The Cohort Effect: Insights and Explanations

Abstract

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.

Phantoms Never Die: Living with Unreliable Population Data

“…In the right-hand plot, the lines are more or less parallel, but the positions of the five curves reflects a greater rate of improvement in mortality rates between these cohorts. This greater rate of improvement between cohorts born around 1930 is well known (see, for example, Willets, 2004). These plots are typical of most birth cohorts.…”

“…The format of the data varies from country to country. For example, in England & Wales, death counts, D(t, x) represents the number of persons who died in year t, 1 For example, Booth et al, 2002, andHyndman andUllah, 2007. 2 For example, Brouhns et al, 2002, Czado et al, 2005, Li et al, 2009 For example, Blake and Burrows, 2001, Coughlan et al, 2007, Cairns et al, 2008, Dahl et al, 2008, Li and Hardy, 2011, Li and Luo, 2012, Cairns 2011, 2013, 2014 4 For example, Cairns et al, 2006, 2009, Hyndman and Ullah, 2007, Plat, 2009, Currie, 2011, Hunt and Blake, 2014, and Mavros et al, 2014 5 For example, Willets, 2004, Renshaw and Haberman, 2006, Cairns et al 2009, 2011a For example, Li and Lee, 2005, Cairns et al, 2011b, Dowd et al, 2011, Jarner and Kryger, 2011, Li and Hardy, 2011, and Börger et al, 2014 7 Cairns (2014) has charted the genealogy of these new models. He argues that the accompanying complexity might not deliver improved forecasts, and that there is a need to get back to simpler, more robust models (see, also, Mavros et al, 2014, Hunt andBlake, 2014).…”

The Modern Tontine: An Innovative Instrument for Longevity Risk Management in an Aging Society

“…changes of mortality within a cohort. Willets (2004) empirically substantiates cohort effects as characteristic of mortality dynamics.…”

Mapping Mortality: Using Lexis surfaces to identify complex patterns in age-year specific mortality rates within and between populations

“…For example, people born around the time of the 'Spanish Flu' of 1918 appear to have somewhat higher mortality risks at any given age than might be expected from trends observed in earlier and later cohorts, (Almond, 2006;J Minton, Vanderbloemen, & Dorling, 2013) and people born in England and Wales in the 1950s to have a somewhat lower mortality risk as they age than might be expected from broader trends. (Willets, 2003) There have been various attempts to uniquely partition away cohort effects from age effects and period effects in statistical models (for example (Yang, Fu, & Land, 2004;Yang, Schulhofer-Wohl, Fu, & Land, 2008)), but doing so is logically impossible, because each of the three effects cannot be uniquely identified (Wilmoth, 2006), leading to effects to do so being branded 'futile'. (Bell & Jones, 2014) 1.2.…”