‘HIV Infection Does Not Disproportionately Affect the Poorer in Sub-Saharan Africa’

Macro International Inc., Calverton, Maryland, USA.
AIDS (London, England) (Impact Factor: 5.55). 12/2007; 21 Suppl 7(Suppl 7):S17-28. DOI: 10.1097/01.aids.0000300532.51860.2a
Source: PubMed


Wealthier populations do better than poorer ones on most measures of health status, including nutrition, morbidity and mortality, and healthcare utilization.
This study examines the association between household wealth status and HIV serostatus to identify what characteristics and behaviours are associated with HIV infection, and the role of confounding factors such as place of residence and other risk factors.
Data are from eight national surveys in sub-Saharan Africa (Kenya, Ghana, Burkina Faso, Cameroon, Tanzania, Lesotho, Malawi, and Uganda) conducted during 2003-2005. Dried blood spot samples were collected and tested for HIV, following internationally accepted ethical standards and laboratory procedures. The association between household wealth (measured by an index based on household ownership of durable assets and other amenities) and HIV serostatus is examined using both descriptive and multivariate statistical methods.
In all eight countries, adults in the wealthiest quintiles have a higher prevalence of HIV than those in the poorer quintiles. Prevalence increases monotonically with wealth in most cases. Similarly for cohabiting couples, the likelihood that one or both partners is HIV infected increases with wealth. The positive association between wealth and HIV prevalence is only partly explained by an association of wealth with other underlying factors, such as place of residence and education, and by differences in sexual behaviour, such as multiple sex partners, condom use, and male circumcision.
In sub-Saharan Africa, HIV prevalence does not exhibit the same pattern of association with poverty as most other diseases. HIV programmes should also focus on the wealthier segments of the population.

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    • "Nevertheless, studies during the early stage of the epidemic suggested that HIV incidence initially occurred not amongst the poorest, but among better off members of society in this region. A decade later, infections still appear more concentrated among the urban employed and more mobile members of society, and consequently the wealthier groups (Mishra, et.al., 2007). There is however, no result that the AIDS/HIV infection has strong association with low socio-economic status and poverty. "
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    • "In a recent supplement to the Journal of the International AIDS Society devoted entirely to structural drivers of HIV transmission, Seeley et al. (2012) noted that elimination of HIV will require 'a comprehensive HIV response, that includes meaningful responses to the social, political, economic and environmental factors that affect HIV risk and vulnerability'. Also, a prevailing view emphasizes the role of poverty in the spread of HIV, despite numerous studies demonstrating an inverse relationship between HIV serostatus and poverty status in sub-Saharan Africa, which is opposite to the case in the developed world and contrary to common expectations about disease susceptibility and poverty status (Shelton et al., 2005; Gillespie et al., 2007; Mishra et al., 2007; Parkhurst, 2010). Commenting in The Lancet, Shelton et al. (2005, p. 1058) suggested that both wealth and economic disadvantage may play pivotal roles in HIV transmission through sexual concurrency networks, with wealth being 'associated with the mobility, time, and resources to maintain concurrent partnerships' and where women 'might improve their economic situation by having more than one concurrent partner.' "
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    ABSTRACT: Summary This paper investigates whether community-level wealth inequality predicts HIV serostatus using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding 5%. The analysis relates the binary dependent variable HIV-positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each statistical enumeration area, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behaviour mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behaviour variables attenuates the effects of both inequality measures. Reporting eleven plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behaviour differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioural mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention.
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    • "" Gender inequality " and poverty, the major social drivers of HIV vulnerability among young women in sub-Saharan Africa (s-SA), are examples of this complexity. The fact that there are countries outside s-SA with greater poverty and gender inequalities with dissimilar gender differentials in HIV infection as compared to s-SA (Mishra et al., 2007; Obermeyer, 2006) adds to this complexity. There are specific pathways through which these social drivers operate in influencing vulnerability to HIV and these pathways include other contextual factors which can be targeted for intervention efforts (World Health Organization [WHO], 2007). "
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