Sexual role and HIV-1 set point viral load among men who have sex with men

Stansfield, Sarah E.; Mittler, John E.; Gottlieb, Geoffrey S.; Murphy, James T.; Hamilton, Deven T.; Detels, Roger; Wolinsky, Steven M.; Jacobson, Lisa P.; Margolick, Joseph B.; Rinaldo, Charles R.; Herbeck, Joshua T.; Goodreau, Steven M.

doi:10.1016/j.epidem.2018.08.006

Cited by 5 publications

(15 citation statements)

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“…Within-host viral dynamics, CD4 progression patterns, probabilities of dying of AIDS, testing procedures, and transmission probabilities were, for the most part, identical to those in previous studies using Evonet_HIV [33–35]. In the sections below, we summarize features that differ from these previous studies and/or that relate specifically to age (i.e., the key assumptions of this study).…”

Section: Methodsmentioning

confidence: 57%

“…We used the Evonet_HIV package (https://github.com/EvoNetHIV), a stochastic, agent-based HIV epidemic model that accounts for a broad set of virological, immunological, behavioral, and epidemiological phenomena [33–35]. Each agent has attributes such as age, sex, HIV status, viral load, CD4 count (discretized into 5 bins), and HIV diagnosis status.…”

Section: Methodsmentioning

confidence: 99%

“…Following an adaptation of data from Hughes et al . [31] used in our previous studies [33–35], we also assumed that the relative per-act risk of transmission is higher when the susceptible partner is young (default: RR 1.492 for each decade under 46).…”

Section: Methodsmentioning

confidence: 99%

“…We followed the general strategy used in our previous studies [33–35] in which the initial placement into a CD4 category (1 = CD4>500 units, 2 = 350<CD4<500, 3 = 200<CD4<350, 4 = CD4<200, 5 = died of AIDS) and the daily probability of transitioning to a different CD4 category depend on set point viral load (SPVL) and treatment status. However, we modified the SPVL-dependent probability of progression to a higher CD4 category, in the absence of ART, to reflect the slower disease progression observed in people infected at younger ages [35] (Table 1).…”

Section: Methodsmentioning

confidence: 99%

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Large benefits to youth-focused HIV treatment-as-prevention efforts in generalized heterosexual populations: An agent-based simulation model

et al. 2019

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Predominantly heterosexual HIV-1 epidemics like those in sub-Saharan Africa continue to have high HIV incidence in young people. We used a stochastic, agent-based model for age-disparate networks to test the hypothesis that focusing uptake and retention of ART among youth could enhance the efficiency of treatment as prevention (TasP) campaigns. We used the model to identify strategies that reduce incidence to negligible levels (i.e., < 0.1 cases/100 person-years) 20–25 years after initiation of a targeted TasP campaign. The model was parameterized using behavioral, demographic, and clinical data from published papers and national reports. To keep a focus on the underlying age effects we model a generalized heterosexual population with average risks (i.e., no MSM, no PWIDs, no sex workers) and no entry of HIV+ people from other regions. The model assumes that most people (default 95%, range in variant simulations 60–95%) are “linkable”; i.e., could get linked to effective care given sufficient resources. To simplify the accounting, we assume a rapid jump in the number of people receiving treatment at the start of the TasP campaign, followed by a 2% annual increase that continues until all linkable HIV+ people have been treated. Under historical scenarios of CD4-based targeted ART allocation and current policies of untargeted (random) ART allocation, our model predicts that viral replication would need to be suppressed in 60–85% of infected people at the start of the TasP campaign to drive incidence to negligible levels. Under age-based strategies, by contrast, this percentage dropped by 18–54%, depending on the strength of the epidemic and the age target. For our baseline model, targeting those under age 30 halved the number of people who need to be treated. Age-based targeting also minimized total and time-discounted AIDS deaths over 25 years. Age-based targeting yielded benefits without being highly exclusive; in a model in which 60% of infected people were treated, ~87% and ~58% of those initiating therapy during a campaign targeting those <25 and <30 years, respectively, fell outside the target group. Sensitivity analyses revealed that youth-focused TasP is beneficial due to age-related risk factors (e.g. shorter relationship durations), and an age-specific herd immunity (ASHI) effect that protects uninfected adolescents entering the sexually active population. As testing rates increase in response to UNAIDS 90-90-90 goals, efforts to link all young people to care and treatment could contribute enormously to ending the HIV epidemic.

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Section: Methodsmentioning

confidence: 57%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Large benefits to youth-focused HIV treatment-as-prevention efforts in generalized heterosexual populations: An agent-based simulation model

et al. 2019

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“…In He et al (2019) Chinese patent trade networks are inspected and Benton and You (2017) use a TERGM for analyzing shareholder activism. Applications of STERGMs are given for example by Stansfield et al (2019) that model sexual relationships and Broekel and Bednarz (2019) that study the network of research and development cooperation between German firms.…”

Section: Introductionmentioning

confidence: 99%

Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time

Fritz

Lebacher

Kauermann

2019

Statistica Neerlandica

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Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. It is often difficult to compare the different models with respect to their applicability and interpretation. The goal of this survey is to provide an overview of popular dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time, respectively. First, we introduce the Temporal Exponential Random Graph Model (TERGM) and its extension, the Separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the Relational Event Model (REM). We additionally show how the REM can handle time-clustered observations, i.e., continuous time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models using a network that represents international arms transfers. The data allow to demonstrate the applicability and interpretation of the network models.

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Why does age at HIV infection correlate with set point viral load? An evolutionary hypothesis

Goodreau

Stansfield²,

Mittler³

et al. 2022

Epidemics

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Sexual role and HIV-1 set point viral load among men who have sex with men

Abstract: Mode of acquisition may be an important aspect of SPVL evolution in MSM, with clinical implications.

Cited by 5 publications

References 48 publications

Large benefits to youth-focused HIV treatment-as-prevention efforts in generalized heterosexual populations: An agent-based simulation model

Large benefits to youth-focused HIV treatment-as-prevention efforts in generalized heterosexual populations: An agent-based simulation model

Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time

Why does age at HIV infection correlate with set point viral load? An evolutionary hypothesis

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