Application of Network Analysis to Multiple
Causes of Death Data in Japan
Futoshi Ishii∗, Reiko Hayashi†, Emiko Shinohara‡, Motomi Beppu†

Introduction
As the old age mortality has been improved, main causes of deaths have shiftedto chronic diseases that increased the number of people suffered from multiple diseases. It has increased the importance of the analysis of multiple causes of death.
In Japan, there have been few studies that uses multiple causes of death since thatkind of data has just become available in recent years. However, there is a large
number of studies on the multiple causes of death in Europe and the United States,
where many researchers join the ”MultiCause network”.
The MultiCause network (https://mcod.web.ined.fr/) is an international research
network dedicated to the analysis of all causes reported on death certificates. In addition to the reassessment of mortality levels, research is conducted on how causes arecombined with one another. Established in 2012, the network regroups researchers
from about 20 countries.
Amongst the various research articles by the members of the MultiCause network,
Egidi et al. (2018) is a seminal study that applied a network analysis to explore relationships between causes of death.
In this study, we analyze multiple causes of death in Japan using network analysis.
Moreover, we apply community detection methods used in network analysis for∗ Keio University† National Institute of Population and Social Security Research‡ University of Tokyo1

better understanding of complex relationship between causes of death, which is not
examined in Egidi et al. (2018).

1 Data and Method
1.1 Data
First, we describe the data used in this study. We use data on multiple causes ofdeath from the vital statistics in Japan for the year 2009. We transformed the namesof causes of death to the ICD-10 codes, as in Figure 1(Shinohara et al. 2021). On thedeath certificate, there are fields for cause and period. We coded both fields whereas
we used only causes in this study.

Coding multiple causes and period
text in “cause” field

Text in “period” field

Normalization

Pattern matching••

Character
Word

Spelling correction
Exact matching

Mapping table

Seq. of ICD codes

Seq. of periods

Pairs of ICD code and period

Figure 1 Coding Multiple Causes and Period

We used the list for classification of causes of death given in the guidelines forthe computation of the standard MCOD indicators by the MultiCause network. The
list includes 74 causes as in Table 2 and 3 shown in the end of this article. We also
followed the processing rules in the guidelines, which requires eliminations of ill2

defined causes/mechanisms of death (groups 66, 67 and 68), counting the multiplecauses coded in one certificate only once when they belong to the same item in thelist, and so on.

1.2 Method
Next, we describe the method used in this study. Egidi et al. (2018) used the causeof-death data classified by the European shortlist for the Italian people aged 65 andover in 2011, and considered non-directed networks, where the ”nodes” (membersof a network) are causes of death and the two nodes are connected with an ”edge”
when both causes are mentioned in the same certificates. They formulated the raw
adjacency matrix adding one for (i, j) component when the causes i and j are mentioned in the same death certificate. Then, they normalized the matrix using theiterative proportional fitting (IPF) method, and considered only the strongest relationships by selecting normalized joint frequencies higher than the ninety-fifth percentile.
We constructed network structures using almost the same method as in Egidi et al.
(2018), although we used the classification of causes that are used in the MultiCause
network. Then, we analyzed them applying network analysis methods.
As the application of the network analysis, first we calculated centrality measuresthat are proposed to measure the prominence of the members of a network. Here,
we calculated three centrality measures such as degree centrality, closeness centrality
and betweenness centrality as in Egidi et al. (2018). They are defined as follows.
The degree centrality CD (ni ) is the simplest central measure defined by the number of links connecting a node ni to the others. It is also called the degree d(ni ) of thenode ni . Here, we define the variable xij as{
1 (if ni and n j have a relationship)xij =0 (otherwise)
Then, the degree centrality is expressed as follows.
CD (ni ) = d(ni ) = ∑ xij = ∑ x jijjThe closeness centrality CC (ni ) indicates how close a node is to all others in the setof nodes. If we denote d(ni ; n j ) as the geodesic distance between nodes ni and n j , the3closeness centrality is defined as follows.
[CD ( n i ) =N] −1∑ d ( ni ; n j )j =1

where N is the total number of nodes in the connected component to which ni belongs.
The betweenness centrality CC (ni ) refers to the role of certain nodes in connecting
nonadjacent nodes, defined as
CB (ni ) = ∑ g jk (ni )/g jkj<kwhere g jk (ni ) is the geodesic distance between nodes n j and nk that include the nodeni , g jk is the geodesic distance between nodes n j and nk .
Next, we applied community detection methods to the network structures. Egidi
et al. (2018) investigated the subgroup (community) of the network with graphs
showing the strongest relationship considering the 15 chapters of the ICD-10
classification. In this study, we applied the 7 community detection methods usedin network anlaysis to examine strong links between causes of death, such as
Fast-greedy[cfg], Walktrap[cw], Edge-betweenness[ceb], Spinglass[cs],Label propagation[clp], Leading eigenvector[clp], and Louvain[cl] that are mentioned in Luke(2015).

2 Results
We show the results for Japanese females aged 65 and over in the following.

2.1 Network structure
Figure 2 shows the whole network structure. The red markers show the causes(nodes). Two causes with strongest relationship are linked with a line (edge). There
are isolated nodes such as ”malignant neoplasm of prostate (19 Prost)”, ”hyperplasia of prostate (63 Hyppro)”, and ”other external causes (74 Oext)”. We can also observe that ”drug dependence, toxicomania (35 Drug)” and ”hypertensive diseases(46 Hypten)” form an independent connected component. These causes are omitted
4

Figure 2 Network Structure (Female, 65 and over, 2009)

in the calculation of closeness centrality and the analysis of community detection,
since we can apply the methods only to the connected component.
From Figure 2, we can observe that the prominence of each node in the network isdifferent, which is measured by central measures.

2.2 Central measures
Figure 3 shows the five highest causes for degree centrality. The five causes are
exhibited in the left table and shown in yellow markers in the right graph. The5

Figure 3 Five Highest Degree Centrality (Female, 65 and over, 2009)

malignant neoplasms of ill-defined, unspecified, independent multiple sites show
the highest degree centrality.

Figure 4 Five Highest Closeness Centrality (Female, 65 and over, 2009)

Figure 4 shows the five highest causes for closeness centrality. Alcoholic psychosis
and chronic alcohol abuse show the highest closeness centrality.
Figure 5 shows the five highest causes for betweenness centrality. As for the betweenness centrality, alcoholic psychosis and chronic alcohol abuse show the highest
6Figure 5 Five Highest Betweenness Centrality (Female, 65 and over, 2009)

value.
Comparing the results for three measures, we can see that alcoholic psychosis andchronic alcohol abuse is included in all three lists.

2.3 Community detection
Next, we show the results for community detection.
Figure 6 shows the result of community detection by the fast-greedy method(Newman2004). Here, the colored circle shows each cause. All causes are divided into 6 communities, and the colors of the circles show their communities.
Community A with green circles includes many causes relating malignant neoplasms. Community B with red circles includes chronic liver diseases, other diseasesof digestive system, and so on. Community C with blue circles includes Alzheimer sdiseases, Parkinson s disease, other diseases of the nervous system and so on. Community D with yellow circles includes diabetes mellitus, obesity and so on. Community E with orange circles includes tuberculosis, influenza, other diseases of the respiratory system, and so on. And Community F with purple circles is others. We cansee that the community detection method is useful to understand the relationships
between causes showing subgroups consisting with strongly linked causes.
Table 1 shows the comparison of the results by the 7 community detection meth7

Figure 6 Community Detection (Japanese Females, 65 and over, 2009)

ods. We can see that all the methods derive similar communities, although somedifferences are observed. For example, dementias (36 Demen) is grouped into community E by the fast-greedy method, althogh it is grouped into community B or C
by other methods.

8Table 1 Comparison of Community Detection Methods(Japanese Females, 65and over, 2009)

3 Concluding Remarks
In this study, we investigated the applications of the network analysis to Japanesemultiple causes of deaths using the methods developed by Egidi et al. (2018). From
the results of this study, we understood that the network analysis is effective foranalyzing the relationship between the multiple causes of death for Japan. Further research would be needed to study methods for network structure construction
and/or community detection that would be more adaptable for Japanese multiplecauses of death data.
This work is supported by MHLW Research on Statistics and Information Program Grant Number
JPMH20AB1001, and a research project ”Modeling and Comprehensive Analysis of Population, Economy
and Society of Ultra-Longevity” by the National Institute of Population and Social Security Research.

9References
Egidi, V., M. A. Salvatore, G. Rivellini, and S. D’Angelo (2018) “A network approachto studying cause-of-death interrelations”, Demographic Research, Vol. 38, pp. 373–400.
Luke, D. A. (2015) A User’s Guide to Network Analysis in R: Springer.
Newman, M. E. (2004) “Fast algorithm for detecting community structure in networks”, Physical review E, Vol. 69, No. 6, p. 066133.
Shinohara, E., M. Beppu, R. Hayashi, and F. Ishii (2021) “An analysis of ’Cause ofDeath’ fields written in death certificates”. Presented at the 41st Joint Conference
on Medical Informatics (The 22nd Annual Meeting of JAMI), Nagoya Congress
Center, Nov. 20 2021.

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Table 2 List of Causes (Group 1-5)

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Table 3 List of Causes (Group 6-15)

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