Modelling suicide risk in later life

Affective disorder is generally regarded as the prominent risk factor for suicide in the old age population. Despite the large number of empirical studies available in the literature, there is no attempt in modelling the dynamics of an individual’s level of suicide risk theoretically yet. In particular, a dynamic model which can simulate the time evolution of an individual’s level of risk for suicide and provide quantitative estimates of the probability of suicide risk is still lacking. In the present study we apply the contingent claims analysis of credit risk modelling in the field of quantitative finance to derive a theoretical stochastic model for estimation of the probability of suicide risk in later life in terms of a signalling index of affective disorder. Our model is based upon the hypothesis that the current state of affective disorder of a patient can be represented by a signalling index and exhibits stochastic movement and that a threshold of affective disorder, which signifies the occurrence of suicide, exists. According to the numerical results, the implications of our model are consistent with the clinical findings. Hence, we believe that such a dynamic model will be essential to the design of effective suicide prevention strategies in the target population of older adults, especially in the primary care setting.