Verdecchia Arduino, De Angelis Giovanni, Capocaccia Riccardo
Laboratorio di Epidemiologia e Biostatistica, Istituto Superiore di Sanita', Rome, Italy.
Stat Med. 2002 Nov 30;21(22):3511-26. doi: 10.1002/sim.1304.
A method, PIAMOD (Prevalence, Incidence, Analysis MODel), which allows the estimation and projection of cancer prevalence patterns by using cancer registry incidence and survival data is presented. As a first step the method involves the fit of incidence data by an age, period and cohort model to derive incidence projections. Prevalence is then estimated from modelled incidence and survival estimates. Cancer mortality is derived as a third step from modelled incidence, prevalence and survival. An application to female breast cancer is given for the Connecticut State by using data from the Connecticut Tumor Registry (CTR), 1973-1993. The age, period and cohort model fitted incidence quite well and allowed us to derive long-term projections up to 2030. Patients' survival was also projected to future years according to a scenario approach based on two extreme hypotheses: steady, that is, no more improvements after 1993 (conservative), and continuously improving at the same rate as during the observation period. Age-standardized estimated incidence shows a changing trend around the year 2005, when it starts decreasing. Age-standardized prevalence is expected to increase and change trend at a later date. Breast cancer mortality is projected as decreasing, as the combined result of no further increase in incidence and improving cancer patients' survival. An easy-to-use PIAMOD software package, on which work is in progress, will be made available to individual cancer registries and/or health planning institutions or authorities once it is developed. The use of the PIAMOD method for cancer registries will allow them to provide results of paramount importance for the whole community involved in the assessment of future disease burden scenarios in an evolving society.
本文介绍了一种名为PIAMOD(患病率、发病率、分析模型)的方法,该方法可利用癌症登记处的发病率和生存数据来估计和预测癌症患病率模式。第一步,该方法通过年龄、时期和队列模型对发病率数据进行拟合,以得出发病率预测值。然后根据模型化的发病率和生存估计值来估计患病率。第三步,从模型化的发病率、患病率和生存数据中得出癌症死亡率。通过使用康涅狄格肿瘤登记处(CTR)1973 - 1993年的数据,对康涅狄格州的女性乳腺癌进行了应用分析。年龄、时期和队列模型对发病率的拟合效果很好,使我们能够得出直至2030年的长期预测值。还根据基于两种极端假设的情景方法对患者未来几年的生存情况进行了预测:稳定假设,即1993年后不再有改善(保守假设),以及以与观察期相同的速率持续改善。年龄标准化估计发病率在2005年左右呈现出变化趋势,此后开始下降。年龄标准化患病率预计会上升,并在稍后时间改变趋势。预计乳腺癌死亡率将下降,这是发病率不再进一步上升和癌症患者生存率提高的综合结果。一个易于使用的PIAMOD软件包正在开发中,一旦开发完成,将提供给各个癌症登记处和/或卫生规划机构或部门。对癌症登记处使用PIAMOD方法将使它们能够为参与评估不断发展的社会中未来疾病负担情景的整个社区提供至关重要的结果。