All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-ofdeath mortality allows one to understand the drivers of the recent changes in all-cause mortality. However, the existing literature has argued that all-cause mortality projections based on cause-specific mortality experience have a number of serious drawbacks, including the inferior cause-of-death mortality data and the complex dependence structure between causes of death. In this article, we use the recent World Health Organization causes-of-death data to address this issue in a multipopulation context. We construct a new model in the spirit of N. Li and Lee (2005) but in terms of cause-specific mortality. A new two-step beta convergence test is used to capture the cause-specific mortality dynamics between different countries and between different causes. We show that the all-cause mortality estimations produced by the new model perform in the sample similarly to the estimations by the Lee-Carter and Li-Lee all-cause mortality models. However, in contrast to results from earlier studies, we find that the all-cause mortality projections of the new model have better out-of-sample performance in a long forecast horizon. Moreover, for the case of The Netherlands, an approximately 1-year higher remaining life expectancy projection for a 67-year-old Dutch male in a 30-year forecast horizon is obtained by this new model, compared to the all-cause Li-Lee mortality model.

This paper proposes a coherent multi-population approach to mortality forecasting for less developed countries. The majority of these countries have witnessed faster mortality declines among the young and the working age populations during the past few decades, whereas in the more developed countries, the contemporary mortality declines have been more substantial among the elders. Along with the socioeconomic developments, the mortality patterns of the less developed countries may become closer to those of the more developed countries. As a consequence, forecasting the long-term mortality of a less developed country by simply extrapolating its historical patterns might lead to implausible results. As an alternative, this paper proposes to incorporate the mortality patterns of a group of more developed countries as the benchmark to improve the forecast for a less developed one. With long-term, between-country coherence in mind, we allow the less developed country’s age-specific mortality improvement rates to gradually converge with those of the benchmark countries during the projection phase. Further, we employ a data-driven, threshold hitting approach to control the speed of this convergence. Our method is applied to China, Brazil, and Nigeria. We conclude that taking into account the gradual convergence of mortality patterns can lead to more reasonable long-term forecasts for less developed countries.

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Objective We investigate whether there are changes over time in years in good health people can expect to live above (surplus) or below (deficit) the pension age, by level of attained education, for the past (2006), present (2018) and future (2030) in the Netherlands. Methods We used regression analysis to estimate linear trends in prevalence of four health indicators: self-assessed health (SAH), the Organization for Economic Co-operation and Development (OECD) functional limitation indicator, the OECD indicator without hearing and seeing, and the activities-of-daily-living (ADL) disability indicator, for individuals between 50 and 69 years of age, by age category, gender and education using the Dutch National Health Survey (1989–2018). We combined these prevalence estimates with past and projected mortality data to obtain estimates of years lived in good health. We calculated how many years individuals are expected to live in good health above (surplus) or below (deficit) the pension age for the three points in time. The pension ages used were 65 years for 2006, 66 years for 2018 and 67.25 years for 2030. Results Both for low educated men and women, our analyses show an increasing deficit of years in good health relative to the pension age for most outcomes, particularly for the SAH and OECD indicator. For high educated we find a decreasing surplus of years lived in good health for all indicators with the exception of SAH. For women, absolute inequalities in the deficit or surplus of years in good health between low and high educated appear to be increasing over time. Conclusions Socio-economic inequalities in trends of mortality and the prevalence of ill-health, combined with increasing statutory pension age, impact the low educated more adversely than the high educated. Policies are needed to mitigate the increasing deficit of years in good health relative to the pension age, particularly among the low educated.