Modeling Smoking and Depression Co-Morbidity and Treatment Interventions: a System Dynamics Approach

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Project Summary Smoking and depression are leading contributors to mortality and morbidity in the U.S. They are also significantly associated with each other, with higher rates of smoking among depressed populations compared to the general population. This smoking disparity persists despite ongoing campaigns to reduce smoking in the U.S. Comorbidity of smoking and depression are subject to feedback effects, as smoking is known to predict future depression, while depression also predicts future smoking. This suggests that health interventions targeting either depression or smoking are likely to have positive effects on the other, resulting in even greater health gains. Little research has examined the impact of mental health treatment strategies on smoking outcomes. Greater use of mental health services could reduce depressive symptoms, and in so doing, reduce propensity for smoking. National organizations also recommend providing smoking cessation treatment to patients with depression. However, smoking cessation interventions in mental health settings have not been widely employed, nor has the population benefit of implementation been estimated. Systems modeling can simulate intervention scenarios that cannot be easily tested in the real world. Existing models of population smoking have yet to consider mental health status while models of depression have not accounted for smoking behaviors. This proposed study aims to develop a system dynamics model of smoking and depression comorbidity in the U.S. population—the first of its kind. The model will project future smoking prevalence by depressive state, depression prevalence, and population deaths due to tobacco or other causes over the next 50 years. The effects of two potential intervention strategies will then be analyzed: 1) integration of a smoking cessation intervention in mental health settings, and 2) increased utilization of mental health services in the population. Results from this study can be used to identify policy approaches likely to produce the largest population health gains, and guide decision-making to address mental illness and smoking disparities.