Abstract
Predictive risk assessment and risk stratification models based on geodemographic postcode-based consumer classification are widely used in the pension and life insurance industry. However, these are static socio-economic models not directly related to health information. Health information is increasingly used for annuity underwriting in the UK, using health status when the annuity is purchased. In real life, people develop new health conditions and lifestyle habits and can start and stop a certain treatment regime at any time. This requires the ability to dynamically classify clients into time-varying risk profiles based on the presence of evolving health-related conditions, treatments and outcomes. We incorporate landmark analysis of electronic health records (EHR), in combination with the baseline hazards described by Gompertz survival distributions, for dynamic prediction of survival probabilities and life expectancy. We discuss a case-study based on landmark analysis of the survival experience of a cohort of 110,243 healthy participants who reached age 60 between 1990–2000.
Highlights
Life expectancy (LE) and longevity projections are of the greatest importance both to the pension and insurance industry and to their clients
As an application of our methodology, we developed a life expectancy calculator that is available on https://mylongevity.org
Adherence of statin prescription defined as a continuing prescription at least 75% of follow-up time differed by birth cohort, from approximately 90% at any age in patients born in 1936–40, to age-dependent and somewhat lower adherence in patients born in 1930–35, increasing from 75% at age 61 to 90% at age 65, after which it slowly levelled to 80% by age 75 and dropped down after age 82
Summary
Life expectancy (LE) and longevity projections are of the greatest importance both to the pension and insurance industry and to their clients. When pricing and reserving for their longevity risks, it is becoming increasingly important that insurance and pensions providers allow for heterogeneity of health conditions and lifestyle as well as socioeconomic status, how these change over time and the impact of changes to treatment regimes. The premium calculation would account for possible future health trajectories of the individual, appropriately weighted according to the results of a dynamic model In simple terms, this will involve pricing to allow for an individual’s medical conditions and lifestyle as with underwritten annuities. In our previous work (Kulinskaya et al, 2020) we developed a method to incorporate proportional hazards modelling of EHRs into actuarial modelling of hypothetical changes in population or group life expectancy due to medical advances and health interventions To do this successfully, some parametric assumptions about the shapes of survival distributions are necessary. As an application of our methodology, we developed a life expectancy calculator that is available on https://mylongevity.org
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