Search

Abstract: Hepatitis C virus (HCV) is a blood-borne virus that disproportionately affects people who inject drugs (PWIDs). Based on extensive interview and blood test data from a longitudinal study in Melbourne, Australia, we describe an individual-based transmission model for HCV spread amongst PWID. We use this model to simulate the transmission of HCV on an empirical social network of PWID. A feature of our model is that sources of infection can be both network neighbours and non-neighbours via “importing”. Data-driven estimates of sharing frequency and rate of importing are provided. Compared to an appropriately calibrated fully connected network, the empirical network provides some protective effect on the time to primary infection. We also illustrate heterogeneities in incidence rate of infection, both across and within node degrees (i.e., number of network partners). We explore the reduced risk of infection from spontaneously clearing cutpoint nodes whose infection status oscillates, both in theory and in simulation. Further, we show our model-based estimate of per-event transmission probability largely agrees with previous estimates at the lower end of the range 1–3% commonly cited. [Copyright &y& Elsevier]

Background: Compared to the general population, people who inject drugs have poor health and wellbeing. Longitudinal studies can provide insight into factors driving these worse health outcomes but are subject to methodological challenges, such as cohort attrition. The aim of this study was to assess and characterise attrition in a prospective cohort of people who inject drugs in Victoria, Australia., Methods: Using annually collected self-reported data from The Melbourne Injecting Drug User Cohort Study (SuperMIX) from September 2008 to January 2021, we estimated the incidence of participants being lost-to-follow-up (LTFU), with an episode of being LTFU defined as participants not undertaking a follow-up interview within two years of their last interview. We utilised a multiple event discrete-time survival analysis on participant period-observation data to estimate the associations between key factors and LTFU. Key areas of exposure measurement in analyses were sociodemographic, drug use and mental health., Results: A total of n = 1328 SuperMIX participants completed a baseline interview, with n = 489 (36.8%) LTFU, i.e. not completing a follow-up interview in the following two years. Increased attrition was observed among SuperMIX participants who were: born outside Australia, younger than 30 years, reporting having completed fewer years of education, not residing in stable accommodation, not in stable employment and not on opioid agonist therapy (OAT)., Conclusions: The attrition rate of the SuperMIX cohort has largely been stable throughout the duration of the study. Higher attrition rates among individuals at greater sociodemographic disadvantage and not on OAT suggest that additional efforts are required to retain these participants. Findings also suggest that SuperMIX might not be capturing data on adverse health and wellbeing outcomes among subpopulations at high risk of harm., (© 2024. The Author(s).)

Background: Coronavirus disease 2019 (COVID-19) outbreaks in acute care settings can have severe consequences for patients due to their underlying vulnerabilities, and can be costly due to additional patient bed-days and the need to replace isolating staff. This study assessed the cost-effectiveness of clinical staff N95 respirators and admission screening testing of patients to reduce COVID-19 hospital-acquired infections., Methods: An agent-based model was calibrated to data on 178 outbreaks in acute care settings in Victoria, Australia between October 2021 and July 2023. Outbreaks were simulated under different combinations of staff masking (surgical, N95) and patient admission screening testing [none, rapid antigen test (RAT), polymerase chain reaction]. For each scenario, average diagnoses, COVID-19 deaths, quality-adjusted life years from discharged patients, and costs (masks, testing, patient COVID-19 bed-days, staff replacement costs while isolating) from acute COVID-19 were estimated over a 12-month period., Findings: Compared with no admission screening testing and staff surgical masks, all scenarios were cost saving with health gains. Staff N95 respirators + RAT admission screening of patients was the cheapest scenario, saving A$78.4M [95% uncertainty interval (UI) 44.4M-135.3M] and preventing 1543 (95% UI 1070-2146) deaths state-wide per annum. Both interventions were individually beneficial: staff N95 respirators saved A$54.7M and 854 deaths state-wide per annum, while RAT admission screening of patients saved A$57.6M and 1176 deaths state-wide per annum., Interpretation: In acute care settings, staff N95 respirators and admission screening testing of patients can reduce hospital-acquired COVID-19 and COVID-19 deaths, and are cost saving because of reduced patient bed-days and staff replacement needs., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia., Methods: The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%)., Results: Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs., Conclusion: Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 McAndrew, Sacks-Davis, Abeysuriya, Delport, West, Parta, Majumdar, Hellard and Scott.)

Background: Prior to mid-2021, Australia's approach to COVID-19 was to eliminate community transmission. However, between August-November 2021, the state of Victoria, Australia, experienced an outbreak of the Delta variant that continued to grow despite extensive lockdowns and public health measures in place. While these public health restrictions were ultimately unable to stop community transmission, they likely had a major impact reducing transmission and adverse health outcomes relative to voluntary risk-mitigation only (e.g., in response to rising cases and deaths, some people may avoid crowded settings, hospitality, retail, social occasions, or indoor settings). This study aims to estimate the impact of the August-November 2021 enforced public health restrictions in Victoria, compared to voluntary risk-mitigation only., Methods: An agent-based model was calibrated to Victorian epidemiological, health and behavioural data from 1 August to 30 November 2021, as well as policies that were implemented over that period. Two counter-factual scenarios were run for the same period with (a) no restrictions in place; or (b) voluntary risk-mitigation only, based on behaviour measured over the December-January Omicron BA.1 epidemic wave when restrictions were not in place., Results: Over August-November 2021, the baseline model scenario resulted in 97,000 (91,000-102,000) diagnoses, 9100 (8500-9700) hospital admissions, and 480 (430-530) deaths. Without any restrictions in place, there were 3,228,000 (3,200,000-3,253,000) diagnoses, 375,100 (370,200-380,900) hospital admissions, and 16,700 (16,000-17,500) deaths. With voluntary risk-mitigation equal to those observed during the Omicron BA.1 epidemic wave, there were 1,507,000 (1,469,000-1,549,000) diagnoses, 130,300 (124,500-136,000) hospital admissions, and 5500 (5000-6100) deaths., Conclusion: Public health restrictions implemented in Victoria over August-November 2021 are likely to have averted more than 120,000 hospitalizations and 5000 deaths relative to voluntary risk-mitigation only. During a COVID-19 epidemic wave voluntary behaviour change can reduce transmission substantially, but not to the same extent as enforced restrictions., Competing Interests: Declaration of Competing Interest DD, RSD, RGA MH, NS have received funding from the Department of Health, State Government of Victoria, Australia for COVID-19-related work. DD is the recipient of a PhD scholarship from the National Health and Medical Research Council, Australia., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Policy responses to COVID-19 in Victoria, Australia over 2020-2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period., Methods: An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions., Results: Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a 'mystery case'. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures., Conclusions: Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation., (© 2023. The Author(s).)

Objective: To estimate the proportion of Victorians infected with COVID-19 in January 2022., Methods: Between 11-19 February 2022 we conducted a nested cross-sectional survey on experiences of COVID-19 testing, symptoms, test outcome and barriers to testing during January 2022 in Victoria, Australia. Respondents were participants of the Optimise Study, a prospective cohort of adults considered at increased risk of COVID-19 or the unintended consequences of COVID-19-related interventions., Results: Of the 577 participants, 78 (14%) reported testing positive to COVID-19, 240 (42%) did not test in January 2022 and 91 of those who did not test (38%) reported COVID-19-like symptoms. Using two different definitions of symptoms, we calculated symptomatic (27% and 39%) and asymptomatic (4% and 11%) test positivity. We extrapolated these positivity rates to participants who did not test and estimated 19-22% of respondents may have had COVID-19 infection in January 2022., Conclusion: The proportion of Victorians infected with COVID-19 in January 2022 was likely considerably higher than officially reported numbers., Implications for Public Health: Our estimate is approximately double the COVID-19 case numbers obtained from official case reporting. This highlights a major limitation of diagnosis data that must be considered when preparing for future waves of infection., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Monitoring trends in hepatitis C virus (HCV) incidence is critical for evaluating strategies aimed at eliminating HCV as a public health threat. We estimate HCV incidence and assess trends in incidence over time among primary care patients., Methods: Data were routinely extracted, linked electronic medical records from 12 primary care health services. Patients included were aged ≥16 years, tested HCV antibody negative on their first test recorded and had at least one subsequent HCV antibody or RNA test (January 2009-December 2020). HCV incident infections were defined as a positive HCV antibody or RNA test. A generalised linear model assessed the association between HCV incidence and calendar year., Results: In total, 6711 patients contributed 17,098 HCV test records, 210 incident HCV infections and 19,566 person-years; incidence was 1.1 per 100 person-years (95% confidence interval (CI): 0.9 to 1.2). Among 559 (8.2%) patients ever prescribed opioid-related pharmacotherapy (ORP) during the observation period, 135 infections occurred during 2,082 person-years (incidence rate of 6.5 per 100 person-years (95% CI: 5.4 to 7.7)). HCV incidence declined 2009-2020 overall (incidence rate ratio per calendar year 0.8 (95% CI: 0.8 to 0.9) and among patients ever prescribed ORT (incidence rate ratio per calendar year 0.9, 95% CI: 0.75 to 1.0)., Conclusion: HCV incidence declined among patients at primary care health services including among patients ever prescribed ORP and during the period following increased access to DAA therapy., Competing Interests: Declarations of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: In settings with zero community transmission, any new SARS-CoV-2 outbreaks are likely to be the result of random incursions. The level of restrictions in place at the time of the incursion is likely to considerably affect possible outbreak trajectories, but the probability that a large outbreak eventuates is not known., Methods: We used an agent-based model to investigate the relationship between ongoing restrictions and behavioural factors, and the probability of an incursion causing an outbreak and the resulting growth rate. We applied our model to the state of Victoria, Australia, which has reached zero community transmission as of November 2020., Results: We found that a future incursion has a 45% probability of causing an outbreak (defined as a 7-day average of > 5 new cases per day within 60 days) if no restrictions were in place, decreasing to 23% with a mandatory masks policy, density restrictions on venues such as restaurants, and if employees worked from home where possible. A drop in community symptomatic testing rates was associated with up to a 10-percentage point increase in outbreak probability, highlighting the importance of maintaining high testing rates as part of a suppression strategy., Conclusions: Because the chance of an incursion occurring is closely related to border controls, outbreak risk management strategies require an integrated approaching spanning border controls, ongoing restrictions, and plans for response. Each individual restriction or control strategy reduces the risk of an outbreak. They can be traded off against each other, but if too many are removed there is a danger of accumulating an unsafe level of risk. The outbreak probabilities estimated in this study are of particular relevance in assessing the downstream risks associated with increased international travel., (© 2022. The Author(s).)

Objectives: To assess the risks associated with relaxing coronavirus disease 2019 (COVID-19)-related physical distancing restrictions and lockdown policies during a period of low viral transmission., Design: Network-based viral transmission risks in households, schools, workplaces, and a variety of community spaces and activities were simulated in an agent-based model, Covasim., Setting: The model was calibrated for a baseline scenario reflecting the epidemiological and policy environment in Victoria during March-May 2020, a period of low community viral transmission., Intervention: Policy changes for easing COVID-19-related restrictions from May 2020 were simulated in the context of interventions that included testing, contact tracing (including with a smartphone app), and quarantine., Main Outcome Measure: Increase in detected COVID-19 cases following relaxation of restrictions., Results: Policy changes that facilitate contact of individuals with large numbers of unknown people (eg, opening bars, increased public transport use) were associated with the greatest risk of COVID-19 case numbers increasing; changes leading to smaller, structured gatherings with known contacts (eg, small social gatherings, opening schools) were associated with lower risks. In our model, the rise in case numbers following some policy changes was notable only two months after their implementation., Conclusions: Removing several COVID-19-related restrictions within a short period of time should be undertaken with care, as the consequences may not be apparent for more than two months. Our findings support continuation of work from home policies (to reduce public transport use) and strategies that mitigate the risk associated with re-opening of social venues., (© 2020 The Authors. Medical Journal of Australia published by John Wiley & Sons Australia, Ltd on behalf of AMPCo Pty Ltd.)

Social network characteristics of people who inject drugs (PWID) have previously been flagged as potential risk factors for HCV transmission such as increased injection frequency. To understand the role of the injecting network on injection frequency, we investigated how changes in an injecting network over time can modulate injecting risk behaviour. PWID were sourced from the Networks 2 Study, a longitudinal cohort study of PWID recruited from illicit drug street markets across Melbourne, Australia. Network-related correlates of injection frequency and the change in frequency over time were analysed using adjusted Cox Proportional Hazards and Generalised Estimating Equations modelling. Two-hundred and eighteen PWID followed up for a mean (s.d.) of 2.8 (1.7) years were included in the analysis. A greater number of injecting partners, network closeness centrality and eigenvector centrality over time were associated with an increased rate of infection frequency. Every additional injection drug partner was associated with an increase in monthly injection frequency. Similarly, increased network connectivity and centrality over time was also associated with an increase in injection frequency. This study observed that baseline network measures of connectivity and centrality may be associated with changes in injection frequency and, by extension, may predict subsequent HCV transmission risk. Longitudinal changes in network position were observed to correlate with changes in injection frequency, with PWID who migrate from the densely-connected network centre out to the less-connected periphery were associated with a decreased rate of injection frequency.

Introduction: Students comprised the majority of early cases of influenza A(H1N1)pdm09 in Melbourne, Australia. Students and school settings were targeted for public health interventions following the emergence of pH1N1. This study was conducted to describe changes in social contacts among the earliest confirmed student cases of pH1N1 in Melbourne, Australia, to inform future pandemic control policy and explore transmission model assumptions., Methods: A retrospective cross-sectional behavioural study of student cases with laboratory-confirmed pH1N1 between 28 April and 3 June 2009 was conducted in 2009. Demographics, symptom onset dates and detailed information on regular and additional extracurricular activities were collected. Summary measures for activities were calculated, including median group size and median number of close contacts and attendance during the students' exposure and infectious periods or during school closures. A multivariable model was used to assess associations between rates of participation in extracurricular activities and both school closures and students' infectious periods., Results: Among 162 eligible cases, 99 students participated. Students reported social contact in both curricular and extra-curricular activities. Group size and total number of close contacts varied. While participation in activities decreased during the students' infectious periods and during school closures, social contact was common during periods when isolation was advised and during school closures., Discussion: This study demonstrates the potential central role of young people in pandemic disease transmission given the level of non-adherence to prevention and control measures. These finding have public health implications for both informing modelling estimates of future pandemics and targeting prevention and control strategies to young people., ((c) 2018 The authors; licensee World Health Organization.)

Background: The hepatitis C virus (HCV) epidemic is a major health issue; in most developed countries it is driven by people who inject drugs (PWID). Injecting networks powerfully influence HCV transmission. In this paper we provide an overview of 10 years of research into injecting networks and HCV, culminating in a network-based approach to provision of direct-acting antiviral therapy., Methods: Between 2005 and 2010 we followed a cohort of 413 PWID, measuring HCV incidence, prevalence and injecting risk, including network-related factors. We developed an individual-based HCV transmission model, using it to simulate the spread of HCV through the empirical social network of PWID. In addition, we created an empirically grounded network model of injecting relationships using exponential random graph models (ERGMs), allowing simulation of realistic networks for investigating HCV treatment and intervention strategies. Our empirical work and modelling underpins the TAP Study, which is examining the feasibility of community-based treatment of PWID with DAAs., Results: We observed incidence rates of HCV primary infection and reinfection of 12.8 per 100 person-years (PY) (95%CI: 7.7-20.0) and 28.8 per 100 PY (95%CI: 15.0-55.4), respectively, and determined that HCV transmission clusters correlated with reported injecting relationships. Transmission modelling showed that the empirical network provided some protective effect, slowing HCV transmission compared to a fully connected, homogenous PWID population. Our ERGMs revealed that treating PWID and all their contacts was the most effective strategy and targeting treatment to infected PWID with the most contacts the least effective., Conclusion: Networks-based approaches greatly increase understanding of HCV transmission and will inform the implementation of treatment as prevention using DAAs., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Unlabelled: With the development of new highly efficacious direct-acting antiviral (DAA) treatments for hepatitis C virus (HCV), the concept of treatment as prevention is gaining credence. To date, the majority of mathematical models assume perfect mixing, with injectors having equal contact with all other injectors. This article explores how using a networks-based approach to treat people who inject drugs (PWID) with DAAs affects HCV prevalence. Using observational data, we parameterized an exponential random graph model containing 524 nodes. We simulated transmission of HCV through this network using a discrete time, stochastic transmission model. The effect of five treatment strategies on the prevalence of HCV was investigated; two of these strategies were (1) treat randomly selected nodes and (2) "treat your friends," where an individual is chosen at random for treatment and all their infected neighbors are treated. As treatment coverage increases, HCV prevalence at 10 years reduces for both the high- and low-efficacy treatment. Within each set of parameters, the treat your friends strategy performed better than the random strategy being most marked for higher-efficacy treatment. For example, over 10 years of treating 25 per 1,000 PWID, the prevalence drops from 50% to 40% for the random strategy and to 33% for the treat your friends strategy (6.5% difference; 95% confidence interval: 5.1-8.1)., Conclusion: Treat your friends is a feasible means of utilizing network strategies to improve treatment efficiency. In an era of highly efficacious and highly tolerable treatment, such an approach will benefit not just the individual, but also the community more broadly by reducing the prevalence of HCV among PWID., (© 2014 by the American Association for the Study of Liver Diseases.)

Objectives: To develop a mathematical model to project the potential impact of hepatitis C virus (HCV) treatment on HCV infection prevalence among people who inject drugs (PWID)., Design and Setting: An existing model of HCV transmission among PWID was parameterised using data from Victoria, Australia, including specific parameter estimates of the number of people who are currently active injecting drug users, average duration of injecting, chronic HCV infection prevalence among PWID, annual mortality, and annual HCV treatment rate. We also explored the impact of prevalence uncertainty, program scale-up, and new treatments., Main Outcome Measure: Prevalence of chronic HCV infection among people who are currently active injecting drug users., Results: With annual treatment rates of 13, 17, or 25 per 1000 PWID, the model predicts relative prevalence reductions of 20%, 30%, and 50%, respectively, within 30 years. If new treatments giving higher sustained viral response rates are available in 5 years, estimated impact is increased by 21%–23% at 15 years, and 17%–38% at 30 years, depending on treatment rates., Conclusions: This model suggests that modest rates of current HCV treatment among PWID in Victoria, Australia could halve HCV infection prevalence among PWID in 30 years. This finding suggests that interventions aimed at increasing access to HCV treatment in community clinics will benefit individual PWID and reduce HCV infection prevalence.