ArticleFrontiers in oncology2023
Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Augmenting fact and date of death in electronic health records using internet media sources: a validation study from two large healthcare systems.American journal of epidemiology · 2026Article
- Increased incidence, survival, and registration quality of primary hepato-pancreato-biliary cancers in the Netherlands Cancer Registry.International journal of cancer · 2026Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Population-based cancer survival estimates can provide insight into the real-world impacts of healthcare interventions and preventive services. However, estimation of survival rates obtained from population-based cancer registries can be biased due to missed incidence or incomplete vital status data. Long-term survival estimates in particular are prone to overestimation, since the proportion of deaths that are missed, for example through unregistered emigration, increases with follow-up time. This also applies to registry-based long-term prevalence estimates. The aim of this report is to introduce a method to detect missed deaths within cancer registry data such that long-term survival of cancer patients does not exceed survival in the general population. Methods: We analyzed data from 15 German epidemiologic cancer registries covering the years 1970-2016 and from Surveillance, Epidemiology, and End Results (SEER)-18 registries covering 1975-2015. The method is based on comparing survival times until exit (death or follow-up end) and ages at exit between deceased patients and surviving patients, stratified by diagnosis group, sex, age group and stage. Deceased patients with both follow-up time and age at exit in the highest percentile were regarded as outliers and used to fit a logistic regression. The regression was then used to classify each surviving patient as a survivor or a missed death. The procedure was repeated for lower percentile thresholds regarding deceased persons until long-term survival rates no longer exceeded the survival rates in the general population. Results: For the German cancer registry data, 0.9% of total deaths were classified as having been missed. Excluding these missed deaths reduced 20-year relative survival estimates for all cancers combined from 140% to 51%. For the whites in SEER data, classified missed deaths amounted to 0.02% of total deaths, resulting in 0.4 percent points lower 20-year relative survival rate for all cancers combined. Conclusion: The method described here classified a relatively small proportion of missed deaths yet reduced long-term survival estimates to more plausible levels. The effects of missed deaths should be considered when calculating long-term survival or prevalence estimates.
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