Jean Hai Ein Yong

Background: The burden of out-of-pocket costs among cancer patients/survivors in Canada is not well understood. The objective of this study was to examine the health-related out-of-pocket cost burden experienced by households with a cancer patient/survivor compared to those without, examine the components of health-related costs and determine who experiences a greater burden. Data and methods: This study used a data linkage between the Survey of Household Spending and the Canadian Cancer Registry to identify households with a cancer patient/survivor (cases) and those without (controls). The out-of-pocket burden (out-of-pocket costs measured relative to household income) and mean costs were described and regression analyses examined the characteristics associated with the household out-of-pocket burden and annual out-of-pocket costs. Results: The health-related out-of-pocket cost burden and annual costs measured in households with a cancer patient/survivor were 3.08% (95% CI: 2.55–3.62%) and CAD 1600 (95% CI: 1456–1759), respectively, compared to a burden of 2.84% (95% CI: 2.31–3.38) and annual costs of CAD 1511 (95% CI: 1377–1659) measured in control households, respectively. Households with a colorectal cancer patient/survivor had a significantly higher out-of-pocket burden compared to controls (mean difference: 1.0%, 95% CI: 0.18, 0.46). Among both cases and controls, the lowest income quintile households experienced the highest health-related out-of-pocket cost burden. Interpretation: Within a universal health care system, it is still relevant to monitor health-related out-of-pocket spending that is not covered by existing insurance mechanisms; however, this is not routinely assessed in Canada. We demonstrate the feasibility of measuring such costs in households with a cancer patient/survivor using routinely collected data. While the burden and annual health-related out-of-pocket costs of households with a cancer patient/survivor were not significantly higher than control households in this study, the routine measurement of out-of-pocket costs in Canada could be systemized, providing a novel, system-level, equity-informed performance indicator, which is relevant for monitoring inequities in the burden of out-of-pocket costs.

Cancer patients and their families experience considerable financial hardship; however, the current published literature on the economic burden of cancer at the population level has typically focused on the costs from the health system’s perspective. This study aims to estimate the economic burden of cancer in Canada from a societal perspective. The analysis was conducted using the OncoSim-All Cancers model, a Canadian cancer microsimulation model. OncoSim simulates cancer incidence and deaths using incidence and mortality data from the Canadian Cancer Registry and demography projections from Statistics Canada. Using a phase-based costing framework, we estimated the economic burden of cancer in Canada in 2021 by incorporating published direct health system costs and patients’ and families’ costs (out-of-pocket costs, time costs, indirect costs). From a societal perspective, cancer-related costs were CAD 26.2 billion in Canada in 2021; 30% of costs were borne by patients and their families. The economic burden was the highest in the first year after cancer was diagnosed (i.e., initial care). During this time, patients and families’ costs amounted to almost CAD 4.8 billion in 2021. This study provides a comprehensive estimate of the economic burden of cancer, which could inform cost–benefit analyses of proposed cancer prevention interventions.

Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive and ductal carcinoma in situ tumours. It combines Canadian cancer incidence, mortality, screening program, and cost data to project population-level outcomes. Users can change the model input to answer specific questions. Here, we compared its projections with observed data. First, we compared the model’s projected breast cancer trends with the observed data in the Canadian Cancer Registry and from Vital Statistics. Next, we replicated a screening trial to compare the model’s projections with the trial’s observed screening effects. Results: OncoSim-Breast’s projected incidence, mortality, and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and from Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Conclusions: OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.