## Seven questions for projections by cause of death

I have written several times about the challenges in creating mortality projections based on cause-of-death data.  Those interested in the details can consult my recent paper published in a special edition of the British Actuarial Journal.  For anyone else looking to build (or buy) a methodology based on cause-of-death projections, here is a quick checklist of questions to ask yourself (or your supplier):

1. How is the inherent bias towards projecting lower improvements corrected?  This point is well documented for both cause-of-death projections and also expectations based on expert opinion.  This issue is of critical importance for reserving for pensions and annuities.
2. How is socio-economic bias handled?  The lives with the largest liabilities in pensioner portfolios have a different cause-of-death mix than the wider population.
3. How are correlations in the data handled?  Causes of death are linked in complex ways, not all of them understood.  The most obvious example is the link between smoking and many causes of death.
4. How are correlations in projections handled?  It is tricky to project correlated time series in such a way that they will recombine to produce sensible all-cause rates.  Remember also that cause-of-death projections are not just correlated, but functionally dependent.  Think of three broad categories: heart, cancer and other. Each has its own projection, but the sum of the proportions must always be one.
5. How are changes in the classification systems handled?  The International Classification of Diseases (ICD) has been revised many times, and countries have used different versions of the ICD over time.
6. How are ambiguities in coding handled?  Some codes are surprisingly non-specific.
7. How are changes in coding guidelines handled?  Even within a given ICD system within a single country, the coding guidelines for certifying doctors can change, resulting in discontinuities which distort trends.

Assume we have a random variable, $$X$$, with expected value ... Read more