Annual hepatitis C testing and positive tests among gay and bisexual men in Australia from 2016 to 2022: a serial cross-sectional analysis of sentinel surveillance data.

OBJECTIVE: Guidelines recommend annual hepatitis C virus (HCV) testing for gay and bisexual men (GBM) with HIV and GBM prescribed HIV pre-exposure prophylaxis (PrEP). However, there is a limited understanding of HCV testing among GBM. We aimed to examine trends in HCV testing and positivity from 2016 to 2022. METHODS: Using sentinel surveillance data, we examined the proportion of GBM with at least one test and the proportion with a positive test in each year for HCV antibody testing among GBM with no previous HCV positive test, HCV RNA testing among GBM with a positive antibody test but no previous positive RNA test (naïve RNA testing), and HCV RNA testing among people who had a previous RNA positive test and a subsequent negative test (RNA follow-up testing). Trends were examined using logistic regression from 2016 to 2019 and 2020 to 2022. RESULTS: Among GBM with HIV, from 2016 to 2019 antibody testing was stable averaging 55% tested annually. Declines were observed for both naïve HCV RNA testing (75.4%-41.4%: p<0.001) and follow-up HCV RNA testing (70.1%-44.5%: p<0.001). Test positivity declined for HCV antibody tests (2.0%-1.3%: p=0.001), HCV RNA naïve tests (75.4%-41.4%: p<0.001) and HCV RNA follow-up tests (11.3%-3.3%: p=0.001). There were minimal or no significant trends from 2020 to 2022.Among GBM prescribed PrEP, antibody testing declined from 2016 to 2019 (79.4%-69.4%: p<0.001) and was stable from 2020 to 2022. Naïve and follow-up HCV RNA testing was stable with an average of 55% and 60% tested each year, respectively. From 2016-2019, the proportion positive from HCV RNA naïve tests declined (44.1%-27.5%: p<0.046) with no significant change thereafter. Positive follow-up HCV RNA tests fluctuated with no or one new positive test among this group in most years. CONCLUSION: The proportion of GBM with positive HCV tests has declined, however a substantial proportion are not tested annually. A renewed focus on HCV testing, and treatment where required, is warranted to achieve HCV elimination among GBM in Australia.

Hepatitis C virus reinfection incidence among gay and bisexual men with HIV in Australia from 2016 to 2020.

BACKGROUND: There is some concern that hepatitis C virus (HCV) reinfection might impact HCV micro-elimination efforts among gay and bisexual men (GBM) with HIV. However, there is a limited understanding of reinfection incidence in the context of unrestricted government-funded HCV treatment. We aimed to estimate HCV reinfection incidence among GBM with HIV in Australia from 2016 to 2020. METHODS: Data were from 39 clinics participating in ACCESS, a sentinel surveillance network for blood borne viruses and sexually transmissible infections across Australia. GBM with HIV who had evidence of treatment or spontaneous clearance with at least one positive HCV RNA test, a subsequent negative HCV RNA test, and at least one additional HCV RNA test between 1st January 2016 and 31st December 2020 were eligible for inclusion. A new HCV RNA positive test and/or detectable viral load was defined as a reinfection. Generalised linear modelling was used to examine trends in reinfection. RESULTS: Among 12 213 GBM with HIV who had at least one HCV test, 540 were included in the reinfection incidence analysis, of whom 38 (7%) had evidence of reinfection during the observation period. Over 1124 person-years of follow-up, the overall rate of reinfection was 3.4/100PY (95% CI 2.5-4.6). HCV reinfection incidence declined on average 30% per calendar year (Incidence Rate Ratio 0.70, 95% CI 0.54-0.91). CONCLUSION: HCV reinfection incidence has declined among GBM with HIV in Australia since government-funded unrestricted DAAs were made available. Ongoing HCV RNA testing following cure and prompt treatment for anyone newly diagnosed is warranted to sustain this.

COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020-2021.

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.

The Incidence of Hepatitis C Among Gay, Bisexual, and Other Men Who Have Sex With Men in Australia, 2009-2019.

BACKGROUND: Hepatitis C virus (HCV) infection has been reported among gay, bisexual, and other men who have sex with men (GBM) globally including GBM with human immunodeficiency virus (HIV) and HIV-negative GBM, particularly those using HIV preexposure prophylaxis (PrEP). In Australia, HCV direct-acting antiviral treatment (DAA) was government-funded from 2016. Large implementation studies of PrEP also began in 2016. We examined HCV incidence among GBM to assess whether HCV incidence has changed since 2015. METHODS: Data were drawn from the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance. We included GBM who tested HCV antibody negative at their first test and had ≥1 subsequent test. Generalized linear modeling (Poisson distribution) was used to examine HCV incidence from 2009 to 2019 stratified by HIV status, and among HIV-negative GBM prescribed PrEP from 2016 to 2019. RESULTS: Among 6744 GBM with HIV, HCV incidence was 1.03 per 100 person-years (PY). Incidence declined by 78% in 2019 compared to 2015 (incidence rate ratio [IRR], 0.22 [95% confidence interval {CI}: .09-.55]). Among 20 590 HIV-negative GBM, HCV incidence was 0.20/100 PY, with no significant change over time. Among 11 661 HIV-negative GBM prescribed PrEP, HCV incidence was 0.29/100 PY. Compared to 2016, incidence among GBM prescribed PrEP declined by 80% in 2019 (IRR, 0.20 [95% CI: .06-.64]). CONCLUSIONS: HCV incidence among GBM living with HIV declined following DAA availability. There was no observed change in HCV incidence among HIV-negative GBM overall. Among GBM prescribed PrEP, incidence declined since the early years of PrEP implementation in Australia. Australia is on track to eliminate HCV among GBM before global 2030 targets.

Mathematical Modeling for Removing Border Entry and Quarantine Requirements for COVID-19, Vanuatu.

The Pacific Island country of Vanuatu is considering strategies to remove border restrictions implemented during 2020 to prevent imported coronavirus disease. We performed mathematical modeling to estimate the number of infectious travelers who had different entry scenarios and testing strategies. Travel bubbles and testing on entry have the greatest importation risk reduction.

Impact of COVID-19 lockdown restrictions on hepatitis C testing in Australian primary care services providing care for people who inject drugs.

In 2020, the Australian state of Victoria experienced the longest COVID-19 lockdowns of any jurisdiction, with two lockdowns starting in March and July, respectively. Lockdowns may impact progress towards eliminating hepatitis C through reductions in hepatitis C testing. To examine the impact of lockdowns on hepatitis C testing in Victoria, de-identified data were extracted from a network of 11 services that specialize in the care of people who inject drugs (PWID). Interrupted time-series analyses estimated weekly changes in hepatitis C antibody and RNA testing from 1 January 2019 to 14 May 2021 and described temporal changes in testing associated with lockdowns. Interruptions were defined at the weeks corresponding to the start of the first lockdown (week 14) and the start (week 80) and end (week 95) of the second lockdown. Pre-COVID, an average of 80.6 antibody and 25.7 RNA tests were performed each week. Following the first lockdown in Victoria, there was an immediate drop of 23.2 antibody tests and 8.6 RNA tests per week (equivalent to a 31% and 46% drop, respectively). Following the second lockdown, there was an immediate drop of 17.2 antibody tests and 4.6 RNA tests per week (equivalent to a 26% and 33% drop, respectively). With testing and case finding identified as a key challenge to Australia achieving hepatitis C elimination targets, the cumulative number of testing opportunities missed during lockdowns may prolong efforts to find, diagnose and engage or reengage in care of the remaining population of PWID living with hepatitis C.

A multi-site, nurse-coordinated hepatitis C model of care in primary care and community services in Melbourne, Australia.

BACKGROUND: Hepatitis C virus (HCV) treatment through primary care and community-based services will be a critical component of HCV elimination. We evaluated a nurse-coordinated programme providing care across eight sites and analysed progression through the HCV care cascade. METHODS: People-accessing services from six primary care clinics, a homeless crisis accommodation provider and a mental health service were directly referred to nurses or engaged by nurses during regular clinic visits. Nurses supported HCV testing, treatment and follow-up. The prescription was provided by affiliated clinicians. Logistic regression was used to examine factors associated with treatment commencement and sustained virological response (SVR) testing. RESULTS: Of 640 people referred to and/or engaged by the nurses from January 2017 to July 2019, 518 had an HCV RNA test of whom 381 (74%) were HCV RNA positive. Treatment was commenced by 281 (74%) people of whom 161 had an SVR test, 157 (97.5%) were cured. Opioid agonist therapy was associated with treatment commencement (aOR 2.68, 95% CI 1.48-4.88). People who were homeless/unstably housed were less likely to commence treatment (aOR 0.45, 95% CI 0.23-0.87). Treatment prescription from a specialist (aOR 2.39, 95% CI 1.20-4.74) and recent injection drug use (<6 months) (aOR 2.15, 95% CI 1.07-4.31) was associated with SVR testing. CONCLUSION: A nurse-coordinated model of care led to high levels of HCV treatment uptake and cure amongst people attending primary care and community services. More tailored models of care may be beneficial for people who are homeless or have unstable housing. These results support primary care and community-based hepatitis C treatment.

Preventing a cluster from becoming a new wave in settings with zero community COVID-19 cases.

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.

Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs.

Transmission of Hepatitis C (HCV) continues via sharing of injection equipment between people who inject drugs (PWID). Network-based modelling studies have produced conflicting results about whether random treatment is preferable to targeting treatment at PWID with multiple partners. We hypothesise that differences in the modelled injecting network structure produce this heterogeneity. The study aimed to test how changing network structure affects HCV transmission and treatment effects. We created three dynamic injecting network structures connecting 689 PWID (UK-net, AUS-net and USA-net) based on published empirical data. We modelled HCV in the networks and at 5 years compared prevalence of HCV 1) with no treatment, 2) with randomly targeted treatment and 3) with treatment targeted at PWID with the most injecting partnerships (degree-based treatment). HCV prevalence at 5 years without treatment differed significantly between the three networks (UK-net (42.8%) vs. AUS-net (38.2%), p < 0.0001 and vs. USA-net (54.0%), p < 0.0001). In the treatment scenarios UK-net and AUS-net showed a benefit of degree-based treatment with a 5-year prevalence of 1.0% vs. 9.6% p < 0.0001 and 0.15% vs. 0.44%, p < 0.0001. USA-net showed no significant difference (29.3% vs. 29.2%, p = 0.0681). Degree-based treatment was optimised with low prevalence, moderate treatment coverage conditions whereas random treatment was optimised in low treatment coverage, high prevalence conditions. In conclusion, injecting network structure determines the transmission rate of HCV and the most efficient treatment strategy. In real-world injecting network structures, the benefit of targeting HCV treatment at individuals with multiple injecting partnerships may have been underestimated.

Modelling the impact of relaxing COVID-19 control measures during a period of low viral transmission.

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.