Understanding the Correlations between Wealth, Poverty and Human Immunodeficiency Virus Infection in African countries/Comprehension Des Correlations Entre Richesse, Pauvrete et Infection Par le Virus De L'immunodeficience Humaine Dans Des Pays africains/Entender Las Correlaciones Entre Riqueza, Pobreza E Infeccion Por El Virus De la Inmunodeficiencia Humana En Los Paises Africanos

Article excerpt

Introduction

A long-held belief in the field of human immunodeficiency virus (HIV) infection prevention is that poverty drives HIV epidemics. The World Bank's 1997 report Confronting AIDS explained that "widespread poverty and unequal distribution of income that typify underdevelopment appear to stimulate the spread of HIV" (1) Similarly, the United Nations Joint Programme on HIV/ AIDS (UNAIDS) stated in 2001 that "[p]overty, underdevelopment, the lack of choices and the inability to determine one's own destiny fuel the [HIV] epidemic" (2) As recently as 2004, in the Lancet, Fenton reviewed evidence on how poverty leads people to high-risk behaviours and concluded that reducing poverty may be the only viable long-term response to the epidemic. (3)

However, the argument that poverty "fuels" the spread of HIV has been challenged by recent studies based on statistical correlations of epidemiological and socioeconomic data. These studies show that in many African countries, the prevalence of HIV infection correlates directly with wealth. For example, Shelton et al. illustrated a strong positive relationship between household wealth and HIV infection prevalence in the United Republic of Tanzania. (4) Chin, who analysed data from Kenya, also showed that national HIV prevalence rates appeared to correlate directly with national income across sub-Saharan Africa (5)--a trend noticed as early as 2000. (6) More recently, Mishra et al. analysed HIV infection prevalence by wealth group with national survey data for eight African countries (Burkina Faso, Cameroon, Ghana, Kenya, Lesotho, Malawi, the United Republic of Tanzania and Uganda) and concluded that there was a positive association between household economic status and prevalence. (7) However, they did not look at how trends differed according to national income or changed with time.

This has left many wondering whether it is poverty or wealth that correlates with HIV infection prevalence. Peter Plot (former executive director of UNAIDS) et al. attempted to answer this question by showing that in African countries HIV infection rates correlate not only with wealth, but also with income inequality. (8) Arguments about this issue, however, often suffer from a key conceptual weakness that may hinder progress in the prevention of HIV infection: the assumption that prevalence correlates with wealth (or relative wealth) in only one way. But attempts to correlate relative wealth directly with prevalence do not accurately reflect the dynamics that characterize the way in which underlying social drivers and structural factors manifest themselves as risk of HIV infection, or how these factors change with time. The relationship between wealth and HIV infection is not direct, nor does it always act in the same direction in every setting. Instead, the ways structural factors lead to situations of risk or non-risk in a given setting must be conceptualized through a more nuanced approach that does not assume that either wealth or poverty leads to risky behaviours. A better approach is one that understands that both wealth and poverty may have associated risks and protective effects in different contexts. No previous analyses have compared the association between trends in HIV infection and wealth within countries according to national income levels or longitudinal factors. Since publication of the study by Mishra et al., several additional country surveys have been conducted with relevant data. I used this expanded set of surveys to investigate the relationships between HIV infection prevalence and underlying structural factors of poverty and wealth in several African countries.

Methods

I conducted an ecological comparison and trend analysis with data from nationally representative HIV sero-surveys that related prevalence with linked indicators of socioeconomic status in sub-Saharan African countries. In addition, I performed a longitudinal study with data from two surveys done at different times in one of the countries. …