The COVID‐19 pandemic has affected cancer care worldwide. This study aimed to estimate the long‐term impacts of cancer care disruptions on cancer mortality in Canada using a microsimulation model. The model simulates cancer incidence and survival using cancer incidence, stage at diagnosis and survival data from the Canadian Cancer Registry. We modeled reported declines in cancer diagnoses and treatments recorded in provincial administrative datasets in March 2020 to June 2021. Based on the literature, we assumed that diagnostic and treatment delays lead to a 6% higher rate of cancer death per 4‐week delay. After June 2021, we assessed scenarios where cancer treatment capacity returned to prepandemic levels, or to 10% higher or lower than prepandemic levels. Results are the median predictions of 10 stochastic simulations. The model predicts that cancer care disruptions during the COVID‐19 pandemic could lead to 21 247 (2.0%) more cancer deaths in Canada in 2020 to 2030, assuming treatment capacity is recovered to 2019 prepandemic levels in 2021. This represents 355 172 life years lost expected due to pandemic‐related diagnostic and treatment delays. The largest number of expected excess cancer deaths was predicted for breast, lung and colorectal cancers, and in the provinces of Ontario, Québec and British Columbia. Diagnostic and treatment capacity in 2021 onward highly influenced the number of cancer deaths over the next decade. Cancer care disruptions during the COVID‐19 pandemic could lead to significant life loss; however, most of these could be mitigated by increasing diagnostic and treatment capacity in the short‐term to address the service backlog.

(1) Background: Preventive measures taken in response to the coronavirus disease 2019 (COVID-19) pandemic have adversely affected an entire range of cancer-related medical activities. The reallocation of medical resources, staff, and ambulatory services, as well as critical shortages in pharmaceutical and medical supplies have compelled healthcare professionals to prioritize patients with cancer to treatment and screening services based on a set of classification criteria in cancer-related guidelines. Cancer patients themselves have been affected on multiple levels, and addressing their concerns poses another challenge to the oncology community. (2) Methods: We conducted a Canada-wide search of cancer-related clinical practice guidelines on the management and prioritization of individuals into treatment and screening services. We also outlined the resources provided by Canadian cancer charities and patient advocacy groups to provide cancer patients, or potential cancer patients, with useful information and valuable support resources. (3) Results: The identified provincial guidelines emphasized cancer care (i.e., treatment) more than cancer control (i.e., screening). For cancer-related resources, a clear significance was placed on knowledge & awareness and supportive resources, mainly relating to mental health. (4) Conclusion: We provided a guidance document outlining cancer-related guidelines and resources that are available to healthcare providers and patients across Canada during the COVID-19 pandemic.

Objectives: The COVID-19 pandemic has affected cancer care worldwide. This study aimed to estimate the long-term impacts of the pandemic on cancer incidence and mortality in Canada using a mathematical model. Methods: We developed a stochastic microsimulation model to estimate the cancer care disruptions and its long-term impact on cancer incidence and mortality in Canada. The model reproduces cancer incidence, survival, and epidemiology in Canada, by using cancer incidence, stage at diagnosis and survival data from the Canadian Cancer Registries. We modeled reported declines in cancer diagnoses and treatments recorded in provincial administrative datasets from March 2020-June 2021. We assumed that diagnostic and treatment delays lead to an increased rate of death. Based on the literature, we assumed each 4-week delay in diagnosis and treatment would lead to a 6% to 50% higher rate of cancer death. Results are the median predictions of 10 stochastic simulations. Findings: The model predicts that cancer care disruptions during the COVID-19 pandemic could lead to 21,247 (2.0%) more cancer deaths in Canada in 2020-2030, assuming treatment capacity is recovered to 2019 pre-pandemic levels in 2021. This represents 355,172 life years lost expected due to pandemic-related diagnostic and treatment delays. The highest absolute expected excess cancer mortality was predicted in breast, lung, and colorectal cancers, and in the provinces of Ontario, Québec, and British Columbia. Diagnostic and treatment capacity in 2021 onwards highly influenced the number of predicted cancer deaths over the next decade. Interpretation: Cancer care disruptions during the Covid-19 pandemic could lead to significant life loss; however, most of these could be mitigated by increasing diagnostic and treatment capacity in the post-pandemic era to address the service backlog. Funding: Canadian Institutes of Health Research

Background: The COVID-19 pandemic has disrupted cancer care, raising concerns regarding the impact of wait time, or 'lag time', on clinical outcomes. We aimed to contextualize pandemic-related lag times by mapping pre-pandemic evidence from systematic reviews and/or meta-analyses on the association between lag time to cancer diagnosis and treatment with mortality- and morbidity-related outcomes. Methods: We systematically searched MEDLINE, EMBASE, Web of Science, and Cochrane Library of Systematic Reviews for reviews published prior to the pandemic (1 January 2010-31 December 2019). We extracted data on methodological characteristics, lag time interval start and endpoints, qualitative findings from systematic reviews, and pooled risk estimates of mortality- (i.e., overall survival) and morbidity- (i.e., local regional control) related outcomes from meta-analyses. We categorized lag times according to milestones across the cancer care continuum and summarized outcomes by cancer site and lag time interval. Results: We identified 9,032 records through database searches, of which 29 were eligible. We classified 33 unique types of lag time intervals across 10 cancer sites, of which breast, colorectal, head and neck, and ovarian cancers were investigated most. Two systematic reviews investigating lag time to diagnosis reported different findings regarding survival outcomes among pediatric patients with Ewing's sarcomas or central nervous system tumours. Comparable risk estimates of mortality were found for lag time intervals from surgery to adjuvant chemotherapy for breast, colorectal, and ovarian cancers. Risk estimates of pathologic complete response indicated an optimal time window of 7-8 weeks for neoadjuvant chemotherapy completion prior to surgery for rectal cancers. In comparing methods across meta-analyses on the same cancer sites, lag times, and outcomes, we identified critical variations in lag time research design. Conclusions: Our review highlighted measured associations between lag time and cancer-related outcomes and identified the need for a standardized methodological approach in areas such as lag time definitions and accounting for the waiting-time paradox. Prioritization of lag time research is integral for revised cancer care guidelines under pandemic contingency and assessing the pandemic's long-term effect on patients with cancer. Funding: The present work was supported by the Canadian Institutes of Health Research (CIHR-COVID-19 Rapid Research Funding opportunity, VR5-172666 grant to Eduardo L. Franco). Parker Tope, Eliya Farah, and Rami Ali each received an MSc. stipend from the Gerald Bronfman Department of Oncology, McGill University.

Income, a component of socioeconomic status, influences cancer risk as a social determinant of health. We evaluated the independent associations between individual‐ and area‐level income and site‐specific cancer incidence in Canada. We used data from the 2006 and 2011 Canadian Census Health and Environment Cohorts, which are probabilistically linked datasets constituted by 5.9 million and 6.5 million respondents of the 2006 Canadian long‐form census and 2011 National Household Survey, respectively. Individuals were linked to the Canadian Cancer Registry through 2015. Individual‐level income was derived using after‐tax household income adjusted for household size. Annual tax return postal codes were used to assign area‐level income quintiles to individuals for each year of follow‐up. We calculated age‐standardized incidence rates (ASIR) and rate ratios for cancers overall and by site. We conducted multivariable negative binomial regression to adjust these rates for other demographic and socioeconomic variables. Individuals of lower individual‐ and area‐level income had higher ASIRs compared to those in the wealthiest income quintile for head and neck, oropharyngeal, esophageal, stomach, colorectal, anal, liver, pancreas, lung, cervical and kidney and renal pelvis cancers. Conversely, individuals of wealthier individual‐ and area‐level income had higher ASIRs for melanoma, leukemia, Hodgkin's lymphoma, non‐Hodgkin's lymphoma, breast, uterine, prostate and testicular cancers. Most differences in site‐specific incidence by income quintile remained after adjustment. Although Canada's publicly funded healthcare system provides universal coverage, inequalities in cancer incidence persist across individual‐ and area‐level income gradients. Our estimates suggest that individual‐ and area‐level income affect cancer incidence through independent mechanisms.