Dataset Information

A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015.

ABSTRACT:

Background

A high prevalence (92.3%) of hepatitis C virus (HCV) co-infection among HIV patients identified during a large HIV outbreak associated with injection of oxymorphone in Indiana prompted genetic analysis of HCV strains.

Methods

Molecular epidemiological analysis of HCV-positive samples included genotyping, sampling intra-host HVR1 variants by next-generation sequencing (NGS) and constructing transmission networks using Global Hepatitis Outbreak and Surveillance Technology (GHOST).

Findings

Results from the 492 samples indicate predominance of HCV genotypes 1a (72.2%) and 3a (20.4%), and existence of 2 major endemic NS5B clusters involving 49.8% of the sequenced strains. Among 76 HIV co-infected patients, 60.5% segregated into 2 endemic clusters. NGS analyses of 281 cases identified 826,917 unique HVR1 sequences and 51 cases of mixed subtype/genotype infections. GHOST mapped 23 transmission clusters. One large cluster (n?=?130) included 50 cases infected with ?2 subtypes/genotypes and 43 cases co-infected with HIV. Rapid strain replacement and superinfection with different strains were found among 7 of 12 cases who were followed up.

Interpretation

GHOST enabled mapping of HCV transmission networks among persons who inject drugs (PWID). Findings of numerous transmission clusters, mixed-genotype infections and rapid succession of infections with different HCV strains indicate a high rate of HCV spread. Co-localization of HIV co-infected patients in the major HCV clusters suggests that HIV dissemination was enabled by existing HCV transmission networks that likely perpetuated HCV in the community for years. Identification of transmission networks is an important step to guiding efficient public health interventions for preventing and interrupting HCV and HIV transmission among PWID. FUND: US Centers for Disease Control and Prevention, and US state and local public health departments.

SUBMITTER: Ramachandran S

PROVIDER: S-EPMC6284413 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015.

Ramachandran Sumathi S Thai Hong H Forbi Joseph C JC Galang Romeo Regi RR Dimitrova Zoya Z Xia Guo-Liang GL Lin Yulin Y Punkova Lili T LT Pontones Pamela R PR Gentry Jessica J Blosser Sara J SJ Lovchik Judith J Switzer William M WM Teshale Eyasu E Peters Philip P Ward John J Khudyakov Yury Y

EBioMedicine 20181115

<h4>Background</h4>A high prevalence (92.3%) of hepatitis C virus (HCV) co-infection among HIV patients identified during a large HIV outbreak associated with injection of oxymorphone in Indiana prompted genetic analysis of HCV strains.<h4>Methods</h4>Molecular epidemiological analysis of HCV-positive samples included genotyping, sampling intra-host HVR1 variants by next-generation sequencing (NGS) and constructing transmission networks using Global Hepatitis Outbreak and Surveillance Technology ...[more]

PMID: 30448155

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Project description:INTRODUCTION:From late 2014 through 2015, Scott County, Indiana faced an HIV outbreak triggered by opioid abuse and transition to injection drug use. Investigating the origins, risk factors, and responses related to this outbreak is critical to inform future surveillance, interventions, and policymaking. In response, this retrospective infoveillance study identifies and characterizes user-generated messages related to opioid abuse, heroin injection drug use, and HIV status using natural language processing (NLP) among Twitter users in Indiana during the period of this HIV outbreak. MATERIALS AND METHODS:Our study consisted of two phases: data collection and processing, and data analysis. We collected Indiana geolocated tweets from the public Twitter API using Amazon Web Services EC2 instances filtered for geocoded messages in the immediate pre and post period of the outbreak. In the data analysis phase we applied an unsupervised machine learning approach using NLP called the Biterm Topic Model (BTM) to identify tweets related to opioid, heroin/injection, and HIV behavior and then examined these messages for HIV risk-related topics that could be associated with the outbreak. RESULTS:More than 10 million geocoded tweets occurring in Indiana during the immediate pre and post period of the outbreak were collected for analysis. Using BTM, we identified 1350 tweets thought to be relevant to the outbreak and then confirmed 358 tweets using human annotation. The most prevalent themes identified were tweets related to self-reported abuse of illicit and prescription drugs, opioid use disorder, self-reported HIV status, and public sentiment regarding the outbreak. Geospatial analysis found that these messages clustered in population dense areas outside of the outbreak, including Indianapolis and neighboring Clark County. DISCUSSION:This infoveillance study characterized the social media conversations of communities in Indiana in the pre and post period of the 2015 HIV outbreak. Behavioral themes detected reflect discussion about risk factors related to HIV transmission stemming from opioid and heroin abuse for priority populations, and also help identify community attitudes that could have motivated or detracted the use of HIV prevention methods, along with helping identify factors that can impede access to prevention services. CONCLUSIONS:Infoveillance approaches, such as the analysis conducted in this study, represent a possibly strategy to detect "signal" of the emergence of risk factors associated with an outbreak though may be limited in their scope and generalizability. Our results, in conjunction with other forms of public health surveillance, can leverage the growing ubiquity of social media platforms to better detect opioid-related HIV risk knowledge, attitudes and behavior, as well as inform future prevention efforts.

Project description:BackgroundPeople who inject drugs (PWID) experience high incarceration rates, with current/recent incarceration being associated with increased hepatitis C virus (HCV) transmission. We assess the contribution of incarceration to HCV transmission amongst PWID in Perry County (PC), Kentucky, USA, and the impact of scaling-up community and in-prison opioid substitution therapy (OST), including the potential for reducing incarceration.MethodsA dynamic model of incarceration and HCV transmission amongst PWID was calibrated in a Bayesian framework to epidemiological and incarceration data from PC, incorporating an empirically estimated 2.8-fold (95%CI: 1.36-5.77) elevated HCV acquisition risk amongst currently incarcerated or recently released (<6 months) PWID compared to other PWID. We projected the percentage of new HCV infections that would be prevented among PWID over 2020-2030 if incarceration no longer elevated HCV transmission risk, if needle and syringe programmes (NSP) and OST are scaled-up, and/or if drug use was decriminalized (incarceration/reincarceration rates are halved) with 50% of PWID that would have been imprisoned being diverted onto OST. We assume OST reduces reincarceration by 10-42%.ResultsOver 2020-2030, removing the effect of incarceration on HCV transmission could prevent 42.7% (95% credibility interval: 15.0-67.4%) of new HCV infections amongst PWID. Conversely, scaling-up community OST and NSP to 50% coverage could prevent 28.5% (20.0-37.4%) of new infections, with this increasing to 32.7% (24.5-41.2%) if PWID are retained on OST upon incarceration, 36.4% (27.7-44.9%) if PWID initiate OST in prison, and 45.3% (35.9-54.1%) if PWID are retained on OST upon release. decriminalization (with diversion to OST) could further increase this impact, preventing 56.8% (45.3-64.5%) of new infections. The impact of these OST interventions decreases by 2.1-28.6% if OST does not reduce incarceration.ConclusionIncarceration is likely to be an important contributor to HCV transmission amongst PWID in PC. Prison-based OST could be an important intervention for reducing this risk.

Dataset Information

A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015.

Background

Methods

Findings

Interpretation

Publications

A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015.

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