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Increasing risk behavior can outweigh the benefits of anti-retroviral drug treatment on the HIV incidence among men-having-sex-with-men in Amsterdam

Shan Mei, Rick Quax, David van de Vijver, Yifan Zhu, A. V. Boukhanovsky, P. M. A. Sloot

arXiv:1012.5752v1cs.SIphysics.med-phq-bio.PE

TL;DR

HIV incidence among MSM continues to increase despite potent antiretroviral therapy. Using a previously presented Complex Agent Network model, the study finds that an increase in risk behavior of at least 30% can outweigh therapy’s positive influence in the long run.

  • Problem

    HIV incidence among MSM continues to increase despite potent antiretroviral therapy.

    Source: Conclusion · p. 12

  • Method

    The study applies a previously presented Complex Agent Network model to the HIV epidemic among MSM in Amsterdam.

    Source: Background · pp. 3–4

  • Results

    An increase in risk behavior of at least 30% for MSM can outweigh the positive influence of effective therapy in the long run.

    Source: Conclusion · p. 12

  • Takeaways & Limitations

    Lowering risk behavior is the prominent control mechanism of HIV incidence even in the presence of effective treatment.

    Source: Conclusion · p. 12

    Validation compares simulated yearly incidence with ACS historical incidence, ignoring prevalence and other indicators.

    Source: Model validation · p. 12

Abstract

from arXiv · show

The transmission through contacts among MSM (men who have sex with men) is one of the dominating contributors to HIV prevalence in industrialized countries. In Amsterdam, the capital of the Netherlands, the MSM risk group has been traced for decades. This has motivated studies which provide detailed information about MSM's risk behavior statistically, psychologically and sociologically. Despite the era of potent antiretroviral therapy, the incidence of HIV among MSM increases. In the long term the contradictory effects of risk behavior and effective therapy are still poorly understood. Using a previously presented Complex Agent Network model, we describe steady and casual partnerships to predict the HIV spreading among MSM. Behavior-related parameters and values, inferred from studies on Amsterdam MSM, are fed into the model; we validate the model using historical yearly incidence data. Subsequently, we study scenarios to assess the contradictory effects of risk behavior and effective therapy, by varying corresponding values of parameters. Finally, we conduct quantitative analysis based on the resulting incidence data. The simulated incidence reproduces the ACS historical incidence well and helps to predict the HIV epidemic among MSM in Amsterdam. Our results show that in the long run the positive influence of effective therapy can be outweighed by an increase in risk behavior of at least 30% for MSM. Conclusion: We recommend, based on the model predictions, that lowering risk behavior is the prominent control mechanism of HIV incidence even in the presence of effective therapy.

Background

The study uses an evidence-based agent-based model of sexual contact networks, infection dynamics, and individual behavior to reproduce historical HIV incidence among MSM in Amsterdam and examine how risk behavior and therapy affect HIV spread. It assesses therapy and risk-behavior scenarios while noting limitations in the data, validation, treatment assumptions, and representation of individual dynamics.

Background

  • MSM contact transmission is a major or dominating contributor to HIV prevalence in industrialized countries, and Amsterdam’s long-running cohort provides detailed behavioral information.The Amsterdam Cohort Study has been conducted since 1984 with thousands of MSM recruited.Source: Background · pp. 2–3
  • Agent-based modeling using a previously developed Complex Agent Network represents heterogeneous individuals and their steady and casual partnerships to simulate HIV spread among Amsterdam MSM.The model incorporates partnership durations and sexual-action frequencies, allowing social-behavior data to inform its in silico population.Source: Background · p. 3
  • Responsibility beliefs among people living with HIV about preventing transmission and partners’ protection are poorly understood, motivating assessment of risk behavior alongside therapy.Source: Background · p. 3
  • The study aims to reproduce historical data and assess how varying risk behavior and therapy may influence HIV spread among MSM in Amsterdam.The authors describe this as the first report of a computational epidemic model combining sexual contact networks, HIV infection dynamics, and individual behavior using evidence-based data.Source: Background · pp. 3–4
  • With moderate treatment reduction, P1 incidence exceeds the reference scenario, while optimistic-treatment P2–P4 scenarios trend downward and P2 has the largest declines.P1 is 10.95% higher in 2030 and 2.87% higher in 2040; P2 declines 11.24% in 2030 and 28.7% in 2040 relative to the reference scenario.Source: Model validation · pp. 9–10
  • The yearly diagnosed fraction fluctuates between 0.35 and 0.56 and approaches 0.41, limiting therapy’s positive influence because many infected people remain unaware of infection.Source: Model validation · pp. 10–11
  • The study contributes an agent-based probabilistic Complex Agent Network model that incorporates detailed Amsterdam MSM behavior data to represent sexual-contact networks and HIV transmission.The authors describe this as an alternative, reliable approach for modeling virus spread in a highly connected population with individual-level detail.Source: Model validation · pp. 10–11
  • The model’s simulated incidence for 1985–2006 reproduces ACS historical incidence well, as assessed by a null-hypothesis T-test, with the RS run using default parameters unchanged throughout.The comparison is shown in Figures 1 and 2a.Source: Figure 1 · p. 8, Model validation · p. 11
  • A risk-behavior increase of at least 30% can counterbalance optimistic therapy’s effects on HIV incidence in the long term.The authors recommend lowering risk behavior as the prominent control mechanism even when effective therapy is available.Source: Model validation · p. 11
  • More precise treatment data are needed to estimate infectiousness while accounting for adherence, post-therapy behavior changes, and resistance; improved risk modeling could capture condom-use patterns.Source: Model validation · pp. 11–12
  • The model is validated only against ACS yearly incidence, owing to data availability and incidence’s dimensionless nature despite yearly variation in recruits; other indicators could help reduce incidence-based bias.Source: Model validation · p. 12
  • Assuming HAART reduces infectiousness by up to 99% is optimistic; undetectable viral load does not necessarily mean noninfectious, and estimating who reaches it requires further surveillance.Source: Model validation · p. 12
  • The study omits individually dynamic risk behavior and individual risky-contact histories because of limited data; adding data-driven detail may dramatically increase computational costs.Source: Model validation · p. 12

Conclusion

The model indicates that a risk-behavior increase of at least 30% can outweigh the positive influence of effective therapy. The authors conclude that lowering risk behavior is a prominent way to control HIV incidence even with effective treatment.

Conclusion

  • A risk-behavior increase of at least 30% for MSM can outweigh the positive influence of effective therapy in the long term.The authors base this conclusion on model predictions.Source: Conclusion · p. 12
  • Lowering risk behavior is a prominent control mechanism for HIV incidence even in the presence of effective treatment.The authors recommend this when universal voluntary HIV testing is impossible or impractical.Source: Conclusion · p. 12
  • HIV incidence among MSM continues to increase despite potent antiretroviral therapy.Source: Conclusion · p. 12

Authors contributions

SM led most of the model work, analyses, interpretation, and manuscript preparation. Other contributors worked on model design and validation, data collection, and the complex agent network approach; all authors approved the final manuscript.

Authors contributions

ACS data

The ACS data section presents yearly incidence alongside a reference scenario and multiple prediction scenarios.

ACS data

  • The plotted series include ACS data, a reference scenario, and prediction scenarios 1 through 5.Source: ACS data · p. 17
  • The figure labels its horizontal axis as year and its vertical axis as incidence per 100 person-years.Source: ACS data · p. 18
  • A second scenario listing names the reference scenario and prediction scenarios 1 through 4.Source: ACS data · p. 19
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