Modeling the gender-specific impact of vaginal microbicides on HIV transmission

Abstract

Vaginal microbicides (VMB) are currently among the few women-initiated biomedical interventions for preventing heterosexual transmission of HIV. In this paper we use a deterministic model of HIV transmission to assess the public-health benefits of a VMB intervention and evaluate its gender-specific impact over short (initial) and extended periods of time. We define two distinct quantitative benefit ratios (QBRs) based on infections prevented in men and women to create and study regions of male advantage in different parameter spaces. Our analysis exposes complicated temporal correlations between the QBRs and series of pre-intervention (e.g., HIV acquisition risks per act) and intervention parameters (e.g., VMB efficacy mechanisms, rates of resistance development and reversion) and indicates that different QBRs may often disagree on the gender distribution of the benefits from a VMB intervention. We also outline the strong influence of some modeling assumptions on the reported results and conclude that the assessment of VMB and other biomedical interventions must be based on more comprehensive analyses than calculations of infections prevented over a fixed period of time.

Figure 1

Flow diagram of a vaginal microbicide (VMB) intervention in a heterosexual population. The departure rates μ from each compartment are omitted for simplicity.

Figure 2

Initial dynamics (up to 1 year after the start of the intervention) of the QBR for A)–B) baseline scenarios biR, biNR, uniR, and uniNR; C) biR scenarios with different levels of pre-enrollment screening. Marked horizontal lines (y = 1) represent the level at which both genders benefit the same.

Figure 3

Long-term dynamics of the QBRs for A)–B) biR scenarios with R₀ < 1 (α_i = 0.4, α_s = 0.5, β_w = 0.001, β_m = 0.002); C)–D) biR scenarios with R₀ > 1 (α_i = 0.4, α_s = 0.5, β_w = 0.002, β_m = 0.004) and different levels of pre-enrollment screening. Marked horizontal lines (y = 1) represent the level at which both genders benefit the same.

Figure 4

Regions of male advantage (RMA) in the α_s−α_i parameter space over 5, 10, 15, 20, and 25 years with respect to A) the cumulative indicator (C_I) and B) the fractional indicator (F_I) for biR scenarios. Dotted lines represent the boundaries of the 25-year region assuming higher HIV acquisition risk for women than for men (biRa scenarios).

Figure 5

Regions of male advantage (RMA) in the β_w − β_m parameter space over 5, 10, 15, 20, and 25 years with respect to A) the cumulative indicator (C_I) and B) the fractional indicator (F_I) for biR scenarios (α_i = α_s). Dotted lines represent the boundaries of the 25 year region assuming uni-directional protection (uniR scenarios, α_i = 0).

Figure 6

Regions of male advantage (RMA) in the α_s − r parameter space over 5, 10, 15, 20, and 25 years for interventions with A)–B) bi-directional protection assuming equal VMB efficacy in reducing susceptibility and infectiousness (biR scenarios, α_i = α_s) and C)–D) uni-directional protection (uniR scenarios, α_i = 0). The dotted lines represent the boundaries of the 25-year region for the scenario assuming that the acquisition risk for women is higher than for men (biRa scenarios, α_i = α_s).

Figure 7

Dynamics of A) the prevalence of drug-resistance, B) the cumulative indicator (C_I), and C) the fractional indicator (F_I) for biRr scenarios with different rates of resistance reversion per year and active post-enrollment screening (δ = 1). The rate of resistance development is assumed relatively high (r₁ = 0.7).

Figure 8

Characteristics of a hypothetical VMB intervention: A) yearly number of new infections; B) population size; C) HIV-prevalence and incidence; D) QBR based on cumulative prevented infections (C_I), cumulative prevented fractions (F_I), reduction in HIV-prevalence (F_P) and incidence (F_inc).