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Identification of High-risk Groups of Suicide from the Depressed Elderly using Decision Tree Analysis

Article information

Res Community Public Health Nurs. 2019;30(2):130-140
Publication date (electronic) : 2019 June 28
doi : https://doi.org/10.12799/jkachn.2019.30.2.130
Sehoon Hong1orcid_icon, Dongwon Lee2orcid_icon
1Associate Professor, College of Nursing, CHA University, Pocheon, Korea.
2Assistant Professor, School of Business Administration, Hansung University, Seoul, Korea.
Corresponding author: Lee, Dongwon. School of Business Administration, Hansung University, 116 Samseongyo-ro, 16 gil, Seongbuk-gu, Seoul 02876, Korea. Tel: +82-2-760-4250, Fax: +82-00-2-760-4482, dongwonlee@hansung.ac.kr
Received 2018 October 31; Revised 2019 March 18; Accepted 2019 April 12.

Abstract

Purpose

The aim of this study is to explore levels of suicidal ideation and identify subgroups of high suicidal risk among the depressed elderly in Korea.

Methods

A descriptive cross-sectional design was adopted on secondary data from the 6th (1st year) Korean national health and nutrition examination survey (KNHANES). A total of 239 depressed elders aged 60 or over who participated in the KNHANES. The prevalence of suicidal ideation and its related factors, including sociodemographic, physical, psychological characteristics and quality of life (EQ-5D index) were examined. Descriptive statistics and a decision tree analysis were performed using the SPSS/WIN 23.0 and SPSS Modeler 14.2 programs.

Results

Of the depressed elderly, 28.9% had suicidal ideation. Three groups with high suicidal ideation were identified. Predictive factors included perceived stress level, household income level, quality of life and restriction of activity. In the highest risk group were those depressed elderly with moderate and low levels of stress, less than .71 of EQ-5D index and restriction of activity, and 80.0% of these participants had suicidal ideation. The accuracy of the model was 80.8%, its sensitivity 85.9%, and its specificity 68.1%.

Conclusion

Multi-dimensional intervention should be designed to decrease suicide among the depressed elderly, particularly focusing on subgroups with high risk factors. This research is expected to contribute itself to the policy design and solution building in the future as it suggests policy implications in preventing the suicide of the depressed elderly.

Keywords: Depression; Aged; Suicide; Decision trees

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Article information Continued

© 2019 Korean Academy of Community Health Nursing
Korean Academy of Community Health Nursing

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Figure 1

Figure 1

Decision tree model to identify high-risk subgroups of suicide in depressed elderly.

Table 1

Differences in Suicidal Ideation according to Characteristics of Subjects (N=239)

Table 1

BMI=body mass index; EQ-5D=Euro quality of life 5-dimensions; †Separated, bereaved, divorced, single and no answer; ‡t-test.

Table 2

Predictive Performance according to Modeling Methods

Table 2

C&RT=classification and regression tree; QUEST=quick, unbiased, efficient, statistical tree; CHAID=chi-squared automatic interaction detection.

Table 3

Classification Matrix of CHAID

Table 3

CHAID=Chi-squared automatic interaction detection.