A network intervention that locates and intervenes with recently HIV-infected persons: The Transmission Reduction Intervention Project (TRIP)

Nikolopoulos, Georgios K.; Pavlitina, Eirini; Muth, Stephen Q.; Schneider, John A.; Psichogiou, Mina; Williams, Leslie D.; Paraskevis, Dimitrios; Sypsa, Vana; Magiorkinis, Gkikas; Smyrnov, Pavlo; Korobchuk, Anya; Vasylyeva, Tetyana I.; Skaathun, Britt; Malliori, Melpomeni; Kafetzopoulos, E.; Hatzakis, Angelos; Friedman, Samuel R.

doi:10.1038/srep38100

Cited by 70 publications

(119 citation statements)

References 41 publications

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“…Furthermore, cost comparisons indicate that TRIP techniques locate PwRI less expensively than does IBBS. These findings support the conclusions of a TRIP‐related study in Athens, Greece , which concluded that “efforts to seek, test, and treat PwRI can be accelerated using strategic, network‐based approaches.” The results in this paper show that, in Odessa, the risk network recruitment approach used in TRIP, together with recruiting seeds who were HIV positive, located more PwRI (at less expense) than a standard RDS epidemiologic study.…”

Section: Discussionsupporting

confidence: 82%

“…Contrary to our expectations, the two arms of TRIP recruited similar proportions of PwRI in Odessa. In Athens TRIP, the networks of PwRI contained higher proportions of PwRI than did the networks of longer‐term HIV‐positives . These different results may well be due to the different percentages in the two cities of risk network members who were recruited from venues as opposed to from among named risk network members.…”

Section: Discussionmentioning

confidence: 93%

“…Past research on injectors’ networks in Athens by Nikolopoulos et al. and research by Green et al. in San Diego showed that partner services that traced the sexual contacts of the recently infected are effective at finding other PwRI.…”

Section: Discussionmentioning

confidence: 99%

“…The logic for focusing on people with recent infection (PwRI) has been previously described . Research from Athens (Greece) provides evidence that recruiting the risk networks of PwRI is a good strategy for finding additional PwRI, since in that study, PwRI were recruited at higher rates in the risk networks of PwRI seeds than in the comparison group risk networks of longer‐term infected seeds .…”

Section: Introductionmentioning

confidence: 99%

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Social network approaches to locating people recently infected with HIV in Odessa, Ukraine

et al. 2019

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Introduction This paper examines the extent to which an intervention succeeded in locating people who had recently become infected with HIV in the context of the large‐scale Ukrainian epidemic. Locating and intervening with people who recently became infected with HIV (people with recent infection, or Pw RI ) can reduce forward HIV transmission and help Pw RI remain healthy. Methods The Transmission Reduction Intervention Project ( TRIP ) recruited recently‐infected and longer‐term infected seeds in Odessa, Ukraine, in 2013 to 2016, and asked them to help recruit their extended risk network members. The proportions of network members who were Pw RI were compared between TRIP arms (i.e. networks of recently‐infected seeds vs. networks of longer‐term infected seeds) and to the proportion of participants who were Pw RI in an RDS ‐based Integrated Biobehavioral Surveillance of people who inject drugs in 2013. Results The networks of Pw RI seeds and those of longer‐term infected seeds had similar (2%) proportions who were themselves Pw RI . This was higher than the 0.25% proportion in IBBS ( OR = 7.80; p = 0.016). The odds ratio among the subset of participants who injected drugs was 11.17 ( p = 0.003). Cost comparison analyses using simplified ingredients‐based methods found that TRIP spent no more than US $4513 per Pw RI located whereas IBBS spent $11,924. Conclusions Further research is needed to confirm these results and improve TRIP further, but our findings suggest that interventions that trace the networks of people who test HIV ‐positive are a cost‐effective way to locate Pw RI and reduce HIV transmission and should therefore be implemented.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Social network approaches to locating people recently infected with HIV in Odessa, Ukraine

et al. 2019

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“…Finally, on the largest scales, one can reconstruct networks of interactions among members of a particular species [50][51][52], or different species in an ecosystem [53][54][55][56][57][58]. This knowledge may help in forecasting ecological catastrophes [59,60] and addressing the spread of infectious disease [61] (or other epidemics [62]). …”

Section: Etdq Rkqvmidgsamird Ymrtgegflc Mldf Rkqvvicgysfmifdqymrtgegfmentioning

confidence: 99%

Reverse-engineering biological networks from large data sets

Natale

Hofmann

Hernández

et al. 2017

Preprint

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Much of contemporary systems biology owes its success to the abstraction of a network, the idea that diverse kinds of molecular, cellular, and organismal species and interactions can be modeled as relational nodes and edges in a graph of dependencies. Since the advent of high-throughput data acquisition technologies in fields such as genomics, metabolomics, and neuroscience, the automated inference and reconstruction of such interaction networks directly from large sets of activation data, commonly known as reverse-engineering, has become a routine procedure. Whereas early attempts at network reverse-engineering focused predominantly on producing maps of system architectures with minimal predictive modeling, reconstructions now play instrumental roles in answering questions about the statistics and dynamics of the underlying systems they represent. Many of these predictions have clinical relevance, suggesting novel paradigms for drug discovery and disease treatment. While other reviews focus predominantly on the details and effectiveness of individual network inference algorithms, here we examine the emerging field as a whole. We first summarize several key application areas in which inferred networks have made successful predictions. We then outline the two major classes of reverse-engineering methodologies, emphasizing that the type of prediction that one aims to make dictates the algorithms one should employ. We conclude by discussing whether recent breakthroughs justify the computational costs of large-scale reverse-engineering sufficiently to admit it as a mainstay in the quantitative analysis of living systems.

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Risk network approaches to locating undiagnosed HIV cases in Odessa, Ukraine

et al. 2018

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IntroductionProviding HIV healthcare and Treatment as Prevention both depend on diagnosing HIV cases, preferably soon after initial infection. We hypothesized that tracing risk networks recruits higher proportions of undiagnosed positives than outreach‐based testing or respondent‐driven sampling (RDS) in Odessa, Ukraine.MethodsThe Transmission Reduction Intervention Project (TRIP) used risk network tracing to recruit sexual and injection networks of recently‐infected and longer‐term infected (LTs) seeds (2013 to 2016). Integrated Biobehavioural Surveillance (IBBS) (2013) used RDS to recruit people who inject drugs (PWID). Outreach Testing tested PWID for HIV at community outreach sites (2013 to 2016). Proportions of undiagnosed positives among those tested were compared TRIP versus IBBS; TRIP versus Outreach Testing and between TRIP arms. Costs were compared across the projects.Results TRIP tested 1252 people (21% women) in seeds’ risk networks; IBBS tested 400 (18% women); Outreach Testing 13,936 (31% women). TRIP networks included a higher proportion of undiagnosed positives (14.6%) than IBBS (5.0%) or Outreach Testing (2.4%); odds ratio (OR) 3.25 (95% CI 2.07, 5.12) versus IBBS and 7.03 (CI 5.95, 8.31) versus Outreach Testing respectively. Findings remained significant in analyses stratified by sex and when PWID in TRIP networks were compared with Outreach Testing and IBBS. Within TRIP, recently‐infected participants’ networks contained higher proportions of undiagnosed positives (16.3%) than LTs’ networks (12.2%); OR 1.41 (CI 1.01, 1.95). TRIP located undiagnosed positives less expensively than did RDS or Outreach Testing.Conclusions TRIP's recruiting techniques, including prioritizing networks of the recently infected, find undiagnosed HIV‐positive people efficiently. They should be integrated with standard practice to improve case‐finding. Research should test these techniques in other socio‐epidemiologic contexts.Clinical trial registryRegistered ClinicalTrials.gov: NCT01827228.

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A network intervention that locates and intervenes with recently HIV-infected persons: The Transmission Reduction Intervention Project (TRIP)

Cited by 70 publications

References 41 publications

Social network approaches to locating people recently infected with HIV in Odessa, Ukraine

Social network approaches to locating people recently infected with HIV in Odessa, Ukraine

Reverse-engineering biological networks from large data sets

Risk network approaches to locating undiagnosed HIV cases in Odessa, Ukraine

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