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The Interaction of Risk Network Structures and Virus Natural History in the Non-spreading of HIV Among People Who Inject Drugs in the Early Stages of the Epidemic

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Abstract

This article explores how social network dynamics may have reduced the spread of HIV-1 infection among people who inject drugs during the early years of the epidemic. Stochastic, discrete event, agent-based simulations are used to test whether a “firewall effect” can arise out of self-organizing processes at the actor level, and whether such an effect can account for stable HIV prevalence rates below population saturation. Repeated simulation experiments show that, in the presence of recurring, acute, and highly infectious outbreaks, micro-network structures combine with the HIV virus’s natural history to reduce the spread of the disease. These results indicate that network factors likely played a significant role in the prevention of HIV infection within injection risk networks during periods of peak prevalence. They also suggest that social forces that disturb network connections may diminish the natural firewall effect and result in higher rates of HIV.

Resumen

Este artículo explora cómo las dinámicas de redes sociales pueden haber reducido la propagación de la infección por VIH-1 entre las personas que se inyectan drogas durante los primeros años de la epidemia. Estocásticas, eventos discretos, las simulaciones basadas en agentes se utilizan para probar la de si un “efecto cortafuegos” puede surgir de los procesos de auto-organización, al nivel de actor, y si este efecto puede dar cuenta de las tasas estables de la prevalencia del VIH por debajo de la saturación de la población. Repetidos experimentos de simulación muestran que, en la presencia de brotes recurrentes, agudos, y altamente infecciosos, las estructuras micro-red se combinan con la historia natural del virus del VIH para reducir la propagación de la enfermedad. Estos resultados indican que los factores de la red probablemente jugaron un papel importante en la prevención de la infección por el VIH dentro de las redes de riesgo de inyección durante los períodos de pico de prevalencia. Además, sugieren que las fuerzas sociales que perturban las conexiones de red pueden disminuir el efecto cortafuegos natural y resultan en tasas más altas de VIH.

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Acknowledgments

Original data collection was supported by NIH/NIDA Grant R01DA006723 (PI Friedman). Co-author Friedman was Principal Investigator of the SFHR project and has full access to all the data in the study and takes responsibility for the integrity of the data analysis upon which this study is based. The modeling research presented here is supported by NIH/NIDA Grants R01DA037117-01 (PIs Dombrowski and Khan) and R01DA034637-01 (PI Hagan). The original simulation platform was developed under 1RC1DA-028476-01/02 (PIs Dombrowski and Khan). In addition, all of the authors have received support and assistance from the NYU’s Center for Drug Use and HIV Research, funded by NIH P30 DA011041 (PIs Deren, Hagan), and Friedman (PI) under DP1 DA034989. We wish to acknowledge the work performed by Katherine McLean, Ric Curtis, and Travis Wendel at several points during this research. Special thanks to Colleen Syron for drawing the illustrations in Fig. 1. The opinions, findings, conclusions and recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the National Institutes of Health/National Institute on Drug Abuse, or the National Science Foundation. The analyses discussed in this paper were carried out at the REACH Lab at the University of Nebraska-Lincoln (reach.unl.edu). Initial funding for a pilot version of this project was provided by the NSF Office of Behavioral, Social, and Economic Sciences, Anthropology Program Grant BCS-0752680 (PI Dombrowski).

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Authors and Affiliations

  1. Department of Sociology, University of Nebraska-Lincoln, 711 Oldfather Hall, Lincoln, NE, 68588, USA

    Kirk Dombrowski, Bilal Khan & Patrick Habecker

  2. College of Nursing, New York University, New York, NY, USA

    Holly Hagan

  3. National Development Research Institute, New York, NY, USA

    Samuel R. Friedman

  4. Microjan Inc, Waterloo, ON, Canada

    Mohamed Saad

Authors
  1. Kirk Dombrowski
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  2. Bilal Khan
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  4. Holly Hagan
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  5. Samuel R. Friedman
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Corresponding author

Correspondence to Kirk Dombrowski.

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Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All original data collection with human subjects was carried out under Institutional Review Board supervisions, and informed consent was obtained from all individual participants included in the study. The current study involves secondary data analysis using only de-identified data. This article does not contain any studies with animals performed by any of the authors. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Glossary of Network Terms

Agent/actor

Simulation objects that act as PWID; each is characterized by a range of individual characteristics (gender, risk propensity…) that condition their risk interactions with other agents/actors

Churn

The effect of network agents changing partners over time; a measure or approximation of overall change of network connections

Clusters

Parts of a network characterized by a high number of mutual connections; dense parts of a network

Component

A part of a network that is not connected to other parts of network; an isolated cluster of agents

Core

A highly connected section of a network where those with high numbers of connections are linked to others with high numbers of connections

Degree distribution

A histogram of how many people or agents have how many connections (i.e. this network contains five people with one connection, eight people with two connections, etc)

Network transitivity

The process where agents tend to make connections with the connections of their current connections

Node

General term for the objects that are connected

Partner/network neighbour

In a PWID risk network, an agent with whom an agent often shares a risk behavior; on the street, a “running partner”

Risk network

A network where the agents are meant to simulate people and the connections show potential avenues of infection due to risk behaviors

Small world

A network configuration where even large numbers of actors are connected by a small number of intermediaries—similar to “six degrees of separation”

Stochastic

A simulation strategy where random “rolls of the dice” determine situational outcomes

Sociometric

A formal network rendering of human social interaction

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Dombrowski, K., Khan, B., Habecker, P. et al. The Interaction of Risk Network Structures and Virus Natural History in the Non-spreading of HIV Among People Who Inject Drugs in the Early Stages of the Epidemic. AIDS Behav 21, 1004–1015 (2017). https://doi.org/10.1007/s10461-016-1568-6

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