Stock Christian, Mons Ute, Brenner Hermann
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany.
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Cancer Prevention Unit, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
Cancer Epidemiol. 2018 Dec;57:110-119. doi: 10.1016/j.canep.2018.10.011. Epub 2018 Oct 30.
Cancer incidence projections are of major interest for resource allocation in healthcare and medical research. Previous reports of cancer incidence projections have often been deterministic, i.e. lacking quantification of uncertainty. We project cancer incidence in Germany by applying an approach that allows for probabilistic interpretation of outcomes.
German cancer registry data from 1999 to 2013 are used to predict cancer incidence for 27 sites until the year 2030. We apply Bayesian Poisson and negative binomial models to obtain probabilistic estimates of future site-, year-, sex- and age-specific cancer incidence rates. Results from cancer incidence models are combined with probabilistic population projections to estimate numbers of incident cancer cases. Comparisons of overall and stratum-specific cancer incidence rates and case numbers are made between the years 2015 and 2030 by estimating absolute and relative change along with uncertainty intervals.
The overall standardized incidence rate is expected to increase by 5% (95%-credible interval: 0%, 13%) until 2030. Incident case numbers are expected to increase by 23% (95%-credible interval: 17%, 29%) which is mostly driven by demographic change. The probability (expressed as %) that the change will be >10%, >20% or >30% was calculated to be >99%, 66% and 7%, respectively.
The analysis provides evidence on the future cancer burden in Germany by applying a fully Bayesian approach that offers advantages in terms of flexibility, probabilistic interpretability, and transparency. It may especially be an alternative when long-term cancer incidence data are missing.
癌症发病率预测对于医疗保健和医学研究中的资源分配至关重要。以往关于癌症发病率预测的报告往往是确定性的，即缺乏不确定性的量化。我们通过应用一种允许对结果进行概率解释的方法来预测德国的癌症发病率。
使用1999年至2013年德国癌症登记数据来预测到2030年27个部位的癌症发病率。我们应用贝叶斯泊松模型和负二项式模型来获得未来特定部位、年份、性别和年龄的癌症发病率的概率估计。癌症发病率模型的结果与概率性人口预测相结合，以估计癌症发病病例数。通过估计绝对和相对变化以及不确定性区间，对2015年至2030年期间总体和特定分层的癌症发病率及病例数进行比较。
预计到2030年总体标准化发病率将上升5%（95%可信区间：0%，13%）。预计发病病例数将增加23%（95%可信区间：17%，29%），这主要是由人口结构变化驱动的。计算得出变化大于10%、大于20%或大于30%的概率（以%表示）分别为>99%、66%和7%。
该分析通过应用一种完全贝叶斯方法提供了关于德国未来癌症负担的证据，该方法在灵活性、概率可解释性和透明度方面具有优势。当缺乏长期癌症发病率数据时，它可能尤其具有替代性。
