title: Estimation of cancer incidence in Japan with an age-period-cohort model.
abstract: Cancer has been the primary cause of death in Japan for many years and accurate cancer incidence data are necessary in order to make plans for cancer control. Although population-based cancer registries are the best answer, regrettably there are still many regions with low accuracy registries. In an alternative estimation, cancer incidences have been analyzed by age-period-cohort (APC) models, allowing future prediction of cancer incidences in 2004. Considering the unexpectedly rapid aging of the Japanese population after this figure was reported, it would be worthwhile to examine more recent data. In this study, we therefore projected major cancer incidences based on the earlier results leaving estimated values for the age and cohort effects. Relating to the period effect, the most adequate scenario was selected from 12 projection methods. Furthermore, incidences when registration rates varied between 70 and 100% were calculated. As a result, different trends from reported incidences were observed for liver cancer in males, and trends of registration rates differed by sites. Until stable accurate registration data become available, it is difficult to judge whether predicted increase is real or only looks so because the registration rate is not 100%. However, it is clearly necessary to continuously observe variation in cancer incidences.
authors.affiliation: Osaka University, Osaka, Japan. m-utada@sahs.med.osaka-u.ac.jp
authors.forename: Mai
authors.initials: M
authors.lastname: Utada
authors.forename: Yuko
authors.initials: Y
authors.lastname: Ohno
authors.forename: Midori
authors.initials: M
authors.lastname: Soda
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Kamo
mesh_terms: D000367:Age Factors; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D007564:Japan / Q000453:epidemiology; D008297:Male; D009369:Neoplasms / Q000453:epidemiology* / Q000401:mortality*; D012042:Registries; D012044:Regression Analysis; D013997:Time Factors