Background: Beginning in 2004, the Africa Health Research Institute in KwaZulu-Natal, South Africa, has been testing a random sample of adults for HIV infection in a study area extending about 21 km x 21 km northwest of Mtubatuba town. The study area has one of the worst HIV epidemics in the world: as of 2014, 36% of women and 27% of men aged ≥15 years were infected (about 9,000 out of 30,000 adults)[1,2].
Recently, the Institute “sequenced” HIV (determined the order of small molecules in each HIV) from 1,376 adults in the study area. From these sequences, the Institute found a group (cluster) of 63 very similar sequences. Because HIV sequences change over time, if a cluster of sequences are almost the same, it means HIV from one person not long ago infected everyone in the cluster.
How long did it take for HIV from one person to infect 63 people? The Institute estimated it took one year only, from mid-2013 to mid-2014 for HIV to pass from one person directly and indirectly (through others, in short transmission chains) to 63 people (see the large cluster in the upper right in slide 10 of Coltart’s 2018 presentation).
To go from 1 to 63 infections in 12 months the number of infections doubled almost 6 times – doubling on average every 2 months from 1 to 2 infections, 2 to 4 infections, etc, to 63 infections. Everything we know about sexual transmission of HIV says sex doesn’t do that! With hetero sex, it takes on average 1,000 coital acts for one person to infect another; that takes a lot more than 2 months. Even for male-male sex, it takes ≥30 penis-in-anus events for one transmission; that also takes time.
Such fast transmission is possible when hospitals or clinics reuse unsterilized skin-piercing instruments. Governments investigating unexplained HIV infections have found such tragedies in Russia, Romania, Libya, and other countries.
The cluster of 63 sequences in KwaZulu-Natal looks like a cluster of sequences from an investigated outbreak in Roka, Cambodia: In late 2014, several residents in Roka, a rural community in Cambodia, found they were HIV-positive although they had no sex risks. The Cambodian government investigated, testing all people in the community. The investigation found 242 HIV-positive residents and traced most infections to injections and other skin-piercing procedures from a local private healthcare provider.
Foreign organizations helping with the investigation sequenced several hundred HIV from the community. Almost all sequences were very similar, showing fast transmission from 1 to 198 infections in a few short years. These sequences can be presented as branches in a “tree” (Figure 1, below). The upper right section of the tree shows the cluster of very similar sequences from Roka. (Most sequences in the lower part of the tree are “controls,” which means the HIV came from other times and places.) The tree shows each HIV infection as the right end-point of a horizontal line. The left ends of these lines show estimated connections to earlier estimated infections. Because the cluster includes very recent infections only, all lines in the upper right are very short. The timeline at the bottom of the figure shows time going from left to right, showing the estimated dates of transmission from earlier to later infections.
Figure 1: Cluster of 198 infections in Roka, Cambodia, linked by transmissions during 2011-14
If 63 sequences from KwaZulu-Natal came from unsafe healthcare, how many people got infected in the outbreak? The cluster of 63 HIV sequences (see slide 10) is from a 15% sample of HIV-positive adults in the study area (1,347 out of an estimated 9,000 infected adults). If someone could sequence HIV from all 9,000, one could expect to find 420 (= 63/0.15) sequences in the cluster, all from people with new and closely linked infections. Moreover, many of the 63 infections came from Mtubaba town on the southwest edge of the study area. If a hospital or clinic in Mtubatuba town was infecting patients, it’s likely the outbreak also extends south and east of the town. Hence, the number of people infected from whatever caused the cluster might well exceed 1,000. And transmission looked like it was continuing when the Institute collected the last HIV samples it sequenced (see slide 10).
What’s the response to this evidence? Investigating the cluster – to find all who have been infected in the outbreak and to find and stop the sources – is a job for public health. As long as the sources of unexpected infections are not found and stopped, public health should also be warning people about blood-borne risks.
If the South African government were to investigate, what could it do? A first task would be to interview people in the cluster to find where they got health care, dental care, tattooing, or other skin-piercing procedures in 2013-14. Once one or more facilities are identified as the possible sources of at least some infections, public health staff could visit the facilities to look for – and fix – dangerous mistakes. At the same time, government could make a public request for people who got skin-piercing procedures at suspected facilities to come for tests. If someone is infected, start treatment. At the same time, investigators could sequence their HIV to see if it’s similar to HIV in the cluster.
1. Vandormael A, Barnighausen T, Herbeck J, et al. Longitudinal trends in the prevalence of detectable HIV viremia: population-based evidence from rural KwaZulu-Natal, South Africa. Clin Infect Dis 2018; 66: 1254-1260. Abstract available at: https://www.ncbi.nlm.nih.gov/pubmed/29186391 (accessed 16 November 2018).
2. Larmarange J, Mossong J, Barnighausen T, et al. Participation dynamics in population-based longitudinal HIV surveillance in rural South Africa. PLoS ONE 2015; 10: e012345. Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123345 (accessed 16 November 2018).
3. Slide 10 in: Coltart C, Shahmanesh M, Hue S, et al. Ongoing HIV micro-epidemics in rural South Africa: the need for flexible interventions. Conference on Retroviruses and Opportunistic Infections, 4-7 March 2018. Available at: http://www.croiwebcasts.org/console/player/37090?mediaType=slideVideo&&crd_fl=0&ssmsrq=1522772955419&ctms=5000&csmsrq=5001 (accessed 4 April 2018).
4. Roka/HIV/bayesian_timetree. Evolutionary and epidemiological analysis of the Roka HIV outbreak. Bedford Lab. Available at: https://bedford.io/projects/roka/HIV/bayesian_timetree/ (accessed 15 November 2018). This figure has been copied by permission from Bedford Lab.