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Non-communicable diseases are the most frequent causes of death in most countries in the Americas, the Eastern Mediterranean, Europe, South-East Asia, and the Western Pacific. In the African Region, there are still more deaths from infectious diseases than NCDs. WHO projections show that NCDs will be responsible for a significantly increased total number of deaths in the next decade (WHO, 2014). In this context, the market of illness insurance is strongly being developed, allowing policyholders to reduce the financial impact of diseases. Indeed, critical illness insurance typically provides a payment of a lump sum in the event of the person insured suffering a condition covered under the policy. In other words, the insured receives a fixed sum on the diagnosis of a specified list of critical illnesses. The contract terms may also be structured to pay out regular income cash-flows on the policyholder. In general, since the policy face amount has to be paid on diagnosis, the incidence rates or diagnosis rates have to be accurately estimated. The research is here developed around the following focal and original points:  the estimation of the diagnosis rates by means of an analysis by cause of death for obtaining cause-specific diagnosis rates: in particular, the author modelі the probability of death by cause as a proxy of the estimate of the diagnosis rates;  the cause-specific death rates are modelled by a stratified stochastic model for avoiding the durable problem in literature of the dependence among different causes of death;  a fair valuation framework is adopted for pricing a specific product of critical illness insurance. The analysis is completed by empirical findings.

Tackling non-communicable diseases by a forecasting model for critical illness cover