Assessment of Suicide Risk in Individuals With Schizophrenia

Suicide risk in individuals with schizophrenia is a critical clinical challenge, with an estimated lifetime risk of approximately 5%. Despite extensive research, accurately predicting suicide in this population remains difficult due to the complex interplay of multiple risk factors and the heterogeneity of clinical presentations.¹ In this article, key findings are summarized to provide a guideline for a structured, multidimensional approach to suicide risk assessment and intervention.

1. Initial Screening for Suicide Risk Factors in Individuals With Schizophrenia

It is important to conduct a brief clinical interview to identify key static and dynamic risk factors for suicide in patients with schizophrenia, as identified in systematic reviews. Important aspects to document and prioritize include the following:

• Depressive and anxiety symptoms
• Feelings of hopelessness and worthlessness
• Prior suicide attempts
• Substance abuse (with emphasis on cannabis use)
• Frequent or recent psychiatric hospitalizations or illness onset (< 1 year)
• Demographic risks: younger age, male sex, unemployment
• Poor adherence to treatment^2-5

Recommendations for prevention emphasize close monitoring of high-risk patients, especially in inpatient settings and during transitions such as hospital discharge. Regular suicide risk assessments should be conducted for newly admitted patients, with attention to family history, substance abuse, depressive symptoms, hopelessness, and treatment adherence.⁴

2. Suicide Risk Formulation

Traditional suicide risk assessment often yields false positives and may be insufficient for guiding clinical management. The field is shifting toward a more comprehensive suicide risk formulation (SRF) approach, which integrates multiple dimensions to inform treatment strategies.⁶ The SRF process involves the following 4 key areas:

• Risk status: Demographic and clinical characteristics placing the patient in a higher risk group (eg, young males, recent illness onset).

Recommendation: If a high-risk status is present, increase monitoring frequency and consider inpatient care if imminent risk is present.

• Risk state: Current risk level assessed through ongoing evaluations to detect changes or periods of elevated risk.

Recommendation: Initiate safety planning, crisis intervention, medication review, and psychosocial support when required.

• Available resources: Identification and strengthening of patient support systems and crisis services.

Recommendation: Engage family and social supports, and connect the patient to crisis services and community resources.

• Foreseeable events: Anticipation of triggers or stressors (eg, hospital discharge) that may exacerbate risk, allowing proactive planning.⁶

Recommendation: Develop proactive transition plans and schedule follow-up assessments to ensure continuity of care.

This multidimensional framework enhances understanding of individual risk profiles beyond diagnosis alone, accommodating the dynamic nature of suicidality in schizophrenia.

3. Evaluation of Anosognosia as a Suicide Risk

Anosognosia, or poor insight into illness, is a prevalent symptom in schizophrenia linked to neurocognitive deficits and brain abnormalities, particularly in the frontal and prefrontal cortex.⁷ It is associated with medication nonadherence and functional impairments. The relationship between insight and suicidality is complex. Better illness insight can paradoxically increase suicide risk, particularly when accompanied by depression, hopelessness, or demoralization.^8,9 Patients with greater awareness may experience self-stigma and diminished quality of life, heightening suicidal ideation. Insight fluctuates over time, and changes in insight level may signal increased suicide risk.¹⁰ Suicide risk related to insight appears mediated by mood and self-perception factors rather than insight alone. Psychoeducational and cognitive behavioral interventions aimed at improving insight must be carefully managed to avoid exacerbating suicidal ideation.¹¹

Recommendations to assess and manage anosognosia as a suicide risk include the following:

• Assess illness insight through a clinical interview and, if available, validated scales.
• Recognize the “insight paradox”: Better insight may elevate suicide risk via increased depression, hopelessness, and self-stigma.
• Monitor and manage mood symptoms and internalized stigma alongside insight levels.
• Carefully tailor psychoeducational and cognitive behavioral interventions to improve insight while mitigating psychological distress.
• Implement close monitoring during high-risk periods such as hospital discharge.

4. Cannabis Use and Suicide Risk

Cannabis use adds complexity to suicide risk assessment in schizophrenia due to its effects on neurobiology and impairments in social cognition.¹² Chronic cannabis use may increase suicide risk through dysregulation of the endocannabinoid system and hypothalamic-pituitary-adrenal axis.¹³ Dissociative symptoms linked to cannabis may be misinterpreted as psychosis but correlate with worse clinical outcomes and higher suicide risk. The bidirectional relationship between cannabis use and dissociative symptoms may perpetuate psychosis and suicidal behaviors, particularly during the first episode of psychosis.¹⁴ Patterns of cannabis use (frequency, potency, age of onset) influence risk, with daily use, high-potency cannabis, and early onset associated with greater suicidality.¹⁵

Recommendations for substance use assessment include the following:

• Screen for cannabis and other substance use patterns, including frequency, potency, and age of onset.
• Evaluate cognitive and dissociative symptoms related to cannabis use that may influence suicide risk.
• It is imperative to monitor the heightened risk of suicide associated with cannabis use in individuals experiencing a first episode of psychosis.
• Integrate findings into the overall risk profile and treatment planning, including psychoeducation about risks and integration of substance use rehabilitation.

5. Neurocognitive Profiles

Emerging research highlights the role of neurocognitive factors in suicide risk. Impairments in social cognition (eg, emotion recognition, theory of mind) and general cognition (eg, attentional control) are more pronounced in patients who attempt suicide compared with those with suicidal ideation alone.¹⁶ Cognitive assessment is frequently overlooked when evaluating the prominent symptom clusters of schizophrenia. Deficits in neurocognition and social cognition result in challenges within social, educational, and occupational domains. When individuals diagnosed with schizophrenia fail to achieve these typical adult goals due to cognitive and social deficits, it may directly or indirectly contribute to an elevated risk of suicide.¹⁷

Recommendations for assessment of neurocognitive domains include the following:

• Briefly assess neurocognitive domains relevant to suicide risk, including social cognition and attentional control.
• It is advisable to employ standardized cognitive assessments, such as the Brief Cognitive Assessment Tool for Schizophrenia or the Screen for Cognitive Impairment in Psychiatry.
• Adapt interventions to cognitive limitations and consider neuropsychological support and rehabilitation.

6. Adverse Childhood Experiences

The presence of a history of adverse childhood experiences (ACEs) correlates with more severe illness, treatment resistance, and increased suicide risk in schizophrenia spectrum disorders. While researchers must avoid oversimplification or stigmatization when linking ACEs to suicide risk, screening for ACEs should be integrated into suicide risk assessments to inform personalized interventions.¹⁸

Recommendations for screening and management of developmental profiles include the following:

• Conduct a comprehensive assessment of ACEs alongside other developmental milestones to identify potential developmental vulnerabilities that may influence risk factors and treatment responses.
• Integrate trauma-informed care methodologies into the approach.

7. Technological Advances and Risk Algorithms

There is a growing consensus that suicide prevention in schizophrenia requires moving beyond static risk factor identification and toward dynamic, algorithm-based risk prediction models.^6,19 Machine learning can analyze complex interactions among multiple risk factors and provide individualized risk predictions. Risk factors incorporated into algorithms may include demographic variables, clinical history, insight levels, substance abuse, social support, cognitive functioning, practical aspects (knowledge of and access to lethal means), personality traits, and acquired aspects (eg, ACEs and views on death).²⁰

Frequent, repeated assessments (potentially via mobile technology) allow real-time tracking of risk fluctuations, especially during the critical first decade post diagnosis. Wearable devices and digital phenotyping offer promise for continuous monitoring but raise ethical concerns regarding privacy, data security, informed consent, and potential algorithmic bias. Ethical frameworks must guide the development and implementation of artificial intelligence–based suicide risk tools to ensure transparency, fairness, and patient safety.^21,22

Recommendations include the following:

• Conduct repeated suicide risk assessments, especially during critical periods such as postdischarge or early illness phases.
• Where feasible and ethically appropriate, consider digital tools or mobile assessments for real-time risk monitoring.

Concluding Thoughts

Suicide risk assessment in schizophrenia demands a multidimensional, dynamic approach that transcends diagnosis alone. It must be comprehensive, integrating clinical, cognitive, social, and environmental factors within an SRF framework. The SRF model offers a structured framework for evaluating risk status, current risk state, resources, and foreseeable stressors. Insight-related interventions require careful balancing to mitigate the “insight paradox” and associated demoralization and depression.

Cannabis use, neurocognitive impairments, and consideration of patients’ developmental histories, including ACEs, enriches understanding of individual vulnerabilities and can enhance personalized care. Future directions emphasize the integration of machine learning algorithms and digital monitoring to capture the complexity and temporal variability of suicide risk, balanced with ethical safeguards to protect patient rights and well-being. This holistic approach aims to improve prediction, prevention, and ultimately reduce suicide rates in this vulnerable population.

Dr Kotzé is an associate professor at Weskoppies Psychiatric Hospital, Gauteng Health and Department of Psychiatry at the University of Pretoria.

Dr Roos is an emeritus professor in the Department of Psychiatry at the University of Pretoria.

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