Risk of unnatural death following self-harm in South Africa: development and validation of multivariable prognostic models - Takeaways - MDSpire

Risk of unnatural death following self-harm in South Africa: development and validation of multivariable prognostic models

  • By

  • Veronika Whitesell Skrivankova

  • Roxanne Pelteret

  • Stephan Rabie

  • Mpho Tlali

  • Naomi Folb

  • Eliane Rohner

  • Chido Chinogurei

  • Yann Ruffieux

  • Soraya Seedat

  • Mary-Ann Davies

  • Gary Maartens

  • John Joska

  • Andreas D Haas

  • June 25, 2026

  • 0 min

Share

  • 1

    Prognostic models were developed to predict unnatural death risk after non-fatal self-harm in South Africa using data from 6846 medical insurance beneficiaries.

  • 2

    The final models achieved optimism-corrected C-indices of 0.74 at presentation and 0.75–0.76 at discharge, identifying 86–88% of observed unnatural deaths.

  • 3

    The models included variables such as age, sex, prior psychotropic medication use, and selected mental disorder diagnoses for risk prediction.

  • 4

    The observed 2-year risk of unnatural death was 0.15% among the lowest predicted risk group, comparable to individuals without a self-harm history.

  • 5

    These models aim to support clinical decision-making and prioritize individuals at high risk for targeted mental health interventions.

Original Source(s)

Risk of unnatural death following self-harm in South Africa: development and validation of multivariable prognostic models

  • by Veronika Whitesell Skrivankova, Roxanne Pelteret, Stephan Rabie, Mpho Tlali, Naomi Folb, Eliane Rohner, Chido Chinogurei, Yann Ruffieux, Soraya Seedat, Mary-Ann Davies, Gary Maartens, John Joska, Andreas D Haas

  • June 25, 2026

Related Content

The relationship between depression symptoms and cortisol levels in adolescents: the role of somatic symptoms and cognitive function

Module-specific diagnostic accuracy of ADOS-2 in real-world clinical referral populations: an updated systematic review and HSROC meta-analysis

Navigating specific targets of psychoneurological symptom cluster in breast cancer: a computer-simulated network analysis