What Drives the US and Peruvian HIV Epidemics in Men Who Have Sex with Men (MSM)?

Abstract

In this work, we estimate the proportions of transmissions occurring in main vs. casual partnerships, and by the sexual role, infection stage, and testing and treatment history of the infected partner, for men who have sex with men (MSM) in the US and Peru. We use dynamic, stochastic models based in exponential random graph models (ERGMs), obtaining inputs from multiple large-scale MSM surveys. Parallel main partnership and casual sexual networks are simulated. Each man is characterized by age, race, circumcision status, sexual role behavior, and propensity for unprotected anal intercourse (UAI); his history is modeled from entry into the adult population, with potential transitions including HIV infection, detection, treatment, AIDS diagnosis, and death. We implemented two model variants differing in assumptions about acute infectiousness, and assessed sensitivity to other key inputs. Our two models suggested that only 4–5% (Model 1) or 22–29% (Model 2) of HIV transmission results from contacts with acute-stage partners; the plurality (80–81% and 49%, respectively) stem from chronic-stage partners and the remainder (14–16% and 27–35%, respectively) from AIDS-stage partners. Similar proportions of infections stem from partners whose infection is undiagnosed (24–31%), diagnosed but untreated (36–46%), and currently being treated (30–36%). Roughly one-third of infections (32–39%) occur within main partnerships. Results by country were qualitatively similar, despite key behavioral differences; one exception was that transmission from the receptive to insertive partner appears more important in Peru (34%) than the US (21%). The broad balance in transmission contexts suggests that education about risk, careful assessment, pre-exposure prophylaxis, more frequent testing, earlier treatment, and risk-reduction, disclosure, and adherence counseling may all contribute substantially to reducing the HIV incidence among MSM in the US and Peru.

Impact of the Centers for Disease Control's HIV Preexposure Prophylaxis Guidelines for Men Who Have Sex With Men in the United States

“…Similar to prior studies [18], this study modeled HIV transmission over sexual contact networks using exponential random graph models, a flexible statistical method for simulating dynamic partnerships parameterized from behavioral data [19]. The modeled network had 3 components: main partners, shorter-term casual partners with repeated contacts, and one-time partners.…”

Relationship Characteristics Differ Based on Use of Substances with Sex among an Urban Internet Sample of HIV-Discordant and HIV-Positive Male Couples

“…[32][33][34] Although these few studies provide some insight about male couples' use of substances (with sex), additional research with male couples is needed because: (1) substance use with sex is fairly common among MSM, (2) substance use and UAS are strongly correlated and may increase the likelihood of HIV acquisition, (3) substance use may impair adherence to ART among those living with HIV, (4) many US male couples practice UAS within their relationship 35,36 , and (5) between one and two thirds of MSM in the USA acquire HIV from their main partners while in a same-sex relationship (e.g., male couples). 37,38 Given the public health relevance for studying how substance use may affect male couples' health (e.g., HIV prevention and adherence), research is lacking on how dynamics of their relationships may be impacted by their use of substances with sex. To date, the few studies that have been conducted with male couples have primarily focused on UAS with respect to their use of substances.…”

Development of an Agent-Based Model to Investigate the Impact of HIV Self-Testing Programs on Men Who Have Sex With Men in Atlanta and Seattle (Preprint)

“…These dynamic network models are ideal for investigating effects of complex, interacting interventions along structured and evolving contact networks [16]. Our key improvement was adding four different types of testers to this model (never testers, opportunistic only testers, regular testers, and risk-based testers), and deriving estimates of the prevalence as well as patterns of testing behaviors for each tester type.…”

“…diagnosis status, treatment status, stage of infection, plasma HIV RNA level). Similar to prior studies [16][17], sexual contact networks employed separable-temporal exponential random graph models (STERGMs) [14]-a flexible statistical framework for simulating partnership formation and dissolution across networks [15] in ways that allow one to match data on complex cross-sectional network structure as well as reported relational durations. STERGMs were implemented in the R package suite statnet (www.statnet.org/trac/wiki) and EpiModel (www.epimodel.org).…”

“…HIV viral loads in the absence of ART progressed in three stages: peak viremia during acute HIV infection, a set point viral load during clinical latency, and a subsequent rise leading to AIDS and disease-induced mortality [19]. HIV transmission probabilities in serodiscordant pairs included were mediated by the viral load of the positive partner [20], condom use [21], the presence of the CCR5-Δ32 genetic allele [22][23], receptive versus insertive sexual position of the HIV-negative partner [16], and circumcision status of an insertive HIV-negative partner [24]. After infection, men were assigned into three clinical care trajectories: men who never initiate treatment, men who initiate treatment and become partially suppressed, and men who initiate treatment and become fully suppressed.…”