Common to all previous studies assessing the cost of adverse selection associated with genetics has been the assumption of an established market, i.e., the adverse selectors have been buying insurance at that rate for such a period that premiums have already absorbed it. Their analyses involve calculating the percentage difference between premiums in a market with adverse selection and one without adverse selection. They can shed no light on how the premiums would get to this stage over time and what losses might be incurred in the process. We take the modelling further by outlining a multiple state Markov model for a start-up market of long-term care insurance. With this model, we explicitly show the progression of adverse selection costs using the development of information that an insurer would gain from analysing the claims history of its existing business, to reprice premiums for new business. To overcome the complication of insurance benefit amounts, which depend on the value of previous benefit payments, we develop a simulation approach of estimating the expected present values of insurance benefits and premium payments. In applying our modelling to a UK setting, we find genetic testing of the apolipoprotein E gene (whose variants can cause a high risk of developing dementia) to be of a relatively small impact compared with our hypothetical state of intermediate dementia progression. Furthermore, we find that the government’s cap on care costs has little effect on adverse selection costs as it benefits only a small proportion of people.