For changes in STD incidence to serve as a surrogate for changes in HIV incidence, STD risk reduction must be strongly associated with HIV infection risk reduction. The analyses presented here indicate that the strength of association between HIV risk reduction and STD risk reduction critically depends upon the infectivity but not the prevalence of the STD. Strong associations were noted for STDs with infectivities of approximately 0.05 or less, and moderate associations were noted with infectivities of less than approximately 0.2 (Figure 3). In contrast, STDs with infectivities greater than 0.2 produced weak associations.
In these analyses, the prevalence of STD infection among sex partners was not an especially important factor in determining the strength of association. For STDs with suitably low infectivities, the prevalence of infection was essentially immaterial. However, the prevalence of infection is likely to be more important for populations in which the number of partners is greater than it was for the heterosexual participants in Project Light and for homosexual populations, because of the substantially greater infectivity of receptive anal intercourse in comparison with receptive vaginal intercourse.
These analyses indicate that stronger HIV–STD associations will be obtained when the observation period for incident STD infections is kept relatively short. The base case results were with an assumed 3-month clinical follow-up, limiting the potential number of sex acts and partners. The sensitivity analyses show that doubling the number of partners strengthens the association between STD and HIV infection risk reduction, whereas doubling the number of acts diminishes the association by diluting the impact of condom use, which is greater for HIV than for other STDs because of the greater infectivity of the latter. 24 However, doubling both acts and partners produces results that are almost identical to those obtained in the base case. Although many people are likely to engage in twice as many acts of intercourse in 6 months as in 3, few individuals will have twice as many sex partners. Therefore, on balance, we would expect a 6-month follow-up schedule to produce weaker associations than a 3-month schedule.
The simple Bernoulli model used in this analysis is both a strength and a limitation. This transmission model is easily described and manipulated, requires few parameters, has high biologic plausibility, and has been empirically verified in an HIV seroconversion study in Africa. 53,54 However, this simple model omits important STD transmission dynamics, including nonrandom mixing patterns, selective condom use (e.g., with new partners but not with steady partners), sexual contact network characteristics, and concurrent partnerships. 8,55–63 These complexities could introduce unknown errors into the analyses. For example, this analysis might underestimate the utility of a marker STD that “inhabits” the same networks as HIV (this is discussed further below). In addition, there is evidence that the per-act Bernoulli model may underestimate or overestimate risk in some situations. 64,65 These concerns are ameliorated somewhat by the use of the model in the present context, in which changes in HIV risk are compared with changes in STD risk.
The HIV infectivity values used in the current study, which were estimated from transmission rates for long-term HIV-serodiscordant couples, 29–31 may fail to capture the brief period of very high viral load—and presumably highly elevated infectiousness—that occurs soon after initial infection and before the development of an effective immune response. Predictions from mathematical models suggest that a substantial proportion of all HIV transmission occurs during this brief period of primary infection, 66,67 which lasts a few weeks to several months. During this time, HIV acts more like a high-infectivity STD than a low-infectivity STD. Consequently, the utility of highly infectious STDs as markers for HIV will be greater in situations in which there is a nonnegligible probability of encountering a partner who was recently infected with HIV—for example, in populations with high partner-change rates. A related issue concerns the facilitative effect on HIV transmission of infection with non-HIV STDs. 68–70 If the same people who are at risk for HIV infection are also at risk for other STDs, which is a fundamental assumption underlying the use of non-HIV STDs as surrogate markers of HIV, then the “effective” infectivity of HIV is likely to be increased in at-risk populations, because of STD in one partner or the other. However, it is not clear how large this “cofactor effect” is likely to be or to what proportion of the population it applies.
Additional limitations of this study include the use of self-reported behavior in the modeling exercise and uncertainty in the HIV and STD infectivity estimates. 29 Notably, the Project Light intervention data were used here to provide a concrete illustration of the relationship between HIV infection risk reduction and STD risk reduction; therefore, slight distortions of the data due to self-reporting bias are of minimal consequence. Although quantitatively the results of the analysis are sensitive to the infectivity estimates, qualitatively the main finding appears robust: namely, that the best marker STD is one with an infectivity near that of HIV. This result was anticipated by Aral and Peterman, who proposed that “for trends in one STD to serve as a good biomarker for another STD, the two STDs need to be identical or very similar” in terms of epidemiologic and other characteristics. 7
The results of this analysis can be used to guide the selection of STD markers for use in HIV prevention trials. However, they also suggest the need for caution in interpreting the results of trials employing surrogate markers. Transmission of HIV and other STDs is differentially sensitive to such factors as partnership acquisition rates and frequency of condom use. Behavioral changes that affect the incidence of a marker STD might not be the same as those that would reduce the incidence of HIV, and vice-versa. Therefore, the failure to detect a measurable reduction in STD incidence does not necessarily imply that an HIV prevention trial has failed to achieve its stated objective of reducing HIV infection risk. 1 Conversely, interventions that successfully decrease STD incidence could have both direct and indirect HIV prevention benefits: first by reducing participants’ behavioral risk and second by diminishing the facilitative effect of STDs on HIV transmission. 71,72
There also are practical obstacles to the use of STDs as surrogate markers of HIV infection risk. This study assumed perfect diagnosis of incident STDs. Existing diagnostic tests for STDs have imperfect sensitivity and specificity, 11 which would impair the performance of the STD as a surrogate for HIV infection risk. Some STDs are readily curable, so between follow-up visits for a study, a participant might become infected with an STD, experience symptoms, receive treatment, and be cured. If the infection was not detected on the subsequent visit, this would be equivalent to a false-negative test result, which would reduce the performance of the marker STD. Another factor that could influence the performance of an STD as a surrogate for HIV infection risk is the distribution of the STD in the population, compared with the distribution of HIV. If an STD was concentrated in the same subgroups of the population as HIV, its utility as a marker for HIV infection risk would be enhanced, whereas if the STD and HIV had different distributions, the performance of the STD as a surrogate would be diminished. For some STDs, acquired immunity could also affect their performance as markers for HIV.
The current analysis focused on HIV and STD acquisition by individuals rather than on the spread of these pathogens throughout a population. As noted above, dissemination of an STD within a population is affected not only by the behavioral characteristics of the individuals who make up that population but also by such factors as the extent of mixing between different risk strata and nonuniform distribution of infection. Population-level models that take these complexities into account are needed. However, because STDs may be differently affected by various network and population-level factors, caution will be required when interpreting the results of these analyses, just as caution is needed here.
This analysis is a first step toward the goal of identifying a suitable marker STD. Further research is needed to better quantify the infectivity of HIV and other STDs and to develop more sophisticated transmission models to examine the complex relationship between HIV and STD risk reduction.
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