A Simple Monte Carlo Framework to Assess Suicide Risk in Adolescents: A Study at a High School in Colombia

• Main Authors: Elias David Nino-Ruiz, Ana Maria Trejos-Herrera, Maria Yaquelin Exposito-Concepcion, Marjorie Rodriguez-Giraldo, Randy Steven Consuegra-Ortega, Claudia Guevara-Novoa
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: Monte Carlo; covariance estimation; suicide risk; psychological instrument
• Online Access: https://www.mdpi.com/1660-4601/17/10/3674

It is very common to perform statistical tests to obtain insights about populations based on samples. For instance, in the context of psychology, when a set of instruments are applied to individuals, psychologists typically look for an explanation of particular psychological constructs (variables), such as personality, intelligence, or emotional functioning. It is common to cross statistical information from the results of different psychological tests to measure certain variables or to confirm prior beliefs. Here, we estimate the Joint Probability Density Function of suicide-related vulnerability and protective factors to assess suicide risk in adolescents. A Markov Chain Monte Carlo Method is employed to move away from the typical Gaussian assumption on data. This allows us to estimate probabilities of the development of suicidal ideation based on samples (which form a Markov chain). We employ our proposed statistical method at a high school in Colombia. The results reveal that adolescents can develop suicidal ideation as a consequence of the following factors, together with their corresponding probabilities: poor school performance 52%, low academic expectations 27%, school integration problems 68%, risky eating behaviors (binge-purge) 42%, risky eating behaviors (compensatory measurements) 21%, risky eating habits (restriction) 22%, and low family functionality 16%.