Secondary Logo

Journal Logo

Epidemiology and Social

Determinants of HIV-1 transmission in men who have sex with men: a combined clinical, epidemiological and phylogenetic approach

Fisher, Martina; Pao, Davida; Brown, Alison Eb,c; Sudarshi, Darshana; Gill, O Noelb; Cane, Patriciab; Buckton, Andrew Jb; Parry, John Vb; Johnson, Anne Mc; Sabin, Carolinec; Pillay, Deenanb,d

Author Information
doi: 10.1097/QAD.0b013e32833ac9e6
  • Free



In many areas of the developed world, the number of new diagnoses of HIV infection is either stable or increasing, with reports of rising incidence in men who have sex with men (MSM) who continue to be particularly affected [1–3]. This epidemic among MSM continues despite the widespread availability of effective antiretroviral therapy (ART), which reduces viraemia and, therefore, also infectivity [4–6].

The brief stage of primary HIV infection is thought to be disproportionately responsible for transmission [7–9], with evidence that individuals experiencing seroconversion with high viral loads are more likely to transmit HIV to their partners [10,11]. Conversely, risk of transmission is reduced by effective treatment with ART [12,13], an effect that may be enhanced if it were possible to introduce treatment during recent infection. However, because the primary phase is relatively brief compared with the entire duration of HIV infection, it has been argued that chronic infection contributes to a higher proportion of onward infections overall [9,14]. There is also evidence that high-risk sexual behaviour is particularly evident in recently infected individuals [15], and it may be difficult to distinguish between the effects of viral load and behaviour on transmission. Once diagnosed, however, awareness of HIV-positive status is usually associated with reduced risk-taking behaviour in both chronic and recent infection [16,17], although there is a minority in whom behaviour becomes more risky [18,19]. Other risk factors that increase viral load, and, therefore, infectivity, include treatment interruption and the presence of a sexually transmitted disease (STD) [20–22]. Although all these factors are likely to be important, the extent to which they individually contribute to ongoing transmission is unknown, making it difficult to identify effective prevention initiatives.

Previous phylogenetic studies [23–26] have shown high levels of clustering among individuals diagnosed during recent HIV infection, estimating that between 24 and 49% of these infections are linked to other recent infections. However, in some of these studies, each recent infection has been considered as fixed from diagnosis throughout the entire study period rather than for a transient time interval. For example, a transmission event at the current time from an individual diagnosed with recent infection 3 years earlier would be incorrectly assigned as a transmission from a recent infection, thereby overestimating the contribution of recent infection to new infections [27]. Furthermore, previous studies have seldom been able to include other relevant risk factors for transmission (viral load, ART and STD status) and, if they have, not throughout the entire study period.

Epidemiological data on sexual partnerships between HIV-infected patients are rarely available to validate the results empirically, and if available would arguably render phylogenetic analysis unnecessary. If the limitations of phylogenetic analysis are fully understood, and it is accepted that transmission by a third party (either infecting both individuals in a cluster or being an intermediary between the two) cannot entirely be excluded, such analysis is sufficiently accurate for public health purposes [28].

We aimed to improve upon many limitations of previous phylogenetic methodology and maximally ascertain in an MSM cohort, covering a relatively small geographic area, the single most likely transmitter to each individual with recent infection by time-specific phylogenetic analysis. By describing the biological risk factors of those single most likely transmitters at the time of transmission, we thereby determine the factors associated with onward HIV transmission.


Study population

The study population was a cohort of HIV-infected MSM attending the HIV treatment clinic at the Brighton and Sussex University Hospital between 2000 and 2006; all were eligible for baseline genotypic resistance testing. The study period was stratified into a series of 3-month intervals and the characteristics of patients under follow-up were summarized at the start of each interval. Clinical data considered for each individual under follow-up for each 3-month period were date of diagnosis, disease stage [recent (see below) or chronic infection], latest CD4 cell count, latest viral load, prior receipt of ART and diagnosis of an STD. The STDs included in the analysis were Neisseria gonorrhoeae, Chlamydia trachomatis, nonspecific urethritis, primary syphilis, genital herpes simplex and Trichomonas vaginalis.

Characterization of disease stage

Recent HIV infection was defined on the basis of a negative HIV antibody result within 6 months, a p24 antigen-positive result in conjunction with a negative anti-HIV antibody test, a limited western blot (≤4 bands, including p24 and gp160) or the Serological Testing Algorithm for Recent HIV Seroconversion (STARHS) [29] if the viral subtype was B and patients were not presenting with advanced disease [30]. STARHS, combined with clinical data including subtype and CD4 cell count, was available for over 95% of individuals, so that categorization of newly diagnosed infections as recent or chronic was high. STARHS testing was with the bioMerieux Vironostika HIV-1 assay (bioMerieux UK Ltd., Basingstoke, UK), as previously described [30]. For this study, an optical density of less than 1.0 was used to identify recent infection, and this cutoff is associated with a seroconversion within 4–6 months. Testing was performed retrospectively for those diagnosed in 1996–2000, and prospectively thereafter.

The study design allowed for individuals to progress from recent to chronic infection during the study period. For all recently infected individuals, an earliest and latest infection date was calculated according to the marker that had been used to identify the recent infection (60–183 days prior to diagnosis for STARHS, 1–30 days prior to diagnosis for Ab negative/p24 Ag positive and 30–60 days prior to diagnosis for evolving western blot). An estimated infection date was taken as the midpoint between the earliest and latest infection date for each patient. Depending on this window period, as well as intervals between first-positive and last-negative HIV tests in some cases, the calendar period dates that individuals were potentially experiencing seroconversion were defined, after which they were reclassified as chronic infection.

Phylogenetic analysis

Pol sequences were generated from the plasma RNA of new diagnosis specimens. When baseline genotype specimens were initially unavailable (i.e. if an individual had low or no detectable viraemia throughout the study period), sequences were obtained from an EDTA cell pellet. Pol sequences were generated through an in-house assay, spanning from position 1 in protease to at least position 300 in reverse transcriptase. Sequences were aligned using a Sequence Analyser. Because of the large sample size, a neighbour-joining tree was constructed with gamma rate heterogeneity set at 0.5 and 500 bootstrap replications. Clusters were defined as sequences that shared a common node that had bootstrap support of over 99% and genetic distance under 0.015 nucleotide substitutions per site. Those sequences that formed a cluster were grouped with 50 random sequences that did not cluster and run through ModelTest. A heuristic search was conducted for a maximum likelihood tree using the selected model (K81uf + I + G) and its derived parameters (proportion of invariable sites set at 0.4477 and the heterogeneity set at 0.6946). The same definition of cluster was used; those clusters that were identical between the neighbour joining and the maximum likelihood trees were retained.

Reconstruction of transmission events

The single most likely transmission source subject for each recently infected individual was sought. All individuals, regardless of disease stage, were considered potential transmission sources, and the period of potential transmission was determined to be from the earliest possible date of infection (in the case of recent infections) or from the date of diagnosis (for chronic infections) to study end. Potential transmission source subjects for each recently infected individual were defined as having any other sequence that was in the same robust phylogenetic cluster and who was diagnosed before or in the same calendar quarter as the recently infected subject. When there was more than one candidate transmission source subject, the candidate sequence with the shortest genetic distance was chosen as the most likely subject for the infection source. Any individual in the cluster whose earliest date of infection was probably after the latest time of transmission to the infected individual was not considered a potential transmission source subject in that particular cluster.

Statistical analysis

Using the information from individuals who were potential transmitters during each 3-month period, and those who were thought not to have transmitted HIV during that period, we identified factors independently associated with transmission using multivariable Poisson regression models (SAS version 9.1; SAS Institute Inc., Cary, North Carolina, USA). To do this, each 3-month period of follow-up for an individual was included as a separate observation in the analysis with the outcome of interest being a binary covariate that indicated whether the individual was thought to have transmitted HIV during that period or not. Thus, each individual contributed as many observations to the analyses as 3-month periods when he remained under follow-up as a potential transmitter. Factors included in the model were age at the start of each period (categorized as <35, 35–44 and ≥45 years and also treated as a continuous covariate), latest viral load (unknown or ≤50, 50–1000, 1001–10 000, 10 001–100 000 and >100 000 copies/ml and also treated as a continuous covariate), latest CD4 cell count (unknown or ≤200, 201–350, 351–500 and >500 cells/μl and also treated as a continuous covariate), disease stage and receipt of ART combined (recent infection, chronic untreated, chronic treated and chronic treatment interruption), whether the patient had a diagnosis of AIDS at the start of the period and whether the patient had an STD in the 3-month period or not and calendar year (2000–2001, 2002–2003 and 2004 onwards). In this way, each individual's covariates (including disease stage) were allowed to change over time as his infection progressed. Transmission rates were calculated as the number of transmissions that occurred divided by the total person-years of follow-up (PYFU) within each strata of interest (and expressed per 100 PYFU).

Sensitivity analyses were conducted to assess the impact of the methods used to allocate infection category. Multivariate analyses were repeated while adjusting relevant variables.

Ethical approval and procedures

The study was approved by Brighton and Hove Research Ethics Committee (06/Q1907/93). When additional specimens were required over and above routine clinical care (i.e. for generation of sequences from proviral DNA), individual informed consent was required. All clinical information and patient identifiers were anonymized prior to phylogenetic analysis, so that individuals would not be identifiable to the researchers once phylogenetic analysis had been performed. By aggregating data into 3-month intervals, we also limited identification opportunities prior to data linkage (e.g. dates of CD4 and viral load tests, AIDS events and STDs).


Study participants

Between 2000 and 2006, 1144 MSM were seen at least once in the clinic; these MSM contributed a total of 6176 PYFU to the analysis. Pol sequence data were obtained for 859 (75%) individuals. Of the remaining 285 individuals, 118 had an incomplete pol sequences (and were consequently excluded from the analysis) and 167 did not have any sequence available. Of these, 134 (80%) were diagnosed before the study period.

Clustering of participants

Of the 859 participants' sequences, 209 (24%) fell into discrete clusters under the neighbour-joining tree (Fig. 1). These 209 sequences were selected with a random selection of 50 sequences that did not form a cluster in the initial neighbour-joining tree and underwent a maximum likelihood reconstruction. Of the 209 original cluster sequences, 129 were retained in the maximum likelihood tree, resulting in 15% (129/859) of sequences clustering overall.

Fig. 1:
Phylogenetic reconstruction of HIV transmission clusters among diagnosed HIV-infected men who have sex with men, with the most likely transmission source individuals highlighted.

Ascertainment of transmission events within clusters

One hundred and fifty-nine (19%) of the 859 participants with sequence data available were classified as being recently infected at diagnosis, and were, therefore, infections for whom a potential transmission source subject was sought. Out of these, 47 (30%) fell into clusters and a single most likely transmission source subject was identified for 41 (26%) (Fig. 2) [transmission rate 0.66/100 PYFU, 95% confidence interval (CI) 0.46–0.87]. During the 3-month period, when transmission was thought to have occurred, 11 out of 41 (27%) of these transmission source subjects were categorized as recently infected and 30 (73%) as chronic infections.

Fig. 2:
Phylogenetic clustering and determination of most likely transmitter by infection stage.

Transmission rates

In descriptive analyses (Table 1), transmission rates were higher in those aged less than 35 years, those with viral loads above 10 000 copies/ml, those with recent or chronic untreated infection, those with CD4 cell counts above 350 cells/μl and those experiencing an STD during the 3-month interval. Univariable analyses (Table 1) confirmed that younger age (rate ratio per 5 years older 0.52, 95% CI 0.41–0.65, P = 0.0001), higher viral load (rate ratio per log higher 2.32, 95% CI 1.79–3.01, P = 0.0001), recent infection (rate ratio 4.44, 95% CI 2.11–9.33, P = 0.0001) and a recent STD (RR 12.13, 95% CI 5.95–24.74, P = 0.0001) were all associated with transmission risk. In multivariable analyses, younger age (rate ratio per 5 years older 0.68, 95% CI 0.54–0.86, P = 0.0009), higher viral load (rate ratio per log higher 1.61, 95% CI 1.15–2.25, P = 0.005), recent infection (rate ratio 3.88, 95% CI 1.76–8.55, P = 0.0008) and a recent STD (rate ratio 5.32, 95% CI 2.51–11.29, P = 0.0001] were confirmed as independent risk factors for transmission.

Table 1:
Transmission rate (per 100 person-years of follow-up) and factors associated with the 41 transmission sources of recent HIV infections identified through phylogenetic analysis.

Although use of HAART was significantly associated with transmission risk in univariable analyses (rate ratio 0.14, 95% CI 0.07–0.27, P = 0.0001], this effect was attenuated towards unity after adjustment for the other covariates (including latest viral load) in a multivariable model (rate ratio 0.77, 95% CI 0.35–1.69, P = 0.51).

Each patient had an estimated infection date calculated as the midpoint of their earliest and latest infection date, based on the markers used to ascertain recent infection. Two sensitivity analyses were undertaken. For the first sensitivity analysis, the earliest date of the estimated transmission period was chosen as the estimated infection date. The association with recent infection stage again was reduced (rate ratio 1.65) and did not retain its significance (P = 0.46), but the relationship with viral load did (rate ratio 1.60, P = 0.004). For the second analysis, the latest possible infection date from the estimated transmission period was chosen as the infection date. The associations between increased transmission rate and infection category and viral load were both retained (rate ratio 3.25, P = 0.005 and rate ratio 1.63, P = 0.004, respectively).


There is debate about the suitability of phylogenetics for reconstructing HIV transmission events because viral genomes can be extremely similar, through parallel or convergent evolution, making it difficult to prove definitively that two viruses have a recent common origin. Like other phylogenetic studies, transmission cannot be proven and the existence of a third party infecting both or being an intermediary source cannot be excluded. The anonymized nature of this study does not allow confirmation of putative transmission with sexual network determination. However, the bootstrapping and genetic distance cutoffs used have been shown to reduce the proportion of false-positive clusters among phylogenetic analysis of UK HIV sequences [28]. The technique is limited in that it only considers pair-wise transmissions and does not attempt to analyse the implications of larger transmission clusters. Additionally, even if two individuals shared a closely related virus, phylogenetic analysis could not verify the direction of transmission and it is difficult to rule out both individuals being infected via a third partner, or that a third party was an intermediary partner between the two.

A better understanding of the relative impact of the biological drivers of the ongoing MSM HIV epidemic is urgently required. Previous work has focused on phylogenetic approaches to identify linkages between individuals and groups but the absence of integrated demographic and clinical data in earlier studies represents a major limitation to interpretation. This study addresses these limitations as fully as possible and contains a number of methodological improvements.

First, participants are from a geographically confined cohort where 88% of MSM attend a single HIV clinic for both HIV and STD treatment (Brown AE, personal communication). Second, disease stage was classified and updated over time, giving an accurate representation of disease stage over the course of the study period. This, and the search for the transmission source subjects of the recently HIV-infected, allowed for the period of transmission events to be approximated, enabling the direction of transmission to be ascertained, and risk attributes of the infection source subjects at the time of transmission to be analysed. Third, selection bias was minimized because baseline genotypic testing had been routine since the beginning of the study. When baseline genotype specimens were unavailable (i.e. if an individual had low or no detectable viraemia throughout the study period), sequences were obtained from proviral DNA. Fourth, STARHS, combined with clinical data including subtype and CD4 cell count, was available for over 95% of individuals to ensure that recent infection was maximally ascertained. Finally, matched clinical data – CD4 cell count, viral load, ART and presence of STD – were available for the majority of individuals throughout the study period.

Our findings show that HIV infections are disproportionately generated by those who are recently infected, untreated and with a concomitant STD. Furthermore, we could not ascertain a likely transmitter for 74% of recently infected individuals. This is consistent with modelling data from the United States and The Netherlands, which suggest that between 54 and 90% of infections come from undiagnosed sources [31,32]. With such a high ascertainment of viral sequence data from the study cohort, these data suggest that the largest source of new infections is the undiagnosed population; around 30% of HIV-positive MSM are undiagnosed in Brighton [33]. We cannot, of course, exclude infection derived from other geographical areas of the UK and beyond. Ongoing work in terms of broader phylogenetic analyses comparing these sequences with those from other MSM in the UK (through the UK Resistance Database [34]) will quantify the degree of cross-transmission between Brighton and other cities in the UK.

We have demonstrated an association between recent infection and onward transmission, consistent with modelling, serodiscordant couple studies and supportive of assertions from previous phylogenetic studies [7–10,23–26]. This potentially enhanced role of recent infection in generating new infections is further highlighted by 11 out of 41 (27%) transmitters being classified as recently infected despite recent infection only representing 194 out of 6176 PYFU (2%) of time under follow-up within the study period.

Recent infection remains independently associated with transmission, suggesting that factors other than higher viraemia – probably behavioural – also contribute to the disproportionate transmission during this disease stage. These findings suggest that further biobehavioural research is warranted, for example, including measurements of partnership dynamics and social–sexual network structure within a phylogenetic framework [35]. Others have identified the importance of STDs in increasing infectivity [36], as we do. It is not possible in this analysis to determine whether this is a marker of higher risk sexual activity or the result of increased transmission risk due to increased genital HIV shedding. However, it suggests that it is prudent that STD diagnosis and care should be included within routine HIV care, so that prompt STD treatment and risk-reduction advice may reduce onward HIV transmission.

We have shown an association between viral load and onward transmission, consistent with that expected by biological plausibility and that seen in heterosexual serodiscordant couple studies [10,11]. We found use of ART to be significant in the univariable but not in multivariable analysis, consistent with the hypothesis that the impact of ART on reducing transmission is mediated through its reduction in viraemia.

Reduction of infectivity is one of the potential advantages of earlier initiation of ART, as is being studied in the Strategic Timing of Anti-Retroviral Treatment trial ( Our data add further weight to the argument that treating the HIV-infected population regardless of clinical need may substantially impact transmission at a population level. Over 70% of likely transmission source individuals were ART naive and ineligible for ART according to current and past British HIV Association guidelines [37]. This supports the need not only for higher ascertainment of diagnosed HIV but also the potential benefit of widening treatment to reduce transmissions, which will be studied in the HIV Prevention Trials Network study, HPTN 052 [38].

Nine out of 41 transmitters were undergoing a treatment interruption at the presumed time of transmission. This is consistent with modelling within the Strategies for Management of Anti-Retroviral Therapy study [39], suggesting that, unless accompanied by changes in risk behaviour, treatment interruption may be associated with increased onward transmission. Clinicians need to highlight this to individuals who are interrupting ART, as reduction in risk behaviour may not immediately accompany the increased infectiousness that results from stopping ART. Two of the transmitters identified in this study were classified as having a viral load below detection limits at the estimated time of transmission. For one individual, the next available viral load was above detection, suggesting that transmission may actually have occurred with detectable viraemia. For the other, there is no evident explanation for apparent transmission while undetectable on ART. Nevertheless, caution must be exercised in the interpretation of such data – for instance, viral rebound during the interval between undetectable viral loads or seminal/plasma discordance [40,41] cannot be excluded. Alternative explanations include that the reconstruction was incorrect or that the most likely transmission source subject was incorrectly identified. Given that the data were unlinked prior to phylogenetic analysis, it was not possible further to evaluate this case with the rigor necessary to determine whether transmission truly occurred while ‘undetectable’, as other authors have been able to do in this scenario [42]. Nevertheless, given the large size of the cohort – of whom approximately 70% (circa 800) were receiving ART – the absolute risk of transmission associated with an undetectable viral load was low.

The robustness of the association between recent infection and onward transmission was examined by two sensitivity analyses, which varied the estimated infection dates of each transmitter (which in the original analysis was the midpoint of the window period of whichever test determined recent infection). Varying the length of recent infection from this midpoint to the earliest and latest possible date of infection substantially impacted upon the association between infection category and transmission risk. When a later date was given to recent infection (sensitivity analysis 2), there was a higher likelihood that the transmission occurred from a transmitter with recent infection. When an earlier date of recent infection is given (sensitivity analysis 1), there was less chance that the transmission occurred from a transmitter experiencing recent infection. This demonstrates that the timing and duration allocated to infection stages need to be meticulously considered in such analyses, as they have the potential to substantially affect results.


We provide important information on the biological factors affecting continuing transmission of HIV within the MSM community. Our data are consistent with a significant but not an absolute effect of ART in reducing onward HIV transmission and our findings contribute required real-life data to inform future modelling of prevention strategies in this risk group [43].


We acknowledge part funding from the UCLH/UCL NIHR Comprehensive Biomedical Research Centre. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under the project ‘Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN)’ – grant agreement number 223131. Other acknowledgements – Dr Gillian Dean, who set up the original Brighton database; Dr Kate Nambiar and Stuart Tilbury for maintaining the Brighton database; Gary Murphy, Denis McElborough and Gary Homer for laboratory work.


1. Health Protection Agency UK. Testing times. [Accessed 17 February 2010]
2. Hall HI, Song R, Rhodes P, Prejean J, An Q, Lee LM, et al, for the HIV Incidence Surveillance Group. Estimation of HIV incidence in the United States. JAMA 2008; 300:520–529.
3. Jaffe HW, Valdiserri RO, De Cock KM. The reemerging HIV/AIDS epidemic in men who have sex with men. JAMA 2007; 298:2412–2414.
4. Gupta P, Mellors J, Kingsley L, Riddler S, Singh MK, Schreiber S, et al. High viral load in semen of human immunodeficiency virus type 1-infected men at all stages of disease and its reduction by therapy with protease and nonnucleoside reverse transcriptase inhibitors. J Virol 1997; 71:6271–6275.
5. Montaner JSG, Hogg R, Wood E, Kerr T, Tyndall M, Levy AR, et al. The case for expanding access to highly active antiretroviral therapy to curb the growth of the HIV epidemic. Lancet 2006; 368:531–536.
6. Vernazza P, Hirschel B, Bernasconi E, Flepp M. HIV-positive individuals without another sexually transmitted infection and on effective antiretroviral therapy do not transmit HIV sexually. Bulletin des médecins suisses 2008; 89.
7. Xiridou M, Geskus R, de Wit J, Coutinho R, Kretzschmar M. Primary HIV Infection as source of HIV transmission within steady and casual partnerships among homosexual men. AIDS 2004; 18:1311–1320.
8. Pilcher CD, Joaki G, Hoffman IF, Martinson FE, Mapanje C, Stewart PW, et al. Amplified transmission of HIV-1: comparison of HIV-1 concentrations in semen and blood during acute and chronic infection. AIDS 2007; 21:1723–1730.
9. Hollingsworth TD, Anderson RM, Fraser C. HIV-1 transmission, by stage of infection. J Infect Dis 2008; 198:687–693.
10. Wawer MJ, Gray RH, Sewankambo NK, Serwadda D, Li X, Laeyendecker O, et al. Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis 2005; 191:1403–1409.
11. Tovanabutra S, Robison V, Wongtrakul J, Sennum S, Suriyanon V, Kingkeow D, et al. Male viral load and heterosexual transmission of HIV-1 subtype E in northern Thailand. J Acquir Immune Defic Syndr 2002; 29:275–283.
12. Castilla J, Del Romero J, Hernando V, Marincovich B, García S, Rodríguez C. Effectiveness of highly active antiretroviral therapy in reducing heterosexual transmission of HIV. J Acquir Immune Defic Syndr 2005; 40:96–101.
13. Blower SM, Gershengorn HB, Grant RM. A tale of two futures: HIV and antiretroviral therapy in San Francisco. Science 2000; 287:650–654.
14. Pinkerton SD. How many sexually-acquired HIV infections in the USA are due to acute-phase HIV transmission? AIDS 2007; 21:1625–1629.
15. Colfax GN, Buchbinder SP, Cornelisse PG, Vittinghoff E, Mayer K, Celum C. Sexual risk behaviors and implications for secondary HIV transmission during and after HIV seroconversion. AIDS 2002; 16:1529–1535.
16. Bunnell R, Ekwaru JP, Solberg P, Wamai N, Bikaako-Kajura W, Were W, et al. Changes in sexual behavior and risk of HIV transmission after antiretroviral therapy and prevention interventions in rural Uganda. AIDS 2006; 20:85–92.
17. Fox J, White PJ, Macdonald N, Weber J, McClure M, Fidler S, et al. Reductions in HIV transmission risk behaviour following diagnosis of primary HIV infection: a cohort of high-risk men who have sex with men. HIV Med 2009; 10:432–438.
18. Marks G, Crepaz N, Senterfitt JW, Jansenn RS. Meta-analysis of high-risk sexual behaviour in persons aware and unaware they are infected with HIV in the United States. J Acquir Immune Defic Synd 2005; 39:446–453.
19. Gorbach PM, Drumright LN, Daar ES, Little SJ. Transmission behaviors of recently HIV-infected men who have sex with men. J Acquir Immune Defic Syndr 2006; 42:80–85.
20. Wasserheit JN. Epidemiological synergy: interrelationships between human immunodeficiency virus infection and infection with other sexually transmitted diseases. Sex Transm Dis 1992; 19:61–77.
21. Powers KA, Poole C, Pettifor AE, Cohen MS. Rethinking the heterosexual infectivity of HIV-1: a systematic review and meta-analysis. Lancet Infect Dis 2008; 8:553–556.
22. Johnson LF, Lewis DA. The effect of genital tract infections on HIV-1 shedding in the genital tract: a systematic review and meta-analysis. Sex Transm Dis 2008; 35:946–959.
23. Yerly S, Vora S, Rizzardi P, Chave JP, Vernazza PL, Flepp M, et al. Swiss HIV Cohort Study. Acute HIV infection: impact on the spread of HIV and transmission of drug resistance. AIDS 2001; 15:2287–2292.
24. Pao D, Fisher M, Hué S, Dean G, Murphy G, Cane PA, et al. Transmission of HIV-1 during primary infection: relationship to sexual risk and sexually transmitted infections. AIDS 2005; 19:85–90.
25. Brenner BG, Roger M, Routy JP, Moisi D, Ntemgwa M, Matte C, et al. High rates of forward transmission events after acute/early HIV-1 infection. J Infect Dis 2007; 195:951–959.
26. Bezemer D, van Sighem A, Lukashov VV, van der Hoek L, Back N, Schuurman R. ATHENA observational cohort. Transmission networks of HIV-1 among men having sex with men in the Netherlands. AIDS 2010; 24:271–282.
27. Brown AE, Gifford RJ, Clewley JP, Kucherer C, Masquelier B, Porter K, et al. Phylogenetic reconstruction of transmission events from individuals with acute HIV infection: toward more-rigorous epidemiological definitions. J Infect Dis 2009; 199:427–431.
28. Hue S, Clewley JP, Cane PA, Pillay D. HIV-1 pol gene variation is sufficient for the reconstruction of transmissions in the era of antiretroviral therapy. AIDS 2004; 18:719–728.
29. Janssen RS, Satten GA, Stramer SL, Rawal BD, O'Brien TR, Weiblen BJ, et al. New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. JAMA 1998; 280:42–48. Erratum in: JAMA 1999; 281:1893.
30. Fisher M, Pao D, Murphy G, Dean G, McElborough D, Homer G, et al. Serological Testing Algorithm for Recent HIV Seroconversion (STARHS): 10 years of clinical application. AIDS 2007; 21:2309–2314.
31. Bezemer D, de Wolf F, Boerlijst MC, van Sighem A, Hollingsworth TD, Prins M, et al. A resurgent HIV-1 epidemic among men who have sex with men in the era of potent antiretroviral therapy. AIDS 2008; 22:1071–1077.
32. Marks G, Crepaz N, Janssen RS. Estimating sexual transmission of HIV from persons aware and unaware that they are infected with the virus in the USA. AIDS 2006; 20:1447–1450.
33. Dodds JP, Johnson AM, Parry JV, Mercey DE. A tale of three cities: persisting high HIV prevalence, risk behaviour and undiagnosed infection in community samples of men who have sex with men. Sex Transm Infect 2007; 83:392–396.
34. Dunn D, Pillay D. UK HIV drug resistance database: background and recent outputs. J HIV Ther 2007; 12:97–98.
35. Smith DM, May SJ, Tweeten S, Drumright L, Pacold ME, Kosakovsky Pond SL, et al. A public health model for the molecular surveillance of HIV transmission in San Diego, California. AIDS 2009; 23:225–232.
36. Aral SO, Padian NS, Holmes KK. Advances in multilevel approaches to understanding the epidemiology and prevention of sexually transmitted infections and HIV: an overview. J Infect Dis 2005; 191(Suppl 1):S1–S6.
37. British HIV Association. [Accessed 17 February 2010]
38. A randomized trial to evaluate the effectiveness of antiretroviral therapy plus HIV primary care versus HIV primary alone to prevent the sexual transmission of HIV-1 in serodiscordant couples. HIV Prevention Trials Network. [Accessed 17 February 2010]
39. Burman W, Grund B, Neuhaus J, Douglas J Jr, Friedland G, Telzak E, et al. Episodic antiretroviral therapy HIV transmission risk compared with continuous therapy: results of a randomised controlled trial. J Acquir Immun Defic Syndr 2008; 49:142–150.
40. Sheth PM, Kovacs C, Kemal KS, Jones RB, Raboud JM, Pilon R, et al, for the Toronto Mucosal Immunology Group. Persistent HIV RNA shedding in semen despite effective antiretroviral therapy. AIDS 2009; 23:2050–2054.
41. Marcelin AG, Tubiana R, Lambert-Niclot S, Lefebvre G, Dominguez S, Bonmarchand M, et al. Detection of HIV-1 RNA in seminal plasma samples from treated patients with undetectable HIV-1 RNA in blood plasma. AIDS 2008; 22:1677–1679.
42. Stürmer M, Doerr HW, Berger A, Gute P. Is transmission of HIV-1 in nonviremic serodiscordant couples possible? Antivir Ther 2008; 13:729–732.
43. Granich RM, Gilks CF, Dye C, De Cock KM, Williams BG. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet 2009; 373:48–57.

HAART; HIV testing; HIV-1; phylogenetics; primary HIV infection; recent HIV infection; sexually transmitted disease; transmission

© 2010 Lippincott Williams & Wilkins, Inc.