Prediction and network modelling of self-harm through daily self-report and history of self-injury.

Abstract

Self-harm is a significant public health issue, and both our understanding and ability to predict adverse outcomes are currently inadequate. The current study explores how preventative efforts could be aided through short-term prediction and modelling of risk factors for self-harm. Patients (72% female, Mage = 40.3 years) within an inpatient psychiatric facility self-reported their psychological distress, interpersonal circumstances, and wish to live and die on a daily basis during 3690 unique admissions. Hierarchical logistic regressions assessed whether daily changes in self-report and history of self-harm could predict self-harm, with machine learning used to train and test the model. To assess interrelationships between predictors, network and cross-lagged panel models were performed. Increases in a wish to die (β = 1.34) and psychological distress (β = 1.07) on a daily basis were associated with increased rates of self-harm, while a wish to die on the day prior [odds ratio (OR) 3.02] and a history of self-harm (OR 3.02) was also associated with self-harm. The model detected 77.7% of self-harm incidents (positive predictive value = 26.6%, specificity = 79.1%). Psychological distress, wish to live and die, and interpersonal factors were reciprocally related over the prior day. Short-term fluctuations in self-reported mental health may provide an indication of when an individual is at-risk of self-harm. Routine monitoring may provide useful feedback to clinical staff to reduce risk of self-harm. Modifiable risk factors identified in the current study may be targeted during interventions to minimise risk of self-harm.