title: Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer.
abstract: 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.
authors.affiliation: Tampere School of Public Health, University of Tampere, Tampere, Finland. chiayun.wu@gmail.com
authors.forename: Jenny Chia-Yun
authors.initials: JC
authors.lastname: Wu
authors.forename: Matti
authors.initials: M
authors.lastname: Hakama
authors.forename: Ahti
authors.initials: A
authors.lastname: Anttila
authors.forename: Amy Ming-Fang
authors.initials: AM
authors.lastname: Yen
authors.forename: Nea
authors.initials: N
authors.lastname: Malila
authors.forename: Tytti
authors.initials: T
authors.lastname: Sarkeala
authors.forename: Anssi
authors.initials: A
authors.lastname: Auvinen
authors.forename: Sherry Yueh-Hsia
authors.initials: SY
authors.lastname: Chiu
authors.forename: Hsiu-Hsi
authors.initials: HH
authors.lastname: Chen
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control*; D003198:Computer Simulation; D005260:Female; D005387:Finland / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D008327:Mammography / Q000706:statistics & numerical data*; D008390:Markov Chains; D008403:Mass Screening / Q000379:methods / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D010349:Patient Compliance / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D015397:Program Evaluation; D012042:Registries; D012680:Sensitivity and Specificity; D013997:Time Factors