| Literature DB >> 26396521 |
Sung Man Bae1, Seung A Lee2, Seung-Hwan Lee3.
Abstract
OBJECTIVE: This study aimed to develop a prediction model for suicide attempts in Korean adolescents.Entities:
Keywords: delinquency; intimacy with family; predictor; severity of depression; suicide
Year: 2015 PMID: 26396521 PMCID: PMC4577255 DOI: 10.2147/NDT.S91111
Source DB: PubMed Journal: Neuropsychiatr Dis Treat ISSN: 1176-6328 Impact factor: 2.570
Sociodemographic characteristics
| Variable | Sample size (persons)
| Rate (%)
|
|---|---|---|
| 2,754 | 100 | |
| Sex | ||
| Male | 1,110 | 40.3 |
| Female | 1,644 | 59.7 |
| School level | ||
| Middle school | 1,208 | 43.9 |
| High school | 1,546 | 56.1 |
| Location of school | ||
| Metropolitan | 1,137 | 41.3 |
| Micropolitan | 1,326 | 48.1 |
| Rural areas | 291 | 10.6 |
| Socioeconomic status | ||
| High | 338 | 12.3 |
| Middle | 2,168 | 78.7 |
| Low | 241 | 8.9 |
| Non-response | 7 | 0.3 |
Figure 1Decision tree for prediction of suicide attempt.
Notes: “Yes” means “suicide attempt”, while “No” is “no suicide attempt”. Scores for stress, family intimacy, and delinquency are scores of inventory used in this study to measure these variables. Numbering 1–20 is each node of the decision tree analysis.
Abbreviation: SES, socioeconomic status.
Gain index of predicting suicide attempts
| Node | Gain index
| Accumulated gain index
| ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Node: n | Node: % | Gain: n | Gain: % | Resp: % | Index: % | Node: n | Node: % | Gain: n | Gain: % | Resp: % | Index: % | |
| 20 | 32 | 1.7 | 16 | 8.6 | 50.0 | 518.8 | 32 | 1.7 | 16 | 8.6 | 50.0 | 518.8 |
| 19 | 46 | 2.4 | 12 | 6.5 | 26.1 | 270.7 | 78 | 4.0 | 28 | 15.1 | 35.9 | 372.5 |
| 18 | 306 | 15.9 | 65 | 34.9 | 21.2 | 220.4 | 416 | 21.6 | 100 | 53.8 | 24.0 | 249.4 |
| 15 | 69 | 3.6 | 14 | 7.5 | 20.3 | 210.5 | 485 | 25.1 | 114 | 61.3 | 23.5 | 243.9 |
| 12 | 68 | 3.5 | 8 | 4.3 | 11.8 | 122.1 | 553 | 28.7 | 122 | 65.6 | 22.1 | 228.9 |
| 17 | 147 | 7.6 | 15 | 8.1 | 10.2 | 105.8 | 700 | 36.3 | 137 | 73.7 | 19.6 | 203.1 |
| 16 | 87 | 4.5 | 7 | 3.6 | 8.0 | 83.5 | 787 | 40.8 | 144 | 77.4 | 18.3 | 189.9 |
| 14 | 28 | 1.5 | 2 | 1.1 | 7.1 | 74.1 | 815 | 42.2 | 146 | 78.5 | 17.9 | 185.9 |
| 8 | 554 | 28.7 | 31 | 16.7 | 5.6 | 58.1 | 1,369 | 70.9 | 177 | 95.2 | 12.9 | 134.2 |
| 6 | 58 | 3.0 | 3 | 1.6 | 5.2 | 53.7 | 1,427 | 73.9 | 180 | 96.8 | 12.6 | 130.9 |
| 11 | 210 | 10.9 | 6 | 3.2 | 2.9 | 29.6 | 1,637 | 84.8 | 186 | 100.0 | 11.4 | 117.9 |
| 13 | 293 | 15.2 | 0 | 0 | 0 | 0 | 1,930 | 100.0 | 186 | 100.0 | 9.6 | 100.0 |
Notes: Node = each node number. Node: n = sample size of each node. Node: % = rate of target category of total sample. Gain: n = sample size of target category of each node. Gain: % = rate of target category of each node of total target category. Resp: % = rate of sample size of target category of sample size of each node. Index: % = resp (%) versus rate of target category of sample size.
Risk estimate for prediction of suicide attempt
| Final value | |
|---|---|
| Risk estimates | 0.0963731 |
| Standard error | 0.00671728 |