When measured in terms of total numbers of cases and deaths averted, the benefits obtained by all four alternative TB control options rose considerably with increasing HIV prevalence. The only exception occurred at the extreme HIV prevalence of 35% when only reducing treatment duration, in which the number of new cases slightly increased. This result was evident when considering the whole population (Fig. 2a), but was due to an increase in only the HIV-infected subpopulation (Fig. 2c). A reduction in treatment duration always decreased the number of TB cases in the HIV-uninfected subpopulation (Fig. 2b). The absolute number of deaths avoided increased with HIV prevalence for all control scenarios (Fig. 3).
Relative benefits, measured as the proportional reductions in numbers of TB cases and deaths, exhibited markedly different patterns from those of the absolute benefits. In the scenario in which only treatment time is reduced, relative reductions in incidence and mortality decreased slightly as HIV prevalence increased (Figs 4a and 5a). As for the absolute benefits, this effect culminated at the 35% HIV prevalence, with a slightly greater number of cases in the whole population (Fig. 4a) and in the people infected with HIV (Fig. 4c). For the people not infected with HIV, reducing treatment duration always provided a net increase in TB cases and deaths avoided (Figs 4b and 5b). The reduction in treatment duration combined with increased detection and/or cure provided a different pattern regarding the relative reductions in cases and deaths: benefits initially increased until an HIV prevalence of approximately 15%, after which they gradually decreased, both for the whole population and when partitioning the population into HIV uninfected and infected (Figs 4 and 5). On a relative scale, the improved TB control strategies provided greater benefits in terms of deaths avoided than in cases avoided.
In terms of absolute cases and deaths avoided, benefits were proportionally greater among the people infected with HIV than among those not infected with HIV for the same HIV prevalence. This pattern was most noticeable at higher HIV prevalence levels (Figs 2 and 3). Again, the only exception arose for the 35% HIV prevalence, because the model predicted a greater number of HIV-infected persons would become infected with TB when only reduced treatment duration is implemented (Fig. 2c). In contrast, the relative benefits for all alternative TB control programs considered were consistently smaller for people infected with HIV than those not infected with HIV at the same HIV prevalence (Figs 4 and 5).
When comparing the benefits obtained from combining reduced treatment duration with either increased detection or cure, results varied depending on HIV prevalence and on whether we considered the people not infected with HIV together with, or separately from, those infected with HIV. The qualitative impact of HIV on TB control already became evident when investigating avoided cases under the different scenarios in the whole population Figs 2a and 4a: for an HIV prevalence less than 15%, increasing detection provided slightly greater benefits, but for higher HIV levels, increasing cure became progressively more effective. Detection always avoided a greater number of deaths when considering the whole population, but the relative gains were minimal at 35% HIV prevalence (Figs 3a and 5a).
Additional insight was gained when comparing benefits obtained from increased detection in comparison with increased cure according to HIV status. In the whole population, improved cure rate out-performed improved detection in terms of reducing TB incidence for HIV prevalences of 15% or higher. In the HIV-uninfected people, this shift was observed only at 35% HIV prevalence (Figs 2b and 4b), whereas among the HIV infected people, it held at all HIV levels (Figs 2c and 4c). A greater number of deaths were avoided by decreasing treatment duration combined with increased detection, as compared with increased cure, for all HIV levels in the HIV-uninfected people (Figs 3b and 5b) and HIV-infected people for HIV prevalences less than 35% (Figs 3c and 5c). At the extreme HIV prevalence of 35%, the deaths avoided in the HIV-infected people by the two TB control policies, although comparable, begin to favor an increased cure rate.
Our investigation into the impact of HIV on TB control shows that, as expected, the number of new TB cases and deaths increases substantially as HIV prevalence increases. More effective measures, therefore, have the potential to provide correspondingly greater absolute benefits at higher HIV levels [2,3,30,33]. The relative benefits of improved TB control, however, either become progressively smaller as HIV prevalence increases when only implementing reduced treatment duration, or increase with rising HIV prevalence up to 15%, then decrease at higher prevalence levels for the three remaining scenarios (reduced treatment time combined with increased detection, increased cure, or all concurrently). We do stress that, because of the great uncertainties regarding TB–HIV codynamics [4,14,39,40], we focused our analysis on understanding interactions and potential qualitative outcomes rather than obtaining precise numerical predictions. In particular, we consider cure rates close to those officially reported for Kenya (supporting document ); substantially lower population-level cure rates may influence the response of the TB epidemic to the interventions considered, highlighting the critical need for thorough data collection and evaluation on appropriate managerial scales.
TB control measures are less effective among the HIV-infected TB patients. Although in absolute terms benefits are proportionally greater among the HIV-infected patients for a given HIV prevalence (a pattern most noticeable at higher levels, with the exception of decreasing treatment duration at 35% HIV prevalence), relative benefits are always smaller. We therefore observed substantially different epidemiological responses to TB control in the HIV-uninfected and HIV-infected patients, even when assuming the two groups mixed randomly (and hence TB transmission was not biased by contact patterns).
Extremely high HIV prevalence appears to reverse the positive epidemiological impact of reducing TB treatment duration: the absolute number of new TB cases and deaths always decreases when changing from a 6-month to a 2-month regimen, except at 35% HIV prevalence when the number of new TB cases increases over the 25-year period considered. This pattern occurs in the HIV-infected patients but not in the HIV-uninfected patients. Moreover, because of the overwhelming proportion of HIV-infected patients, it translates into an increased number of new cases when considering the whole population. This outcome is due to immunocompromised persons being very susceptible to TB infection, reinfection, progression, and relapse following treatment completion. In effect, extended TB treatment can act as a prophylactic and transmission blocker for this population, such that in our model there is an epidemiological benefit to longer treatment duration when HIV prevalence reaches these levels. In this regard, TB relapse rates in TB–HIV-coinfected patients may be reduced when TB treatment is extended beyond the routinely recommended treatment duration with current drugs . In any case, we need to bear in mind that our evaluation does not account for the potentially toxic effects of extended chemotherapy, negative interactions with HIV treatments, or the additional burden on public health systems of longer TB treatment regimes .
Considering benefits both in absolute and relative terms provides complementary information regarding the potential impact of different TB interventions. Considering first the benefits among HIV-uninfected patients, we find that absolute benefits rise with increasing HIV prevalence, because of the greater TB burden present in the population at higher HIV levels; in contrast, relative benefits rise as HIV prevalence increases to 15% but then decline at higher HIV prevalence levels because of diminishing per capita efficiency of TB control measures. Comparisons of absolute and relative benefits in the HIV-infected patients yield further insights: the absolute number of cases avoided when increasing cure versus detection is substantially greater at higher HIV levels among the HIV-infected patients; conversely, increasing cure provides similar relative benefits in terms of cases avoided in comparison to increasing detection at all HIV levels. Therefore, although knowledge of absolute measures is essential, relative numbers can provide further critical insights into relevance to cost-effective analyses , particularly when differences in absolute terms are small.
The magnitude of the impact of HIV on TB control will vary across geographic areas and social groups, but will be greatest in settings of high HIV prevalence, including countries (sub-Saharan Africa ), regions or communities within countries (Carletonville South African gold mining community in South Africa [42,43]), and high-risk groups (the homeless, incarcerated, and drug users in industrialized nations [8,10,11], military personnel ). Our study further indicates that if HIV treatment is effective in making the treated HIV-infected patients behave epidemiologically as if they were not infected with HIV, then such treatment can provide valuable secondary benefits in TB control both on an absolute and on a per capita basis. However, if the mortality of treated HIV-infected persons is reduced by antiretrovirals (ARVs) but their susceptibility to disease remains higher than that of the HIV-uninfected persons, the overall TB burden in the population will likely increase [4,6,45–48]. Further detailed investigations are required to fully evaluate the public health impact of shortening TB treatment duration, in particular regarding the rollout of antiretroviral drugs, and the generation and spread of multidrug-resistant and extensively drug-resistant tuberculosis [6,49–52]. Moreover, results also indicate greater benefits may be obtained by interventions targeted at communities according to their TB and HIV risk [11,53–56]. For instance, we observed that the benefits of increasing detection versus cure varied according to HIV levels, depending on how HIV interacts with the intervention. As the proportion of HIV-infected persons increased, increasing cure gradually became more beneficial compared with increasing detection; this pattern was most apparent in the HIV-infected fraction. A targeted approach may be particularly fruitful for those groups with extremely high HIV prevalence compared with the general population, as evidenced by the exceptional results obtained at the 35% HIV prevalence level.
Our study shows how reducing treatment duration, alone or in combination with other TB control strategies, can provide enormous benefits at high HIV prevalence, although both absolute and relative benefits vary substantially according to the proportion of the population that is HIV infected. Challenges arise not only because in absolute terms the total amount of infected people and deaths increases dramatically with increasing HIV prevalence [2,3,30,33] but also because the relative efficacy of TB control measures display a nonlinear pattern whereby they become less effective on a per capita basis at HIV prevalences higher than 15%. Therefore, a qualitative shift in TB dynamics occurs as HIV levels increase – the population's response to TB control becomes more in consonance with the response of the HIV-infected fraction ( and references therein) up to the point where the benefits of reducing treatment duration may even be reversed at extreme HIV levels. Moreover, the benefits of increasing detection versus cure decrease as HIV prevalence increases. Our results indicate that TB control policies need to consider HIV levels because the efficacy of different interventions varies substantially with HIV prevalence [58,59].
This research was supported by NIH-NIDA (M.S.S. and W.M.G.), the Global Alliance for TB Drug Development (W.M.G., J.A.S.), the James S. McDonnell Foundation 21st Century Science Initiative (W.M.G.), SACEMA, the South African Center for Epidemiological Modeling and Analysis (W.M.G.), and the Center for Infectious Disease Dynamics, Pennsylvania State University (J.L.S.). We thank three anonymous reviewers for their insightful suggestions, together with David Bangsberg, Jason Barbour, Frank Cobelens, Judy Hahn, Philip Hopewell, Gwynne Oosterbaan, Perry de Valpine, Suzanne Verver, and members of Wayne Getz's and Phil Hopewell's laboratories for their help and orientation. We would also like to thank the Kenyan public health authorities and the persons participating in the studies for their invaluable data collection.
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