OBJECTIVES: To estimate the incidence and prevalence of the most burdensome smoking related morbidities in the US population as part of the Benefits of Smoking Cessation on Outcomes (BENESCO) model. METHODS: We have developed a micro-simulation Markov model to estimate the outcomes and costs of a hypothetical cohort of US current smokers, a proportion of whom (25%) will make a single attempt to quit smoking in the first year of a lifetime model. The BENESCO model estimates the incidence and prevalence of smoking related diseases by using the relative risks for mortality of the diseases as a proxy. The hazard ratios from the Cancer Prevention Study II (Thun 2000) were used as the basis for the required transformations. The incidence and prevalence of the following smoking related diseases were included in the model: lung cancer, chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke and asthma exacerbations (attributed to smoking). Although the model includes smokers aged 18 years and older, no excess events were assumed to occur before the age of 35. RESULTS: Of the morbidities modelled, COPD was found as the most prevalent [peak estimates of individuals affected at model entry point] disease among smokers [2.89 million], followed by CHD [1.8 million], stroke [449,991], asthma exacerbations [395,829] and lung cancer [68,348]. Incidence rates at model entry followed a similar pattern to prevalence. The morbidities, which were modelled, were more prevalent overall in female smokers than their male counterparts. CONCLUSION: The estimates of the incidence and prevalence of smoking related diseases in the US population rely on methodology, which has been used elsewhere in published, validated models (Orme 2001, Hoogendoorn 2003) and use underlying hazard ratios from a large, independent US public health study. As such it could be expected that the external validity of the estimates in the BENESCO model is acceptable.