Understanding and forecasting mortality by cause is an essential branch of actuarial science, with wide-ranging implications for decision-makers in public policy and industry. To accurately capture trends in cause-specific mortality, it is critical to consider dependencies between causes of death and produce forecasts by age and cause coherent with aggregate mortality forecasts. One way to achieve these aims is to model cause-specific deaths using compositional data analysis (CODA), treating the density of deaths by age and cause as a set of dependent, nonnegative values that sum to one. A major drawback of standard CODA methods is the challenge of zero values, which frequently occur in cause-of-death mortality modeling. Thus, we propose using a compositional power transformation, the $\alpha$-transformation, to model cause-specific life-table death counts. The $\alpha$-transformation offers a statistically rigorous approach to handling zero value subgroups in CODA compared to ad hoc techniques: adding an arbitrarily small amount. We illustrate the $\alpha$-transformation in England and Wales and US death counts by cause from the Human Cause-of-Death database, for cardiovascular-related causes of death. The results demonstrate the $\alpha$-transformation improves forecast accuracy of cause-specific life-table death counts compared with log-ratio-based CODA transformations. The forecasts suggest declines in the proportions of deaths from major cardiovascular causes (myocardial infarction and other ischemic heart diseases).

This paper assesses inequality in longevity across education and gender groups in 23 OECD countries around 2011. Data on mortality rates by age, gender, educational attainment, and, for 17 countries, cause of death were collected from national sources, with similar treatment applied to all countries in order to derive comparable measures of longevity at age 25 and 65 by gender and education. These estimates show that, on average, the gap in life expectancy between high and low-educated people is 7.6 years for men and 4.8 years for women at age 25 years, and 3.6 years for men and 2.6 years for women at age 65. At the age of 25, the gap in life expectancy between high and low-educated people varies between 4.1 years (in Canada) and 13.9 years (in Hungary) for men, and between 2.5 years (in Italy) and 8.3 years (in Latvia) for women; in the United States, the gap is 10.0 years for men and 7.0 years for women. Cardiovascular diseases are the first cause of death for all gender and education groups after age 65 years, and the first cause of mortality inequality between the high and low-education elderly.

We examined the differences in the suicide characteristics between areas directly and indirectly affected by war activities and in war and post-war periods according to the following variables: suicide rate, sex, age and method of suicide. Analysis was done on 5349 suicides committed in the period 1993–1998 (war and post-war years). The suicide rates in the Republic of Croatia oscillated in the pre-war, war and post-war periods (1985–2000) but without significant differences. In the areas directly affected by war, the suicide rate was significantly lower than in other areas during the study period 1993–1998 (chi-square = 10.3245; P = 0.0017). The number of suicides in both sexes declined in the areas directly affected by war—more in men than in women; the difference between sexes was statistically significant (chi-square = 3.6697; P = 0.055). Middle- and old-aged people were the population with high suicide risk in both areas (t = 1.76; P = 0.078). There were significant differences in the methods of suicides between war and non-war areas (chi-square = 108.8473; P = 0.001). Firearms or explosive devices were the methods used more significantly for suicides in the areas directly affected by war than in other areas, whereas hanging was more frequently used in the areas indirectly affected by war.

The life expectancy gap between people with severe mental illness (SMI) and the general population persists and may even be widening. This study aimed to estimate contributions of specific causes of death to the gap. Age of death and primary cause of death were used to estimate life expectancy at birth for people with SMI from a large mental healthcare case register during 2007–2012. Using data for England and Wales in 2010, death rates in the SMI cohort for each primary cause of death category were replaced with gender- and age-specific norms for that cause. Life expectancy in SMI was then re-calculated and, thus, the contribution of that specific cause of death estimated. Natural causes accounted for 79.2% of lost life-years in women with SMI and 78.6% in men. Deaths from circulatory disorders accounted for more life-years lost in women than men (22.0% versus 17.4%, respectively), as did deaths from cancer (8.1% versus 0%), but the contribution from respiratory disorders was lower in women than men (13.7% versus 16.5%). For women, cancer contributed more in those with non-affective than affective disorders, while suicide, respiratory and digestive disorders contributed more in those with affective disorders. In men, respiratory disorders contributed more in non-affective disorders. Other contributions were similar between gender and affective/non-affective groups. Loss of life expectancy in people with SMI is accounted for by a broad range of causes of death, varying by gender and diagnosis. Interventions focused on multiple rather than individual causes of death should be prioritised accordingly.

Over the course of the 20th century Ireland moved from being a largely young population with a high death rate from infectious diseases to an increasingly older population with a consequent rise in chronic diseases. Understanding the changes that occurred in Irish mortality over the 20th century and how these changes compare with those experienced by similar countries can help us plan for the challenges of our aging population. This paper analyses trends in mortality in Ireland over the period 1901 to 2006 by age group, gender and five broad categories of cause of death – infectious diseases, circulatory diseases, respiratory diseases, cancer and external causes. To place the changes in an international context the trends are compared with those experienced by Northern Ireland and England and Wales. Ireland experienced the fastest improvements in mortality of the three regions in the early years of the 21st century. By 2006 the mortality of Irish males ranked between that of Northern Ireland and England and Wales while Irish females experienced the lowest mortality of the three regions. The improvement in Irish mortality in the 21st century can be attributed mainly to the drop in deaths from circulatory diseases for both males and females.

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.

The Actuarial Profession is currently undertaking a review of its research strategy, and has decided to focus more resources on researching mortality developments. It is important that the profession is involved in partnerships with researchers from outside of the profession, bringing actuarial expertise into closer contact with other disciplines. We believe that collaboration between these different areas of expertise will provide important new insights in understanding mortality trends. This report represents the first step by the Mortality Developments Scoping Project Steering Group to map current research into mortality developments across a wide range of disciplines. The steering group is aware that the themes and literature discussed in this report are not exhaustive; however it does include areas of research not normally covered by the Actuarial Profession. The steering group welcomes comments on the report and suggestions for any areas which may not yet have been covered.

The main aim of this report is to provide an overview of the key areas of research into mortality developments across a wide range of disciplines, as well as areas of overlap and gaps in the research. The literature described is compiled from recommendations received by the Mortality Developments Scoping Project Steering Group from experts from different disciplines working in the area of mortality.

Key themes identified from recommended literature:

— the role of medicine in mortality reduction;

— the role of lifestyle and environment in mortality reduction, including smoking, socio-economic conditions and obesity;

— causes of death contributing to mortality reduction, in particular coronary heart disease;

— mortality reduction attributable to differing age groups;

— the relationship of active life expectancy to total gains in life expectancy;

— evidence of cohort effects on mortality improvement; and

— future trends in mortality developments.

Areas of overlap identified from recommended literature are:

— the overlap between literature examining the role of medicine in mortality decline and the influences on the decline in mortality from coronary heart disease; and

— the areas of overlap between various disciplines working in the field of mortality developments.

Gaps identified in recommended literature are:

— a lack of recommendations from social policy;

— few papers recommended on the role of lifestyle and behavioural factors on mortality;

— few papers recommended on causes of death other than coronary heart disease; and

— few papers recommended on potential threats to future mortality improvement.

Patterns and trends in late-life mortality are of growing financial importance. The growth in pension liabilities, both public and private, are of crucial interest to governments, insurers and companies with defined benefit pension schemes. This paper explores the patterns in international mortality data, and draws important lessons for actuaries in the United Kingdom.

The purpose of the report is to achieve a greater understanding of the United Kingdom ‘cohort effect’ by exploring research in other fields and analysing population mortality data by cause of death in more detail. The ‘cohort effect’ in this context is the observed phenomenon that people born in the U.K. between 1925 and 1945 (centred on the generation born in 1931) have experienced more rapid improvement in mortality than generations born either side of this period.

In a Continuous Mortality Investigation (CMI) Bureau working paper published in 2002, a similar trend was noted in the mortality experience of male pensioners and males with life assurance policies. The CMI Bureau investigation showed peak rates of improvement for the cohort born in 1926. Interim projection bases for future mortality experience were produced as a result of the study. The projections made various assumptions about the extent to which the observed cohort effect would continue to shape the pattern of future mortality improvement.

This report suggests that it is highly likely that the cohort effect has been caused by a number of different factors in combination. Prevalence of smoking from one generation to the next has certainly been one such factor. Furthermore, an analysis of patterns of cigarette smoking suggests that there is a degree of inevitability in some element of likely future improvement, especially for mortality at older ages from conditions strongly linked to smoking.

However, trends in heart disease and breast cancer mortality suggest that smoking is not the only factor. The differences between lung cancer and heart disease trends by year of birth are especially interesting. The report shows that there are two ‘sub-cohorts’ of the 1925 to 1945 cohort: an earlier group where the improvements may be largely due to smoking; and a later one where other factors, such as diet in early life, may have played a greater role.

Historic patterns of smoking behaviour by socio-economic class provide an explanation for the five-year difference in the year of birth showing the fastest improvements, i.e. the difference between 1926 for the CMI Bureau investigation and 1931 for the general population. It is also notable that the second ‘sub-cohort’ of high improvement, applying to people born in the early 1940s, can be seen in both population and CMI experience.

A case study examining Japanese mortality experience shows that strong cohort trends can be projected well into old age. This does not provide proof that the U.K. cohort effect will do the same. However, it does counter arguments that year of birth effects will inevitably wear off with age. It is especially interesting given recent epidemiological research linking early life experience with markers of ageing.

There are a number of reasons to believe that the U.K. cohort effect will have an enduring impact on rates of mortality improvement in future decades. These include historical patterns of smoking behaviour and the impact of early life experience on health in later life. There appears to be little evidence to support the idea that the width of the generation experiencing rapid improvement will reduce with time.

Continuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee–Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee–Carter age-period-cohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.