Skip to main content
  • Loading metrics

HIV Treatment as Prevention: The Utility and Limitations of Ecological Observation

  • M. Kumi Smith,

    Affiliation Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • Kimberly A. Powers,

    Affiliations Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • Kathryn E. Muessig,

    Affiliation Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • William C. Miller,

    Affiliations Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • Myron S. Cohen

    Affiliations Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, North Carolina, United States of America


Results from several observational studies of HIV-discordant couples and a randomized controlled trial (HIV Prevention Trials Network 052) show that antiretroviral therapy (ART) can greatly reduce heterosexual HIV transmission in stable HIV-discordant couples. However, such data do not prove that ART will reduce HIV incidence at the population level. Observational investigations using ecological measures have been used to support the implementation of HIV treatment for the specific purpose of preventing transmission at the population level. Many of these studies note ecological associations between measures of increased ART uptake and decreased HIV transmission. Given the urgency of implementing HIV prevention measures, ecological studies must de facto be used to inform current strategies. However, the hypothesis that widespread ART can eliminate HIV infection may have raised expectations beyond what we may be able to achieve. Here we review and discuss the construct of the exposure and outcome measures and analysis methods used in ecological studies. By examining the strengths and weaknesses of ecological analyses, we aim to aid understanding of the findings from these studies to inform future policy decisions regarding the use of ART for HIV prevention.


Ecological studies use observational data to examine relationships between exposures and outcomes at the level of groups rather than individuals [1]. When individual-level data are unavailable, ecological studies can provide important insight into population-level trends [2],[3]. Ecological studies appeal to researchers and policy-makers because they are inexpensive, use existing data, and are applicable to a broad range of issues. However, statistical models using only group-level data cannot evaluate person-level details and are therefore unable to test etiological hypotheses [2],[4][6]. Further, because ecological studies often use separate data sources to measure exposures and their potential effects, the link between exposures and outcomes cannot be determined at the individual level.

Concern over these limitations has focused on “ecological fallacy,” in which associations detected at the population level are mistakenly interpreted to reflect the experience of individuals in that population [1]. The first study describing ecological fallacy presented an analysis of literacy and immigration in the US, in which states with higher proportions of immigrants were shown to have higher average literacy rates [7]. An “ecologically fallacious” interpretation of this association would be that immigrants have higher literacy rates than native-born individuals; in fact, individual-level analysis shows lower literacy rates among immigrants. The best explanation for this particular population-level observation is that immigrants tend to settle in sites where the native-born individuals have higher literacy levels [7].

Despite their limitations, ecological studies play an important role in generating hypotheses that can be tested in experimental or individual-level observational studies [2],[8]. For instance, ecological analyses were successfully applied during the exploratory phases of research on male circumcision to prevent HIV, in which geographical associations between circumcision rates and HIV prevalence [9][11] provided the foundation for two decades of observational research [12],[13] on the topic. All three randomized clinical trials that followed were halted because of a readily demonstrable reduction of HIV acquisition in circumcised men [14],[15]. A Cochrane review published in 2009 concluded that male circumcision is a clinically viable HIV prevention strategy [16].

Here we describe an illustrative set of observational studies that use ecological measures to examine the population-level effects of antiretroviral therapy (ART) on HIV transmission. We critically review what these studies measure, how they measure it, and how their findings are interpreted. These results are used to provide insight into the strengths and limitations of this approach.

Ecological Studies of ART for HIV Prevention

The narrative of exploring the effects of treatment on prevention shares similarities with the narrative for male circumcision, though with a somewhat different chronology. The hypothesis that antiretroviral agents can prevent sexual HIV transmission was suggested in 1988 shortly after the development of azidothymidine, which was found to effectively penetrate the genital tract [17]. This report was followed by more intensive study of the effect of newly developed antiretroviral agents on HIV replication in the male and female genital tract [18][21]. In 1994, Musicco et al. observed that azidothymidine could reduce transmission of HIV in a cohort of discordant couples by 50% [22]. Several clinical trials in the late 1990s showing that ART stopped mother-to-child transmission lent further credibility to the potential use of ART to prevent sexual transmission [23]. In 2000, a randomized clinical trial, HIV Prevention Trials Network 052 (HPTN 052), was launched to determine the magnitude and durability of the effect of combination antiretroviral agents on the prevention of sexual transmission of HIV [24]. After this trial was launched, several [25][28], but not all [29], individual-level observational studies reported a protective effect of ART against HIV transmission in serodiscordant couples. In addition, many modeling exercises suggested varying degrees of population-level prevention benefit from broader use of ART [30], the most widely discussed of which predicted elimination of HIV within five years under ideal conditions [31]. These models are discussed in a review by Eaton et al. [32] in the July 2012 PLoS Medicine Collection, “Investigating the Impact of Treatment on New HIV Infections.” A third group of eight ecological studies examined the population-level effects of widespread ART on HIV incidence using ecological measures, and reported significant effects [33][38] in all but two cases [39],[40]. These eight studies are the focus of this review. Finally, in mid-2011, the HPTN 052 investigators reported a 96% reduction of HIV transmission in heterosexual couples over the 1.7 years of follow-up [41].

The promise of ART to control—and perhaps even eliminate [31]—HIV has mobilized calls from public health leaders to integrate preventive and clinical applications of ART [42][45]. In light of several trials showing markedly improved survival for those initiating ART earlier in the course of disease, such initiatives often emphasize the clinical benefits that early treatment can bring HIV-infected persons [46],[47]. However, numerous behavioral, epidemiological, and programmatic challenges may well limit the ability to translate the individual-level prevention benefits of ART to a larger population [48][52]. As such, demonstration of a minimally biased population-level benefit is critical. Not surprisingly, there is a credible tension between the need for more randomized individual- and community-level trials (also called cluster randomized controlled trials), and the immediate scale-up of HIV treatment to prevent the spread of HIV [53][55]. The arguments for immediate and broader roll-out of ART for the sake of prevention are based on the HPTN 052 study [41], observational studies of transmission within HIV-discordant couples [25][29], ecological reports [33]–, and modeling exercises [31],[56][59].

In this report we examine eight influential ecological studies that assess the population-level effects of ART on HIV transmission (Table 1). Most of the studies are from North America [33],[34],[36],[38][40], with one set in Taiwan [35] and one in Australia [37]. Each study uses an ecological measure of the exposure, such as access to ART, or the outcome, such as HIV incidence, or both (summarized in Table 1; further considerations detailed in Table 2).

Table 1. Summary of exposure and outcome measures in studies using ecological measures to assess population-level effects of ART on HIV transmission.

Table 2. Summary of measures used and considerations for their use.

Measuring Population Exposure to ART

The simplest way investigators have characterized ART exposure in a population of HIV-infected persons is to use a dichotomous “before/after” measure, as in the case of Fang et al. [35] and Porco et al. [38], based on the time at which scale-up of local HIV treatment policies improved access to ART. Other investigators have used more detailed measures of ART exposure, including Montaner et al. [33], who estimated the number of HIV-infected persons known to be receiving ART in a population, or Katz et al. [40], who used prevalence of ART use among all identified HIV patients. How well these measures reflect actual ART exposure of an entire HIV-infected population depends on the extent to which some subpopulations remain “hidden” to investigators. ART exposure of the entire HIV-infected population can only be measured if every person with HIV infection can be identified and their treatment status assessed.

The hypothesis that population ART usage will decrease HIV incidence relies on the assumption that ongoing HIV care will sustain viral suppression, which is essential to transmission prevention [60]. However, large numbers of HIV-infected persons are lost to follow-up along the path from testing to suppressive treatment [61],[62]; the US Centers for Disease Control and Prevention recently estimated that only about 24% of the 1.2 million people in the US with HIV infection in 2010 were virally suppressed (Figure 1) [61][63]. Even once in care, rates of treatment refusal by eligible individuals can be substantial [64].

Figure 1. Estimated numbers of HIV-infected individuals in the US retained (and corresponding percentages lost) at various stages of the test, link, and treat cascade.

This figure is based on data from [61],[62].

To address the shortcomings of measures that do not fully reflect suppressive ART use, alternative metrics incorporate viral load information, based on the well-understood relationship between sexual transmission of HIV from infected individuals and viral concentrations in their blood [65] and genital fluids [66]. One such measure uses the proportion (or absolute number) of treated individuals in a study population with undetectable viral load, usually defined as having fewer than 400 copies/ml [33],[37][39].

A related measure, community viral load (CVL), is used by Das et al. [34], Montaner et al. [33], and Wood et al. [36], and is defined as the total, mean [33],[34], or median [36] viral load for a particular group or geographic region in a given period of time (usually a year). CVL may be a useful biomarker for describing population-level treatment outcomes over time, particularly in cases in which geospatial information about the patients' primary residence or point of medical care is available, allowing investigators to compare geographic disparities in CVL with other predictive factors such as socioeconomic status or proximity to health care programs [34],[39]. However, because most CVL measures rely on public health surveillance data [33],[34],[39], these exposure measures reflect the treatment outcomes only for the subset of the HIV-infected population who get tested for HIV, link to care, and remain in care long enough to contribute such measurements. Patients with acute infection unidentified by serological testing are de facto not considered in the calculation of CVL, but may well be expected to contribute disproportionately to onward HIV transmission [67],[68]. Additionally, the use of an aggregate measure of viral load in a community cannot capture other important drivers of HIV transmission, such as the distribution of viral loads within the population, sexual and drug-using behaviors, and the sexual or drug-use networks through which these behaviors spread HIV.

Outcome Assessment: Tracking Population HIV Transmission

Accurate assessment of HIV incidence is critical for evaluating the population-level effect of interventions, but such assessment is challenging. The simplest approach is taken by Law et al. [37], who simply refer to HIV incidence trends cited in past publications [37]. Another approach, taken by Montaner et al. [33], Das et al. [34], and Castel et al. [39], estimates population-based incidence from information on newly identified cases, using new HIV diagnoses as a direct proxy for new infections. Obviously, newly diagnosed patients acquired HIV at some unknown earlier time, and so they are not “incident” in the traditional use of the word. Using new diagnoses as a proxy for incidence also misses populations that do not seek testing and that may have lower access to health care and a corresponding higher risk for acquiring HIV [69],[70]. Changes in the number of new HIV diagnoses may reflect actual changes in incidence, but will also be affected by changes in availability of services and testing behaviors [71].

A second population-based incidence estimation method, used by Fang et al. [35], back-calculates past incidence from new diagnoses [35]. This method relies in part on assumptions of uniform parameters for disease progression markers such as the onset of AIDS symptoms or the proportion of newly diagnosed individuals with CD4 cell count less than 200 cells/µl, although variation in the rate of decline of CD4 cell count over time and across gender, ethnicity, and HIV subtypes undermines the validity of this method [72][74].

Laboratory assays to identify persons with recent HIV infection can be applied to stored biospecimens collected in the course of routine surveillance or epidemiological research studies and may provide a more rigorous method to determine current HIV incidence from new diagnoses. The serologic testing algorithm for recent HIV seroconversion (STARHS) [75] derives current HIV incidence from the prevalence of recent infections, based on the assay window period, delineated by the seroconversion dates as detected by the original HIV-1 antibody test and the STARHS method, and adjusting for the estimated prevalence among non-testers and the probability that HIV-infected individuals will test, receive treatment, and/or have missing specimens. Although the investigations that use this method take advantage of existing surveillance data, as in the case of Das et al. [34] and Katz et al. [40], logistical challenges in storing and tracking remnant blood can affect the completeness of data. Furthermore, even relatively new laboratory methods such as the detuned enzyme-linked immunosorbent assay or the newer BED capture enzyme immunoassay have been known to misclassify established infections as incident infections; therefore, results must be interpreted with some caution [76]. The use of this approach has generally fallen out of favor pending development of better laboratory-based tests or algorithms [77].

In contrast to population-based methods, longitudinal cohort follow-up data have also been used to define population incidence, as in the analyses carried out by Wood et al. [36] and Porco et al. [38]. Although long considered the gold standard of HIV incidence estimation, cohort follow-up is not immune to bias, such as that which can result from the choice of testing intervals and HIV assay [78]. Moreover, cohort participants may be a poor proxy for the rest of the population, especially if the individuals who enroll and remain in the cohort have fewer risk behaviors than their unobserved counterparts.

Identifying the Effects of ART on HIV Transmission

The ultimate aim of these investigations is to determine whether population-level ART exposure has affected HIV transmission. Some investigators used inductive reasoning to synthesize either their own results [38],[40] or a combination of their own results and other published reports [37]. The remaining studies quantify the association by comparing transmission rates, defined as the ratio of new cases to prevalent cases in an interval of time, before and after introduction of ART [35], or by using time series regression modeling [33],[34],[36],[39]. Although these methods of analysis differ considerably, it is worth consideration that nearly every study arrives at the same conclusion: that increased population exposure to ART leads to lower HIV transmission (Table 3).

Table 3. Analysis methods and conclusions regarding effects.

However, inaccurate assessment of exposures or outcomes can generate bias [51]. Overestimating the decline in incidence, for instance—perhaps because of an unrecognized change in testing behaviors—could produce an upward bias in the estimated impact of ART on HIV transmission. Additionally, statistical associations do not show causation, and observed trends in HIV diagnoses may be due to factors other than population-level exposure to ART. For example, declines in HIV incidence in settings worldwide—most of which started to occur before ART was available or could be expected to have had an effect—have been ascribed to various phenomena, including the saturation of HIV in high-risk groups [79] and changes in sexual behavior in response to the HIV pandemic [71],[78]. Although the potential confounding effects of changes in HIV-related risk behaviors have been widely acknowledged, only one report, from Vancouver [36], formally controls for them in a regression model (Table 3). By comparison, another study from British Columbia attributes large numbers of averted HIV infections among injection drug users (IDUs) to broader uptake of ART in the community, but some have suggested that the analysis underestimates the potential protective effects of other HIV prevention measures directed at the same community [80]. Indeed, the protective effects of Vancouver's safer injection sites have been documented in the past [81],[82]. Also, consistent ART adherence may be difficult to sustain in IDUs [83], further suggesting that factors beyond viral suppression may have contributed to the reduction in HIV incidence in this population.

The ability of ART to visibly reduce the number of newly diagnosed cases of HIV takes time, because most new diagnoses are made years after infection occurs, and many patients present with a reduced CD4 count, reflecting substantial progression of HIV disease. But in some ecological studies, the effect of ART is presumed to be almost immediate. In the report from British Columbia [33], where combination ART was introduced in 1996, the largest decrease in documented new HIV diagnoses took place between 1997 and 2000, but it is reasonable to question whether enough suppressive combination ART was immediately available to most patients to explain this decline.

Alternative Results and Other Considerations

The comparative lack of reports investigating the ecological effects of population-level ART in settings where rising incidence rates have been detected [84],[85] suggests potential publication bias. It is also noteworthy that ecological studies of ART for HIV prevention are almost exclusively from developed western settings, likely because of the limited availability of surveillance data, viral load measurements, or registry data in resource-constrained settings.

Stable or rising HIV incidence among certain population subgroups with ready access to ART suggests the possibility that identified relationships between ART access and declines in HIV diagnoses in the studies reviewed here may be overstated. For example, HIV incidence (estimated by STARHS) increased and then stabilized among voluntary testers in San Francisco between 1999 and 2006 [85], and model-estimated numbers of new HIV infections among men who have sex with men (MSM) in British Columbia increased by 13% from 2005 to 2008 [86]. In parts of Australia, the number of HIV diagnoses among MSM between 2000 and 2006 doubled, although cohort data suggest that this observation may be largely driven by new infections among older MSM [87],[88]. In Denmark [89] and the UK [90], incidence rates among MSM have reportedly increased. In Canada, some subgroups of IDUs have experienced rising HIV incidence, including Aboriginals [91], women [92], and youth [93], prompting a call for renewed prevention efforts [94].

More than 50 experimental studies of treatment as prevention are in some stage of development, and more can be anticipated [95],[96]. Policy-makers often do not have the luxury of waiting years for trial data, and all decisions take place under a certain degree of uncertainty. To this end, several studies, including some considered in this review, have successfully applied novel tools of geospatial mapping and phylogenetic analysis to aid interpretation of observational data. A study in the UK [97] used viral molecular phylogeny to determine the single most likely transmitter among MSM, allowing the investigators to account for the higher transmission probability of individuals with acute and early infection. Other studies, including those from San Francisco [34] and Washington, D. C. [39], used geospatial analysis to illustrate the spatial distribution of HIV-infected individuals in communities. Most recently, investigators at the Africa Centre for Health and Population Studies in South Africa have been able to identify a relationship between the density of ART use and HIV acquisition risk within a community by studying HIV incidence in a longitudinal cohort of more than 16,000 individuals (personal communication, F. Tanser). Additional strengths of this study include the use of information about the patients' primary residence and attempts to control for at least some possible confounders of the relationship between ART uptake and HIV incidence in the same community.

Several large cluster randomized controlled trials are being developed [98]. A team from the Harvard School of Public Health AIDS Initiative, working with partners in Botswana, will target individuals with sustained, high plasma viral loads for immediate treatment, a strategy that could have exponential public health benefits [73]. A second group from the London School of Hygiene & Tropical Medicine and Imperial College London plans to test the feasibility and impact of a universal test-and-treat strategy along with other combination prevention measures, including male circumcision [99]. But clinical trials have their own limitations, including time, cost, ethical challenges, and perturbations to the underlying community that can cause bias. And there is never a guarantee that approaches employed in a trial will prove effective outside of the trial setting.


Suppressive ART prevents HIV transmission in stable, monogamous, heterosexual couples. While ART seems to hold great promise as a public health tool, its population-level benefits have not been proven. Although ecological studies can play an important role in the development of new HIV prevention strategies, they are methodologically limited to building justification of further formal scientific inquiry into population-level effects of the potential policies in question. They are therefore the first of many steps in the path from science to policy, beginning with the establishment of biological plausibility, and progressing to assessment of an individual-level effect and then a group-level effect. Though most policy decisions must be made under conditions of uncertainty, the hypothesis that widespread ART can eliminate HIV infection [31],[100] may have raised expectations beyond what can actually be achieved. Additionally, implementation of treatment as prevention is not without its risks, including the rise of population-level drug resistance with the rapid uptake of ART in the face of continued limited infrastructure, and increased risk compensation by treated individuals who believe that treatment alone may justify forgoing other forms of protection [101][103].

Although we expect an impact of ART at the population level, the magnitude of the effect may not be as great as some hope; measuring the impact of ART roll-out on HIV spread, as in several planned cluster randomized controlled trials, therefore remains a critical step. Much as combination prevention methods are believed to be better than single interventions for HIV prevention [104], all the methods available to determine the benefits of prevention interventions, including ecological studies, should be deployed. The results must be weighed and used with a full understanding of the methods used to define the outcomes of treatment of HIV infection for prevention of transmission.

Key Points

  • Several strong observational studies and one randomized controlled trial, HPTN 052, demonstrate that ART reduces the transmission of HIV in stable, heterosexual HIV-discordant couples.
  • A number of ecological studies, which use observational data to examine relationships between exposures and outcomes at the level of groups instead of individuals, have found associations between the broader use of antiretroviral agents and reductions in new HIV diagnoses in at-risk populations or in the general population. Ecological studies may generate hypotheses that can be explored using other experimental or observational methods.
  • A better understanding of the strengths and limitations of the various exposure and outcome measures used in ecological studies that examine the population-level effects of ART on HIV transmission, and the methods used to analyze them, is essential for the effective application of the findings in policy-making processes.
  • Methodological challenges such as measurement error, selection bias, confounding, and assumptions about the time lag of effects must be taken into account when interpreting the results of these studies.
  • Prospective measurement of the population-level impact of ART can be approached through cluster randomized controlled trials, but the cost, time, and degree of difficulty of designing and conducting these studies are appreciable, and the potential for the intervention itself to introduce bias can threaten the validity of the results.
  • Measuring the population-level benefits of ART is critical to HIV prevention efforts, and consideration of results of all methods may be used to inform ongoing research and public health policy. A firm understanding of the strengths and weaknesses of each approach is crucial to the interpretation of results and allocation of resources.

Author Contributions

Conceived and designed the experiments: MKS KAP KEM WCM MSC. Wrote the first draft of the manuscript: MKS. Contributed to the writing of the manuscript: MKS KAP KEM WCM MSC. ICMJE criteria for authorship read and met: MKS KAP KEM WCM MSC. Agree with manuscript results and conclusions: MKS KAP KEM WCM MSC.


  1. 1. Rothman KJ, Greenland S, Lash TL (2008) Modern epidemiology. Philadelphia: Lippincott Williams & Wilkins.
  2. 2. Piantadosi S, Byar DP, Green SB (1988) The ecological fallacy. Am J Epidemiol 127: 893–904.
  3. 3. Hallett TB, White PJ, Garnett GP (2007) Appropriate evaluation of HIV prevention interventions: from experiment to full-scale implementation. Sex Transm Infect 83: Suppl 1i55–i60.
  4. 4. Morgenstern H (1995) Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health 16: 61–81.
  5. 5. Greenland S, Robins J (1994) Invited commentary: ecologic studies—biases, misconceptions, and counterexamples. Am J Epidemiol 139: 747–760.
  6. 6. Schwartz S (1994) The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. Am J Public Health 84: 819–824.
  7. 7. Robinson W (1950) Ecological correlations and the behavior of individuals. Am Sociol Rev 15: 351–357.
  8. 8. Durrheim D, Ogunbanjo G (2004) Making sense of statistics for the family practitioner: “what are ecological studies?” S Afr Fam Pract 46: 48.
  9. 9. Moses S, Bradley JE, Nagelkerke NJ, Ronald AR, Ndinya-Achola JO, et al. (1990) Geographical patterns of male circumcision practices in Africa: association with HIV seroprevalence. Int J Epidemiol 19: 693–697.
  10. 10. Bongaarts J, Reining P, Way P, Conant F (1989) The relationship between male circumcision and HIV infection in African populations. AIDS 3: 373–377.
  11. 11. Caldwell JC, Caldwell P (1996) The African AIDS epidemic. Sci Am 274: 62-63, 66-68.
  12. 12. Weiss HA, Quigley MA, Hayes RJ (2000) Male circumcision and risk of HIV infection in sub-Saharan Africa: a systematic review and meta-analysis. AIDS 14: 2361–2370.
  13. 13. Siegfried N, Muller M, Deeks J, Volmink J, Egger M, et al. (2005) HIV and male circumcision—a systematic review with assessment of the quality of studies. Lancet Infect Dis 5: 165–173.
  14. 14. Bailey RC, Plummer FA, Moses S (2001) Male circumcision and HIV prevention: current knowledge and future research directions. Lancet Infect Dis 1: 223–231.
  15. 15. Gray RH, Kigozi G, Serwadda D, Makumbi F, Watya S, et al. (2007) Male circumcision for HIV prevention in men in Rakai, Uganda: a randomised trial. Lancet 369: 657–666.
  16. 16. Siegfried N, Muller M, Deeks JJ, Volmink J (2009) Male circumcision for prevention of heterosexual acquisition of HIV in men. Cochrane Database Syst Rev 2009: CD003362.
  17. 17. Henry K, Chinnock BJ, Quinn RP, Fletcher CV, de Miranda P, et al. (1988) Concurrent zidovudine levels in semen and serum determined by radioimmunoassay in patients with AIDS or AIDS-related complex. JAMA 259: 3023–3026.
  18. 18. Dumond JB, Yeh RF, Patterson KB, Corbett AH, Jung BH, et al. (2007) Antiretroviral drug exposure in the female genital tract: implications for oral pre- and post-exposure prophylaxis. AIDS 21: 1899–1907.
  19. 19. Nicol MR, Kashuba ADM (2010) Pharmacologic opportunities for HIV prevention. Clin Pharmacol Ther 88: 598–609.
  20. 20. Pereira AS, Kashuba AD, Fiscus SA, Hall JE, Tidwell RR, et al. (1999) Nucleoside analogues achieve high concentrations in seminal plasma: relationship between drug concentration and virus burden. J Infect Dis 180: 2039–2043.
  21. 21. Kashuba AD, Dyer JR, Kramer LM, Raasch RH, Eron JJ, et al. (1999) Antiretroviral-drug concentrations in semen: implications for sexual transmission of human immunodeficiency virus type 1. Antimicrob Agents Chemother 43: 1817–1826.
  22. 22. Musicco M, Lazzarin A, Nicolosi A, Gasparini M, Costigliola P, et al. (1994) Antiretroviral treatment of men infected with human immunodeficiency virus type 1 reduces the incidence of heterosexual transmission. Italian Study Group on HIV Heterosexual Transmission. Arch Intern Med 154: 1971–1976.
  23. 23. Siegfried N, van der Merwe L, Brocklehurst P, Sint TT (2011) Antiretrovirals for reducing the risk of mother-to-child transmission of HIV infection. Cochrane Database Syst Rev 2011: CD003510.
  24. 24. Cohen MS, McCauley M, Gamble TR (2012) HIV treatment as prevention and HPTN 052. Curr Opin HIV AIDS 7: 99–105.
  25. 25. Donnell D, Baeten JM, Kiarie J, Thomas KK, Stevens W, et al. (2010) Heterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. Lancet 375: 2092–2098.
  26. 26. Bunnell R, Ekwaru JP, Solberg P, Wamai N, Bikaako-Kajura W, et al. (2006) Changes in sexual behavior and risk of HIV transmission after antiretroviral therapy and prevention interventions in rural Uganda. AIDS 20: 85–92.
  27. 27. Sullivan P, Kayitenkore K, Chomba E, Karita E, Mwananyanda L, et al. (2010) Reduction of HIV transmission risk while prescribed anti-retroviral therapy (ARVT): Misclassification of ARVT status as a methodological issue [abstract]. AIDS Vaccine 2010; 28 Sept–1 Oct 2010; Atlanta, Georgia, US. AIDS Res Hum Retroviruses 26: A-1–A-184.
  28. 28. Del Romero J, Castilla J, Hernando V, Rodriguez C, Garcia S (2010) Combined antiretroviral treatment and heterosexual transmission of HIV-1: cross sectional and prospective cohort study. BMJ 340: c2205–c2205.
  29. 29. Wang L, Ge Z, Luo J, Shan D, Gao X, et al. (2010) HIV transmission risk among serodiscordant couples: a retrospective study of former plasma donors in Henan, China. J Acquir Immune Defic Syndr 55: 232–238.
  30. 30. Gay CL, Cohen MS (2008) Antiretrovirals to prevent HIV infection: pre- and postexposure prophylaxis. Curr Infect Dis Rep 10: 323–331.
  31. 31. Granich RM, Gilks CF, Dye C, De Cock KM, Williams BG (2009) Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet 373: 48–57.
  32. 32. Eaton JW, Johnson LF, Salomon JA, Bärnighausen T, Bendavid E, et al. (2012) HIV treatment as prevention: systematic comparison of mathematical models of the potential impact of antiretroviral therapy on HIV incidence in South Africa. PLoS Med 9: e1001245.
  33. 33. Montaner JS, Lima VD, Barrios R, Yip B, Wood E, et al. (2010) Association of highly active antiretroviral therapy coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study. Lancet 376: 532–539.
  34. 34. Das M, Chu PL, Santos G-M, Scheer S, Vittinghoff E, et al. (2010) Decreases in community viral load are accompanied by reductions in new HIV infections in San Francisco. PLoS ONE 5: e11068.
  35. 35. Fang C-T, Hsu H-M, Twu S-J, Chen M-Y, Chang Y-Y, et al. (2004) Decreased HIV transmission after a policy of providing free access to highly active antiretroviral therapy in Taiwan. J Infect Dis 190: 879–885.
  36. 36. Wood E, Kerr T, Marshall BDL, Li K, Zhang R, et al. (2009) Longitudinal community plasma HIV-1 RNA concentrations and incidence of HIV-1 among injecting drug users: prospective cohort study. BMJ 338: b1649.
  37. 37. Law MG, Woolley I, Templeton DJ, Roth N, Chuah J, et al. (2011) Trends in detectable viral load by calendar year in the Australian HIV observational database. J Int AIDS Soc 14: 10.
  38. 38. Porco TC, Martin JN, Page-Shafer KA, Cheng A, Charlebois E, et al. (2004) Decline in HIV infectivity following the introduction of highly active antiretroviral therapy. AIDS 18: 81–88.
  39. 39. Castel AD, Befus M, Willis S, Griffin A, West T, et al. (2011) Use of the community viral load as a population-based biomarker of HIV burden, Washington, DC. AIDS 26: 345–353.
  40. 40. Katz MH, Schwarcz SK, Kellogg TA, Klausner JD, Dilley JW, et al. (2002) Impact of highly active antiretroviral treatment on HIV seroincidence among men who have sex with men: San Francisco. Am J Public Health 92: 388–394.
  41. 41. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, et al. (2011) Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med 365: 493–505.
  42. 42. 6th IAS Conference on HIV Pathogenesis, Treatment and Prevention Conference Programme (2011) Fauci: looking to Rome, with optimism. Available: Accessed 26 December 2011.
  43. 43. del Rio C (2011) Treatment is prevention: the results of HPTN 052. Journal Watch. Available: Accessed 25 November 2011.
  44. 44. Squires KE, Hughes JM (2011) [Letter to the President of the United States on behalf of the Infectious Diseases Society of America and the HIV Medicine Association.] Available: Accessed 3 June 2012.
  45. 45. Maugh TH 2nd (2011 May 13) Anti-HIV drugs prove highly effective in preventing transmission of virus. Los Angeles Times. Available: Accessed 29 May 2012.
  46. 46. Panel on Antiretroviral Guidelines for Adults and Adolescents (2012) Guidelines for the use of antiretroviral agents in HIV-1 infected adults and adolescents. US Department of Health and Human Services. Available: Accessed 29 May 2012.
  47. 47. World Health Organization (2011) The strategic use of antiretrovirals for treatment and prevention of HIV. Geneva: World Health Organization. Available: Accessed 29 May 2012.
  48. 48. Epstein H, Morris M (2011) HPTN 052 and the future of HIV treatment and prevention. Lancet 378: 225.
  49. 49. Cates W (2011) HPTN 052 and the future of HIV treatment and prevention. Lancet 378: 224–225.
  50. 50. Anglemyer A, Rutherford GW, Egger M, Siegfried N (2011) Antiretroviral therapy for prevention of HIV transmission in HIV-discordant couples. Cochrane Database Syst Rev 2011: CD009153.
  51. 51. Schwartländer B, Stover J, Hallett T, Atun R, Avila C, et al. (2011) Towards an improved investment approach for an effective response to HIV/AIDS. Lancet 377: 2031–2041.
  52. 52. Geffen N (2011) When to start antiretroviral therapy in adults: the results of HPTN 052 move us closer to a “test-and-treat” policy. South Afr J HIV Med 12: 9–11. Available: Accessed 29 May 2012.
  53. 53. Montaner JSG (2011) Treatment as prevention—a double hat-trick. Lancet 378: 208–209.
  54. 54. (2011) HIV treatment as prevention—it works. Lancet 377: 1719.
  55. 55. Seale A, Lazarus JV, Grubb I, Fakoya A, Atun R (2011) HPTN 052 and the future of HIV treatment and prevention. Lancet 378: 226–225.
  56. 56. Law MG, Prestage G, Grulich A, Van de Ven P, Kippax S (2001) Modelling the effect of combination antiretroviral treatments on HIV incidence. AIDS 15: 1287–1294.
  57. 57. Blower S, Bodine E, Kahn J, McFarland W (2005) The antiretroviral rollout and drug-resistant HIV in Africa: insights from empirical data and theoretical models. AIDS 19: 1–14.
  58. 58. Lima VD, Johnston K, Hogg RS, Levy AR, Harrigan PR, et al. (2008) Expanded access to highly active antiretroviral therapy: a potentially powerful strategy to curb the growth of the HIV epidemic. J Infect Dis 198: 59–67.
  59. 59. Meyer-Rath G, Over M (2012) HIV treatment as prevention: modelling the cost of antiretroviral treatment—state of the art and future directions. PLoS Med 9: e1001247.
  60. 60. Cohen MS, Gay CL (2010) Treatment to prevent transmission of HIV-1. Clin Infect Dis 50: S85–S95.
  61. 61. Gardner EM, McLees MP, Steiner JF, del Rio C, Burman WJ (2011) The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis 52: 793–800.
  62. 62. Burns DN, Dieffenbach CW, Vermund SH (2010) Rethinking prevention of HIV type 1 infection. Clin Infect Dis 51: 725–731.
  63. 63. US Centers for Disease Control and Prevention (2011 Nov 29) Only one quarter of Americans with HIV have virus under control. Available: Accessed 29 May 2012.
  64. 64. Katz IT, Essien T, Marinda ET, Gray GE, Bangsberg DR, et al. (2011) Antiretroviral therapy refusal among newly diagnosed HIV-infected adults. AIDS 25: 2177–2181.
  65. 65. Quinn TC, Wawer MJ, Sewankambo N, Serwadda D, Li C, et al. (2000) Viral load and heterosexual transmission of human immunodeficiency virus type 1. Rakai Project Study Group. N Engl J Med 342: 921–929.
  66. 66. Baeten JM, Kahle E, Lingappa JR, Coombs RW, Delany-Moretlwe S, et al. (2011) Genital HIV-1 RNA predicts risk of heterosexual HIV-1 transmission. Sci Transl Med 3: 77ra29.
  67. 67. Powers KA, Ghani AC, Miller WC, Hoffman IF, Pettifor AE, et al. (2011) The role of acute and early HIV infection in the spread of HIV and implications for transmission prevention strategies in Lilongwe, Malawi: a modelling study. Lancet 378: 256–268.
  68. 68. Brenner BG, Roger M, Routy JP, Moisi D, Ntemgwa M, et al. (2007) High rates of forward transmission events after acute/early HIV-1 infection. J Infect Dis 195: 951–959.
  69. 69. Lopez-Quintero C, Shtarkshall R, Neumark YD (2005) Barriers to HIV-testing among Hispanics in the United States: analysis of the National Health Interview Survey, 2000. AIDS Patient Care STDS 19: 672–683.
  70. 70. Spielberg F, Branson BM, Goldbaum GM, Lockhart D, Kurth A, et al. (2003) Overcoming barriers to HIV testing: preferences for new strategies among clients of a needle exchange, a sexually transmitted disease clinic, and sex venues for men who have sex with men. J Acquir Immune Defic Syndr 32: 318–327.
  71. 71. Shelton JD, Cohen M, Barnhart M, Hallett T (2010) Is antiretroviral therapy modifying the HIV epidemic? Lancet 376: 1824–1825.
  72. 72. Wand H, Wilson D, Yan P, Gonnermann A, McDonald A, et al. (2009) Characterizing trends in HIV infection among men who have sex with men in Australia by birth cohorts: results from a modified back-projection method. J Int AIDS Soc 12: 19.
  73. 73. Novitsky V, Wang R, Bussmann H, Lockman S, Baum M, et al. (2010) HIV-1 subtype C-infected individuals maintaining high viral load as potential targets for the “test-and-treat” approach to reduce HIV transmission. PLoS ONE 5: e10148.
  74. 74. Wolbers M, Babiker A, Sabin C, Young J, Dorrucci M, et al. (2010) Pretreatment CD4 cell slope and progression to AIDS or death in HIV-infected patients initiating antiretroviral therapy—the CASCADE collaboration: a collaboration of 23 cohort studies. PLoS Med 7: e1000239.
  75. 75. Janssen RS, Satten GA, Stramer SL, Rawal BD, O'Brien TR, et al. (1998) New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. JAMA 280: 42–48.
  76. 76. Le Vu S, Pillonel J, Semaille C, Bernillon P, Le Strat Y, et al. (2008) Principles and uses of HIV incidence estimation from recent infection testing—a review. Euro Surveill 13: 18969.
  77. 77. Busch MP, Pilcher CD, Mastro TD, Kaldor J, Vercauteren G, et al. (2010) Beyond detuning: 10 years of progress and new challenges in the development and application of assays for HIV incidence estimation. AIDS 24: 2763–2771.
  78. 78. Joint United Nations Programme on HIV/AIDS (2010) Methods for estimating HIV incidence. Epi Alert, UNAIDS Quarterly Update on HIV Epidemiology. Available: Accessed 3 June 2012.
  79. 79. Shelton JD, Halperin DT, Wilson D (2006) Has global HIV incidence peaked? Lancet 367: 1120–1122.
  80. 80. Grulich AE, Wilson DP (2010) Is antiretroviral therapy modifying the HIV epidemic? Lancet 376: 1824.
  81. 81. Kerr T, Small W, Buchner C, Zhang R, Li K, et al. (2010) Syringe sharing and HIV incidence among injection drug users and increased access to sterile syringes. Am J Public Health 100: 1449–1453.
  82. 82. Stoltz J-A, Wood E, Small W, Li K, Tyndall M, et al. (2007) Changes in injecting practices associated with the use of a medically supervised safer injection facility. J Public Health (Oxf) 29: 35–39.
  83. 83. Wood E, Montaner JSG, Yip B, Tyndall MW, Schechter MT, et al. (2003) Adherence and plasma HIV RNA responses to highly active antiretroviral therapy among HIV-1 infected injection drug users. CMAJ 169: 656–661.
  84. 84. Murray JM, Prestage G, Grierson J, Middleton M, McDonald A (2011) Increasing HIV diagnoses in Australia among men who have sex with men correlated with the growing number not taking antiretroviral therapy. Sex Health 8: 304–310.
  85. 85. Truong HM, Kellogg T, Klausner JD, Katz MH, Dilley J, et al. (2006) Increases in sexually transmitted infections and sexual risk behavior without a concurrent increase in HIV incidence among men who have sex with men in San Francisco: a suggestion of HIV serosorting? Sex Transm Infect 82: 461–466.
  86. 86. British Columbia Centre for Disease Control (2011) HIV and sexually transmitted infections 2010. Available: Accessed 29 May 2012.
  87. 87. Guy RJ, McDonald AM, Bartlett MJ, Murray JC, Giele CM, et al. (2007) HIV diagnoses in Australia: diverging epidemics within a low-prevalence country. Med J Aust 187: 437–440.
  88. 88. Grulich AE, Kaldor JM (2008) Trends in HIV incidence in homosexual men in developed countries. Sex Health 5: 113–118.
  89. 89. Dukers NH, Spaargaren J, Geskus RB, Beijnen J, Coutinho RA, et al. (2002) HIV incidence on the increase among homosexual men attending an Amsterdam sexually transmitted disease clinic: using a novel approach for detecting recent infections. AIDS 16: F19–F24.
  90. 90. Dougan S, Elford J, Chadborn TR, Brown AE, Roy K, et al. (2006) Does the recent increase in HIV diagnoses among men who have sex with men in the UK reflect a rise in HIV incidence or increased uptake of HIV testing? Sex Transm Infect 83: 120–125.
  91. 91. Duncan KC, Reading C, Borwein AM, Murray MC, Palmer A, et al. (2011) HIV incidence and prevalence among aboriginal peoples in Canada. AIDS Behav 15: 214–227.
  92. 92. Spittal PM, Craib KJP, Wood E, Laliberté N, Li K, et al. (2002) Risk factors for elevated HIV incidence rates among female injection drug users in Vancouver. CMAJ 166: 894–899.
  93. 93. Miller CL, Wood E, Spittal PM, Li K, Frankish JC, et al. (2004) The future face of coinfection: prevalence and incidence of HIV and hepatitis C virus coinfection among young injection drug users. J Acquir Immune Defic Syndr 36: 743–749.
  94. 94. Bruneau J, Daniel M, Abrahamowicz M, Zang G, Lamothe F, et al. (2011) Trends in human immunodeficiency virus incidence and risk behavior among injection drug users in montreal, Canada: a 16-year longitudinal study. Am J Epidemiol 173: 1049–1058.
  95. 95. Granich R, Gupta S, Suthar AB, Smyth C, Hoos D, et al. (2011) Antiretroviral therapy in prevention of HIV and TB: update on current research efforts. Curr HIV Res 9: 446–469.
  96. 96. Boily MC, Buvé A, Baggaley RF (2010) HIV transmission in serodiscordant heterosexual couples. BMJ 340: c2449.
  97. 97. Fisher M, Pao D, Brown AE, Sudarshi D, Gill ON, et al. (2010) Determinants of HIV-1 transmission in men who have sex with men: a combined clinical, epidemiological and phylogenetic approach. AIDS 24: 1739–1747.
  98. 98. Boily MC, Mâsse B, Alsallaq R, Padian NS, Eaton JW, et al. (2012) HIV treatment as prevention: considerations in the design, conduct, and analysis of cluster randomized controlled trials of combination HIV prevention. PLoS Med 9: e1001250.
  99. 99. Imperial College London Faculty of Medicine (2011) About PopART. Available: Accessed 3 June 2012.
  100. 100. (2011 Jun 2) The end of AIDS? The Economist. Available: Accessed 29 May 2012.
  101. 101. Crepaz N, Lyles CM, Wolitski RJ, Passin WF, Rama SM, et al. (2006) Do prevention interventions reduce HIV risk behaviors among people living with HIV? A meta-analytic review of controlled trials. AIDS 20: 143–157.
  102. 102. Hasse B, Ledergerber B, Hirschel B, Vernazza P, Glass TR, et al. (2010) Frequency and determinants of unprotected sex among HIV-infected persons: the Swiss HIV Cohort Study. Clin Infect Dis 51: 1314–1322.
  103. 103. Cohen MS (2010) HIV treatment as prevention and “the Swiss statement”: in for a dime, in for a dollar? Clin Infect Dis 51: 1323–1324.
  104. 104. Coates TJ, Richter L, Caceres C (2008) Behavioral strategies to reduce HIV transmission: how to make them work better. Lancet 372: 669–684.