Objectives To estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer.
Methods We employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England.
Results Declines in urgent referrals (median=−70.4%) and chemotherapy attendances (median=−41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=−44.5%) and chemotherapy attendances (median=−31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity.
Conclusions Dramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.
- health informatics
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AL, ML and HH are joint senior authors.
Twitter @alvinaglai, @amibanerjee1, @richarddneal, @davie_charlie, @profhhemingway
Contributors Research question: AL and HH. Funding: AL, AB, ML and HH. Study design and analysis plan: AL, LP, AB, MK, WHC and HH. Preparation of data, including electronic health record phenotyping in the CALIBER portal: AL, LP and SD. Provision of weekly hospital data: GH, KP-J, MF, DH, ML, KB and CD. Statistical analysis: AL, LP, WHC and MK. Drafting initial versions of manuscript: AL, ML and HH. Drafting final versions of manuscript: AL, GH, CD, ML and HH. Critical review of early and final versions of manuscript: AL, LP, AB, GH, SD, WHC, MK, BW, DP, MN, DL, DH, MF, CT, NF, KB, GF, TE, VN, BH, RDN, MC, MJ, KP-J, RS, CD, ML and HH. Joint second authors: LP, AB and GH.
Funding DATA-CAN (MC_PC_19006) is part of the Digital Innovation Hub Programme, delivered by HDR UK and funded by UK Research and Innovation through the government’s Industrial Strategy Challenge Fund. AL is supported by funding from the Wellcome Trust (204841/Z/16/Z), National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre (BRC714/HI/RW/101440), NIHR Great Ormond Street Hospital Biomedical Research Centre (19RX02) and the Health Data Research UK Better Care Catalyst Award. AB is supported by research funding from NIHR, British Medical Association, Astra-Zeneca and UK Research and Innovation. KP-J is supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. CD and KP-J are funded by UCLPartners. HH is an NIHR senior investigator and is funded by the NIHR University College London Hospitals Biomedical Research Centre, supported by Health Data Research UK (grant No. LOND1), which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, Wellcome Trust, the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116 074.
Competing interests ML has received honoraria from Pfizer, EMD Serono and Roche for presentations unrelated to this research, and an unrestricted educational grant from Pfizer for research unrelated to the research presented in this paper. AB has received research funding from AstraZeneca. MF has received research funding from AstraZeneca, Boehringer Ingelheim, Merck and MSD and honoraria from Achilles, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Meyers Squibb, Celgene, Guardant Health, Merck, MSD, Nanobiotix, Novartis, Pharmamar, Roche and Takeda for advisory roles or presentations unrelated to this research. GF receives funding from companies that manufacture drugs for hepatitis C virus (AbbVie, Gilead, MSD) and is a consultant for GSK, Arbutus and Shionogi in areas unrelated to this research.
Patient consent for publication Not required.
Ethics approval The study was approved by the MHRA (UK) Independent Scientific Advisory Committee (20_074R2), under Section 251 (NHS Social Care Act 2006).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data may be obtained from a third party and are not publicly available. Data used in this study were accessed through the Clinical Practice Research Datalink that is subject to protocol approval by an Independent Scientific Advisory Committee and cannot directly be shared. All results are reported in the manuscript and no additional data are available.
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