Although our findings suggest that the association between suicidal ideation and later suicide is stronger in schizophrenia spectrum psychosis than in mood disorders this result should be interpreted cautiously due to the high degree of between-study heterogeneity and because studies that used stronger methods of reporting had a weaker association between suicidal ideation and suicide.

Current substance users with psychosis may have more severe positive symptoms than patients who have never used substances, but this result should be interpreted with caution because of demographic differences between substance users and non-substance users.

background. Catheter-associated urinary tract infections (CAUTIs) are among the most common hospital-acquired infections (HAIs). Reducing CAUTI rates has become a major focus of attention due to increasing public health concerns and reimbursement implications.

The results suggest that substance use contributes to both the symptoms and the burden of disability experienced by patients with psychosis. Patients in the early stages of psychotic illness should be informed about the benefits of giving up substances earlier, rather than later in the illness. Psychiatric services should regard the treatment of substance use as an integral part of the treatment of psychotic disorders.

Māori and Pacific peoples with dementia presented to an NZ memory service at a younger age than NZ Europeans, and Pacific peoples presented with more advanced dementia. A population-based epidemiological study is critical to determine whether Māori and Pacific peoples have indeed a higher risk of developing dementia at a younger age.

Background Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors. Aims To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions. Method Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title. Results In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7–8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0–22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1–10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors. Conclusions Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.

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The absence of significant differences between the two groups suggests that a history of substance use is not a poor prognostic indicator for patients who are able to stop using substances.