著者

Tasuku OKUI

出版者

日本公衆衛生学会

雑誌

日本公衆衛生雑誌 (ISSN:05461766)

巻号頁・発行日

vol.67, no.11, pp.781-790, 2020-11-15 (Released:2020-12-23)

参考文献数

25

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Objectives In this study, we compared the decrease in cancer mortality rates among prefectures in Japan using age-period-cohort (APC) analysis.Methods We used the cancer mortality data of each prefecture in Japan, as determined by the Vital Statistics, over 5-year periods from 1995 to 2015. Records of the number of mortalities in each 5-year age group from 40-44 to 85-89 years age was collected. We fitted a Bayesian APC model to the data of each prefecture and estimated the birth cohort effect on cancer mortality rates in the prefectures over 5-year periods ranging from 1916-1920 to 1971-1975. In addition, we calculated the ratio of the mortality rate of each prefecture to that of the entire country for each birth cohort.Results Our APC analysis revealed that the decrease in the age-adjusted cancer mortality rates was mainly attributable to a reduction in the cohort effect on the rates in men and to reduction in the cohort and period effects on the rates in women. The magnitude of reduction in cohort effect varied by prefecture for men and women. Several prefectures having a government ordinance-designated municipality tended to show a higher reduction than those that do not. Spearman's correlation coefficient between the population size of prefectures and the percentage reduction in cohort effect was 0.370 in men. In addition, the relative ranking of the prefectures based on cancer mortality rates greatly varied by birth cohorts, particularly in men.Conclusion A disparity exists in the percentage reduction in the cohort effect among prefectures. In each prefecture target cohorts with higher than average cancer mortality rates must be identified to implement specific countermeasures for cancer prevention. In addition, for each prefecture, assessment of lifestyle differences that might be related to cancer mortality among birth cohorts is important for reducing cancer mortality in the more recent birth cohorts.

著者

Tasuku Okui

出版者

Information Processing Society of Japan

雑誌

IPSJ Transactions on Bioinformatics (ISSN:18826679)

巻号頁・発行日

vol.13, pp.1-6, 2020 (Released:2020-01-08)

参考文献数

26

被引用文献数

5

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Microbiome data have been obtained relatively easily in recent years, and currently, various methods for analyzing microbiome data are being proposed. Latent Dirichlet allocation (LDA) models, which are frequently used to extract latent topics from words in documents, have also been proposed to extract information on microbial communities for microbiome data. To extract microbiome topics associated with a subject's attributes, LDA models that utilize supervisory information, including LDA with Dirichlet multinomial regression (DMR topic model) or supervised topic model (SLDA, ) can be applied. Further, a Bayesian nonparametric model is often used to automatically decide the number of latent classes for a latent variable model. An LDA can also be extended to a Bayesian nonparametric model using the hierarchical Dirichlet process. Although a Bayesian nonparametric DMR topic model has been previously proposed, it uses normalized gamma process for generating topic distribution, and it is unknown whether the number of topics can be automatically decided from data. It is expected that the total number of topics (with relatively large proportions) can be restricted to a smaller value using the stick-breaking process for generating topic distribution. Therefore, we propose a Bayesian nonparametric DMR topic model using a stick-breaking process and have compared it to existing models using two sets of real microbiome data. The results showed that the proposed model could extract topics that were more associated with attributes of a subject than existing methods, and it could automatically decide the number of topics from the data.

著者

Tasuku Okui Yutaka Matsuyama Shigeyuki Nakaji

出版者

The Biometric Society of Japan

雑誌

計量生物学 (ISSN:09184430)

巻号頁・発行日

vol.39, no.2, pp.55-84, 2019-01-31 (Released:2019-05-11)

参考文献数

59

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Nowadays, many methods that employ the 16S ribosomal RNA gene (16S rRNA sequencing data) have been proposed for the analysis of gut microbial compositional data. 16S rRNA sequencing data is statistically multivariate count data. When multivariate data analysis methods are used for association analysis with a disease, 16S rRNA sequencing data is generally normalized before analysis models are fitted, because the total sequence read counts of the subjects are different. However, proper methods for normalization have not yet been discussed or proposed. Rarefying is one such normalization method that equals the total counts of subjects by subsampling a certain amount of counts from each subject. It was thought that if rarefying were combined with ensemble learning, performance improvement could be achieved. Then, we proposed an association analysis method by combining rarefying with ensemble learning and evaluated its performance by simulation experiment using several multivariate data analysis methods. The proposed method showed superior performance compared with other analysis methods, with regard to the identification ability of response-associated variables and the classification ability of a response variable. We also used each evaluated method to analyze the gut microbial data of Japanese people, and then compared these results.