Risk Prediction of Fatal Suicide in Ilam Province, Iran

Abstract

Background: Suicide is a major public health challenge, with Ilam province in Iran exhibiting a concerning upward trend and one of the highest rates in the country. Objectives: This study aimed to determine the prevalence of fatal suicide in Ilam province and to develop and validate a multivariable predictive model to identify individuals at high risk. Methods: This retrospective case-control study included all fatal suicide cases recorded by the Legal Medicine Organization from May 2024 to June 2025. Age- and gender-matched controls were selected from the primary health care registry. A multivariable logistic regression model was constructed, and its performance was evaluated using the Area Under the Curve (AUC) and the Hosmer-Lemeshow test. Results: The incidence rate of fatal suicide was 18.1 per 100,000 population (95% CI: 14.7 - 21.4). The final model identified a history of psychiatric disorder (OR = 2.7), unemployment (OR = 1.9), and a family history of suicide (OR = 2.1) as significant predictors. The model demonstrated excellent discrimination (AUC = 0.81) and good calibration (Hosmer-Lemeshow P = 0.48). Conclusions: The high incidence of suicide in Ilam necessitates targeted interventions. The validated model provides a robust, evidence-based tool for the early identification of high-risk individuals, which can guide preventive strategies and optimize resource allocation.