Bloodborne HIV: Don't Get Stuck!

Protect yourself from bloodborne HIV during healthcare and cosmetic services

New evidence bloodborne transmission explains most HIV infections in sub-Saharan Africa  

What new evidence?

            Studies that collect HIV from people in a community and then describe how each person’s HIV is organized (sequence their HIV) can find out how HIV has been spreading in the community. People with similar HIVs very likely have linked infections – one infected the other directly or indirectly (through one or more others). If sex is the most important risk, a lot of sex partners would have similar HIVs. If a lot of people with similar HIVs have no sexual connection, then blood-borne transmission must be infecting a lot of people.

            To see what such studies show about how HIV transmits in Africa, we looked at large studies that collected and sequenced from at least 100 adults in a community-based survey (we included studies that sequenced additional HIV collected during local health activities). Most evidence is recent: 9 of 13 studies meeting those criteria were published in 2017 or later.

New evidence: Not much sexual transmission within households!

            Five of 13 studies give good information about the percentages of HIV infections that may be coming from sex within households. These five studies collected HIV from all willing adults in sampled households and identified couples (spouses, steady partners, or men and women living together) with similar sequences.

            For example, a 2010-13 study in Mochudi town, Botswana, looked for similarities among 833 sequenced HIVs representing half of the HIV-positive adults (age 16-64 years) in the community.[1] The study found 322 sequences similar to one or more others, including 30 in 15 pairs from men and women living together. Assuming they were sex partners (the study does not say one way or the other), one partner likely infected the other, providing a sexual explanation for only 1.8% (=15/833) of Mochudi adults with sequenced HIV.

            The other four studies with information to estimate sexual transmission within households [2-5] identified couples with similar HIV sequences to explain from 0.3% of adults with sequenced HIV in a study area in South Africa up to 7.5% in a study area in Malawi (Figure 1). Some men and women who infected household sex partners may have been missed in these studies (not home, not wanting to give blood, divorced, or died), and studies may have mistakenly said some couples had dissimilar sequences. But even if household sexual transmission was 2-3 times greater than estimated from evidence (Figure 1), it would still account for small percentages of HIV infections in any of the studied communities.

New evidence: Bloodborne transmission dominates outside the home

            None of the articles that met our search criteria identified any short-term sexual partners. Hence, to see the frequency of sexual or blood-borne transmission outside the home we considered the sex of people linked in non-household pairs with similar HIVs. We found five studies that reported the sex of people paired together outside the home (see Table 1). Two of these five studies took HIV from only one adult in each sampled household (7,9), and three identified man-woman household pairs, which we exclude in Table 1.

            If sexual transmission accounts for most infections outside the home, one would expect to see mostly man-woman pairs. On the other hand, if people get HIV from contaminated instruments in health care or cosmetic services, then the previous HIV-positive patient or client whose HIV contaminated the transmitting instrument could be either a man or a women – and one could expect an equal percentage of same-sex vs. men-women pairs. (However, some settings with skin-piercing events might serve mostly one sex, such as antenatal clinics, which could cause some bias towards same-sex pairs outside the home.)

            What do the data show? In three of five studies, same sex pairs account for 59% or more of non-household pairs. Overall, combining data from the five studies, 45% of non-household pairs are same-sex. Near 50% frequency of same-sex non-household pairs suggests that most transmission events outside the home were influenced more by chance (e.g., the last previous patient at a hospital or dental clinic) than by sex.

country, yearsnumber of pairs% same-sex pairs% man-woman pairs
Kenya, 2003-5(6)786%14%
South Africa, 2014-15(7)16859%41%
Uganda, 2009-11(8)2259%41%
Uganda, 2011-1(5)36145%55%
Zambia, 2014-18(9)80442%58%

Similar HIVs in people living too far apart to be sexual partners

            Comparing the locations of two or more non-household adults with similar HIVs and reported or reasonable locations for non-household sex partners undermines the view most infections outside the home come from sexual transmission. Consider evidence from two studies:

  • From HIV collected in Rakai District, 2008-9, similar sequences were more likely to link people from different communities compared to reported non-household sex partnerships. Among clusters (two or more similar HIVs) that linked people outside the home, 72% (=38/53) linked people from two or more Rakai communities, whereas only 28% (=929/3,271) of reported non-household sex partners in the previous year lived in other communities in Rakai District.[4]
  • A study in Botswana in 2013-18 identified 25 (page 20 in[10]) “highly supported probable source-recipient [man-woman] pairs,” which linked men and women living a median of 161 kilometers apart; 1/4th lived at least 420 kilometers apart. Similar sequences in people living so far apart may be better explained by unsafe practices at a hospital or other skin-piercing facility serving a large area than by sexual liaisons.

Large groups of people with similar HIVs from new infections

            Two studies report 63 and 10 people with similar HIVs from new infections. Both studies collected blood from a minority of adults in the study area, so the total number with new and linked infections was likely much larger. But even 63 and 10 new infections are hard to explain by heterosexual transmission (which takes on average years, even between married people unaware one is infected, and with regular unprotected sex). On the other hand, such rapid transmission has been documented in HIV outbreaks from health care in other countries (e.g., Russia[11] and Cambodia[12]).

            Here are some details about these African clusters:

  • A study in KwaZulu-Natal, South Africa, found a cluster of 63 similar HIVs from recent infections. From similarities among sequences, researchers estimated HIV from one person in mid-2013 reached and infected, directly and through others, 63 people over 18 months.[13] This was likely part of a much larger cluster: it was found in HIV representing circa 15% of infected adults in the study area.
  • A study of HIV sequences from villages in southern Cameroon, 2011-13, identified a (page 10 in[14]) “recent transmission” linking 10 women in five villages along a road.

Conclusion: Stopping Africa’s blood-borne HIV transmission

            From this evidence, blood-borne transmission almost certainly accounts for a large proportion, and likely a large majority, of HIV infections in Africa. Stopping bloodborne transmission is the key to stopping Africa’s HIV epidemics.

            Whatever the scale of blood-borne transmission, the best way to stop it is to investigate unexplained infections (e.g., in adults with no sexual risks; in children with HIV-negative mothers), testing widely to find other victims, and thereby trace unsafe procedures. Throughout Africa HIV testing year-by-year exposes thousands of unexplained infections. When people talk within their communities about such infections, that is already an informal investigation. When and if such sharing finds more unexplained infections and focuses suspicions on specific facilities, sooner or later reports reach local media and government officials..

           Will new evidence change anything? If and when African communities start informal investigations into unexplained infections, will new evidence from sequencing encourage government leaders to respond favorably when communities ask for help to find more people infected from the same sources and to trace and stop dangerous procedures?

[Note: this post by Gisselquist and Collery is a short version of their article, which is available for free download on SSRN:[17] The full article describes the literature search and more details about the new evidence.]


1. Novitsky V, Bussmann H, Okui L, et al. Estimated age and gender profile of individuals missed by a home-based HIV testing and counselling campaign in a Botswana community. J Int AIDS Soc 2015; 18: 19918. Available at: (accessed 30 May 2022).

2. McCormack GP, Glynn JR, Crampin AC, et al. Early evolution of the human immunodeficiency virus type 1 subtype C epidemic in rural Malawi. J Virol 2002; 76: 12890-12899. Available at: (accessed 10 June 2022).

3. Cuadros DF, de Oliveira T, Graf T, et al. The role of high-risk geographies in the perpetuation of the HIV epidemic in rural South Africa: A spatial molecular epidemiology study. PLOS Glob Pub Health 2022; 2: e0000105. Available at: (accessed 25 June 2022). Supplementary information available at: (accessed 25 June 2022).

4. Grabowski MK, Lessler J, Redd AD, et al. The role of viral introductions in sustaining community-based HIV epidemics in rural Uganda: evidence from spatial clustering, phylogenetics, and egocentric transmission models. PLoS Med 2014; 11: e1001610. Available at: (accessed 17 June 2022).

5. Ratmann O, Grabowski MK, Hall M, et al. Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis. Nat Commun 2019; 10: 1411. Available at: (accessed 17 June 2022).

6. Zeh C, Inzaule SC, Ondoa P, et al. Molecular epidemiology and transmission dynamics of recent and long-term HIV-1 infections in rural Western Kenya. PLoS ONE 2016; 11: e0147436. Available at: (accessed 25 June 2022).

7. de Oliveira T, Kharsany ABM, Gräf T, et al. Transmission networks and risk of HIV infection in KwaZulu-Natal, South Africa: a community-wide phylogenetic study. Lancet HIV 2017; 4: e41–e50. Available at: (accessed 27 April 2022).

8. Kiwuwa-Muyingo S, Nazziwa J, Ssemwanga D, et al. HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: a phylogenetic and epidemiologic approach. PLoS One 2017; 12: e0185818. Available at: (accessed 6 June 2022).

9. Hall M, Golubchik T, Bonsall D, et al. Demographic characteristics of sources of HIV-1 transmission in Zambia. medRxiv [internet] 9 Oct 2021. Available at: (accessed 15 May 2022).

10. Magosi LE, Zhang Y, Golubchik T, et al. Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/Ya Tsie trial. eLife 2022; 11: e72657. Available at: (accessed 27 April 2022).

11. Pokrovsky VV. Localization of nosocomial outbreak of HIV-infection in southern Russia in 1988-1989. 8th Int Conf AIDS. 19-24 July 1992; abstract no. PoC 4138. Available at:;view=fulltext  (accessed 28 June 2022).

12. Vun MC, Galang RR, Fujita M, et al. Cluster of HIV infections attributed to unsafe injections  – Cambodia December 1, 2014-February 28, 2015. MMWR Morb Mortal Wkly Rep 2016; 65: 142-145. Available at:

13. Coltart CEM, Shahmanesh M, Hue S, et al. Ongoing HIV micro-epidemics in rural South Africa: the need for flexible interventionsConference on Retroviruses and Opportunistic Infections, Boston, 4-7 March 2018. Abstract 47LB and oral abstract. Available at: AHRI research at CROI 2018 – Africa Health Research Institute (accessed 1 June 2022).

14. Edoul G, Ghia JE, Vidal N. et al. High HIV burden and recent transmission chains in rural forest areas in southern Cameroon, where ancestors of HIV-1 have been identified in ape populations. Infect Genet Evol  2020; 84: 104358. Available at: (accessed 25 June 2022).

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