  predictor of HIV status in Africa.
• Prevention must cut across all socioeconomic
  strata of society
• No simple explanation
• Poverty is part of the story, but not the key
• Pathways and interactions are complex
• Predisposing factors are different for different
  groups
• Tailor interventions to the specific drivers of
  transmission within different groups
• Education womens economic independence

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Next Steps

• Relating HIV incidence to income, rather than HIV
  prevalence to wealth
• More diverse longitudinal studies on
  socioeconomic conditions, risk and HIV
  acquisition
• Better investigation of the associations with
  inequalities at different levels