@article{ref1,
title="Prediction and network modelling of self-harm through daily self-report and history of self-injury",
journal="Psychological medicine",
year="2020",
author="Kyron, Michael J. and Hooke, Geoff R. and Page, Andrew C.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: 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. <br><br>METHODS: 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. <br><br>RESULTS: 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. <br><br>CONCLUSIONS: 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.<p /> <p>Language: en</p>",
language="en",
issn="0033-2917",
doi="10.1017/S0033291720000744",
url="http://dx.doi.org/10.1017/S0033291720000744"
}