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Predictors of intentional intoxication using decision tree modeling analysis: a retrospective study.
Eun Seok Oh1, Jae Hyung Choi1, Jung Won Lee1
1Department of Emergency Medicine, Soonchunhyang University College of Medicine, Cheonan, Korea.
South Korea faces a high suicide rate, with intentional intoxication being a common method. This study identified key risk factors like alcohol consumption, psychiatric disease, and male sex to help target prevention efforts.
Area of Science:
- Public Health
- Emergency Medicine
- Psychiatry
Background:
- South Korea experiences a high and increasing suicide rate.
- Intentional intoxication is a prevalent method for suicide attempts and a frequent reason for emergency department visits.
Purpose of the Study:
- To identify predictors of suicide risk specifically among individuals attempting suicide via intentional intoxication.
- To utilize decision tree modeling for risk factor identification in intentional intoxication cases.
Main Methods:
- Retrospective analysis of a 4-year hospital registry (2013-2016) involving 4,023 patients presenting with intoxication.
- Data collected included demographic factors (sex, age, smoking, alcohol consumption, psychiatric disease), therapeutic adherence, physical disease, and toxicological factors.
- Decision tree modeling was employed, followed by multiple logistic regression analysis to identify significant risk factors.
Main Results:
- Intentional intoxication accounted for 55.9% (2,247) of all intoxication cases.
- Decision tree analysis identified alcohol consumption, old age, psychiatric disease, smoking, and male sex as significant predictors.
- Multiple logistic regression confirmed these predictors and additionally highlighted poor therapeutic adherence and physical disease.
Conclusions:
- Key predictors for suicide risk via intentional intoxication have been identified.
- Decision tree modeling offers an effective, specific, and realistic approach to identifying at-risk populations for targeted suicide prevention strategies.
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