SynthesisPloS one
People who inject drugs in metropolitan Chicago: A meta-analysis of data from 1997-2017 to inform interventions and computational modeling toward hepatitis C microelimination.
Synthesis in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Modeling Highlights the Challenge of Achieving and Maintaining Hepatitis C Virus Micro-Elimination Among People Who Inject Drugs.Open forum infectious diseases · 2026Article
- A Novel Index Measure of Housing-Related Risk as a Predictor of Overdose Among Young People Who Inject Drugs and Injection Networks.Journal of urban health : bulletin of the New York Academy of Medicine · 2026Article
- Differential risk for Hepatitis C and HIV among people who inject drugs in Kenya: a latent class analysis signaling needs for innovation in service delivery.The International journal on drug policy · 2025Article
- Testing the Relationships Between Multiple Domains of Substance Use‑Related Stigma and Depression Among a Sample of Young (18-30‑Year‑Old) People Who Inject Drugs in the Greater Chicago Area.International journal of mental health and addiction · 2025Article
- Spatial inequities in access to medications for treatment of opioid use disorder highlight scarcity of methadone providers under counterfactual scenarios.PLoS computational biology · 2024Article
- Spatial inequities in access to medications for treatment of opioid use disorder highlight scarcity of methadone providers under counterfactual scenarios.medRxiv : the preprint server for health sciences · 2023Article
- Injection partnership characteristics and HCV status associations with syringe and equipment sharing among people who inject drugs.BMC public health · 2023Article
- Egocentric network characteristics of people who inject drugs in the Chicago metro area and associations with hepatitis C virus and injection risk behavior.Harm reduction journal · 2022Article
- Network ethnic homophily and injection equipment sharing among Latinx and White non-Latinx people who inject drugs.Journal of ethnicity in substance abuseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Progress toward hepatitis C virus (HCV) elimination in the United States is not on track to meet targets set by the World Health Organization, as the opioid crisis continues to drive both injection drug use and increasing HCV incidence. A pragmatic approach to achieving this is using a microelimination approach of focusing on high-risk populations such as people who inject drugs (PWID). Computational models are useful in understanding the complex interplay of individual, social, and structural level factors that might alter HCV incidence, prevalence, transmission, and treatment uptake to achieve HCV microelimination. However, these models need to be informed with realistic sociodemographic, risk behavior and network estimates on PWID. We conducted a meta-analysis of research studies spanning 20 years of research and interventions with PWID in metropolitan Chicago to produce parameters for a synthetic population for realistic computational models (e.g., agent-based models). We then fit an exponential random graph model (ERGM) using the network estimates from the meta-analysis in order to develop the network component of the synthetic population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.