Prediction model for pre-existing mental health difficulties in cases of child sexual assault reporting to Saint Mary's Sexual Assault Referral Centre

Background: Child sexual assault (CSA) is associated with mental health (MH) difficulties, both as a risk factor and as a consequence. Research is lacking on predictive factors that indicate which children attending a Sexual Assault Referral Centre (SARC) are more likely to have pre-existing MH difficulties. Objectives: (1) To identify the prevalence of pre-existing MH difficulties across children attending Saint Marys SARC in Manchester. (2) To develop and internally validate a risk prediction model for children attending SARC with pre-existing MH difficulties, which could be used to triage such patients. Methods: Our primary outcome was any history of self-reported MH difficulty and/or current psychiatric medication. We developed the predictive model for this primary outcome using logistic regression. From a list of 7 candidate predictors for potential inclusion in the model, we used stepwise selection to determine the final variables in the model. Calibration and discrimination of the model was assessed using bootstrap internal validation. Results: The analysis cohort included 492 cases of CSA (aged over 11 years). Of these, 218 cases (44.31 %) had the primary outcome. After applying variable selection, the developed prediction model included 4 predictors of MH difficulties (age, gender, location of alleged assault, and time between alleged assault and SARC attendance), and achieved good performance, upon internal validation, in terms of both calibration (calibration-in-the-large of -0.01 [-0.186, 0.162], calibration slope of 0.77 [0.52, 1.15]) and discrimination (AUC of 0.59 [0.56, 0.61]). Conclusions: Being able to predict which children attending a SARC are likely to have MH difficulties would enable proactive tailoring of interventions and swift referrals. A timely response is known to have a positive impact on children's MH outcomes.