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Cao B, Liu M, Jiang T, Yu Q, Yuan T, Ding P, Zhou X, Huang Y, Zou Y, Huang F. HCV Genotype Distribution and Clinical Characteristics of HCV Mono-Infected and HCV/HIV Co-Infected Patients in Liangshan Prefecture, Sichuan Province, China. J Int Assoc Provid AIDS Care 2023; 22:23259582231217810. [PMID: 38099656 PMCID: PMC10725143 DOI: 10.1177/23259582231217810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
Objective: The present study aimed to characterize the genotype distribution and clinical characteristics of HCV monoinfected and HCV/HIV coinfected patients in the Liangshan Prefecture, Sichuan Province, China. Methods: All the patients were divided into HCV monoinfection and HCV/HIV coinfection groups according to whether they were complicated with HIV infection. The data from the two groups were collected. Results: In this study, HCV genotype 3 was the most common genotype in both groups, while HCV genotype 6 was significantly higher in the coinfection group than in the monoinfection group (p = 0.046). The white blood cell count, total bilirubin level, and HCV RNA were significantly higher in the HCV monoinfection group than that in the HCV/HIV coinfection group (p = 0.031; p < 0.001; p = 0.027, respectively). Conclusion: HCV prevalence was high in HIV-positive patients in the Liangshan Prefecture. Thus, incorporating screening and management of HCV monoinfection and HCV/HIV coinfection is needed in local region programs.
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Affiliation(s)
- Bianchuan Cao
- Department of Infectious Disease, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Mei Liu
- Antiviral Therapy Center, The First People's Hospital of Yuexi County, Liangshan, China
| | - Tao Jiang
- Antiviral Therapy Center, The First People's Hospital of Yuexi County, Liangshan, China
| | - Qinghua Yu
- Antiviral Therapy Center, The First People's Hospital of Yuexi County, Liangshan, China
| | - Tianru Yuan
- Antiviral Therapy Center, The First People's Hospital of Yuexi County, Liangshan, China
| | - Ping Ding
- Antiviral Therapy Center, The First People's Hospital of Yuexi County, Liangshan, China
| | - Xian Zhou
- Antiviral Therapy Center, The First People's Hospital of Yuexi County, Liangshan, China
| | - Yongmao Huang
- Department of Infectious Disease, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yongsheng Zou
- Department of Infectious Disease, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fuli Huang
- Department of Infectious Disease, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Country versus pharmaceutical company interests for hepatitis C treatment. Health Care Manag Sci 2022; 25:725-749. [PMID: 36001218 PMCID: PMC9399601 DOI: 10.1007/s10729-022-09607-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 07/06/2022] [Indexed: 11/04/2022]
Abstract
Hepatitis C virus (HCV) is one of the leading causes of liver disease and is responsible for massive health and economic burden worldwide. The disease is asymptomatic in its early stages, but it can progress over time to fatal end-stage liver disease. Thus, the majority of individuals infected with HCV are unaware of their chronic condition. Recent treatment options for HCV can completely cure the infection but are costly. We developed a game model between a pharmaceutical company (PC) and a country striving to maximize its citizens' utility. First, the PC determines the price of HCV treatment; then, the country responds with corresponding screening and treatment strategies. We employed an analytical framework to calculate the utility of the players for each selected strategy. Calibrated to detailed HCV data from Israel, we found that the PC will gain higher revenue by offering a quantity discount rather than using standard fixed pricing per treatment, by indirectly forcing the country to conduct more screening than it desired. By contrast, risk-sharing agreements, in which the country pays only for successful treatments are beneficial for the country. Our findings underscore that policy makers worldwide should prudently consider recent offers by PCs to increase screening either directly, via covering HCV screening, or indirectly, by providing discounts following a predetermined volume of sales. More broadly, our approach is applicable in other healthcare settings where screening is essential to determine treatment strategies.
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Malloy GSP, Goldhaber-Fiebert JD, Enns EA, Brandeau ML. Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure. Med Decis Making 2021; 41:623-640. [PMID: 33899563 PMCID: PMC8295189 DOI: 10.1177/0272989x211006025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong. METHODS We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium. RESULTS Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy. CONCLUSIONS For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure. HIGHLIGHTS • We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.• We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.• Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.• Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.
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Affiliation(s)
- Giovanni S P Malloy
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Jeremy D Goldhaber-Fiebert
- Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA
| | - Eva A Enns
- School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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Bellerose M, Zhu L, Hagan LM, Thompson WW, Randall LM, Malyuta Y, Salomon JA, Linas BP. A review of network simulation models of hepatitis C virus and HIV among people who inject drugs. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2021; 88:102580. [PMID: 31740175 PMCID: PMC8729792 DOI: 10.1016/j.drugpo.2019.10.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/17/2019] [Accepted: 10/04/2019] [Indexed: 01/22/2023]
Abstract
Network modelling is a valuable tool for simulating hepatitis C virus (HCV) and HIV transmission among people who inject drugs (PWID) and assessing the potential impact of treatment and harm-reduction interventions. In this paper, we review literature on network simulation models, highlighting key structural considerations and questions that network models are well suited to address. We describe five approaches (Erdös-Rényi, Stochastic Block, Watts-Strogatz, Barabási-Albert, and Exponential Random Graph Model) used to model partnership formation with emphasis on the strengths of each approach in simulating different features of real-world PWID networks. We also review two important structural considerations when designing or interpreting results from a network simulation study: (1) dynamic vs. static network and (2) injection only vs. both injection and sexual networks. Dynamic network simulations allow partnerships to evolve and disintegrate over time, capturing corresponding shifts in individual and population-level risk behaviour; however, their high level of complexity and reliance on difficult-to-observe data has driven others to develop static network models. Incorporating both sexual and injection partnerships increases model complexity and data demands, but more accurately represents HIV transmission between PWID and their sexual partners who may not also use drugs. Network models add the greatest value when used to investigate how leveraging network structure can maximize the effectiveness of health interventions and optimize investments. For example, network models have shown that features of a given network and epidemic influence whether the greatest community benefit would be achieved by allocating hepatitis C or HIV treatment randomly, versus to those with the most partners. They have also demonstrated the potential for syringe services and "buddy sharing" programs to reduce disease transmission.
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Affiliation(s)
- Meghan Bellerose
- Prevention Policy Modeling Lab, Department of Global Health and Population, Harvard T. H. Chan School of Public Health, 90 Smith Street, Boston, MA 02120, United States.
| | - Lin Zhu
- Prevention Policy Modeling Lab, Department of Global Health and Population, Harvard T. H. Chan School of Public Health, 90 Smith Street, Boston, MA 02120, United States
| | - Liesl M Hagan
- Division of Viral Hepatitis, U.S. Centers for Disease Control, United States
| | - William W Thompson
- Division of Viral Hepatitis, U.S. Centers for Disease Control, United States
| | | | - Yelena Malyuta
- Prevention Policy Modeling Lab, Department of Global Health and Population, Harvard T. H. Chan School of Public Health, 90 Smith Street, Boston, MA 02120, United States
| | - Joshua A Salomon
- Prevention Policy Modeling Lab, Department of Global Health and Population, Harvard T. H. Chan School of Public Health, 90 Smith Street, Boston, MA 02120, United States; Center for Health Policy / Center for Primary Care and Outcomes Research, Stanford University, United States
| | - Benjamin P Linas
- Boston Medical Center, Boston University School of Public Health, United States
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Zang X, Krebs E, Min JE, Pandya A, Marshall BDL, Schackman BR, Behrends CN, Feaster DJ, Nosyk B. Development and Calibration of a Dynamic HIV Transmission Model for 6 US Cities. Med Decis Making 2019; 40:3-16. [PMID: 31865849 DOI: 10.1177/0272989x19889356] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background. Heterogeneity in HIV microepidemics across US cities necessitates locally oriented, combination implementation strategies to prioritize resources. We calibrated and validated a dynamic, compartmental HIV transmission model to establish a status quo treatment scenario, holding constant current levels of care for 6 US cities. Methods. Built off a comprehensive evidence synthesis, we adapted and extended a previously published model to replicate the transmission, progression, and clinical care for each microepidemic. We identified a common set of 17 calibration targets between 2012 and 2015 and used the Morris method to select the most influential parameters for calibration. We then applied the Nelder-Mead algorithm to iteratively calibrate the model to generate 2000 best-fitting parameter sets. Finally, model projections were internally validated with a series of robustness checks and externally validated against published estimates of HIV incidence, while the face validity of 25-year projections was assessed by a Scientific Advisory Committee (SAC). Results. We documented our process for model development, calibration, and validation to maximize its transparency and reproducibility. The projected outcomes demonstrated a good fit to calibration targets, with a mean goodness-of-fit ranging from 0.0174 (New York City [NYC]) to 0.0861 (Atlanta). Most of the incidence predictions were within the uncertainty range for 5 of the 6 cities (ranging from 21% [Miami] to 100% [NYC]), demonstrating good external validity. The face validity of the long-term projections was confirmed by our SAC, showing that the incidence would decrease or remain stable in Atlanta, Los Angeles, NYC, and Seattle while increasing in Baltimore and Miami. Discussion. This exercise provides a basis for assessing the incremental value of further investments in HIV combination implementation strategies tailored to urban HIV microepidemics.
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Affiliation(s)
- Xiao Zang
- BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.,Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Emanuel Krebs
- BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - Jeong E Min
- BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - Ankur Pandya
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brandon D L Marshall
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Bruce R Schackman
- Department of Healthcare Policy and Research, Weill Cornell Medical College, New York City, NY, USA
| | - Czarina N Behrends
- Department of Healthcare Policy and Research, Weill Cornell Medical College, New York City, NY, USA
| | - Daniel J Feaster
- Department of Epidemiology and Public Health, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bohdan Nosyk
- BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.,Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
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Pitcher AB, Borquez A, Skaathun B, Martin NK. Mathematical modeling of hepatitis c virus (HCV) prevention among people who inject drugs: A review of the literature and insights for elimination strategies. J Theor Biol 2019; 481:194-201. [PMID: 30452959 PMCID: PMC6522340 DOI: 10.1016/j.jtbi.2018.11.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/13/2018] [Accepted: 11/15/2018] [Indexed: 02/07/2023]
Abstract
In 2016, the World Health Organization issued global elimination targets for hepatitis C virus (HCV), including an 80% reduction in HCV incidence by 2030. The vast majority of new HCV infections occur among people who inject drugs (PWID), and as such elimination strategies require particular focus on this population. As governments urgently require guidance on how to achieve elimination among PWID, mathematical modeling can provide critical information on the level and targeting of intervention are required. In this paper we review the epidemic modeling literature on HCV transmission and prevention among PWID, highlight main differences in mathematical formulation, and discuss key insights provided by these models in terms of achieving WHO elimination targets among PWID. Overall, the vast majority of modeling studies utilized a deterministic compartmental susceptible-infected-susceptible structure, with select studies utilizing individual-based network transmission models. In general, these studies found that harm reduction alone is unlikely to achieve elimination targets among PWID. However, modeling indicates elimination is achievable in a wide variety of epidemic settings with harm reduction scale-up combined with modest levels of HCV treatment for PWID. Unfortunately, current levels of testing and treatment are generally insufficient to achieve elimination in most settings, and require further scale-up. Additionally, network-based treatment strategies as well as prison-based treatment and harm reduction provision could provide important additional population benefits. Overall, epidemic modeling has and continues to play a critical role in informing HCV elimination strategies worldwide.
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Affiliation(s)
| | - Annick Borquez
- Division of Infectious Diseases and Global Public Health, University of California San Diego, CA, USA
| | - Britt Skaathun
- Division of Infectious Diseases and Global Public Health, University of California San Diego, CA, USA
| | - Natasha K Martin
- Division of Infectious Diseases and Global Public Health, University of California San Diego, CA, USA.
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Augusta C, Taylor GW, Deardon R. Dynamic contact networks of swine movement in Manitoba, Canada: Characterization and implications for infectious disease spread. Transbound Emerg Dis 2019; 66:1910-1919. [PMID: 31059200 DOI: 10.1111/tbed.13220] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/27/2019] [Accepted: 04/24/2019] [Indexed: 11/28/2022]
Abstract
We use swine shipping data from Manitoba to construct one-mode dynamic contact networks of swine locations and two-mode location-to-truck networks at four time scales: daily, weekly, monthly and for the entire two-year study period. We provide measures of graph evolution and graph characterization for each, useful in the development of statistical models related to infectious disease transmission. We find that Manitoba shipping practices differ from those in other Canadian regions, and particularly that truck sharing is more common in Manitoba than elsewhere in the country.
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Affiliation(s)
- Carolyn Augusta
- Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada
| | - Graham W Taylor
- School of Engineering, University of Guelph, Guelph, Ontario, Canada.,Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Rob Deardon
- Department of Production Animal Health and Department of Mathematics and Statistics, University of Calgary, Calgary, Alberta, Canada
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Cost-effectiveness of alternative strategies for provision of HIV preexposure prophylaxis for people who inject drugs. AIDS 2018; 32:663-672. [PMID: 29334549 DOI: 10.1097/qad.0000000000001747] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Oral HIV preexposure prophylaxis (PrEP) has been recommended as a means of HIV prevention among people who inject drugs (PWIDs) but, at current prices, is unlikely to be cost-effective for all PWID. OBJECTIVE To determine the cost-effectiveness of alternative strategies for enrolling PWID in PrEP. DESIGN Dynamic network model that captures HIV transmission and progression among PWID in a representative US urban center. OUTCOME MEASURES HIV infections averted, discounted costs and quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. INTERVENTION We assume 25% PrEP coverage and investigate four strategies: first, random PWID are enrolled (Unselected Enrollment); second, individuals are randomly selected and enrolled together with their partners (Enroll Partners); third, individuals with the highest number of sexual and needle-sharing partnerships are enrolled (Most Partners); fourth, individuals with the greatest number of infected partners are enrolled (Most Positive Partners). RESULTS PrEP can achieve significant health benefits: compared with the status quo of no PrEP, the strategies gain 1114 QALYs (Unselected Enrollment), 2194 QALYs (Enroll Partners), 2481 QALYs (Most Partners), and 3046 QALYs (Most Positive Partners) over 20 years in a population of approximately 8500 people. The incremental cost-effectiveness ratio of each strategy compared with the status quo (cost per QALY gained) is $272 000 (Unselected Enrollment), $158 000 (Enroll Partners), $124 000 (Most Partners), and $101 000 (Most Positive Partners). All strategies except Unselected Enrollment are cost-effective according to WHO criteria. CONCLUSION Selection of high-risk PWID for PrEP can improve the cost-effectiveness of PrEP for PWID.
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