Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation
BackgroundPopulation-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 s...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1088657/full |
| _version_ | 1811157727601229824 |
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| author | Stefan Dahm Benjamin Barnes Klaus Kraywinkel |
| author_facet | Stefan Dahm Benjamin Barnes Klaus Kraywinkel |
| author_sort | Stefan Dahm |
| collection | DOAJ |
| description | BackgroundPopulation-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.MethodsWe 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.ResultsFor 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.ConclusionThe 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. |
| first_indexed | 2024-04-10T05:11:06Z |
| format | Article |
| id | doaj.art-fced312a961e4151b459ccca28edf920 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T05:11:06Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-fced312a961e4151b459ccca28edf9202023-03-09T07:28:46ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.10886571088657Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimationStefan DahmBenjamin BarnesKlaus KraywinkelBackgroundPopulation-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.MethodsWe 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.ResultsFor 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1088657/fullcancer registry datamissed deathslong-term survivalclassification algorithmrelative survival |
| spellingShingle | Stefan Dahm Benjamin Barnes Klaus Kraywinkel Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation Frontiers in Oncology cancer registry data missed deaths long-term survival classification algorithm relative survival |
| title | Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation |
| title_full | Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation |
| title_fullStr | Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation |
| title_full_unstemmed | Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation |
| title_short | Detection of missed deaths in cancer registry data to reduce bias in long-term survival estimation |
| title_sort | detection of missed deaths in cancer registry data to reduce bias in long term survival estimation |
| topic | cancer registry data missed deaths long-term survival classification algorithm relative survival |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1088657/full |
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