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      New Cancer Diagnoses Before and During the COVID-19 Pandemic

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          Key Points

          Question

          Is there an association between the COVID-19 pandemic and cancer incidence in Manitoba, Canada?

          Findings

          In this cross-sectional study including 48 378 individuals with cancer diagnoses, a significant decrease in cancer diagnosis incidence was observed in the first few months of the pandemic, particularly in breast, colon, and rectal cancer incidence. Other cancer sites showed minimal long-term changes in incidence.

          Meaning

          The COVID-19 pandemic was associated with an initial decrease in cancer incidence followed by a return to previous incidence rates for most cancer sites.

          Abstract

          This cross-sectional study evaluates associations between the COVID-19 pandemic and cancer incidence in Manitoba, Canada.

          Abstract

          Importance

          Disruptions to health care during the COVID-19 pandemic may have led to missed cancer diagnoses. It is critical to evaluate the association between the COVID-19 pandemic and cancer incidence to address public and patient anxiety, inform recovery efforts, and identify strategies to reduce the system’s vulnerability to future disruptions.

          Objective

          To examine the association between the COVID-19 pandemic and cancer incidence in Manitoba, Canada.

          Design, Setting, and Participants

          A population-based cross-sectional study design was conducted using data from the Manitoba Cancer Registry and an interrupted time-series analysis. All individuals diagnosed with cancer in Manitoba, Canada, from January 1, 2015, until December 31, 2021, were included. Individuals diagnosed with breast, colon, rectal, or lung cancer were grouped by age as follows: younger than 50 years, 50 to 74 years, and 75 years and older.

          Exposures

          COVID-19 pandemic.

          Main Outcomes and Measures

          Age-standardized cancer incidence rates and the estimated cumulative difference between the number of cases in the absence of COVID-19 and observed (fitted) number of cancer cases.

          Results

          A total of 48 378 individuals were included. The median (IQR) age at diagnosis was 68 (59-77) years and 23 972 participants (49.6%) were female. In April 2020, there was a 23% decrease in overall cancer incidence. Cancer incidence decreased by 46% for breast, 35% for colon, 47% for rectal, 50% for head and neck, 65% for melanoma, and 33% for endocrine cancer diagnoses and increased by 12% for hematological cancer diagnoses and 8% for diagnoses of cancers with an unknown primary site. Lung cancer incidence remained stable until December 2020 when it decreased by 11%. Brain and central nervous system and urinary cancer diagnoses decreased consistently over time from April 2020 to December 2021 by 26% and 12%, respectively. No association was observed with gynecologic (1% increase), other digestive (1% decrease), or pancreatic (7% increase) cancer incidence. As of December 2021, Manitoba had an estimated deficit of 692 (5.3%) cancers. The largest estimated deficits were for breast (273 cases, 14.1% deficit), colon (133 cases, 12.2% deficit), and lung cancers (132 cases, 7.6% deficit).

          Conclusions and Relevance

          In this study, the COVID-19 pandemic was associated with an initial decrease in cancer diagnosis incidence followed by a recovery for most cancer sites. However, the cumulative deficit for some cancers with high fatality needs immediate attention.

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          Most cited references30

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          Interrupted time series regression for the evaluation of public health interventions: a tutorial

          Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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            The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study

            Summary Background Since a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types. Methods In this national population-based modelling study, we used linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data. Findings We collected data for 32 583 patients with breast cancer, 24 975 with colorectal cancer, 6744 with oesophageal cancer, and 29 305 with lung cancer. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9–9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266–295) and 344 (329–358) additional deaths. For colorectal cancer, we estimate 1445 (1392–1591) to 1563 (1534–1592) additional deaths, a 15·3–16·6% increase; for lung cancer, 1235 (1220–1254) to 1372 (1343–1401) additional deaths, a 4·8–5·3% increase; and for oesophageal cancer, 330 (324–335) to 342 (336–348) additional deaths, 5·8–6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291–3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204–63 229 years. Interpretation Substantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer. Funding UK Research and Innovation Economic and Social Research Council.
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              Mortality due to cancer treatment delay: systematic review and meta-analysis

              Abstract Objective To quantify the association of cancer treatment delay and mortality for each four week increase in delay to inform cancer treatment pathways. Design Systematic review and meta-analysis. Data sources Published studies in Medline from 1 January 2000 to 10 April 2020. Eligibility criteria for selecting studies Curative, neoadjuvant, and adjuvant indications for surgery, systemic treatment, or radiotherapy for cancers of the bladder, breast, colon, rectum, lung, cervix, and head and neck were included. The main outcome measure was the hazard ratio for overall survival for each four week delay for each indication. Delay was measured from diagnosis to first treatment, or from the completion of one treatment to the start of the next. The primary analysis only included high validity studies controlling for major prognostic factors. Hazard ratios were assumed to be log linear in relation to overall survival and were converted to an effect for each four week delay. Pooled effects were estimated using DerSimonian and Laird random effect models. Results The review included 34 studies for 17 indications (n=1 272 681 patients). No high validity data were found for five of the radiotherapy indications or for cervical cancer surgery. The association between delay and increased mortality was significant (P<0.05) for 13 of 17 indications. Surgery findings were consistent, with a mortality risk for each four week delay of 1.06-1.08 (eg, colectomy 1.06, 95% confidence interval 1.01 to 1.12; breast surgery 1.08, 1.03 to 1.13). Estimates for systemic treatment varied (hazard ratio range 1.01-1.28). Radiotherapy estimates were for radical radiotherapy for head and neck cancer (hazard ratio 1.09, 95% confidence interval 1.05 to 1.14), adjuvant radiotherapy after breast conserving surgery (0.98, 0.88 to 1.09), and cervix cancer adjuvant radiotherapy (1.23, 1.00 to 1.50). A sensitivity analysis of studies that had been excluded because of lack of information on comorbidities or functional status did not change the findings. Conclusions Cancer treatment delay is a problem in health systems worldwide. The impact of delay on mortality can now be quantified for prioritisation and modelling. Even a four week delay of cancer treatment is associated with increased mortality across surgical, systemic treatment, and radiotherapy indications for seven cancers. Policies focused on minimising system level delays to cancer treatment initiation could improve population level survival outcomes.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                5 September 2023
                September 2023
                5 September 2023
                : 6
                : 9
                : e2332363
                Affiliations
                [1 ]Paul Albrechtsen Research Institute CancerCare Manitoba, Winnipeg, Manitoba, Canada
                [2 ]Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
                [3 ]Department of Epidemiology and Cancer Registry, CancerCare Manitoba, Winnipeg, Manitoba, Canada
                [4 ]Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
                [5 ]Department of Medical Oncology and Hematology, CancerCare Manitoba, Winnipeg, Manitoba, Canada
                [6 ]Section of General Surgery, Department of Surgery, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
                [7 ]Section of Radiation Oncology, Department of Radiology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
                [8 ]Department of Radiation Oncology, CancerCare Manitoba, Winnipeg, Manitoba, Canada
                Author notes
                Article Information
                Accepted for Publication: July 30, 2023.
                Published: September 5, 2023. doi:10.1001/jamanetworkopen.2023.32363
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Decker KM et al. JAMA Network Open.
                Corresponding Author: Kathleen M. Decker, PhD, Paul Albrechtsen Research Institute CancerCare Manitoba, 825 Sherbrook St, Winnipeg, MB R3A 1M5, Manitoba, Canada ( kdecker@ 123456cancercare.mb.ca ).
                Author Contributions: Dr Decker and Mr Lambert had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Decker, Bucher, Singh, Lambert.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Decker, Bucher, Thiessen, Lambert.
                Critical review of the manuscript for important intellectual content: All authors.
                Statistical analysis: Decker, Feely, Bucher, Lambert.
                Obtained funding: Decker, Bucher, Czaykowski, Singh, Lambert.
                Administrative, technical, or material support: Kim, Pitz.
                Supervision: Decker, Lambert.
                Conflict of Interest Disclosures: Dr Singh reported serving on advisory boards or consulting for Pendopharm, Ferring Canada, Amgen Canada, Roche Canada, Sandoz Canada, Takeda Canada, Bristol Myers Squibb Canada, and Guardant Health. None of these advisory board and consulting activities are related to the submitted manuscript. No other disclosures were reported.
                Funding/Support: This work was supported by a research grant from Research Manitoba and the CancerCare Manitoba Foundation (2020-2021; funding reference No. 4459) and the Canadian Institutes of Health Research (2022-2024; funding reference No. 179890).
                Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Data Sharing Statement: See Supplement 2.
                Additional Contributions: We thank Katie Galloway, MSc, and Grace Musto, BSc (Department of Epidemiology and Cancer Registry, CancerCare Manitoba), for providing feedback on this article; Erin Dean, MD (Department of Medical Oncology, CancerCare Manitoba), for assistance interpreting outcomes; and Kelly Brown, MSc (Department of Epidemiology and Cancer Registry, CancerCare Manitoba), for study coordination. These individuals received no compensation for their contributions.
                Article
                zoi230935
                10.1001/jamanetworkopen.2023.32363
                10481240
                37669049
                1cd99f7a-31ff-4e67-8f7b-e045c0d6b9d9
                Copyright 2023 Decker KM et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 21 March 2023
                : 30 July 2023
                Funding
                Funded by: Research Manitoba
                Funded by: CancerCare Manitoba Foundation
                Funded by: Canadian Institutes of Health Research
                Categories
                Research
                Original Investigation
                Online Only
                Oncology

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