Bloodborne HIV: Don't Get Stuck!

Protect yourself from bloodborne HIV during healthcare and cosmetic services

Please don’t bother me with facts, I like my sex fantasies!


Sex, sex, sex. Beginning in the late 1980s, several years after HIV was recognized in Africa, health bureaucrats, staff, and researchers have peddled salacious and racist fantasies that almost HIV-positive adults got it from sex.

But what about facts?

One way to see how people in a community have been getting HIV is to see who has viruses that are similar. Because HIV changes over time as it multiplies in anyone it infects, when two people are found to have very similar HIV (similar components in a similar order), one likely infected the other. Studies that look for people with similar HIV in African communities provide facts to test the fantasy that male-female sex accounts for almost all HIV-positive adults.

Here’s an example: During 2011-15, research staff drew blood from 25,882 people in 40 communities in Rakai District in Uganda.[1] More than 5,000 were HIV-positive. Researchers were able to describe HIVs (what components, what order) from 2,552 HIV-positive adults. Among the 2,552 HIV, researchers found 537 pairs with very similar HIV (“highly supported phylogenetic linkages”[page 5 in reference 1]), indicating that one person in the pair likely infected the other.

What do those pairs tell us about sexual fantasies?

1. Setting aside 176 spouse pairs with similar HIV (more on spouses below), there were 361 (=537-176) very similar non-spouse pairs. Here’s where the fantasy runs afoul of facts: 161 (45%) of those 361 non-spouse pairs were same-sex pairs, linking a man with a man, or a woman with a woman. Since the sex of whoever infected anyone seems to have been irrelevant (near equal numbers of same-sex pairs as male-female pairs), the obvious conclusion is that most transmission had nothing to do with sex. Most infections likely came from bloodborne risks such as unsterilized needles, syringes, catheters, saline bags, razors, lancets, etc., not from a sex partner. What about the 200 (=361-161) unmarried male-female pairs? Since the study says nothing about the sexual behavior of anyone in those non-spouse pairs, supposing sexual transmission is based on sex fantasy, not evidence.

2. What about spouses with similar HIV? The study collected and described HIV from 331 husband-wife couples. Only 176 (53%) of the 331 couples had similar HIV. Almost half of the couples (155 of 331) had non-matching HIV, which means husbands and wives likely got HIV from other blood or sex risks, not from their partners. In other words: Sexual transmission seems to be inefficient and slow in Africa as it is elsewhere in the world.

Instead of acting like scientist (respecting evidence), the research team that reported the above facts simply rejected same-sex pairs as mistakes: We don’t like the facts, so we ignore them! Let’s stick with sex fantasies! For example:

Example  1: In a 2021 sub-study, the research team used male-female pairs previously identified to fantasize about the ages of men and women having sex, ignoring same-sex pairs.[2] Because the average HIV-positive man is older than the average HIV-positive woman, one could expect pairs to include older men and younger women no matter how one infected the other (sex, or shared skin-piercing instruments). Duh! But the study team opted for sex fantasies: Hah, older men chasing younger women!  

Example 2: To estimate direction of HIV transmission between Rakai’s lakeshore communities and inland communities, the study team rejected 200 same-sex pairs as misleading (not agreeing with sex fantasies). Then, “[w]e further analysed the … male−female linkages to infer the direction of transmission”[page 6 in  reference 3]. Even so, what they found did not agree with sex fantasies – HIV was going from inland communities with lower percentages of adults infected to lakeshore communities with higher percentages infected. If it was going by sex, that doesn’t make a lot of sense – in sex partnerships across communities, the transmitting (HIV-positive) partner would more likely come from the lakeshore, where adults were more likely to be HIV-positive. On the other hand, if it were going by bloodborne risks in clinics and cosmetic services in inland communities along main roads, then the direction of transmission makes sense if, as seems likely, people from lakeshore communities visit facilities along major roads. Hence, it’s likely many male-female pairs were linked not by sex but by reused and unsterilized skin-piercing instruments.

Peddling sex fantasies about Africa’s HIV epidemic is not a victimless lie 

1. Sex fantasies distract everyone’s attention from bloodborne risks that people face in clinics and cosmetic services. That leads to infections.

2. Sex fantasies stigmatize HIV-positive Africans. Consider, for example, a woman who tests HIV-positive during antenatal care, and then her husband tests negative. Here’s what those who peddle sex fantasies are, in effect, saying to the husband: “Your wife had a boyfriend and lied about it!” What about a teenage boy or girl testing HIV-positive, or a husband? All slimed with abusive fantasies.

3. Health pros who push these fantasies suffer as well. If they know it’s a lie, how do they live with themselves? If they are too scared to investigate unexplained infections to find and stop unsafe practices in healthcare, how can they respect themselves and their profession?

References

1. Ratmann O, Grabowski MK, Hall M, et al. Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogeneetic analysis. Nat Commun 2019; 10: 1411. Available at: https://www.nature.com/articles/s41467-019-09139-4.pdf (accessed 13 December 2021).

2. Xi X, Spencer SEF, Hall M. Inferring the sources of HIV infection in Africa from deepsequence data with semi-parametric Bayesian Poisson flow models. arXiv [internet] 29 October 2021. Available at: https://arxiv.org/pdf/2110.12273.pdf (accessed 6 December 2021).

3. Ratmann O, Kagaayi J, Hall M, et al. Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda. Lancet HIV 2020; 7: e173-e183. Available at: https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7167508/ (accessed 13 December 2021).

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