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- DOI: 10.1016/j.jad.2010.05.001
- PMID: 20684848
- UKPMCID: 20684848
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Psychosocial assessment and repetition of self-harm: The significance of single and multiple repeat episode analyses.
Bergen, Helen; Hawton, Keith; Waters, Keith; Cooper, Jayne; Kapur, Navneet
Journal of affective disorders. 2010;.
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Full-text held externally
- DOI: 10.1016/j.jad.2010.05.001
- PMID: 20684848
- UKPMCID: 20684848
Abstract
BACKGROUND: Self-harm is a common reason for presentation to the Emergency Department. An important question is whether psychosocial assessment reduces risk of repeated self-harm. Repetition has been investigated with survival analysis using various models, though many are not appropriate for recurrent events. METHODS: Survival analysis was used to investigate associations between psychosocial assessment following an episode of self-harm and subsequent repetition, including (i) one repeat, and (ii) recurrent repetition (</=5 repeats) using (a) an independent episodes model, and (b) a stratified episodes model based on a conditional risk set. Data were from the Multicentre Study on Self-harm in England, 2000 to 2007. RESULTS: Psychosocial assessment following an index episode of self-harm was associated with a 51% (95% CI 42%-58%) decreased risk of a repeat episode in persons with no psychiatric treatment history, and 26% (95% CI 8%-34%) decreased risk in those with a treatment history. For recurrent repetition, assessment was associated with a 57% (95% CI 51%-63%) decreased risk of repetition assuming independent episodes, and 13% (95% CI 1%-24%) decreased risk accounting for ordering and correlation of episodes by the same person (stratified episodes model). All models controlled for age, gender, method, history of self-harm, and centre differences. LIMITATIONS: Some missing data on psychiatric treatment for non-assessed patients. CONCLUSIONS: Psychosocial assessment appeared to be beneficial in reducing the risk of repetition, especially in the short-term. Findings for recurrent repetition were highly dependent on model assumptions. Analyses should fully account for ordering and correlation of episodes by the same person.