Abstract for: Complexities of modelling tobacco use and its related disease burden
Tobacco use is a risk factor that threatens population health, as it is attributed as cause many chronic disease and cancers (1). Tobacco use is responsible for more than 8 million deaths worldwide. Modelling smoking harms in health system research is challenging, and several approaches have been used in the system dynamics community (6,7). A model was built to capture the complexities and interactions of tobacco use and its related disease burden. Epidemiological estimates for this risk factor and the diseases that are linked to tobacco-use, are described, using one linked disease Chronic Obstructive Pulmonary Diseases (COPD) as an example (2,3,4,5). The model captured the harms of tobacco use in a population by age and gender measuring three different risk measures. Despite the current efforts to reduce the tobacco use, modelling suggests that current trends of smoking will continue to increase the COPD burden and other linked diseases. There is a need to ensure dynamic models are driven by the feedback processes of the system and move away from exogenous-driven models. Modelling is a useful approach to estimate tobacco use epidemiology and disease burden, however, caution is required. Data availability issues inhibit building models with a high level of confidence.