Epidemiologic context and parameters.

<p>Epidemiologic context and parameters.</p

Impact of different prevention policies on the overall HIV incidence in three epidemic types.

<p>A generic intervention that reduces HIV transmission by 80% per sex-act is used. Policy 1 (red) prioritizes the low-activity group based on the largest burden of new HIV infections estimated from the generic Modes of Transmission model (gMOT) in 2012. gMOT-guided Policy 1 redistributes finite resources from condom-use coverage in high-risk groups to a generic intervention focus on low-activity individuals. gMOT-guided Policy 2 (blue) prioritizes the low-activity group but resources are added to existing interventions (baseline condom use in high-risk partnerships is sustained). Policy 3 (green) is guided by an increasing long-term population attributable fraction over time t (PAF_t), and therefore prioritizes epidemic drivers to receive the generic intervention. Policy 4 is not informed by an epidemic appraisal, and randomly allocates additional resources across subgroups. Each policy is implemented in 2012, is immediately scaled-up, and sustained over 30 years of follow-up. The person-years of the generic intervention are fixed throughout the follow-up period, and equivalent within each simulated synthetic epidemic type.</p

Circumcision Status and Incident HSV-2 Infection, Genital Ulcer Disease, and HIV Infection

Objective: We assessed the protective effect of medical male circumcision (MMC) against HIV, HSV-2, and GUD incidence. Design: 2784 men aged 18–24 years living in Kisumu, Kenya were randomly assigned to circumcision (n=1391) or delayed circumcision (n=1393), and assessed by HIV and HSV-2 testing and medical examinations during follow-ups at 1, 3, 6, 12, 18, and 24 months. Methods: Cox regression estimated the risk ratio (RR) of each outcome (incident HIV, GUD, HSV-2) for circumcision status and multivariable models estimated HIV risk associated with HSV-2, GUD and circumcision status as time-varying covariates. Results: HIV incidence was 1.42 per 100 person-years. Circumcision was 62% protective against HIV [RR=0.38; 95% CI: 0.22 - 0.67], and did not change when controlling for HSV-2 and GUD [RR=0.39; 95% CI: 0.23 - 0.69]. GUD incidence was halved among circumcised men [RR=0.52, 95% CI: 0.37 - 0.73]. HSV-2 incidence did not differ by circumcision status [RR=0.94; 95% CI: 0.70 - 1.25]. In the multivariable model, HIV seroconversions were tripled [RR=3.44; 95% CI: 1.52 - 7.80] among men with incident HSV-2 and 7 times greater [RR=6.98; 95% CI: 3.50 - 13.9] for men with GUD. Conclusion: Contrary to findings from the South African and Ugandan trials, the protective effect of MMC against HIV was independent of GUD and HSV-2 and MMC had no effect on HSV-2 incidence. Determining the causes of GUD is necessary to reduce associated HIV risk, and to understand how circumcision confers protection against GUD and HIV

Circumcision Status and Incident HSV-2 Infection, Genital Ulcer Disease, and HIV Infection

Objective: We assessed the protective effect of medical male circumcision (MMC) against HIV, HSV-2, and GUD incidence. Design: 2784 men aged 18–24 years living in Kisumu, Kenya were randomly assigned to circumcision (n=1391) or delayed circumcision (n=1393), and assessed by HIV and HSV-2 testing and medical examinations during follow-ups at 1, 3, 6, 12, 18, and 24 months. Methods: Cox regression estimated the risk ratio (RR) of each outcome (incident HIV, GUD, HSV-2) for circumcision status and multivariable models estimated HIV risk associated with HSV-2, GUD and circumcision status as time-varying covariates. Results: HIV incidence was 1.42 per 100 person-years. Circumcision was 62% protective against HIV [RR=0.38; 95% CI: 0.22 - 0.67], and did not change when controlling for HSV-2 and GUD [RR=0.39; 95% CI: 0.23 - 0.69]. GUD incidence was halved among circumcised men [RR=0.52, 95% CI: 0.37 - 0.73]. HSV-2 incidence did not differ by circumcision status [RR=0.94; 95% CI: 0.70 - 1.25]. In the multivariable model, HIV seroconversions were tripled [RR=3.44; 95% CI: 1.52 - 7.80] among men with incident HSV-2 and 7 times greater [RR=6.98; 95% CI: 3.50 - 13.9] for men with GUD. Conclusion: Contrary to findings from the South African and Ugandan trials, the protective effect of MMC against HIV was independent of GUD and HSV-2 and MMC had no effect on HSV-2 incidence. Determining the causes of GUD is necessary to reduce associated HIV risk, and to understand how circumcision confers protection against GUD and HIV

Sensitivity of the MOT metric to biased input parameters (concentrated epidemic).

<p>The range in the predicted fraction of new HIV infections acquired by the low-activity group (A), clients (B), and female sex workers (FSWs, C) are depicted for the five most influential parameters from the complex Modes of Transmission model (cMOT) using biased inputs. Also shown are the benchmark MOT metric and the unbiased cMOT metric. Across the parameter range examined here, the low-activity group incurred the largest burden of new infections, and the unbiased MOT metric did not identify the epidemic driver (no red regions). Pop. (population); Prev. (prevalence).</p

MOT metrics by subgroups and their contribution to overall HIV transmission (concentrated epidemic).

<p>The predicted fraction of new infections acquired by female sex workers (A, FSWs) and the low-activity group (B), as obtained from the complex Modes of Transmission model (cMOT acquired) and the generic Modes of Transmission model (gMOT acquired), and the benchmark MOT (acquired), are shown in grey. The fraction of HIV infections transmitted from FSWs and the low-activity group is shown in green (cMOT transmitted). The cumulative population attributable fraction (PAF_t) over different time horizons measured from the year of the MOT (2012) for the epidemic driver (FSWs) and low-activity groups are shown in black.</p

Sexual structure of the dynamic model, complex MOT model, and generic MOT model.

<p>(A) In the dynamical model, the population is divided into four different activity classes based on the frequency of yearly partner change (FSWs/clients, two multiple partnership classes, and a low-activity class). Four partnership types are possible: commercial (regular or occasional), casual, or main. In the dynamical model, males and females who engage in higher-risk activity (commercial or casual sex) cease higher-risk activity and enter into the low-activity population reflecting a turn-over in each of the higher-risk activity groups (solid black lines). Multiple concurrent exposures are possible, and subpopulations are linked via bridging groups (individuals with multiple exposures). The partnerships are therefore shown with double-headed arrows to represent bridging between groups. (B) The complex Modes of Transmission model (cMOT) divides the population into the same activity classes as the dynamical model. The cMOT allows for multiple exposures to HIV (i.e. multiple types of partnerships). For visibility, only partnerships where infections are acquired by males are shown. Infections acquired by males and by females are counted separately, and partnerships are therefore shown with single-arrows to represent the lack of bridging between groups. Secondary infections and movement between risk-groups are not possible. (C) The generic Modes of Transmission model (gMOT) uses a simplified sexual structure, and only partnerships where infections are acquired by males are shown. In the gMOT, only one type of HIV exposure or partnership is possible, and subgroups are amalgamated in keeping with the generic MOT template <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101690#pone.0101690-Gouws2" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101690#pone.0101690-UNAIDS2" target="_blank">[9]</a>. Infections acquired by males and by females are counted separately. As with the cMOT, single-headed arrows are used to represent different partnerships without bridging between groups. Hence, indirect transmission via bridging populations and secondary infections, and movement between risk-groups are not possible. MOT (modes of transmission); FSWs (female sex workers).</p

Validation of the Modes of Transmission Model as a Tool to Prioritize HIV Prevention Targets: A Comparative Modelling Analysis

<div><p>Background</p><p>The static Modes of Transmission (MOT) model predicts the annual fraction of new HIV infections acquired across subgroups (MOT metric), and is used to focus HIV prevention. Using synthetic epidemics via a dynamical model, we assessed the validity of the MOT metric for identifying epidemic drivers (behaviours or subgroups that are sufficient and necessary for HIV to establish and persist), and the potential consequence of MOT-guided policies.</p><p>Methods and Findings</p><p>To generate benchmark MOT metrics for comparison, we simulated three synthetic epidemics (concentrated, mixed, and generalized) with different epidemic drivers using a dynamical model of heterosexual HIV transmission. MOT metrics from generic and complex MOT models were compared against the benchmark, and to the contribution of epidemic drivers to overall HIV transmission (cumulative population attributable fraction over t years, PAF_t). The complex MOT metric was similar to the benchmark, but the generic MOT underestimated the fraction of infections in epidemic drivers. The benchmark MOT metric identified epidemic drivers early in the epidemics. Over time, the MOT metric did not identify epidemic drivers. This was not due to simplified MOT models or biased parameters but occurred because the MOT metric (irrespective of the model used to generate it) underestimates the contribution of epidemic drivers to HIV transmission over time (PAF_5–30). MOT-directed policies that fail to reach epidemic drivers could undermine long-term impact on HIV incidence, and achieve a similar impact as random allocation of additional resources.</p><p>Conclusions</p><p>Irrespective of how it is obtained, the MOT metric is not a valid stand-alone tool to identify epidemic drivers, and has limited additional value in guiding the prioritization of HIV prevention targets. Policy-makers should use the MOT model judiciously, in combination with other approaches, to identify epidemic drivers.</p></div

Prevention policies under finite resources, as formulated in 2012.

<p>Prevention policies under finite resources, as formulated in 2012.</p

Model-predicted distribution of new HIV infections over one year (MOT metric) in three epidemic types.

<p>The benchmark Modes of Transmission (MOT) metric is obtained from the 4-stage dynamical model, and corresponding MOT metric obtained from the 1-stage dynamical model (1-stage DM), complex MOT (cMOT), and generic MOT (gMOT) models. The MOT metric reflects the fraction of new HIV infections acquired by different risk groups (colored bars) estimated for 2012 using data from the synthetic epidemics.*local epidemic drivers.</p