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Innovative Research Program on Suicide Countermeasures in FY2021: Report on Commissioned
Research Results

Field 2: Improving support tools for local governments
Topic number：2-1
Research topic:
Research on Promotion of Utilization of Statistical Data in Public Administration

Principal Researcher:
Hiroe Tsubaki
Professor Emeritus of ISM, Inter-University Research Institute Corporation Research Organization ofInformation and Systems (ROIS), The Institute of Statistical Mathematics (ISM)
Co-Researcher:Kazuhiro Minami
Professor, Department of Statistical Data Science, Inter-University Research Institute Corporation Research
Organization of Information and Systems, The Institute of Statistical Mathematics,
Mayumi OkaProject Associate Professor, Research Center for Medical and Health Data Science, Inter-University ResearchInstitute Corporation Research Organization of Information and Systems, The Institute of Statistical
Mathematics
Motoi Okamoto
Chief URA / Project Associate Professor, Office of Strategic Planning, Inter-University Research InstituteCorporation Research Organization of Information and Systems
Isao Takabe
Professor, Department of Data Science, Faculty of Data Science, Rissho University
Satoshi Yamashita
Professor / Vice Director-General / Director of the Risk Analysis Research Center, Inter-University ResearchInstitute Corporation Research Organization of Information and Systems, The Institute of Statistical
Mathematics
Keita Yamauchi
Professor, Graduate School of Health Management, Keio University1

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Michiko Watanabe
Professor, Department of Data Science, Faculty of Data Science, Rissho University

Takahiro Arai
Grad Student/ Data Analyst, Graduate School of Health Management, Keio University/Japan Suicide
Countermeasures Promotion Center
Takafumi Kubota
Associate Professor, School of Management & Information Sciences, Tama University
Yoshitake Takebayashi
Associate Professor, School of Medicine (Life Sciences and Social Medicine), Department of Health Risk
Communication, Fukushima Medical University

Summary:
Research on the formation of an on-site data analysis environment to accelerate the policy use of official
statistical microdata:
Takabe, Yamauchi, Okamoto, and Tsubaki have been preparing for the establishment of on-site data analysis
centers for advanced analysis of microdata in official statistics and expanding the data analysis environment atexisting centers. On-site microdata centers for official statistics will be established at the Faculty of Data Scienceand the Graduate School of Health Management at Keio University. Through this, exploratory data analysis
contributing to policy-making, such as suicide prevention, will be possible at the two universities. Arai, Kubota,
Tsubaki, and Watanabe have been studying data analysis methods that can be used for large-scale data analysis
at the on-site center, and have implemented an environment that enables analysis of latent class models at theon-site center of the Research Organization of Information and Systems.

1)

Research and development of publishable data creation and anonymization tools to accelerate

the utilization of on-site data analysis environments:
Based on the idea of Synthetic Data, Takabe analyzed and examined how to create pseudo microdata of official2

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statistics using actual data. As a result of the analysis, it was found that the created pseudo micro data preserves
the structure of the original data to some extent. Minami has developed a k-anonymization algorithm thatenables the disclosure of detailed regional information, mainly targeting cancer registration information in Japan,and empirically evaluated its safety. As a pre-processing for anonymization, we match the municipal information(e.g., machi, city) in the regional information with the GPS coordinates published by the Ministry of Land,
Infrastructure, Transport, and Tourism, so that we can perform regional division based on the GPS reference
values recursively. He considers age, gender, and regional information as quasi-identifier attributes that anattacker can know age, and evaluates record identification risks with varying values of k.

2)

Research on the Utilization of Micro Data for Comprehensive Suicide Prevention:

Suicide in Japan, which had been declining for more than a decade, began to increase in 2020 and has been
pointed out to be related to the COVID-19 pandemic. Oka, Yamauchi, Kubota, and Tsubaki attempted tounderstand how changes such as unemployment, economic deprivation, decreased social activity, and isolation
affected the increase in suicide.
Referring to previous studies on the suicide problem, they organized the classification of target areas, selectionof indicators, and analytical methods. In particular, we examined the use of public micro data that contributes
to comprehensive suicide countermeasures. They created an indicator, "degree of increase in the suicide rate,"
to estimate changes in the suicide rate around 2020 based on suicide statistics from 1,735 municipalities
nationwide, and analyzed regional and gender differences in the increase in the suicide rate and its backgroundfactors.
It became clear that the rise in Japan's suicide rate due to the Corona disaster did not occur uniformly across thecountry, but rather that disparities emerged between regions where the rate rose more and those where it did not.
Differences in industrial structure and differences in the working patterns of men and women may have caused
the gender difference in the rise in the suicide rate.
In light of the results of this study, they believe it is important to set priorities in the implementation ofcountermeasures and to formulate different support contents according to background factors. To conduct a3【 課題番号 2-1 】
more effective analysis of background factors, it is desirable to develop an environment for public micro-data
analysis using an on-site center and to use this information in the evaluation of countermeasures in the future.
As an analytical technique for promoting the use of microdata from official statistics surveys, the method oflatent class analysis, which is a technique for exploring statistical causal structure models for observational
record data, especially relatively large-scale, so-called real-world data, was taken up for quantitative studies onvisualization of several latent heterogeneous lifestyle behavioral patterns of carers based on anonymous
microdata from the Official Survey of Social Life Time. Watanabe, Yamauchi, and Tanoue indicate the issuesof social isolation as latent factors in the caregiving lifestyle behavior of single elder males. The percentage ofsuicides in Japan caused by "nursing care tiredness" is on the rise every year. In Japan, where the population isaging, this rate is expected to increase further. Arai, Yamauchi, and Watanabe also visualized the variousproblems faced by caregivers through the analysis of information on caregivers' time spent in daily activities
using anonymous data on Time Use and Leisure Activities from the Official Survey of Social Life Time
Takebayashi planned a web-based survey to examine the factors that promote or hinder the utilization of mental
health services. This year, survey items were selected and developed based on a review of previous studies. Thereview of previous studies revealed the existence of numerous psychological barriers based on the stigma ofmental illness and demographic factors such as gender, age, socioeconomic status, etc., as factors that inhibit
the utilization of mental health services. On the other hand, mental health literacy and acceptance of diversity
may promote the use of mental health services. A survey will be conducted in the next fiscal year to examine
the complex relationships among these factors.

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