Articles from Cass Knowledge

A promising new method for forecasting asbestos-related disease mortality

Forecasting the number of deaths from asbestos-related disease has proved an enduring challenge for insurance companies. New research points to a more simple and flexible method.

Since the 1960s, diseases caused by exposure to asbestos, such as mesothelioma, have gained worldwide recognition as a result of their increasing incidence, related medico-legal issues, and frighteningly poor prognoses. Such diseases are mainly caused by occupational exposure to asbestos fibres in industries such as mining, railway, shipyards, and general construction. Brief or indirect exposure to even a small amount of asbestos fibres can be sufficient to put one at severe risk of disease later in life, with the latency period of mesothelioma being on average around 40 years. With the onset of symptoms, mesothelioma is rapidly fatal, and the majority of deaths occur amongst those over 60. In Great Britain, mesothelioma mortality has been steadily increasing in recent years.

The global insurance industry pays billions for mesothelioma claims annually, under policies that covered, for example, employer or product liability at the time of exposure. The result has been multiple insurer insolvencies.

It remains difficult to project the industry’s ultimate loss exposure, due to advances in treatment, increasing life expectancies, changes in litigation, and the number of new claimants emerging. A core uncertainty is whether the amounts of technical reserves set aside to cover future claims are sufficient.

Therefore, with respect to mesothelioma mortality forecasting, it is important that insurers are able to determine when the numbers of deaths are going to peak (and establishing the peak value), and how the deaths will develop after the peak.

In the paper Calendar Effect and In-Sample Forecasting Applied to Mesothelioma Mortality Data, the researchers apply and further illustrate a recently developed extended continuous chain ladder model to forecast mesothelioma deaths.

They applied this method to actual data obtained from the Health and Safety Executive, consisting of the number of deaths due to mesothelioma between 1968 and 2016. From this, they were able to produce an up-to-date forecast for the number of deaths in the future. They also compared the results from this model to those from the discrete age-period cohort (APC) model, and the model using synthetic exposure measures used by the Health and Safety Executive itself.

The new model was found to have important benefits.

First, unlike with standard models of mortality forecasting, it does not require any modelling, estimating, and extrapolating of the exposure when it is unknown. This makes the approach more robust compared with those that are more detailed in this regard, especially when the model for the exposure is mis-specified.

Secondly, it is very intuitive, in no small part due to its connection with the popular APC models.

Thirdly, it takes advantage of the powerful non-parametric structured models, which exhibit excellent theoretical and practical properties compared to the standard APC models.

Finally, forecasting is entirely determined by the data, avoiding the need to use time series modelling and other more sophisticated extrapolation techniques. This further contributes to the robustness of the approach in practice.

The research shows that the extended continuous chain ladder model is a promising benchmark candidate for asbestosis mortality forecasting, due to its flexible and simple forecasting strategy. It illustrates that the method addresses the problem of the lack of exposure data, as it is applied to a real dataset that an insurer’s claim reserving methodology would find challenging. Furthermore, the research demonstrates how the model can be used to update the forecast of the number of deaths due to mesothelioma in Great Britain.

Calendar Effect and In-Sample Forecasting Applied to Mesothelioma Mortality Data is available for download at City Research Online.