Background Population-based cancer screening can reduce cancer burden but was interrupted temporarily due to the COVID-19 pandemic. We estimated the long-term clinical impact of breast and colorectal cancer screening interruptions in Canada using a validated mathematical model. Methods We used the OncoSim breast and colorectal cancers microsimulation models to explore scenarios of primary screening stops for 3, 6, and 12 months followed by 6–24-month transition periods of reduced screening volumes. For breast cancer, we estimated changes in cancer incidence over time, additional advanced-stage cases diagnosed, and excess cancer deaths in 2020–2029. For colorectal cancer, we estimated changes in cancer incidence over time, undiagnosed advanced adenomas and colorectal cancers in 2020, and lifetime excess cancer incidence and deaths. Results Our simulations projected a surge of cancer cases when screening resumes. For breast cancer screening, a three-month interruption could increase cases diagnosed at advanced stages (310 more) and cancer deaths (110 more) in 2020–2029. A six-month interruption could lead to 670 extra advanced cancers and 250 additional cancer deaths. For colorectal cancers, a six-month suspension of primary screening could increase cancer incidence by 2200 cases with 960 more cancer deaths over the lifetime. Longer interruptions, and reduced volumes when screening resumes, would further increase excess cancer deaths. Conclusions Interruptions in cancer screening will lead to additional cancer deaths, additional advanced cancers diagnosed, and a surge in demand for downstream resources when screening resumes. An effective strategy is needed to minimize potential harm to people who missed their screening.
strategies, fobt parameter values associated with high-sensitivity formulations were associated with a substantial increase in test effectiveness. The fit was more cost-effective at the 50 ng/mL threshold than at the 100 ng/mL threshold. ConclusionsThe crmm-crc provides a sophisticated and flexible environment in which to evaluate crc control options. All screening scenarios considered in this study effectively reduced crc mortality, although sensitivity analyses demonstrated some uncertainty in the magnitude of the improvements. Where possible, local data should be used to reduce uncertainty in the parameters.
The Canadian Partnership Against Cancer was created in 2007 by the federal government to accelerate cancer control across Canada. Its OncoSim microsimulation model platform, which consists of a suite of specific cancer models, was conceived as a tool to augment conventional resources for population-level policy-and decision-making. The Canadian Partnership Against Cancer manages the OncoSim program, with funding from Health Canada and model development by Statistics Canada.Microsimulation modelling allows for the detailed capture of population heterogeneity and health and demographic history over time. Extensive data from multiple Canadian sources were used as inputs or to validate the model. OncoSim has been validated through expert consultation; assessments of face validity, internal validity, and external validity; and model fit against observed data. The platform comprises three in-depth cancer models (lung, colorectal, cervical), with another in-depth model (breast) and a generalized model (25 cancers) being in development. Unique among models of its class, OncoSim is available online for public sector use free of charge. Users can customize input values and output display, and extensive user support is provided.OncoSim has been used to support decision-making at the national and jurisdictional levels. Although simulation studies are generally not included in hierarchies of evidence, they are integral to informing cancer control policy when clinical studies are not feasible. OncoSim can evaluate complex intervention scenarios for multiple cancers.Canadian decision-makers thus have a powerful tool to assess the costs, benefits, cost-effectiveness, and budgetary effects of cancer control interventions when faced with difficult choices for improvements in population health and resource allocation.
Background: The coronavirus disease 2019 (COVID-19) pandemic began with a detected cluster of pneumonia cases in Wuhan, China in December 2019. Endemic transmission was recognized in Canada in early February 2020, making it urgent for public health stakeholders to have access to robust and reliable tools to support decision-making for epidemic management. The objectives of this paper are to present one of these tools-an aged-stratified dynamic compartmental model developed by the Public Health Agency of Canada in collaboration with Statistics Canada-and to model the impact of non-pharmaceutical interventions on the attack rate of COVID-19 infection in Canada. Methods: This model simulates the impact of different levels of non-pharmaceutical interventions, including case detection/isolation, contact tracing/quarantine and changes in the level of physical distancing in Canada, as restrictive closures began to be lifted in May 2020.Results: This model allows us to highlight the importance of a relatively high level of detection and isolation of cases, as well as tracing and quarantine of individuals in contact with those cases, in order to avoid a resurgence of the epidemic in Canada as restrictive closures are lifted. Some level of physical distancing by the public will also likely need to be maintained. Conclusion:This study underlines the importance of a cautious approach to lifting restrictive closures in this second phase of the epidemic. This approach includes efforts by public health to identify cases and trace contacts, and to encourage Canadians to get tested if they are at risk of having been infected and to maintain physical distancing in public areas.
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