A multi-population approach to forecasting all-cause mortality using cause-of-death mortality data. (English) Zbl 1460.91231

Summary: All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-of-death 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 R. D. Lee [“Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method”, Demography 42, No. 3, 575–594 (2005; doi:10.1353/dem.2005.0021)] 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.


91G05 Actuarial mathematics
91D20 Mathematical geography and demography
Full Text: DOI


[1] Adam, K.; Jappelli, T.; Menichini, A.; Padula, M.; Pagano., M., Analyse, compare, and apply alternative indicators and monitoring methodologies to measure the evolution of capital market integration in the European Union (2002)
[2] Alai, D. H.; Arnold, S.; Sherris, M., Modelling cause-of-death mortality and the impact of cause-elimination, Annals of Actuarial Science, 9, 167-86 (2015)
[3] Arnold, S.; Sherris., M., Forecasting mortality trends allowing for cause-of-death mortality dependence, North American Actuarial Journal, 17, 273-82 (2013) · Zbl 1412.91218
[4] Barro, R. J., Economic growth in a cross section of countries, The Quarterly Journal of Economics, 106, 407-43 (1991)
[5] Bongaarts, J., Trends in causes of death in low-mortality countries: implications for mortality projections, Population and Development Review, 40, 189-212 (2014)
[6] Booth, H.; Hyndman, R.; Tickle, L.; De Jong, P., Lee-carter mortality forecasting: a multi-country comparison of variants and extensions, Demographic Research, 15, 289-310 (2006)
[7] Booth, H.; Tickle., L., Mortality modelling and forecasting: A review of methods, Annals of Actuarial Science, 3, 3-43 (2008)
[8] Boumezoued, A., H. L. Hardy, N. El Karoui, and S. Arnold. 2018. Cause-of-death mortality: What can be learned from population dynamics? Insurance: Mathematics and Economics78:301-15. · Zbl 1400.91242
[9] Bremberg, S. G., Mortality rates in OECD countries converged during the period 1990-2010, Scandinavian Journal of Public Health, 45, 436-43 (2017)
[10] Cairns, A. J. G.; Blake, D.; Dowd., K., A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration, Journal of Risk and Insurance, 73, 687-718 (2006)
[11] Carrat, F.; Flahault, A.; Boussard, E.; Farran, N.; Dangoumau, L.; Valleron, A.-J., Surveillance of influenza-like illness in France. The example of the 1995/1996 epidemic, Journal of Epidemiology & Community Health, 32S-38S (1998)
[12] Carriere, J. F., Dependent decrement theory, Transactions of the Society of Actuaries, 46, 45-74 (1994)
[13] Carrière, J. F., Removing cancer when it is correlated with other causes of death, Biometrical Journal, 37, 339-50 (1995) · Zbl 0837.62093
[14] Caselli, G.; Vallin, J.; Marsili., M., How useful are the causes of death when extrapolating mortality trends: An update (2019)
[15] d’Albis, H.; Esso, L. J.; Pifarré i. Arolas, H., Mortality convergence across high-income countries: An econometric approach (2012)
[16] Dimitrova, D. S.; Haberman, S.; Kaishev., V. K., Dependent competing risks: Cause elimination and its impact on survival, Insurance: Mathematics and Economics, 53, 464-77 (2013) · Zbl 1304.91099
[17] Gaille, S.; Sherris., M., Modelling mortality with common stochastic long-run trends, The Geneva Papers on Risk and Insurance Issues and Practice., 36, 595-621 (2011)
[18] Gutterman, S.; Vanderhoof., I. T., Forecasting changes in mortality: a search for a law of causes and effects, North American Actuarial Journal, 2, 135-38 (1998) · Zbl 1081.91602
[19] Hatzopoulos, P.; Haberman., S., Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data, Insurance: Mathematics and Economics, 52, 320-37 (2013) · Zbl 1284.91238
[20] Honoré, B. E.; Lleras-Muney., A., Bounds in competing risks models and the war on cancer, Econometrica, 74, 1675-98 (2006) · Zbl 1187.92054
[21] Janssen, F., Cohort patterns in mortality trends among the elderly in seven european countries, 1950-99, International Journal of Epidemiology, 34, 1149-59 (2005)
[22] Jemal, A.; Center, M. M.; DeSantis, C.; Ward., E. M., Global patterns of cancer incidence and mortality rates and trends, Cancer Epidemiology and Prevention Biomarkers, 19, 1893-1907 (2010)
[23] Kaishev, V. K.; Dimitrova, D. S.; Haberman., S., Modelling the joint distribution of competing risks survival times using copula functions, Insurance: Mathematics and Economics, 41, 339-61 (2007) · Zbl 1141.91518
[24] Koene, R. J.; Prizment, A. E.; Blaes, A.; Konety., S. H., Shared risk factors in cardiovascular disease and cancer, Circulation, 133, 1104-14 (2016)
[25] Lee, R. D.; Carter., L. R., Modeling and forecasting us mortality, Journal of the American Statistical Association, 87, 659-71 (1992) · Zbl 1351.62186
[26] Lee, R. D.; Miller., T., Evaluating the performance of the Lee-Carter method for forecasting mortality, Demography, 38, 537-49 (2001)
[27] Li, H.; Lu., Y. (2019)
[28] Li, N.; Lee., R., Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method, Demography, 42, 575-94 (2005)
[29] Mathers, C. D.; Boerma, T.; Ma Fat, D., Global and regional causes of death, British Medical Bulletin (2009)
[30] Millossovich, P.; Haberman, S.; Kaishev, V. K.; Baxter, S.; Gaches, A.; Gunnlaugsson, S.; Sison., M. (2014)
[31] Oeppen, J., Coherent forecasting of multiple-decrement life tables: A test using japanese cause of death data (2008)
[32] Omran, A. R., The epidemiologic transition theory revisited thirty years later, World Health Statistics Quarterly, 51, 99-119 (1998)
[33] Putter, H.; Fiocco, M.; Geskus., R. B., Tutorial in biostatistics: Competing risks and multi-state models, Statistics in Medicine, 26, 2389-2430 (2007)
[34] Renshaw, A. E.; Haberman., S., A cohort-based extension to the lee-carter model for mortality reduction factors, Insurance: Mathematics and Economics, 38, 556-70 (2006) · Zbl 1168.91418
[35] Tabeau, E.; Ekamper, P.; Huisman, C.; Bosch., A., Improving overall mortality forecasts by analysing cause-of-death, period and cohort effects in trends, European Journal of Population/Revue Européenne de Démographie, 15, 153-83 (1999)
[36] Tabeau, E.; van den Berg Jeths, A.; Heathcote, C., Forecasting mortality in developed countries: Insights from a statistical, demographic and epidemiological perspective, 9 (2001), Springer Science & Business Media
[37] Tsiatis, A., A nonidentifiability aspect of the problem of competing risks, Proceedings of the National Academy of Sciences, 72, 20-22 (1975) · Zbl 0299.62066
[38] Tuljapurkar, S.; Li, N.; Boe., C., A universal pattern of mortality decline in the g7 countries, Nature, 405, 789-92 (2000)
[39] Vallin, J.; Meslé., F., Convergences and divergences in mortality: A new approach of health transition, Demographic Research, 2, 11-44 (2004)
[40] White, K. M., Longevity advances in high-income countries, 1955-96, Population and Development Review, 28, 59-76 (2002)
[41] Wilmoth, J. R., Are mortality projections always more pessimistic when disaggregated by cause of death?, Mathematical Population Studies, 5, 293-319 (1995) · Zbl 0876.92032
[42] Wilmoth, J. R., Mortality projections for Japan: A comparison of four methods (1996)
[43] Wilmoth, J. R., Is the pace of japanese mortality decline converging toward international trends?, Population and Development Review, 593-600 (1998)
[44] Wilson, C., On the scale of global demographic convergence 1950-2000, Population and Development Review, 27, 155-71 (2001)
[45] (2018)
[46] (2018)
[47] Zheng, M.; Klein., J. P., Estimates of marginal survival for dependent competing risks based on an assumed copula, Biometrika, 82, 127-38 (1995) · Zbl 0823.62099
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.