Oficina Administrativa:

Biological and behavioural plausibility, empirical and phylogenetic evidence, and mathematical modelling outputs strongly suggest that the phenomena that characterise RHI increase infectiousness, making it a potentially valuable disease stage for behavioural and biological intervention.

However, it has been difficult to make accurate observations because the diagnosis of RHI poses challenges for both the clinician and the laboratory, both contemporaneously and retrospectively. These difficulties are discussed further in section in 1.10.1 and explored in Chapter 2.

Biological Plausibility

RHI is thought to play a disproportionate role in HIV transmission, despite its short duration, mainly because of the high genital tract VL associated with high plasma VL [Pilcher, 2007, Pilcher 2001; Pedraza, 1999]. Among a cohort of heterosexual Malawian men with newly diagnosed HIV infection, the median plasma VL among those with AHI was >106 _copies/mL

compared with 104.5 _{copies/mL observed among men with longstanding}

infection [Pilcher, 2004].

2008; Pilcher, 2007; Pilcher, 2001, Chakraborty, 2001; Quinn, 2000]. In men, the peak VL is estimated to occur at 17 days in plasma and at 30 days in semen, and remains elevated for approximately 10 weeks [Pilcher, 2007]. The entire period of increased infectiousness is thought to extend to 4.9 months [Cohen MS, 2010, personal communication] and therefore it is reasonable to use RHI (infection within 6 months) as opposed to AHI (p24 Ag or RNA positive and HIV Ab negative) as a suitable classification when studying transmission.

In most cases, MSM transmission appears to be mediated by a single infectious unit [Keele, 2008]. Transmission of multiple variants has also been observed, but is associated with factors that compromise the genital mucosa. Therefore, transmissibility is likely influenced by the viral subtype [Derdeyn, 2008]. During AHI, the level of infectiousness per potential transmission event is thought by some to be higher than would be predicted as function of the high plasma VL alone [Hollingsworth, 2008]. These calculations have suggested that viral factors beside the high VL immediately after HIV-1 acquisition are responsible for the increased transmissibility during AHI. Primate studies support this theory. In macaques, SIV virions isolated during AHI are more infectious than those from the longstanding phase of infection. Naïve macaques are productively infected with approximately one hundred-fold less AHI stage virions compared to the number of virions required from the longstanding stage of infection [Ma, 2009].

Potential explanations for this observation include a greater number of defective viruses circulating during the longstanding as compared to the acute stage of disease. Furthermore, host generated antibodies may coat the virus during the longstanding as compared to the acute phase of infection, retarding the ability of the viruses to infect target cells. In addition, modifications that occur over the course of infection may help the virus replicate within an individual host but may be counterproductive during transmission. Although the exact biological mechanism for the greater infectiousness observed among AHI variants is uncertain, these studies

suggest that acute stage circulating viruses possess unique properties that confer fitness for transmission.

Behavioural Plausibility

While most individuals diagnosed with RHI reduce their risk-taking sexual practices there is a significant minority, represented in both heterosexual and MSM groups, who do not [Pettifor, 2011; Seng, 2011; Fox J, 2010]. However, individuals with RHI are usually unaware of their infection. In a study by Rothenberg, individuals with RHI named 2.5 times (95% CI 2.1– 3.0) as many current sexual partners as did individuals diagnosed with longstanding HIV infection [Rothenberg, 2009].

In contexts of high concurrency and/or rate of partner change, this contribution of RHI increases considerably. Concurrency operates not only as an individual factor for increased transmission but also by connecting multiple sexual dyads and clusters to one another at the population level [Marks, 2005]. Eaton and Garnett argue that the combination of long-term concurrent sexual partnerships and high infectiousness in RHI is a key driver of HIV transmission. They used a mathematical model to simulate HIV spreading in sexual networks with different amounts of concurrency. The models show that if HIV infectiousness is constant over the duration of infection, the amount of concurrency has much less influence on HIV spread compared to when infectiousness varies over three stages of infection with high infectiousness in the first few months. The proportion of transmissions during RHI is sensitive to the amount of concurrency and, in this model, is estimated to be between 16 and 28% in spreading epidemics with increasing concurrency [Eaton, 2011].

Empirical evidence

Despite the clear biological and behavioural plausibility outlined above, identifying transmissions from individuals with RHI has been difficult, which has hindered our ability to determine the proportion of transmission events attributable to RHI. The relative paucity of empirical data linking RHI

individuals during or just after the brief disease stage and linking them to HIV-uninfected sexual partners who subsequently seroconvert. Therefore, the contribution of RHI to the spread of HIV remains to be quantified accurately.

In 2005, Wawer et al. published extensive empirical data (with phylogenetic support of transmission events, see Sections 1.7.4 and 1.10.3) from a cohort in Rakai, Uganda, that quantified how heterosexual transmission within stable discordant partnerships varies by disease stage. Notwithstanding the limitations of the study, it presents the best data available from which to directly estimate both the relative transmissibility of HIV during each stage of infection and the duration of periods of high infectiousness. They estimated that during the first 5 months of infection the probability of transmission per coital act was 8-10 times higher than during longstanding infection, and that the probability of transmission also increased by 4–8-fold during the 2 years before death. For a person newly- infected outside of a partnership, the probability of infecting their long-term partner within 2.5 months was 43% [Wawer, 2005].

These data have led Hayes to conclude that RHI may be responsible for 23% of transmissions [Hayes, 2006]. Hollingsworth and Fraser, who reanalysed data from this cohort with modified assumptions about sexual behaviour and transmission timing, estimated the hazard rate of transmission in acute HIV infection to be 26 times the rate compared to longstanding HIV infection [Hollingsworth, 2008].

Phylogenetic evidence

Phylogenetic sequence analysis has been commonly used [Blick, 2007; Wawer, 2005; Zhu, 1996] and is considered sufficient for the reconstruction of transmission events [Hué, 2004]. Such studies have correlated phylogenetic clustering with recent infection [Brenner, 2007; Yerly, 2001], viral factors [Lindström, 2006], risk behaviours and geography [Frost, 2007]. These data collectively suggest that chains or groups of transmission associated with RHI play an important role in transmission within these cohorts, which are predominately made up of MSM.

Phylogenetic studies have estimated that between 24 and 49% of RHI (in MSM and heterosexuals) are linked to other recent infections [Bezemer, 2010; Brenner, 2007; Yerly, 2001]. For example, in Quebec, likely transmission clusters among persons who seroconverted in the previous 6 months were identified. Individuals with treated or untreated longstanding infection were also included. Approximately half of the individuals who seroconverted in the previous 6 months fell into 75 transmission clusters whereas the remaining individuals had unique sequences, suggesting that RHI was responsible for approximately half of HIV transmission events [Brenner, 2007].

Lewis et al. focused on the internal architecture of the sequence clusters and used a combination of epidemiology, immunodynamics and evolutionary biology to infer the dynamics of HIV transmission in an MSM population in London. They applied a viral genetic relatedness cut-off to filter the data down to a computationally manageable subset of 402 HIV- infected individuals that exhibited at least one other close sequence relative in the study population. Nine large putative transmission clusters were identified within this subset of protease and RT sequence data on the basis of  genetic  (Hamming)  distance.  They  then  used  a  “relaxed  clock”  approach   to generate time-scaled phylogenies of these data to infer the timing and distribution of transmission events within the 88 sequences contained in the six clusters that were large enough for analysis. In more than a quarter of cases, transmission events appear to have occurred fewer than six months after infection. Also, their results suggest that the sexual transmission of HIV in London over the previous decade may have occurred not as a slow and steady process, but rather via discrete outbreaks fuelled in part by efficient transmission during RHI [Lewis, 2008].

In contrast, Ambrose’s   recent work at UCL argues against RHI as a predominant factor in transmission. He took all individuals downstream of a particular infection (infection source A), within the same cluster as potential infectees, and used a date cut-off (at 0.18% nucleotide ambiguity, 125  days)  to  determine  if  these  potential  infections  were  during  source  A’s  

phase. He simplified the methodology by defining the date of infection as

actually being 125 days prior to the date the blood sample was taken. He

used nucleotide ambiguity to identify RHI, and applied it to a national database of 43,002 samples, of which 40,627 clustered. Of these, 23.9% were classified as RHI at sample date, and these accounted for a median of only 7.3% of potential onward transmissions in clusters [Ambrose, 2011; Leigh Brown, 2011].

Mathematical models of transmission

Mathematical models provide an explicit framework within which to develop and communicate an understanding of transmission dynamics. They provide a tool to translate biological or behavioural interventions into quantifiable outcomes in terms of HIV incidence and prevalence. Several studies based on the concept of HIV transmission rates have recently appeared in the literature. The transmission rate for a particular group of HIV-infected persons is defined as the mean number of secondary infections per member of the group per unit time.

The degree of contribution from RHI will be influenced by the stage of the epidemic as well as the characteristics of the underlying population.

Models used to describe the proportional contribution of RHI have used a variety of model structures in varying populations and settings with differing parameter estimates [Prabhu, 2009; Abu-Raddad, 2008; Hollingsworth, 2008; Pinkerton, 2008; Pinkerton, 2007; Rapatski, 2005; Kretzschmar, 1998; Koopman, 1997; Pinkerton, 1996; Jacquez, 1994]. Most support the role of RHI as a critical driving force in the epidemic phase, but in some models of the endemic phase however, the impact of RHI has varied considerably, due in part to the varying approaches used and different populations studied [Prabhu, 2009; Abu-Raddad, 2008; Pinkerton, 2007].

The outputs from these modelling studies are interpreted and summarised in Figure 1.8, reproduced with permission from the author [Cohen MS, 2011b]. The methodology used to derive the ranges of estimates of infectiousness for each model in Figure 1.8 exemplifies the effect of studying different populations using widely varying assumptions. The

estimates in the graph reflect the proportion of all transmissions during an individual  patient’s  entire  infectious  period,  which  depends  on the epidemic phase and patterns of sexual behaviour [Abu-Raddad, 2008; Pinkerton, 2008; Kretzschmar, 1998; Koopman, 1997]. For this interpretation, transmission probabilities were drawn from the population category shown (sub-Saharan Africa heterosexual, USA heterosexual and MSM, USA MSM, Europe MSM) but the reported estimates result from a range of hypothetical sexual behaviour variables that do not necessarily reflect a specific population.

Figure 1.8: Role of Acute and Early HIV Infection on the spread of HIV, according to population studies in Sub-Saharan Africa, the United States and Europe [Cohen MS, 2011b]