The use of a single, “one-size-fits-all” value for the heterosexual infectivity of HIV-1 obscures important differences associated with transmission co-factors. Perhaps more importantly, the particular value of ~0·001 (i.e., 1 infection per 1,000 contacts between infected and uninfected individuals) that is commonly used appears to represent a lower bound. As such, it dramatically underestimates the infectivity of HIV-1 in many heterosexual contexts. Of the 11 overall estimates near or below 0·001 identified in this study, 9 were produced in analyses of stable couples with low prevalences of high-risk co-factors. In other contexts – particularly if the susceptible partner has an STI or is uncircumcised, if contact is penile-anal, or if the index case is in early- or late-stage infection – heterosexual infectivity can exceed 0·1 (1 transmission per 10 contacts) for penile-vaginal contact or even 0·3 (1 transmission per 3 contacts) for penile-anal contact (18
). Claims in both the popular media (62
) and the peer-reviewed literature (5
) that HIV is extremely difficult to transmit heterosexually are dangerous in any context where the possibility of HIV exposure exists.
Observation of co-factor effects at the level of cumulative
incidence has been critical to the development of interventions designed to reduce HIV incidence. Understanding co-factor effects at the per-contact
level is also important, as HIV exposure and transmission occur during discrete contacts between infected and uninfected individuals, and many epidemic models rely on parameter inputs at the per-contact level. Our results, which relate to transmission at the per-contact level, are consistent with numerous studies of cumulative HIV incidence showing that STIs, decreased age, and lack of circumcision increase susceptibility; that increased age and both early- and late-stage index infection amplify transmissibility (13
); and that heterosexual transmission is more efficient through penile-anal contact than through penile-vaginal contact (11
). Additionally, our finding that penile-anal transmission is more efficient than penile-vaginal is consistent with infectivity studies conducted among men who have sex with men (64
). Studies of cumulative HIV incidence have provided mixed evidence in support of differences between male-to-female and female-to-male transmission (66
); our results suggest that there is no meaningful difference by direction of transmission at the per-contact level.
The sharply increased infectivity reported among female sex workers' clients in an Asian setting may reflect differences by disease stage, as the infectivity study (33
) conducted in Asia took place at the start of the epidemic when a large proportion of index cases were in early stages of infection (33
). The elevated infectivity in the Asian study also may reflect unmeasured STI co-factor effects, as a large proportion of sex worker index cases were infected with STIs during the study period (67
). We also note that because the study was conducted among commercial sex workers' clients (rather than specifically identifiable index cases), HIV prevalence estimates among the commercial sex worker population were required to estimate the probability of HIV exposure in infectivity calculations. If prevalence were underestimated in these calculations, the infectivity would have been biased upward. The higher infectivity in this setting may also reflect differences by subtype or unmeasured or poorly measured co-factors.
The reduced infectivity observed among circumcised male susceptibles is consistent with results of randomized trials of circumcision for HIV prevention (16
). The observed increases in infectivity associated with STI and GUD are less readily compared to randomized trials of bacterial STI treatment (60
) and HSV-2 suppression (73
). While one bacterial STI treatment trial achieved a 40% reduction in HIV incidence through syndromic STI management (70
), other trials of bacterial STI treatment interventions have failed to show effects on HIV incidence (60
). Various explanations have been offered for the lack of bacterial STI treatment effects, including insufficient power (71
), receipt by the control group of ethically mandated STI services (60
), and high prevalences of HSV-2 in both intervention and control communities (60
). Similarly, recent trials of acyclovir among HSV-2 seropositive individuals did not find an effect of HSV-2 suppression on HIV acquisition (73
), possibly due to high proportions of GUD unrelated to HSV-2 (75
), inadequate ulcer suppression (73
), or insufficient compliance with the acyclovir regimen (74
). Because the “STI” and “GUD” groups in our analyses were not restricted specifically to those with the same treatable
STI targeted in the intervention trials, the results of bacterial STI and HSV-2 treatment trials are not directly comparable to the ”STI” and “GUD” results shown here.
The observed differences in infectivity according to index disease stage deserve particular attention. The estimates produced for “mid-stage” infection were very homogeneous, and the pooled estimate for this stage (0·7 transmissions per 1000 acts) is approximately equal to the commonly cited value of 1 transmission per 1000 acts. The probability of transmission is likely much higher outside of this period, especially during acute (pre-seroconversion) HIV, when viral loads are sharply elevated, acquired immunity in acutely infected individuals' partners is absent, and a substantial portion of transmission events occur (76
). No infectivity study has directly measured transmission during the brief acute phase. The “early” infectivity estimate of Leynaert et al (37
) was based on a retrospective exposure period with crudely estimated dates of index infection, and the estimate of Wawer et al (50
) corresponded to the period up through 5 months after seroconversion. As others have noted (50
), couples in whom transmission occurs during the brief acute phase cannot be selected for “discordant couples” studies, which follow susceptible partners only after the index partner has developed HIV antibodies.
Most infectivity studies have not explicitly accounted for all important cofactors, producing “population-average” estimates that do not capture variations in infectivity. Additionally, most study designs have been subject to at least one potential bias in determining the number of potentially infectious exposures experienced by susceptible individuals. Estimates from both cross-sectional and longitudinal studies of independent individuals (rather than partners of known HIV-infected index cases) have relied on HIV prevalence estimates to calculate the probability of exposure during a sexual contact. Overestimates of the prevalence will have underestimated infectivity; underestimated prevalence will have had the opposite effect. Cross-sectional analyses have relied on reported sexual contacts that occurred well before the cross-section, and in most of these studies, the start of the exposure period was based on a very crude estimate of the index case's infection date. In several studies, the earliest possible index infection date was used, likely resulting in the inclusion of sex acts that occurred prior to the true index infection date. Inclusion of these non-exposures in infectivity calculations will have resulted in deflated estimates.
A number of biases common to all study designs also could have affected infectivity estimates. First, unadjusted inclusion of condom-protected acts in the count of potentially infectious exposures could bias estimates downward. Additionally, infectivity estimates could be biased upward if any transmission occurs through “external” (blood or sexual) contacts that are not included in the count of potentially infectious contacts. All but one study (50
) assumed (without molecular analysis) that transmission events occurred via exposure to index partners, but molecular analysis in other studies has revealed that 10% or more of apparent transmission events within couples result from exposure to an additional sexual partner. Self-report error in the number of sexual contacts could also bias estimates; this bias could be in either direction. Additionally, no studies included separate counts of oral-genital contacts. Because transmission via oral-genital contact is believed to be extremely inefficient (78
), though, the failure to account for oral-genital contact in estimating penile-anal and penile-vaginal infectivity is unlikely to have resulted in substantial bias. Finally, because all studies have used antibody tests to detect transmission to susceptible individuals, those with acute infections at the time of testing would have been misclassified as uninfected, resulting in underestimated infectivity.
We also note that there were insufficient data to conduct even univariable stratified and meta-regression analyses of several co-factors, such as viral load, viral subtype, and ARV use; however, we have some information for assessing these variables. In the single population for which viral load was analyzed (48
), infectivity increased from 0.1 transmission per thousand acts to 2·3 transmissions per thousand acts as blood viral load increased from <1700 copies/ml to >38500 copies/ml. In this same population, infectivity was similar across the subtypes (A, D, and V3) analyzed. The increased infectivity values associated with early- and late-stage infection and with the Thai population at the beginning of the epidemic indirectly suggest amplifying effects of high viral load. All studies were conducted prior to the advent or widespread use of ARV, so the estimates reported here correspond to infectivity in the absence of therapy.
In some co-factor and study method strata, the difference between the estimate obtained from stratified meta-analysis and the estimate produced with meta-regression is quite pronounced. Each estimate obtained from stratified meta-analysis made use only of the data in a particular subgroup, whereas each estimate obtained from meta-regression also made use of the data from the other stratum (or strata), and thus involved modeling or smoothing. The stratified estimates are less precise and less model-dependent; the meta-regression estimates are more precise and more model-dependent. The difference between the two methods' estimates tends to be greater when the data are relatively sparse, which can occur from small sample sizes within studies, from small numbers of studies within strata, or both. The potential for differences is accentuated by the use of random-effects meta-regression, which involves estimation of an among-studies variance. In the meta-regression analyses, this variance is estimated from all studies in either stratum; in the stratified analyses, it is estimated separately within each stratum.
We have focused on one key parameter in HIV transmission dynamics: the conditional probability of HIV transmission given exposure during a single contact. The overall probability of HIV transmission also depends upon the probability of exposure to HIV, which is determined by such factors as HIV prevalence, partner change rate, sexual network position, and contact with partners who are involved in concurrent relationships. These factors, which are outside the scope of this analysis, represent additional, important determinants of HIV transmission.
HIV infectivity studies are extremely difficult to conduct for both logistical and ethical reasons. As a result, information about infectivity is limited, in terms of both the number of existing estimates and the quality of those estimates. Because of the small number of infectivity studies, the shortage of estimates stratified by co-factors, the impossibility of adequately controlling for confounding with multivariable analyses, and the methodological issues of existing studies, the true independent effects of co-factors and study features may differ substantially from the estimates that we obtained. Given these limitations of the existing data, we caution against interpreting any quantitative value reported here as “the” infectivity for a particular study design or co-factor stratum, just as we have cautioned against using a value of 0·001 as “the” overall heterosexual infectivity of HIV-1. Caution is especially warranted for estimates associated with particularly sparse co-factor strata (e.g., estimates stratified by STI status), as well as pooled estimates within strata where heterogeneity exists. While many of the summary infectivity estimates that we report are subject to considerable uncertainty due to systematic and random error, we note that the infectivity differences estimated by our meta-regression analyses (which account for across-study variance) represent advances in understanding the variability of HIV infectivity. Further explanations for the heterogeneity of infectivity estimates may yet be discerned.
In addition to study limitations resulting from shortcomings of the literature, it is possible that we inadvertently excluded some existing infectivity estimates or misclassified some variables, despite a thorough literature search and careful data extraction process. Furthermore, for some infectivity estimates, we were able to obtain only approximate standard errors.
Despite these limitations, our study represents a comprehensive summary and systematic analysis of the current literature on the heterosexual infectivity of HIV-1, a fundamental determinant of the epidemic's spread. Our findings suggest that in many contexts – particularly in the absence of male circumcision or in the presence of STIs, anal sex, or early or late infection – the heterosexual infectivity of HIV-1 may exceed the commonly cited value of 0·001 by more than an order of magnitude. The vast extent of the current epidemic is more easily understood in the context of these biological co-factors, which create a more favorable environment for HIV transmission. In addition to documenting the heterogeneity of infectivity estimates and providing some possible explanations for this heterogeneity, our review describes the limitations of the existing literature, highlights the need for further infectivity research, and reinforces the importance of including co-factor effects in HIV epidemic models, policy considerations, and prevention messages. Future infectivity studies should carefully count infectious exposures and rigorously account for transmission cofactors. Improved infectivity estimates – especially more detailed estimates that quantify the amplifying effects of biological co-factors – will help us to grasp the magnitude of the HIV epidemic, accurately communicate the level of risk involved in heterosexual sex, and identify the optimal intervention strategies for slowing the epidemic's spread.