Journal List > J Korean Acad Community Health Nurs > v.30(2) > 1129030

Hong and Lee: Identification of High-risk Groups of Suicide from the Depressed Elderly using Decision Tree Analysis

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.

Figures and Tables

Figure 1

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

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Table 1

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

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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

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

Table 3

Classification Matrix of CHAID

jkachn-30-130-i003

CHAID=Chi-squared automatic interaction detection.

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TOOLS
ORCID iDs

Sehoon Hong
https://orcid.org/0000-0001-6585-8815

Dongwon Lee
https://orcid.org/0000-0003-4264-7120

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