This review was undertaken for the Faculty and Institute of Actuaries as part of their programme to encourage research collaborations between health researchers and actuaries in order to understand better the factors influencing mortality and longevity. The authors presented their findings in a number of linked sessions at the Edinburgh conference (Joining Forces on Mortality and Longevity) in October 2009 and contributed to this overview. The purpose is to review evidence for the impact on adult mortality of characteristics of the individual's lifetime socioeconomic or psychosocial environment or phenotype at the behavioural; multi-system (e.g. cognitive and physical function); or body system level (e.g. vascular and metabolic traits) that may be common risk factors for a number of major causes of death. This review shows there is growing evidence from large studies and systematic reviews that these individual characteristics, measured in pre-adult as well as the adult life, are associated with later mortality risk. The relative contribution of lifetime environment, genetic factors and chance, whether these contributions change with age, and the underlying social and biological pathways are still to be clarified. This review identifies areas where further life course research is warranted.

In 1900 life expectancy at birth in the UK was only 46 years for men and 53 years for women. Just over a century later life expectancy at birth has increased by around 30 years and by 2007 had reached 77.5 years for men and 81.7 years for women. The population aged 85 years and over, often termed the ‘oldest old’, are now the fastest growing section of our population. For the 1921 cohort only 18% of men and a third of women reached the age of 85 years but for the 1951 birth cohort it is expected that almost half of men and 60% of women will achieve that age. The important question for health care planners and society is whether the large number of those who will reach 85 years in the future are similar in health characteristics to those attaining 85 years now.

This question was addressed by substantive results and by methodological papers in the ‘Cohort’ theme of the Joining Forces on Mortality and Longevity conference in October 2009. Here we provide an overview of the papers, some of which are presented in full in this issue (see Murphy (2009), Di Cesare & Murphy (2009), O'Connell & Dunstan (2009), Forfar (2009)).

The mortality data (registered deaths and population size) over the years 1961–2007 for the population of England and Wales and for Scotland were obtained from the Office for National Statistics (ONS) and from the Scottish Registrar General. This paper addresses the following questions:

(i) Is there statistical evidence for a cohort effect (i.e. a generation effect separate from the period effect) being present in the data?

(ii) Do both males and females exhibit similar cohort (generation) effects?

(iii) Are period effects (i.e. the improvement in mortality with time) more significant than cohort effects?

(iv) How should one allow, in forecasts of population mortality, for age, period and cohort effects?

(v) Is it sensible to combine male and female mortality experience to determine the period effect and the cohort effect?

(vi) How do the forecasts for the expectation of life at birth, using the Extended-Lee–Carter-Combined (ELCC) model (described in the paper) differ from the (2008 based) Office of National Statistics (ONS) forecasts of the expectation of life at birth?

The assumed rate of future mortality improvement has increased over three recent sets of the United Kingdom's national population projections. This optimism has not been so marked in countries which share ancestors with the U.K. population. New Zealand is one such country that provides a data-rich case example in which to investigate the portability of mortality trends.

This paper compares mortality trends in New Zealand with those in England & Wales. Both countries seem to have a ‘golden cohort’ which enjoys faster improving mortality than people born before or after. The birth of the golden cohort in England & Wales coincided with cohort life expectancy there catching up with New Zealand's.

We show that first generation migrants from the U.K. have better mortality than New Zealand born residents likely to have British ancestry. The advantage lasts into older ages, decades after migration. We hypothesise that migrants from the U.K.'s golden cohort brought with them an early life mortality improvement advantage, and additionally benefited from the healthier environment of New Zealand at middle to older ages. Further, given the recent strong mortality improvement in New Zealand, the U.K.'s assumptions for future mortality look relatively optimistic.

Secular variations in longevity and in population aging are of huge interest to actuaries. It is shown here that temporal changes in mortality and natality accompany the recycling of influenza A viruses i.e., the re-exposure of human populations, from time to time, to influenza A viruses antigenically similar to viruses (H1, H2, H3) that circulated in the past. Mortality (and natality) change as birth cohorts (whole population and maternal) with specific types and levels of vulnerability to influenza A re-infections, acquired through early-life effects of infection with one (period-specific) influenza A sub-type, course through subsequent influenza A environments over time. Epidemiologic evidence of association between secular trends in mortality (and natality) and interactions between birth-cohort and period effects of influenza A circulation is presented both for the U.K. and the U.S. New interpretations to several epidemiologic and demographic observations follow from this finding.

Assumptions about future mortality are more important than those for factors such as fertility, migration, disability trends or real interest rates for cost projections of the U.S. Old Age, Survivors, Disability and Health Insurance scheme. Recently, one factor has been assumed to be the key driver of future mortality in both official British population projections and actuarial ones: a ‘cohort effect’ associated with a group who were born in a period centred on the early 1930s who have been identified as having experienced particularly rapid improvements in mortality rates and are often referred to as the ‘golden generations’ or ‘golden cohorts’. The concept of ‘cohort effects’ is discussed; limitations of national-level cohort data considered; and methods for identifying such effects are reviewed. Particular attention is given to the analysis of populations which have been identified as having clear cut cohort effects; those of Britain and Sweden in the later part of the nineteenth century and early twentieth century, as well as the contemporary British population. The likely magnitude of such effects is discussed using a stylised model to assess the extent to which members of the ‘golden generations’ are especially privileged.

Most of the methods of mortality forecasting have been assessed using performance on overall mortality, and few studies address the issue of identifying the appropriate forecasting models for specific causes of deaths. This study analyses trends and forecasts mortality rates for three major causes of death — lung cancer, influenza-pneumonia-bronchitis, and motor vehicle accidents — using Lee–Carter, Booth–Maindonald–Smith, Age-Period-Cohort, and Bayesian models, to assess how far different causes of death need different forecasting methods. Using data from the Twentieth and Twenty-First Century Mortality databases for England and Wales, results show major differences among the different forecasting techniques. In particular, when linearity is the main driver of past trends, Lee–Carter-based approaches are preferred due to their straightforward assumptions and limited need for subjective judgment. When a clear cohort pattern is detectable, such as with lung cancer, the Age-Period-Cohort model shows the best outcome. When complete and reliable historical trends are available the Bayesian model does not produce better results than the other models.

The Nested Binomial Model presented in this paper is a new approach to modelling mortality and survival in humans and other species that seeks to reconcile individual life course risk trajectories and those population mortality patterns that arise from inter-individual heterogeneity. In describing individual trajectories it partitions mortality risk into two main elements: ‘redundancy’ and ‘interactive risk'. Interactive risk is volatile, increasing or decreasing with time and circumstance, while redundancy is a quantity which declines in a linear and largely invariable fashion throughout life. Although a biological correlate for redundancy is not identified, this assumption allows strikingly realistic modelling of mortality and survival curves, late-life mortality deceleration, Strehler-Mildvan correlation, mortality plateaux and slowing of mortality. Simple assumptions with regard to heterogeneity of parameters within the model allow close approximation to the entire human mortality curve, and provide a rationale for observed and otherwise paradoxical population mortality phenomena. As such, it fulfils biodemographic criteria for a comprehensive theory of ageing. Future challenges are to reconcile its theoretical structure with empirical findings in the biology of ageing and to render it in a form that can become a usable actuarial tool.

Mortality improvement has traditionally been analysed using an array of statistical methods, and extrapolated to make actuarial projections. This paper presents a forward-looking approach to longevity risk analysis which is based on stochastic modelling of the underlying causes of mortality improvement, due to changes in lifestyle, health environment, and advances in medical science. The rationale for this approach is similar to that adopted for modelling other types of dynamic insurance risk, e.g. natural catastrophes, where risk analysts construct a stochastic ensemble of events that might happen in the future, rather than rely on a retrospective analysis of the non-stationary and comparatively brief historical record.

Another feature of prospective longevity risk analysis, which is shared with catastrophe risk modelling, is the objective of capturing vulnerability data at a high resolution, to maximise the benefit of detailed modelling capability down to individual risk factor level. Already, the use by insurers of postcode data for U.K. flood risk assessment has carried over to U.K. mortality assessment. Powered by fast numerical computation and parameterised with high quality geographical data, hydrological models of flood risk have superseded the traditional statistical insurance loss models. A decade later, medically-motivated computational models of mortality risk can be expected to gain increasing prominence in longevity risk management.

All UK insurers exposed to longevity risk need to perform stress tests for their Individual Capital Assessment (ICA). Some have put in place deterministic models which are arguably too simple; others have developed stochastic models that can be demanding and complex.

This paper presents a simple model to turn any deterministic mortality scenario into a stochastic model. We propose a simple model of stochastic variation that is easy to explain and to implement, which could be an alternative to and/or complete some of the well known models. The model can be applied to any best estimates of future mortality rates, as it aims to describe how longevity behaves around the projected expected values.

The paper proposes a possible calibration on the England and Wales population mortality that produces a minimum indication of possible future variation and uses the results to validate the model's assumptions. Using sample portfolios and the stochastic model, we can simulate cash flows to determine the distribution of the net present values (NPV) of annuity outgo.

Actuarial practice as regards mortality analysis and projection is changing rapidly. This paper provides a short introduction to some of the limitations and risks in using trends in cause of death as a means for projecting future mortality rates. It also covers recent developments in analysing the mortality of smaller populations, including survival models and “piggyback” models.