Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende
Erweiterte Suche
Anmelden
nach oben
AIDS and Behavior
Erschienen in: AIDS and Behavior 4/2017

03.10.2016 | Original Paper

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

verfasst von: Kirk Dombrowski, Bilal Khan, Patrick Habecker, Holly Hagan, Samuel R. Friedman, Mohamed Saad

Erschienen in: AIDS and Behavior | Ausgabe 4/2017

Einloggen, um Zugang zu erhalten

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.
Glossar
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
Literatur
1.
Huang C-Y, Tsai Y-S, Wen T-H. Simulations for epidemiology and public health education. J Simul. 2010;4(1):68–80.CrossRef
2.
Marshall BD, Galea S. Marshall and Galea respond to “data theory in epidemiology”. Am J Epidemiol. 2015;181(2):106–7.CrossRefPubMed
3.
Goodreau SM. Assessing the effects of human mixing patterns on human immunodeficiency virus-1 interhost phylogenetics through social network simulation. Genetics. 2006;172(4):2033–45.CrossRefPubMedPubMedCentral
4.
Hsieh J-L, Huang C-Y, Sun C-T, Tsai Y-S. Learning to build network-oriented epidemic simulation models in epidemiology education. Int J Simul Process Model. 2009;5(1):31–41.CrossRef
5.
Sloot PMA, Ivanov SV, Boukhanovsky AV, van de Vijver DAMC, Boucher CAB. Stochastic simulation of HIV population dynamics through complex network modelling. Int J Comput Math. 2008;85(8):1175–87.CrossRef
6.
7.
Des Jarlais DC, Friedman SR, Novick DM, Sotheran JL, Thomas P, Yancovitz SR, et al. HIV-1 infection among intravenous drug users in Manhattan, New York City, from 1977 through 1987. JAMA. 1989;261(7):1008–12.CrossRefPubMed
8.
Brown AJL, Lycett SJ, Weinert L, Hughes GJ, Fearnhill E, Dunn DT. Transmission network parameters estimated from HIV sequences for a nationwide epidemic. J Infect Dis. 2011;204(9):1463–9.CrossRef
9.
Dombrowski K, Curtis R, Friedman S, Khan B. Topological and historical considerations for infectious disease transmission among injecting drug users in Bushwick, Brooklyn (USA). World J AIDS. 2013;3(1):1–9.CrossRefPubMedPubMedCentral
10.
Vieira, et al. Small world network models of the dynamics of HIV infection. Ann Oper Res. 2010;178(1):173–200.CrossRef
11.
Dombrowski K, Khan B, McLean K, Curtis R, Wendel T, Misshula E, et al. A reexamination of connectivity trends via exponential random graph modeling in two IDU risk networks. Subst Use Misuse. 2013;2(48):1485–97.CrossRef
12.
Friedman SR, Neaigus A, Jose B, Curtis R, Goldstein M, Ildefonso G, et al. Sociometric risk networks and risk for HIV infection. Am J Public Health. 1997;87(8):1289–96.CrossRefPubMedPubMedCentral
13.
Des Jarlais DC, Perlis T, Arasteh K, Torian LV, Beatrice S, Milliken J, et al. HIV incidence among injection drug users in New York City, 1990 to 2002: use of serologic test algorithm to assess expansion of HIV prevention services. Am J Public Health. 2005;95(8):1439–44.CrossRefPubMedPubMedCentral
14.
Des Jarlais DC, Perlis T, Friedman SR, Deren S, Chapman T, Sotheran JL, et al. Declining seroprevalence in a very large HIV epidemic: injecting drug users in New York City, 1991 to 1996. Am J Public Health. 1998;88(12):1801–6.CrossRefPubMedPubMedCentral
15.
Friedman SR, Des Jarlais DC. HIV among drug injectors: the epidemic and the response. AIDS Care. 1991;3(3):239–50.CrossRefPubMed
16.
Des Jarlais DC, Perlis T, Arasteh K, Torian LV, Hagan H, Beatrice S, et al. Reductions in hepatitis C virus and HIV infections among injecting drug users in New York City, 1990–2001. AIDS. 2005;19(Suppl 3):S20–5.CrossRefPubMed
17.
Stadler T, Kühnert D, Bonhoeffer S, Drummond AJ. Birth–death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc Natl Acad Sci. 2013;110(1):228–33.CrossRefPubMed
18.
Hagan H, Pouget ER, Jarlais DCD, Lelutiu-Weinberger C. Meta-regression of hepatitis C virus infection in relation to time since onset of illicit drug injection: the influence of time and place. Am J Epidemiol. 2008;168(10):1099–109.CrossRefPubMedPubMedCentral
19.
Nelson PK, Mathers BM, Cowie B, Hagan H, Des Jarlais D, Horyniak D, et al. Global epidemiology of hepatitis B and hepatitis C in people who inject drugs: results of systematic reviews. The Lancet. 2011;378(9791):571–83.CrossRef
20.
21.
Galea S, Riddle M, Kaplan GA. Causal thinking and complex system approaches in epidemiology. Int J Epidemiol. 2010;39(1):97–106.CrossRefPubMed
22.
Philippe P, Mansi O. Nonlinearity in the epidemiology of complex health and disease processes. Theor Med Bioeth. 1998;19(6):591–607.CrossRefPubMed
23.
Friedman SR, Kottiri BJ, Neaigus A, Curtis R, Vermund SH, Des Jarlais DC. Network-related mechanisms may help explain long-term HIV-1 seroprevalence levels that remain high but do not approach population-group saturation. Am J Epidemiol. 2000;152(10):913–22.CrossRefPubMed
24.
Friedman SR, Sandoval M, Mateu-Gelabert P, Rossi D, Gwadz M, Dombrowski K, et al. Theory, measurement and hard times: some issues for HIV/AIDS research. AIDS Behav. 2013;17(6):1915–25.CrossRefPubMedPubMedCentral
25.
Valente TW. Social networks and health: models, methods, and applications: models, methods, and applications [Internet]. Oxford: Oxford University Press; 2010.CrossRef
26.
Friedman SR, Curtis R, Neaigus A, Jose B, Jarlais DCD. Social networks, drug injectors’ lives, and HIV/AIDS, vol. 1st ed. New York: Springer; 2010 Softcover of ed. 1999.
27.
Friedman SR, Neaigus A, Jose B, Curtis R, Des Jarlais D. Networks and HIV risk: an introduction to social network analysis for harm reductionists. Int J Drug Policy. 1998;9(6):461–9.CrossRef
28.
Friedman SR, de Jong W, Rossi D, Touzé G, Rockwell R, Des Jarlais DC, et al. Harm reduction theory: users’ culture, micro-social indigenous harm reduction, and the self-organization and outside-organizing of users’ groups. Int J Drug Policy. 2007;18(2):107–17.CrossRefPubMed
29.
Friedman SR, Des Jarlais DC, Sotheran JL, Garber J, Cohen H, Smith D. AIDS and self-organization among intravenous drug users. Int J Addict. 1987;22(3):201–19.CrossRefPubMed
30.
31.
Goodreau SM. A decade of modelling research yields considerable evidence for the importance of concurrency: a response to Sawers and Stillwaggon. J Int AIDS Soc. 2011;14:12.CrossRefPubMedPubMedCentral
32.
Kumar S, Grefenstette JJ, Galloway D, Albert SM, Burke DS. Policies to reduce influenza in the workplace: impact assessments using an agent-based model. Am J Public Health. 2013;103(8):1406–11.CrossRefPubMedPubMedCentral
33.
Marshall BDL, Paczkowski MM, Seemann L, Tempalski B, Pouget ER, Galea S, et al. A complex systems approach to evaluate HIV prevention in metropolitan areas: preliminary implications for combination intervention strategies. PLoS One. 2012;7(9):e44833.CrossRefPubMedPubMedCentral
34.
Marshall BDL, Friedman SR, Monteiro JFG, Paczkowski M, Tempalski B, Pouget ER, et al. Prevention and treatment produced large decreases in HIV incidence in a model of people who inject drugs. Health Aff (Millwood). 2014;33(3):401–9.CrossRefPubMedPubMedCentral
35.
Monteiro JFG, Galea S, Flanigan T, de Monteiro ML, Friedman SR, Marshall BDL. Evaluating HIV prevention strategies for populations in key affected groups: the example of Cabo Verde. Int J Public Health. 2015;60(4):457–66.CrossRefPubMedPubMedCentral
36.
Mniszewski SM, Del Valle SY, Stroud PD, Riese JM, Sydoriak SJ. EpiSimS simulation of a multi-component strategy for pandemic influenza. In: Proceedings of the 2008 Spring simulation multiconference [Internet]. 2008 [cited 2013 Apr 3]. pp. 556–63. Available from: http://​dl.​acm.​org/​citation.​cfm?​id=​1400636.
37.
Snijders TAB, van de Bunt GG, Steglich CEG. Introduction to stochastic actor-based models for network dynamics☆. Soc Netw. 2010;32(1):44–60.CrossRef
38.
Ripley RM, Snijders TA, Preciado P. Manual for RSiena (University of Oxford: Department of Statistics; Nuffield College) 2012.
39.
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M. statnet: Software tools for the representation, visualization, analysis and simulation of network data. J Stat Softw. 2008;24(1):1548–7660.CrossRef
40.
41.
Guizani M, Rayes A, Khan B, Al-Fuqaha A. Network modeling and simulation: a practical perspective, vol. 1st ed. Hoboken: Wiley; 2010.CrossRef
42.
Khan B, Dombrowski K, Saad M. A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks. Simul Trans Soc Model Simul Int. 2014;90(4):460–84.
43.
Khan B, Dombrowski K, Saad M, McLean K, Friedman S. Network firewall dynamics and the subsaturation stabilization of HIV. Discret Dyn Nat Soc. 2013;4(2013):1–16.CrossRef
44.
Yang C, Tobin K, Latkin C. Perceived serosorting of injection paraphernalia sharing networks among injection drug users in Baltimore, MD. AIDS Behav. 2010;15(1):16–21.CrossRef
45.
Jiayu L, Guihong B, Hairui W. A cave small world network model for HIV transmission among intravenous drug users. In IEEE: 31st Chinese Control Conference (CCC), 2012. (2012). pp. 7475–80.
46.
Costenbader EC, Astone NM, Latkin CA. The dynamics of injection drug users’ personal networks and HIV risk behaviors. Addiction. 2006;101(7):1003–13.CrossRefPubMed
47.
Curtis R, Friedman SR, Neaigus A, Jose B, Goldstein M, Ildefonso G. Street-level drug markets: network structure and HIV risk. Soc Netw. 1995;17(3–4):229–49.CrossRef
48.
Latkin CA, Kuramoto SJ, Davey-Rothwell MA, Tobin KE. Social norms, social networks, and HIV risk behavior among injection drug users. AIDS Behav. 2010;14(5):1159–68.CrossRefPubMed
49.
Snijders TAB, Pattison PE, Robins GL, Handcock MS. New specifications for exponential random graph models. Sociol Methodol. 2006;36(1):99–153.CrossRef
50.
Dahari H, Shudo E, Ribeiro RM, Perelson AS. Mathematical modeling of HCV infection and treatment. Methods Mol Biol. 2009;510:439–53.CrossRefPubMed
Metadaten
Titel
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
verfasst von
Kirk Dombrowski
Bilal Khan
Patrick Habecker
Holly Hagan
Samuel R. Friedman
Mohamed Saad
Publikationsdatum
03.10.2016
Verlag
Springer US
Erschienen in
AIDS and Behavior / Ausgabe 4/2017
Print ISSN: 1090-7165
Elektronische ISSN: 1573-3254
DOI
https://doi.org/10.1007/s10461-016-1568-6

Weitere Artikel der Ausgabe 4/2017

AIDS and Behavior 4/2017 Zur Ausgabe

Original Paper

Trajectories of Marijuana Use among HIV-seropositive and HIV-seronegative MSM in the Multicenter AIDS Cohort Study (MACS), 1984–2013

Brief Report

Correlates of Double Risk of HIV Acquisition and Transmission Among Women who Inject Drugs in St. Petersburg, Russia

Original Paper

Prevalence and Predictors of Substance Use Disorders Among HIV Care Enrollees in the United States

Original Paper

Changes in Substance Use Symptoms Across Adolescence in Youth Perinatally Infected with HIV

Original Paper

Social Network Factors as Correlates and Predictors of High Depressive Symptoms Among Black Men Who Have Sex with Men in HPTN 061

Original Paper

Primary Care Physicians’ Willingness to Prescribe HIV Pre-exposure Prophylaxis for People who Inject Drugs

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Kostenlos registrieren

Neu im Fachgebiet Innere Medizin

Notfall-TEP der Hüfte ist auch bei 90-Jährigen machbar

26.04.2024 Hüft-TEP Nachrichten

Ob bei einer Notfalloperation nach Schenkelhalsfraktur eine Hemiarthroplastik oder eine totale Endoprothese (TEP) eingebaut wird, sollte nicht allein vom Alter der Patientinnen und Patienten abhängen. Auch über 90-Jährige können von der TEP profitieren.

Niedriger diastolischer Blutdruck erhöht Risiko für schwere kardiovaskuläre Komplikationen

25.04.2024 Hypotonie Nachrichten

Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Bei schweren Reaktionen auf Insektenstiche empfiehlt sich eine spezifische Immuntherapie

25.04.2024 Allergien und Intoleranzreaktionen Nachrichten

Insektenstiche sind bei Erwachsenen die häufigsten Auslöser einer Anaphylaxie. Einen wirksamen Schutz vor schweren anaphylaktischen Reaktionen bietet die allergenspezifische Immuntherapie. Jedoch kommt sie noch viel zu selten zum Einsatz.

Therapiestart mit Blutdrucksenkern erhöht Frakturrisiko

25.04.2024 Hypertonie Nachrichten

Beginnen ältere Männer im Pflegeheim eine Antihypertensiva-Therapie, dann ist die Frakturrate in den folgenden 30 Tagen mehr als verdoppelt. Besonders häufig stürzen Demenzkranke und Männer, die erstmals Blutdrucksenker nehmen. Dafür spricht eine Analyse unter US-Veteranen.

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen
Bildnachweise