Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs
Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs
Transmission of Hepatitis C (HCV) continues via sharing of injection equipment between people who inject drugs (PWID). Network-based modelling studies have produced conflicting results about whether random treatment is preferable to targeting treatment at PWID with multiple partners. We hypothesise that differences in the modelled injecting network structure produce this heterogeneity. The study aimed to test how changing network structure affects HCV transmission and treatment effects. We created three dynamic injecting network structures connecting 689 PWID (UK-net, AUS-net and USA-net) based on published empirical data. We modelled HCV in the networks and at 5 years compared prevalence of HCV 1) with no treatment, 2) with randomly targeted treatment and 3) with treatment targeted at PWID with the most injecting partnerships (degree-based treatment). HCV prevalence at 5 years without treatment differed significantly between the three networks (UK-net (42.8%) vs. AUS-net (38.2%), p < 0.0001 and vs. USA-net (54.0%), p < 0.0001). In the treatment scenarios UK-net and AUS-net showed a benefit of degree-based treatment with a 5-year prevalence of 1.0% vs. 9.6% p < 0.0001 and 0.15% vs. 0.44%, p < 0.0001. USA-net showed no significant difference (29.3% vs. 29.2%, p = 0.0681). Degree-based treatment was optimised with low prevalence, moderate treatment coverage conditions whereas random treatment was optimised in low treatment coverage, high prevalence conditions. In conclusion, injecting network structure determines the transmission rate of HCV and the most efficient treatment strategy. In real-world injecting network structures, the benefit of targeting HCV treatment at individuals with multiple injecting partnerships may have been underestimated.
elimination, Hepatitis C, injecting, model, network, prevention, treatment
1274-1283
Brown, Chloe
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Siegele, Martin
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Wright, Mark
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Cook, Charlotte
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Parkes, Julie
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I Khakoo, Salim
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Sacks-Davis, Rachel
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Buchanan, Ryan M.
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September 2021
Brown, Chloe
4193c6eb-fccb-49f1-a4bd-9837edbd28c4
Siegele, Martin
a65f3350-ceb0-4087-a9c3-4b4047f2668f
Wright, Mark
43325ef9-3459-4c75-b3bf-cf8d8dac2a21
Cook, Charlotte
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Parkes, Julie
59dc6de3-4018-415e-bb99-13552f97e984
I Khakoo, Salim
6c16d2f5-ae80-4d9b-9100-6bfb34ad0273
Sacks-Davis, Rachel
b4d5c7d0-a67e-40b5-8aca-eb5b5b049bcf
Buchanan, Ryan M.
9499f713-f684-4046-be29-83cd9d6f834d
Brown, Chloe, Siegele, Martin, Wright, Mark, Cook, Charlotte, Parkes, Julie, I Khakoo, Salim, Sacks-Davis, Rachel and Buchanan, Ryan M.
(2021)
Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs.
Journal of Viral Hepatitis, 28 (9), .
(doi:10.1111/jvh.13554).
Abstract
Transmission of Hepatitis C (HCV) continues via sharing of injection equipment between people who inject drugs (PWID). Network-based modelling studies have produced conflicting results about whether random treatment is preferable to targeting treatment at PWID with multiple partners. We hypothesise that differences in the modelled injecting network structure produce this heterogeneity. The study aimed to test how changing network structure affects HCV transmission and treatment effects. We created three dynamic injecting network structures connecting 689 PWID (UK-net, AUS-net and USA-net) based on published empirical data. We modelled HCV in the networks and at 5 years compared prevalence of HCV 1) with no treatment, 2) with randomly targeted treatment and 3) with treatment targeted at PWID with the most injecting partnerships (degree-based treatment). HCV prevalence at 5 years without treatment differed significantly between the three networks (UK-net (42.8%) vs. AUS-net (38.2%), p < 0.0001 and vs. USA-net (54.0%), p < 0.0001). In the treatment scenarios UK-net and AUS-net showed a benefit of degree-based treatment with a 5-year prevalence of 1.0% vs. 9.6% p < 0.0001 and 0.15% vs. 0.44%, p < 0.0001. USA-net showed no significant difference (29.3% vs. 29.2%, p = 0.0681). Degree-based treatment was optimised with low prevalence, moderate treatment coverage conditions whereas random treatment was optimised in low treatment coverage, high prevalence conditions. In conclusion, injecting network structure determines the transmission rate of HCV and the most efficient treatment strategy. In real-world injecting network structures, the benefit of targeting HCV treatment at individuals with multiple injecting partnerships may have been underestimated.
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Journal of Viral Hepatitis - 2021 - Brown - Injecting network structure determines the most efficient strategy to achieve
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In preparation date: 2021
Published date: September 2021
Keywords:
elimination, Hepatitis C, injecting, model, network, prevention, treatment
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Local EPrints ID: 455508
URI: http://eprints.soton.ac.uk/id/eprint/455508
ISSN: 1352-0504
PURE UUID: 6200feae-db40-4b8f-9f66-10c3924d6c94
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Date deposited: 24 Mar 2022 17:32
Last modified: 17 Mar 2024 03:52
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Author:
Chloe Brown
Author:
Martin Siegele
Author:
Mark Wright
Author:
Charlotte Cook
Author:
Rachel Sacks-Davis
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