Mapping Mental Health Support and Suicide Risk: A Markov Model Approach

Background: Suicide remains a significant public health challenge, accounting for approximately 1.4% of all deaths globally. Alarmingly, individuals who report having no friends exhibit a prevalence of distress and mental health struggles that is over 3.5 times greater than those with a robust network of close friends for support. Methods: This study presents a Markov chain model designed to explore the dynamics of mental health recovery and the associated risks of suicide, specifically focusing on the transition roles of peer support, professional support, and self-support. Sensitivity analyses were conducted by adjusting transition probabilities to simulate the impacts of increased awareness and access to peer-support interventions. Results: The findings suggest that enhancing awareness of self-support strategies by approximately 15% is associated with a 0.68% reduction in the suicide rate. Conversely, merely expanding access to self-support services did not yield any observable effects on suicide rates. Furthermore, first passage time (FPT) analysis revealed that peer support facilitates the most rapid recovery,followed by self-support and professional support.A heat map has been employed to further illustrate these findings using python, underscoring the superior efficiency of peer support in expediting recovery. Conclusion: These results emphasize the urgent need to enhance awareness initiatives and integrate peer support into mental health care strategies to improve recovery outcomes and mitigate suicide risk.