Dahm Stefan, Barnes Benjamin, Kraywinkel Klaus
German Center for Cancer Registry Data, Department for Health Monitoring, Robert Koch-Institute, Berlin, Germany.
Front Oncol. 2023 Mar 9;13:1088657. doi: 10.3389/fonc.2023.1088657. eCollection 2023.
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.
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.
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.
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.
基于人群的癌症生存估计可以洞察医疗干预措施和预防服务对现实世界的影响。然而，由于发病率遗漏或生命状态数据不完整，基于人群的癌症登记处获得的生存率估计可能存在偏差。特别是长期生存估计容易被高估，因为例如通过未登记的移民而遗漏的死亡比例会随着随访时间的增加而上升。这也适用于基于登记处的长期患病率估计。本报告的目的是介绍一种方法，用于检测癌症登记数据中的遗漏死亡情况，以使癌症患者的长期生存率不超过一般人群的生存率。
我们分析了来自15个德国癌症流行病学登记处（涵盖1970 - 2016年）以及监测、流行病学和最终结果（SEER）- 18登记处（涵盖1975 - 2015年）的数据。该方法基于比较死亡患者和存活患者直至退出（死亡或随访结束）的生存时间以及退出时的年龄，并按诊断组、性别、年龄组和分期进行分层。随访时间和退出时年龄均处于最高百分位数的死亡患者被视为异常值，并用于拟合逻辑回归。然后使用该回归将每个存活患者分类为幸存者或遗漏死亡者。对于死亡者的较低百分位数阈值重复该过程，直到长期生存率不再超过一般人群的生存率。
对于德国癌症登记数据，总死亡人数的0.9%被分类为遗漏死亡。排除这些遗漏死亡后，所有癌症综合的20年相对生存估计从140%降至51%。对于SEER数据中的白人，分类为遗漏死亡的人数占总死亡人数的0.02%，导致所有癌症综合的20年相对生存率降低0.4个百分点。
此处描述的方法分类出的遗漏死亡比例相对较小，但将长期生存估计降低到了更合理的水平。在计算长期生存或患病率估计时，应考虑遗漏死亡的影响。
