Fight against Aids: How UNAids perpetuated the myth of condom effectiveness in Africa in the face of all the evidence

Meanwhile, some of the poorest countries such as Somalia, Guinea, Liberia, Mali and Eritrea have among the lowest rates of fewer than 3 percent.

Further, another curious link is that persons in the top 20 percentile for income in Kenya, Tanzania, and Ethiopia have HIV infection rates several times higher compared to persons in the lowest 20 percentile.

UNAIDS official response to some of Dr. Chin’s accusations deal mainly with what Chin sees as UNAIDS’ flawed estimation and projection of HIV infections and AIDS cases and deaths (In 2004, for example, UNAIDS discovered a mistake in the analysis of antenatal surveillance data that led to an overestimation of HIV in some rural areas of Africa.

Since the majority of sub-Saharan Africans since live in the rural areas, this led to a downward revision of the UNAIDS prevalence estimates in many countries by 25 to 40 percent) and the prediction (which so far has yet to fully materialize) that there will be an explosion of HIV infections in Asia.

However, the rebuke fails to address UNAIDS’ shortcomings in dealing with the behavioural and social aspects that drive the HIV/AIDS pandemic in developing countries.

But this issue of proclaiming that poverty (or wealth) is causing HIV/AIDS is too simplistic.

Dr. Justin Parkhurst, a lecturer in health policy at the London School of Hygiene and Tropical Medicine believes that those who talk of HIV being caused by poverty are asking the wrong question.

“To me, what we really need to be asking is - what is it about given levels of income, in particular contexts, that are linked to risk behaviour for HIV?”

Parkhurst speculates that is sub-Saharan Africa, while poverty has been linked to particular risk situations - such as reliance on transactional sex, or lack of information about HIV - it may also limit individuals’ mobility and thus limit them to smaller social and sexual networks.

Conversely, individuals with higher incomes would tend to be more mobile and with more social and sexual networks, behaviour that has been linked with the spread of the deadly HIV virus.

Parkhurst also believes that affluence would also be linked to the ability to pay for sex, or to provide some kind of support/assistance for sex (transactional sex, not purely commercial sex). However, he believes that this is context specific.

Available data from Demographic and Health Surveys, which exists for over a dozen African countries and looks at the correlation between wealth and HIV incidence strongly suggests that there is indeed a relationship between increasing HIV prevalence with increasing wealth.

However, this trend is by no means universal, and at times men and women show different patterns.

For example, the southern African country of Swaziland shows a relatively non-existent relationship between wealth and HIV incidence; with seeming impact of wealth on prevalence-in women the wealthiest group has slightly lower prevalence than other income groups.

In Lesotho, men in the middle income bracket have the highest prevalence, while the country’s wealthiest women have a slightly lower prevalence rate than other women. This again reinforces Parkhurst’s notion that the link between wealth/poverty and HIV infection is context specific.