OBJECTIVES: It would be valuable for health care planning to project future population co-morbidity conditions by studying the historical progression of patients through the various possible health states, based on their initial health. This study investigated a methodology of observing co-morbidity prevalence in the UK population. METHODS: By using pre-collected data from The Health Improvement Network (THIN) database of UK primary care records, the proportion of patients who have one or more co-morbidities including hypertension, diabetes, heart disease, COPD and stroke can be observed for any given time period. A THIN data extract was used to determine the transition rates between co-morbidity states, as well as the mortality rates for each co-morbidity group. This was carried out by ‘measuring the central exposed to risk’, an actuarial analysis method, in each group and using ‘forces of transition’ between the states. The number of doctor visits which patients make per year, broken down by co-morbidity grouping, was used as a potential proxy for historic demand for health services. RESULTS: Combining data on the future population by age and sex, and the split between co-morbidity groupings, we can estimate the total likely health service demand in the future. CONCLUSIONS: This framework can be used to consider future scenarios, seeking to address specific clinical issues or design policies that influence health behaviour issues based on assumed changes in transition rates. For example, if there were to be a one-year delay on the average age of diabetes onset, the impact on the population’s health as well as the demand and cost of health provisions can be estimated. Other examples of health scenarios that can be modelled include the effects of giving up smoking and reducing levels of obesity on co-morbidity and thus the impact that lifestyle changes can have on future health service demand.