Tampere School of Public Health, University of Tampere, Tampere, Finland.
Breast Cancer Res Treat. 2010 Jul;122(2):553-66. doi: 10.1007/s10549-009-0701-x. Epub 2010 Jan 7.
Estimating the natural history parameters of breast cancer not only elucidates the disease progression but also make contributions to assessing the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancer. We applied three-state and five-state Markov models to data on a two-yearly routine mammography screening in Finland between 1988 and 2000. The mean sojourn time (MST) was computed from estimated transition parameters. Computer simulation was implemented to examine the effect of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancers. In three-state model, the MST was 2.02 years, and the sensitivity for detecting preclinical breast cancer was 84.83%. In five-state model, the MST was 2.21 years for localized tumor and 0.82 year for non-localized tumor. Annual, biennial, and triennial screening programs can reduce 53, 37, and 28% of advanced cancer. The effectiveness of intensive screening with poor attendance is the same as that of infrequent screening with high attendance rate. We demonstrated how to estimate the natural history parameters using a service screening program and applied these parameters to assess the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced cancer. The proposed method makes contribution to further cost-effectiveness analysis. However, these findings had better be validated by using a further long-term follow-up data.
估算乳腺癌的自然史参数不仅可以阐明疾病的进展过程，还有助于评估筛查间隔、敏感性和参与率对降低晚期乳腺癌的影响。我们应用三状态和五状态马尔可夫模型对 1988 年至 2000 年间芬兰进行的两年一次常规乳房 X 线筛查数据进行了分析。根据估计的转移参数计算平均逗留时间（MST）。通过计算机模拟来检查筛查间隔、敏感性和参与率对降低晚期乳腺癌的影响。在三状态模型中，MST 为 2.02 年，检测临床前乳腺癌的敏感性为 84.83%。在五状态模型中，局部肿瘤的 MST 为 2.21 年，非局部肿瘤的 MST 为 0.82 年。年度、两年和三年一次的筛查计划可以减少 53%、37%和 28%的晚期癌症。参与率低的强化筛查与参与率高的不频繁筛查效果相同。我们展示了如何使用服务筛查计划来估计自然史参数，并应用这些参数来评估筛查间隔、敏感性和参与率对降低晚期癌症的影响。所提出的方法有助于进一步的成本效益分析。然而，这些发现最好通过使用进一步的长期随访数据来验证。
