Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful.

We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours based on a combination of traditional (sociodemographic, physical health, social–environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics).

The study included 5885 unrelated children (50% female, 67% White, 9–11 years of age) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalised logistic regression analysis to distinguish between: (a) children with current or past suicide thoughts or behaviours; (b) children with a mental illness but no suicide thoughts or behaviours (clinical controls); and (c) healthy control children (no suicide thoughts or behaviours and no history of mental illness). The model was subsequently validated with data from seven independent sites involved in the ABCD study (n = 1712).

Our results showed that we were able to distinguish the suicide thoughts or behaviours group from healthy controls (area under the receiver operating characteristics curve: 0.80 child-report, 0.81 for parent-report) and clinical controls (0.71 child-report and 0.76–0.77 parent-report). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.55–0.58 child-report; 0.49–0.53 parent-report). The factors that differentiated the suicide thoughts or behaviours group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and history of mental health treatment.

This work highlights that mostly clinical psychiatric factors were able to distinguish children with suicide thoughts or behaviours from children without suicide thoughts or behaviours. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.

To estimate the proportion of patients who acquire methicillin-resistant Staphylococcus aureus (MRSA) while in hospital and to identify risk factors associated with acquisition of MRSA.

Retrospective cohort study.

Adult patients discharged from 36 general specialty wards of 2 Scottish hospitals that had implemented universal screening for MRSA on admission.

Patients were screened for MRSA on discharge from hospital by using multisite body swabs that were tested by culture. Discharge screening results were linked to admission screening results. Genotyping was undertaken to identify newly acquired MRSA in MRSA-positive patients on admission.

Of the 5,155 patients screened for MRSA on discharge, 2.9% (95% confidence interval [CI], 2.43–3.34) were found to be positive. In the subcohort screened on both admission and discharge (n = 2,724), 1.3% of all patients acquired MRSA while in hospital (incidence rate, 2.1/1,000 hospital bed-days in this cohort [95% CI, 1.5–2.9]), while 1.3% remained MRSA positive throughout hospital stay. Three risk factors for acquisition of MRSA were identified: age above 64 years, self-reported renal failure, and self-reported presence of open wounds. On a population level, the prevalence of MRSA colonization did not differ between admission and discharge.

Cross-transmission of MRSA takes place in Scottish hospitals that have implemented universal screening for MRSA. This study reinforces the importance of infection prevention and control measures to prevent MRSA cross-transmission in hospitals; universal screening for MRSA on admission will in itself not be sufficient to reduce the number of MRSA colonizations and subsequent MRSA infections.