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​By definition, changes in the average human lifespan emerge slowly over time.

It can be useful though to reclassify this emergence as the long term result of a series of various trigger events that occur at a single point in time.



Post war Britain has seen great advances in the extension of average human lifespans.  Life expectancies of men and women as old as 65 have risen by around 6 years since 1960, as the likelihood of "premature" death has fallen.  Much of this success story is attributable to fewer deaths from cardio-vascular diseases such as stroke and heart disease.

Whilst this is great news for us all individually, it creates challenges for organisations that make long term promises to older people such as life insurance companies, pension funds and governments.

Although today's death rates can be readily measured, how these might improve over the remainder of a person's lifetime is much more challenging to predict.  The pace of change over the last two decades has been especially rapid, exceeding the expectations of many demographers and actuaries.

Innovative new approaches to managing what life insurers call "longevity risk" are emerging.  These help to make our society more stable and sustainable, enabling change to be handled with less inter-generational tension.

But, for the insurance world to advance our financial security, it needs a better appreciation of future events which will materially impact survival rates, as well as those events that have already occurred but are not yet affecting today's observed death rates.  We call these "catalysts".

The delayed response between the occurrence of such catalysts and their eventual impact on realised survival rates is a commonly observed phenomenon as illustrAted here.  Consider the initial discovery of the link between smoking and lung cancer.  When this was first announced in the early 1950s by Sir Richard Doll, there was not an immediate fall in death rates. Instead, there was a gradual reduction in smoking rates, nudged along by public health initiatives over the following decades culminating in the 2007 smoking ban.  Similarly, this in itself represents another catalyst which is widely regarded as more potent than the first in that its future impact on survival rates is perceived to be greater over a shorter period - but this remains to be seen.

Both of these represent good examples of the type of catalyst event that would cause expectations about future death rates to be reviewed.

This project seeks to identify and list such potential catalysts for step changes in death rates.  This can include both (a) events that have already occurred but may not be observed in today's statistics and (b) potential future catalysts.



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