Summary
Background
During the COVID-19 lockdown, referrals via the 2-week-wait urgent pathway for suspected cancer in England, UK, are reported to have decreased by up to 84%. We aimed to examine the impact of different scenarios of lockdown-accumulated backlog in cancer referrals on cancer survival, and the impact on survival per referred patient due to delayed referral versus risk of death from nosocomial infection with severe acute respiratory syndrome coronavirus 2.
Methods
In this modelling study, we used age-stratified and stage-stratified 10-year cancer survival estimates for patients in England, UK, for 20 common tumour types diagnosed in 2008–17 at age 30 years and older from Public Health England. We also used data for cancer diagnoses made via the 2-week-wait referral pathway in 2013–16 from the Cancer Waiting Times system from NHS Digital. We applied per-day hazard ratios (HRs) for cancer progression that we generated from observational studies of delay to treatment. We quantified the annual numbers of cancers at stage I–III diagnosed via the 2-week-wait pathway using 2-week-wait age-specific and stage-specific breakdowns. From these numbers, we estimated the aggregate number of lives and life-years lost in England for per-patient delays of 1–6 months in presentation, diagnosis, or cancer treatment, or a combination of these. We assessed three scenarios of a 3-month period of lockdown during which 25%, 50%, and 75% of the normal monthly volumes of symptomatic patients delayed their presentation until after lockdown. Using referral-to-diagnosis conversion rates and COVID-19 case-fatality rates, we also estimated the survival increment per patient referred.
Findings
Across England in 2013–16, an average of 6281 patients with stage I–III cancer were diagnosed via the 2-week-wait pathway per month, of whom 1691 (27%) would be predicted to die within 10 years from their disease. Delays in presentation via the 2-week-wait pathway over a 3-month lockdown period (with an average presentational delay of 2 months per patient) would result in 181 additional lives and 3316 life-years lost as a result of a backlog of referrals of 25%, 361 additional lives and 6632 life-years lost for a 50% backlog of referrals, and 542 additional lives and 9948 life-years lost for a 75% backlog in referrals. Compared with all diagnostics for the backlog being done in month 1 after lockdown, additional capacity across months 1–3 would result in 90 additional lives and 1662 live-years lost due to diagnostic delays for the 25% backlog scenario, 183 additional lives and 3362 life-years lost under the 50% backlog scenario, and 276 additional lives and 5075 life-years lost under the 75% backlog scenario. However, a delay in additional diagnostic capacity with provision spread across months 3–8 after lockdown would result in 401 additional lives and 7332 life-years lost due to diagnostic delays under the 25% backlog scenario, 811 additional lives and 14 873 life-years lost under the 50% backlog scenario, and 1231 additional lives and 22 635 life-years lost under the 75% backlog scenario. A 2-month delay in 2-week-wait investigatory referrals results in an estimated loss of between 0·0 and 0·7 life-years per referred patient, depending on age and tumour type.
Interpretation
Prompt provision of additional capacity to address the backlog of diagnostics will minimise deaths as a result of diagnostic delays that could add to those predicted due to expected presentational delays. Prioritisation of patient groups for whom delay would result in most life-years lost warrants consideration as an option for mitigating the aggregate burden of mortality in patients with cancer.
Funding
None.
Introduction
As the lockdown is lifted, a surge in presentations for non-COVID-19-related medical issues is anticipated.
Evidence before this study
We searched PubMed, with no language or date restrictions, on March 10, 2020, for observational studies of cancer pathway delays in English using the terms ([“cancer” OR “neoplasm”] AND [“delay” OR “interval” OR “wait”] AND [“diagnosis” OR “treatment]). Studies typically reported data extracted from institutional, regional, or national databases. Overall, studies are highly heterogeneous in design and findings, including the durations of delay studied, the duration of survival follow-up, the method by which impact is captured (percentages, odds ratios, hazard ratios), and how and when staging is done. Each study typically focused on a single tumour type and most did not stratify impact by stage of cancer. To our knowledge, no study had modelled the impact in lives and life-years lost of systematic delays in referral pathways in a standardised fashion across tumour types until we recently reported a health-care resource analysis focused on systemic delays at point of surgery
Added value of this study
Across multiple tumour types, we used a standardised approach using per-day fatality hazard ratios to quantify the effect of different durations of delay on survival, examining both the referred patient and the diagnosed patient, and examining delays for individual tumour types and subtypes and aggregated across major tumour types. This study focuses specifically on cancers diagnosed via the 2-week wait pathway because this pathway is most amenable to interventions. Although pertinent to ongoing forecasting of the impact of COVID-19-related delays, these models could apply to any systemic delays to cancer pathways.
Implications of all the available evidence
Incorporating previous observational studies of delay and examining crudely estimated, non-naturalistic per-patient delays, our models predict that COVID-19-related delays in presentation, diagnosis, and subsequent treatment, will result in loss of life and life-years that varies widely according to patient age and tumour type. Data regarding the true duration and extent of service disruption and per-patient cancer pathway delay across the UK as a result of the COVID-19 lockdown are currently immature. Direct predictions regarding attributable cancer deaths will be possible once more accurate patient-level data become available
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Large backlogs of patients accrued as a consequence of the lockdown are predicted to first place pressure on diagnostic services in secondary care, and affect other areas of the pathway thereafter.
Methods
Data sources
We used data on proportion of referrals for suspected cancer that translated into cancer diagnoses (the diagnostic-conversion rate) from Cancer Waits-Faster Diagnosis Standard data for west London, UK, for 2019–20.
We used data from across England for cancer cases diagnosed when the patient was aged 30 years or older. We concentrated our analysis on the 20 most common cancers referred via 2-week-wait pathways, for which we analysed NCRAS survival data from 2 314 822 cancer cases (2008–17) and 2-week-wait diagnoses for 385 156 cancer cases (2013–16) (appendix 1 pp 4–5). We calculated life expectancy on the basis of UK Office of National Statistics life tables for 2016–18.
We estimated nosocomial infection rates and median duration of hospital stay for treatment of each cancer type on the basis of aggregated and anonymised information from three large UK surgical oncology centres (Gronthoud F, unpublished). For the calculation of the case-fatality rate associated with unselected COVID-19 infection, we used published data from China because UK COVID-19 case-fatality rate estimates were only available for patients who had been admitted to hospital.
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Model development
We used the midpoint per 10-year age group for life expectancy to estimate life-years gained, averaged per patient.
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Since sufficient observational data were only available to generate summary delay HRs for breast, colorectal, and bladder cancers, we assigned delay HRs to other tumours on the basis of similarity of 5-year survival, categorising tumour progressiveness as being low (with a 5-year survival for stage II disease being >90%), moderate (50–90%), or high (appendix 1 pp 1–3). Finally, we assumed that delay to treatment for stage IV cancer would not affect 10-year survival.
Because patients younger than 60 years with stage I–III cancers typically have treatment with curative intent, we generated the stage-specific ratio from this group of those having major resection to those having other definitive treatment (eg, endoscopic resection or curative radiotherapy). We applied this ratio to age-specific and cancer stage-specific strata for those aged 60 years and older having major resection to estimate the proportion of patients who have been diagnosed who are having other types of definitive treatment.
We quantified the annual numbers of cancers diagnosed via the 2-week-wait pathway using the 2-week-wait age-specific and stage-specific breakdowns. From these, our outcome measures were estimated aggregate number of lives lost and life-years lost in England for per-patient delays of 1–6 months.
Scenarios analysed using the model
to produce a combined per-referral mortality.
Using the diagnostic-conversion rates, we estimated the survival benefit per patient from an investigatory referral for each age group and tumour group. We considered the potential to delay referral by 2, 4, and 6 months against varying rates of nosocomial infection per investigatory referral (5% being very high, 2·5% being high, 1% being moderate, and 0·5% being low). By age group and tumour type, we compared the benefit of prompt investigatory referral versus different periods of delay or no referral (absolute survival benefit). We estimated benefit in proportional survival and life-years gained from gain in cancer survival versus the combined fatality risk (COVID-19 and technical).
Statistical analysis
Role of the funding source
There was no funding source for this study. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Results
Figure 1Reduction in 10-year net survival incurred from a 3-month delay for the 20 most common tumour types, by age group
Red indicates the highest decile of survival decrement, scaling down through orange and yellow to pale green, which indicates the lowest decile of survival decrement.
Figure 2Annual lives and life-years lost attributable to delay, aggregated for all patients diagnosed via the 2-week-wait pathway for the 20 most common tumour types
Based on 10-year net survival data for England, UK, 2008–17. Greatest decrements in lives and life-years lost are shown in darker shades of orange.
TableCancer diagnoses made through the 2-week-wait pathway for 2013–16
Data shown are proportion of all diagnoses made via 2-week-wait pathway, with a breakdown by cancers diagnosed via this pathway by age and cancer stage, diagnostic conversion rate, and average annual referrals. Diagnostic conversion rates reflect all diagnoses of invasive cancers (exceptions are that breast includes carcinoma in situ, skin excludes basal cell carcinomas, urology excludes pTa bladder tumours). N/A=not applicable.
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Based on data for 2013–16, for these 20 cancer types on average an estimated 149 000 2-week-wait referrals are made each month, resulting in 8024 diagnoses of cancer, of which 6281 are diagnosed at stage I–III (appendix 1 pp 4–5). 1691 (27%) of these 6281 patients will typically die from their cancer within 10 years.
We estimated the national toll of presentational delay accrued over a 3-month lockdown period to be 181 attributable additional lives and 3316 life-years lost for a backlog rate of 25%, 361 additional lives and 6632 life-years lost for a backlog rate of 50%, and 542 additional lives and 9948 life-years lost assuming a backlog rate of 75%, with an average presentational delay of 2 months per patient. Assuming the patients all present in month 1 after lockdown, normal diagnostic capacity would need to work at least 175% under the 25% backlog scenario, 250% under the 50% backlog scenario, and 325% under the 75% backlog scenario to clear the backlog (appendix 2). However, it is unlikely that all extra diagnostic capacity required can be provided in a single month; therefore, we estimated the additional lives and life-years that might be lost due to subsequent diagnostic delays. Rapid provision of additional capacity over months 1–3 would result in 90 additional lives and 1662 live-years lost due to diagnostic delays for the 25% backlog scenario, 183 additional lives and 3362 life-years lost under the 50% backlog scenario, and 276 additional lives and 5075 life-years lost under the 75% backlog scenario (appendix 2). Conversely, delayed additional capacity provided across months 3–8 after lockdown would result in 401 additional lives and 7332 life-years lost due to diagnostic delays under the 25% backlog scenario, 811 additional lives and 14 873 life-years lost under the 50% backlog scenario, and 1231 additional lives and 22 635 life-years lost under the 75% backlog scenario (appendix 2).
Figure 3Per-patient net survival gain from urgent investigatory referral compared with 2-month delay for the 20 most common tumour types, with varying rates of nosocomial SARS-CoV-2 infection, by age group
Red indicates benefit of urgent investigatory referral and green indicates no benefit. SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.
Discussion
For most solid cancers, 10-year survival is generally considered to equate to cure, reflecting the proportion of stage I–III tumours for which their surgery (or radical radiotherapy) has enabled the restoration to normal or near-normal life expectancy. Our estimates suggest that, for many cancers, delays to treatment of 2–6 months will lead to a substantial proportion of patients with early-stage tumours progressing from having curable to incurable disease. However, this varies widely between tumour types, reflecting variation in the proportion of patients diagnosed through the 2-week-wait pathway, the proportion diagnosed with stage I–III tumours, the age profile of patients diagnosed with those cancers, and the diagnostic-conversion rate, which inevitably means that the overall impact of delays in referral via the 2-week-wait pathway is far from uniform between cancers.
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Substantial additional mortality from diagnostic delay on top of the presentational delay accrued during patient deferment is possible, especially if additional diagnostic capacity for catching up with the backlog is delayed. The additional capacity must include not only expanded technical provision for endoscopy, imaging, interventional radiology, and nuclear medicine, but also increased staffing for specialist assessment and pathology. Delivery will be further challenged by new requirements for personal protective equipment, physical distancing, and infection control. Innovative solutions will be required to deliver this extra capacity in a timely fashion, which might include procurement of private sector provision, expanded roles for health-care professionals such as endoscopy nurses, and pathway adaptation—eg, use of faecal immunochemical testing (FIT) for triage of colorectal cancer referrals.
Investment in expansion of capacity for NHS diagnostics and treatment is a priority if cancer services are to become more resilient to future extrinsic disruption, which could include additional waves of COVID-19. Additionally, more responsive informatic connections between primary care, diagnostic, and treatment services would enable improved agility in adaption of pathways and prioritisation of referrals. Furthermore, pre-emptive public education is required to discourage patients from deferring presentation of cancer symptoms along with modification of pathways to and through primary care.
Diagnostic delays will affect patient groups differently. For younger patients (<70 years), all delays should be avoided. Our data show that survival decrement for even small delays (ie, 2 months) is substantial for most tumours. Conversely, for older groups (≥70 years), per-referral risk of death from nosocomial infection is much higher and might exceed the average decrement of a moderate delay, in particular for more indolent cancer types (eg, prostate cancer) or cancers with a poor overall prognosis (eg, upper gastrointestinal tract cancers). Even in the absence of concerns about nosocomial infection, if there are pressures on diagnostic capacity, prioritisation and deprioritisation of patients according to tumour referral group and age warrants consideration as a strategy to mitigate the population-level cost of diagnostic delays in terms of lives and life-years lost.
Whether or not deaths due to SARS-CoV-2 infection, both direct and indirect (eg, compromise in collateral health-care delivery) will be outweighed by the positive impacts on mortality (eg, reduced air pollution, fewer road-traffic accidents, and handwashing) as a result of the COVID-19 and lockdown period is a matter of debate. Although our analyses examine cancer-specific survival only, the estimations of life-years gained would be altered by any sizeable shifts in life expectancy.
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Our analysis focused only on invasive disease in common adult tumour types. Additional analyses might extend across rarer cancers, tumours of childhood, and non-invasive lesions such as dysplastic colonic adenomas. We only considered the impact of delay on patients with stage I–III disease having treatment with curative intent. Additional analyses will be required to assess the impact of delays for those having non-curative treatments.
As with all modelling, the accuracy of our predictions is contingent on the validity of assumptions and parameter estimates. Although we identified suitable observational data for delays in treatment for stages I–III for three tumour types, uniform application of these delay HRs across tumour types and over time will invariably oversimplify the complex, dynamic, tumour type-specific, age-specific, and stage-specific nature of cancer progression. To enable systematic insights across tumour types, routine capture of delays in referral pathways should be incorporated into all national cancer data collections.
The availability of models such as those we have used will also enable more agile prospective resource planning in the face of future instances of systematic disruption of cancer services, which could include future major waves of COVID-19, other pandemics, or economic contractions.
The impact of COVID-19-related disruption on cancer care is likely to be an ongoing issue until a vaccine or effective treatment is identified. Our modelling suggests a clinically significant impact in lives and life-years lost if delays to the 2-week-wait pathway are extensive and prolonged. Unlike acute pathologies, such as stroke and myocardial infarction, the true excess mortality due to COVID-19-related disruption to cancer pathways will not be fully evident for 10 years or longer.
Contributors
AS, CT, and RH designed the model. JB generated and did appropriate check to assert completeness and accuracy of data in the NCRAS datasets. SS generated and did appropriate checks to assert completeness and accuracy of data from the Clinical Commissioning Group-Cancer Waiting Times datasets. MEJ provided cancer progression models. BT provided mitigation models. AS and CT wrote the code for the model. MEJ, JB, ML, EM, CT, RH, AS, GL, ER, DCM, and MW provided epidemiological expertise in the parameterisation of the model and relevant literature. FG provided microbiology expertise in the estimation of nosocomial infection rates. SAB, SJ, DLN, JL, EK, CS, and NN provided details of clinical pathways. BT, AS, AG, and CL assembled the figures. CT drafted the manuscript, with substantial contribution from AS, RH, GL, ML, and EM. All authors contributed to the final manuscript.
Declaration of interests
ML reports personal fees and grants from Pfizer and personal fees from Roche outside of the submitted work. CS reports grants from Pfizer and Boehringer Ingelheim; grants and personal fees from Bristol Myers Squibb, AstraZeneca, Ono Pharmaceutical, and Roche-Ventana; personal fees from Novartis, MSD, Illumina, Celgene, GlaxoSmithKline, Genentech, Medicxi, and Sarah Canon Research Institute; personal fees and stock options from GRAIL; stock options from EPIC Biosciences and Apogen Biotech; and personal fees and being a co-founder of Achilles Therapeutics during the conduct of the study. CS has patents issued for an immune checkpoint intervention in cancer (PCT/EP2016/071471), for a method for treating cancer based on identification of clonal neo-antigens (PCT/EP2016/059401), for methods for lung cancer detection (PCT/US2017/028013), for a method of detecting tumour recurrence (PCT/GB2017/053289), for a method for treating cancer (PCT/EP2016/059401), for a method of treating cancer by targeting insertion/deletion mutations (PCT/GB2018/051893), for a method of identifying insertion/deletion mutation targets (PCT/GB2018/051892), for a method for determining whether an HLA allele is lost in a tumour (PCT/GB2018/052004), for a method for identifying responders to cancer treatment (PCT/GB2018/051912), and for a method of predicting survival rates for cancer patients (PCT/GB2020/050221). JL reports grants and personal fees from Achilles Therapeutics, Bristol-Myers Squibb, MSD, Nektar, Novartis, Pfizer, Roche, and Immunocore; personal fees from AstraZeneca, Boston Biomedical, Eisai, EUSA Pharma, GlaxoSmithKline, Ipsen, Imugene, Incyte, iOnctura, Kymab, Merck Sorono, Pierre Fabre, Secarna, Vitaccess, and Covance; and grants from Aveo and Pharmacyclics outside of the submitted work. All other authors declare no competing interests.
Acknowledgments
AS, CT, RH, and MEJ are supported by the Institute of Cancer Research. MEJ also received funding from Breast Cancer Now. BT and AG are supported by Cancer Research UK ( C61296/A27223 ). CL and CT receive support from the Movember foundation. RH is supported by Cancer Research UK ( C1298/A8362 ) and Bobby Moore Fund for Cancer. GL is supported by a Cancer Research UK Advanced Clinician Scientist Fellowship Award ( C18081/A18180 ) and is Associate Director of the multi-institutional CanTest Collaborative funded by Cancer Research UK ( C8640/A23385 ). DCM is supported by Cancer Research UK ( C57955/A24390 ). AS is in receipt of an Academic Clinical Lectureship from National Institute for Health Research and Biomedical Research Centre post-doctoral support. EM receives post-doctoral support from Health Data Research UK and Cancer Focus Northern Ireland grants. ML is funded by Health Data Research UK and UK Research and Innovation Industrial Strategy Challenge Fund.
Supplementary Materials
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