Rachael Stannard

Objective: Unlike survival measures, life expectancy readily illustrates the burden of cancer on society and the average impact on individuals diagnosed with cancer. Cancer stage at diagnosis is a key prognostic factor and hence it is important to obtain stage-specific life expectancy estimates. However, completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries. Therefore, it can be challenging to obtain the long-term stage-specific survival estimates required for estimating stage-specific life expectancy. We provide the first stage-specific life expectancy estimates using whole population data in England. Methods and analysis: Multiple imputation was used to impute values of cancer stage at diagnosis for patients with missing stage at diagnosis information. The simultaneous application of period analysis to obtain up-to-date estimates restricts the contribution of patients with historical diagnoses and hence improves the overall completeness of stage at diagnosis information. For each of the 10 cancer sites in this study, we fit a flexible parametric excess hazard model for each cancer stage on the cumulative excess hazard scale and estimated stage-specific life expectancy using the relative survival framework. Results: The differences in stage-specific and sex-specific life expectancy were evaluated from 40 to 90 years of age. Colorectal cancer, prostate cancer and bladder cancer yield much lower estimates of life expectancy for patients diagnosed with stage IV cancer compared with stages I–III. For example, female patients diagnosed with stage IV colorectal cancer at 70 years of age have a life expectancy of 72.3 years, while those with stages I–III can expect to live beyond 82.0 years. The remaining cancer sites yield approximately equal reductions in life expectancy with each increase in stage at diagnosis from I to IV. Conclusion: We offer the first stage-specific life expectancy estimates for a range of cancer sites in England using data from the National Cancer Registration and Analysis Service, with follow-up until February 2020. Estimates of stage-specific life expectancy provide a real-world, intuitive metric to evaluate the impact of cancer stage at diagnosis on prognosis up to a lifetime horizon. Stage-specific life expectancy estimates also provide key information regarding the potential benefits of early diagnosis initiatives in terms of gains in life years.

Monitoring trends of cancer incidence, mortality and survival is vital for the planning and delivery of health services, and the evaluation of diagnostics and treatment at the population level. Furthermore, comparisons are often made between population subgroups to explore inequalities in outcomes. During the COVID-19 pandemic routine delivery of health services were severely disrupted. Resources were redeployed to COVID-19 services and patient risk of COVID-19 infection required serious consideration. Cancer screening services were paused, the availability of healthcare providers was reduced and, in some cases, patients faced difficulty in accessing optimal treatment in a timely manner. Given these major disruptions, much care should be taken when interpreting changes in cancer survival estimates during this period. The impact on cancer incidence and mortality statistics that have already been reported in some jurisdictions should drive further thought on the corresponding impact on cancer survival, and whether any differences observed are real, artificial or a combination of the two. We discuss the likely impact on key cancer metrics, the likely implications for the analysis of cancer registration data impacted by the pandemic and the implications for comparative analyses between population groups and other risk factor groups when using data spanning the pandemic period.

Background: Completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries, making it challenging to provide long-term stage-specific survival estimates. Stage-specific survival differences are driven by differences in short-term prognosis, meaning estimated survival metrics using period analysis are unlikely to be sensitive to imputed historical stage data. Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER) Program for lung, colon and breast cancer. To represent missing data patterns in less complete registry data, we artificially inflated the proportion of missing stage information conditional on stage at diagnosis and calendar year of diagnosis. Period analysis was applied and missing stage at diagnosis information was imputed under four different conditions to emulate extreme imputed stage distributions. Results: We fit a flexible parametric model for each cancer stage on the excess hazard scale and the differences in stage-specific marginal relative survival were assessed. Estimates were also obtained from non-parametric approaches for validation. There was little difference between the 10-year stage-specific marginal relative survival estimates, regardless of the assumed historical stage distribution. Conclusions: When conducting a period analysis, multiple imputation can be used to obtain stage-specific long-term estimates of relative survival, even when the historical stage information is largely incomplete.