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Purpose: Cancer survival is generally lower for rural compared with urban residents, but findings have been inconsistent. We aimed to assess inequalities in cancer survival by remoteness of residence in Victoria, Australia.Methods: Incident cancer cases diagnosed in 2001-2015 with 30 cancer types (n = 331,302) were identified through the Victorian Cancer Registry and followed to the end of 2015 through death registries. Five-year net survival was estimated using the Pohar-Perme method and differences assessed by excess mortality rate ratios (EMRRs) using Poisson regression, adjusting for sex, age and year of diagnosis. EMRRs adjusted for socio-economic disadvantage were also estimated.Results: People living outside major cities had lower survival for 11 cancers: esophagus, stomach, colorectum, liver, gallbladder/biliary tract, pancreas, lung, connective/soft tissue, ovary, prostate, kidney. No differences in survival were found for cancers of uterus, small intestine and mesothelioma. After adjusting for socio-economic disadvantage, the observed differences in survival decreased for most cancers and disappeared for colorectal cancer, but they remained largely unchanged for cancers of esophagus, stomach, liver, pancreas, lung, connective/soft tissue, ovary and kidney.Conclusion: People with cancer residing outside major cities had lower survival from some cancers, which is partly due to the greater socio-economic disadvantage of rural residents. [ABSTRACT FROM AUTHOR]

Despite overall improvements in cancer survival due to earlier diagnosis and better treatment, socio-economically disadvantaged people have lower cancer survival than more advantaged people. We aimed to examine differences in cancer survival by area-level socio-economic disadvantage in Victoria, Australia and assess whether these inequalities varied by year of diagnosis, age at diagnosis, time since diagnosis and sex. Cases diagnosed with a first primary cancer in 2001–2015 were identified using the Victorian Cancer Registry and followed to the end of 2016. Five-year net survival and the excess risk of death due to a cancer diagnosis were estimated. People living in more disadvantaged areas had lower five-year survival than residents of less disadvantaged regions for 21 of 29 cancer types: head and neck, oesophagus, stomach, colorectum, anus/anal canal, liver, gallbladder/biliary tract, pancreas, lung, melanoma, connective/soft tissue, female breast, ovary, prostate, kidney, bladder, brain and central nervous system, unknown primary, non-Hodgkin lymphoma, multiple myeloma and leukemia. The observed lower survival in more deprived regions persisted over time, except head and neck cancer, for which the gap in survival has widened. Socio-economic inequalities in survival decreased with increasing age at diagnosis for cancers of connective/soft tissue, bladder and unknown primary. For colorectal cancer, the observed survival disadvantage in lower socio-economic regions was greater for men than for women, while for brain and central nervous system tumours, it was larger for women. Cancer survival is generally lower for residents of more socio-economically disadvantaged areas. Identifying the underlying reasons for these inequalities is important and may help to identify effective interventions to increase survival for underprivileged cancer patients. [ABSTRACT FROM AUTHOR]

The probability of the 6-week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS-CoV-2 Keywords: COVID-19; Infectious diseases; Respiratory tract infections; Models; statistical EN COVID-19 Infectious diseases Respiratory tract infections Models statistical 96 96 1 07/29/21 20210715 NES 210715 B I In reply: i b In response to the letter by Crammond and Kishore, we would like to make a few points. The example Crammond and Kishore offer of tea-room changes in hospitals being ignored and therefore rendering the work invalid is erroneous. [Extracted from the article]

Objective: In 2020, we developed a public health decision-support model for mitigating the spread of SARS-CoV-2 infections in Australia and New Zealand. Having demonstrated its capacity to describe disease progression patterns during both countries' first waves of infections, we describe its utilisation in Victoria in underpinning the State Government's then 'RoadMap to Reopening'., Methods: Key aspects of population demographics, disease, spatial and behavioural dynamics, as well as the mechanism, timing, and effect of non-pharmaceutical public health policies responses on the transmission of SARS-CoV-2 in both countries were represented in an agent-based model. We considered scenarios related to the imposition and removal of non-pharmaceutical interventions on the estimated progression of SARS-CoV-2 infections., Results: Wave 1 results suggested elimination of community transmission of SARS-CoV-2 was possible in both countries given sustained public adherence to social restrictions beyond 60 days' duration. However, under scenarios of decaying adherence to restrictions, a second wave of infections (Wave 2) was predicted in Australia. In Victoria's second wave, we estimated in early September 2020 that a rolling 14-day average of <5 new cases per day was achievable on or around 26 October. Victoria recorded a 14-day rolling average of 4.6 cases per day on 25 October., Conclusions: Elimination of SARS-CoV-2 transmission represented in faithfully constructed agent-based models can be replicated in the real world., Implications for Public Health: Agent-based public health policy models can be helpful to support decision-making in novel and complex unfolding public health crises., (© 2022 The Authors.)

Background: Socio-economic inequalities in colon cancer survival exist in high-income countries, but the reasons are unclear. We assessed the mediating effects of stage at diagnosis, comorbidities, and treatment (surgery and intravenous chemotherapy) on survival from colon cancer., Methods: We identified 2,203 people aged 15 to 79 years with first primary colon cancer diagnosed in Victoria, Australia, between 2008 and 2011. Colon cancer cases were identified through the Victorian Cancer Registry (VCR), and clinical information was obtained from hospital records. Deaths till December 31, 2016 ( n = 807), were identified from Victorian and national death registries. Socio-economic disadvantage was based on residential address at diagnosis. For stage III disease, we decomposed its total effect into direct and indirect effects using interventional mediation analysis., Results: Socio-economic inequalities in colon cancer survival were not explained by stage and were greater for men than women. For men with stage III disease, there were 161 [95% confidence interval (CI), 67-256] additional deaths per 1,000 cases in the 5 years following diagnosis for the most disadvantaged compared with the least disadvantaged. The indirect effects through comorbidities and intravenous chemotherapy explained 6 (95% CI, -10-21) and 15 (95% CI, -14-44) per 1,000 of these additional deaths, respectively. Surgery did not explain the observed gap in survival., Conclusions: Disadvantaged men have lower survival from stage III colon cancer that is only modestly explained by having comorbidities or not receiving chemotherapy after surgery., Impact: Future studies should investigate the potential mediating role of factors occurring beyond the first year following diagnosis, such as compliance with surveillance for recurrence and supportive care services., (©2021 American Association for Cancer Research.)

Importance: Countries have varied enormously in how they have responded to the COVID-19 pandemic, ranging from elimination strategies (eg, Australia, New Zealand, Taiwan) to tight suppression (not aiming for elimination but rather to keep infection rates low [eg, South Korea]) to loose suppression (eg, Europe, United States) to virtually unmitigated (eg, Brazil, India). Weighing the best option, based on health and economic consequences due to lockdowns, is necessary., Objective: To determine the optimal policy response, using a net monetary benefit (NMB) approach, for policies ranging from aggressive elimination and moderate elimination to tight suppression (aiming for 1-5 cases per million per day) and loose suppression (5-25 cases per million per day)., Design Setting and Participants: Using governmental data from the state of Victoria, Australia, and other collected data, 2 simulation models in series were conducted of all residents (population, 6.4 million) for SARS-CoV-2 infections for 1 year from September 1, 2020. An agent-based model (ABM) was used to estimate daily SARS-CoV-2 infection rates and time in 5 stages of social restrictions (stages 1, 1b, 2, 3, and 4) for 4 policy response settings (aggressive elimination, moderate elimination, tight suppression, and loose suppression), and a proportional multistate life table (PMSLT) model was used to estimate health-adjusted life-years (HALYs) associated with COVID-19 and costs (health systems and health system plus gross domestic product [GDP]). The ABM is a generic COVID-19 model of 2500 agents, or simulants, that was scaled up to the population of interest. Models were specified with data from 2019 (eg, epidemiological data in the PMSLT model) and 2020 (eg, epidemiological and cost consequences of COVID-19). The NMB of each policy option at varying willingness to pay (WTP) per HALY was calculated: NMB = HALYs × WTP - cost. The estimated most cost-effective (optimal) policy response was that with the highest NMB., Main Outcome and Measures: Estimated SARS-CoV-2 infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses., Results: In 100 runs of both the ABM and PMSLT models for each of the 4 policy responses, 31.0% of SARS-CoV-2 infections, 56.5% of hospitalizations, and 84.6% of deaths occurred among those 60 years and older. Aggressive elimination was associated with the highest percentage of days with the lowest level of restrictions (median, 31.7%; 90% simulation interval [SI], 6.6%-64.4%). However, days in hard lockdown were similar across all 4 strategies. The HALY losses (compared with a scenario without COVID-19) were similar for aggressive elimination (median, 286 HALYs; 90% SI, 219-389 HALYs) and moderate elimination (median, 314 HALYs; 90% SI, 228-413 HALYs), and nearly 8 and 40 times higher for tight suppression and loose suppression, respectively. The median GDP loss was least for moderate elimination (median, $41.7 billion; 90% SI, $29.0-$63.6 billion), but there was substantial overlap in simulation intervals between the 4 strategies. From a health system perspective, aggressive elimination was optimal in 64% of simulations above a WTP of $15 000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a GDP perspective in half of the simulations, followed by aggressive elimination in a quarter., Conclusions and Relevance: In this simulation modeling economic evaluation of estimated SARS-CoV-infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses in Victoria, Australia, an elimination strategy was associated with the least health losses and usually the fewest GDP losses., Competing Interests: Conflict of Interest Disclosures: Dr Bablani reported receiving grants from the Health Research Council of New Zealand outside of the submitted work. No other disclosures were reported., (Copyright 2021 Blakely T et al. JAMA Health Forum.)