Changing Mortality Patterns and Their Predictability: The Case of the United States

  • Christina Bohk
  • Roland RauEmail author
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 39)


Forecasting U.S. mortality is challenging due to its irregular development. Since the 1990s, men have experienced stronger increases in life expectancy than women. Prospective forecasts up to 2050 generated using the Lee-Carter model and three of its variants illustrate that an extrapolation of this trend involves not just the risk of forecasting only moderate gains in life expectancy, but also the risk of forecasting that men will outlive women in the long run. Jointly forecasting mortality trends of multiple (sub)populations appears to be the key to averting such implausible developments in the model of Hyndman et al. and in our model. Since we also (1) forecast the rates of mortality improvement (to catch dynamic age shifts in survival improvement) and (2) select reference countries in terms of their (a) overall level of mortality, (b) risk factor attributable mortality, and/or (c) cultural and political proximity, our model can also forecast long-term trend changes and accelerating increases in life expectancy.


Forecasting mortality Survival improvements Coherent approaches Reference countries Cigarette smoking Uncertainty 



The European Research Council has provided financial support under the European Community’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 263744.


  1. Bohk, C., & R. Rau (2014a). Bayesian mortality forecasts with a flexible age pattern of change for several European countries. In Proceedings of the sixth Eurostat/Unece work session on demographic projections (pp. 360–371).Google Scholar
  2. Bohk, C., & Rau, R. (2014b). Probabilistic mortality forecasting with varying age-specific survival improvements. arXiv:1311.5380v2[stat.AP]. Google Scholar
  3. Booth, H., Maindonald, J., & Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.CrossRefGoogle Scholar
  4. Booth, H., Hyndman, R. J., Tickle, L., & de Jong, P. (2006). Lee-Carter mortality forecasting: A multi-country comparison of variants and extensions. Demographic Research, 15(1–2), 289–310.CrossRefGoogle Scholar
  5. Cairns, A. J. G., Blake, D., Dowd, K., Coughlan, G. D., & Khalaf-Allah, M. (2011). Bayesian stochastic mortality modelling for two populations. ASTIN Bulletin, 41, 29–59.Google Scholar
  6. Caselli, G., Vaupel, J. W., & Yashin, A. I. (1985). Mortality in Italy: Contours of a century of evolution. Genus, 41(1–2), 39–55.Google Scholar
  7. Crimmins, E. M., Preston, S. H., & Cohen, B. (Eds.). (2011). Explaining divergent levels of longevity in high-income countries. Washington, DC: The National Academy of Sciences.Google Scholar
  8. Danaei, G., Ding, E. L., Mozaffarian, D., Taylor, B., Rehm, J., Murray, C. J. L., & Ezzati, M. (2009). The preventable causes of death in the United States: Comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Medicine, 6(4), 1–23.CrossRefGoogle Scholar
  9. Davis, K., Stremikis, K., Squires, D., & Schoen, C. (2014). Mirror, mirror on the wall. 2014 update: How the performance of the U.S. health care system compares internationally. The Commonwealth Fund.Google Scholar
  10. Ezzati, M., Martin, H., Skjold, S., Hoorn, S. V., & Murray, C. J. L. (2006). Trends in national and state-level obesity in the USA after correction for self-report bias: Analysis of health surveys. Journal of the Royal Society of Medicine, 99, 250–257.CrossRefGoogle Scholar
  11. Gambill, B. A., & Vaupel, J. W. (1985). The LEXIS program for creating shaded contour maps of demographic surfaces. Technical report, International Institute for Applied Systems Analysis (IIASA) (Working Paper WP-85-094).Google Scholar
  12. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741.CrossRefGoogle Scholar
  13. Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society, Series B, 69(Part 2), 243–268.CrossRefGoogle Scholar
  14. Haberman, S., & Renshaw, A. E. (2012). Parametric mortality improvement rate modelling and projecting. Insurance: Mathematics and Economics, 50, 309–333.Google Scholar
  15. Hyndman, R. J. (2014). Demography: Forecasting mortality, fertility, migration and population data.
  16. Hyndman, R. J., & Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics and Data Analysis, 51(10), 4942–4956.CrossRefGoogle Scholar
  17. Hyndman, R. J., Booth, H., & Yasmeen, F. (2013). Coherent mortality forecasting: The product-ratio method with functional time series models. Demography, 50(1), 261–283.CrossRefGoogle Scholar
  18. Janssen, F., van Wissen, L. J. G., & Kunst, A. E. (2013). Including the smoking epidemic in internationally coherent mortality projections. Demography, 50(4), 1341–1362.CrossRefGoogle Scholar
  19. King, G., & Soneji, S. (2011). The future of death in America. Demographic Research, 25(1), 1–38.Google Scholar
  20. Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. Mortality. Journal of the American Statistical Association, 87(419), 659–671.Google Scholar
  21. Lee, R., & Miller, T. (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography, 38, 537–549.CrossRefGoogle Scholar
  22. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421–431.Google Scholar
  23. Li, N., & Lee, R. (2005). Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method. Demography, 42(3), 575–594.CrossRefGoogle Scholar
  24. Li, N., Lee, R., & Gerland, P. (2013). Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography, 50(6), 2037–2051.CrossRefGoogle Scholar
  25. Luy, M. (2002). Die geschlechtsspezifischen Sterblichkeitsunterschiede – Zeit für eine Zwischenbilanz. Zeitschrift für Gerontologie, Band 35, Heft 5, 412–429.Google Scholar
  26. Mitchell, D., Brockett, P., Mendoza-Arriage, R., & Muthuraman, K. (2013). Modeling and forecasting mortality rates. Insurance: Mathematics and Economics, 52(2), 275–285.Google Scholar
  27. National Center for Health Statistics. (2013). Mortality data – Vital statistics. NCHS’s multiple cause of death data. Available at
  28. Oeppen, J., & Vaupel, J. W. (2002). Broken limits to life expectancy. Science, 296, 1029–1031.CrossRefGoogle Scholar
  29. Orzack, S. H. (2012). The philosophy of modelling or does the philosophy of biology have any use? Philosophical Transactions of the Royal Statistical Society, 367(1586), 170–180.CrossRefGoogle Scholar
  30. Plummer, M. (2011). JAGS Version 3.1.0 user manual.Google Scholar
  31. Preston, S. H., & Wang, H. (2006). Sex mortality differences in the United States: The role of cohort smoking patterns. Demography, 43(4), 631–646.CrossRefGoogle Scholar
  32. Preston, S. H., Glei, D. A., & Wilmoth, J. R. (2010). A new method for estimating smoking-attributable mortality in high-income countries. International Journal of Epidemiology, 39(2), 430–438.CrossRefGoogle Scholar
  33. R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria,
  34. Raftery, A. E., & Lewis, S. M. (1992). Comment: One long run with diagnostics: Implementation strategies for Markov Chain Monte Carlo. Statistical Science, 7, 493–497.CrossRefGoogle Scholar
  35. Raftery, A. E., Chunn, J. L., Gerland, P., & Ševčíková, H. (2013). Bayesian probabilistic projections of life expectancy for all countries. Demography, 50(3), 777–801.CrossRefGoogle Scholar
  36. Renshaw, A. E., & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33, 255–272.Google Scholar
  37. Renshaw, A. E., & Haberman, S. (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38, 556–570.Google Scholar
  38. Shang, H. L. (2012). Point and interval forecasts of age-specific life expectancies: A model averaging approach. Demographic Research, 27, 593–644.CrossRefGoogle Scholar
  39. Shang, H. L., Booth, H., & Hyndman, R. (2011). Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods. Demographic Research, 25, 173–214.CrossRefGoogle Scholar
  40. Soneji, S., & King, G. (2010). The future of death in America. Demographic Research, 25(1), 1–38.Google Scholar
  41. Stewart, S. T., Cutler, D. M., & Rosen, A. B. (2009). Forecasting the effects of obesity and smoking on U.S. life expectancy. New England Journal of Medicine, 361, 2252–2260.CrossRefGoogle Scholar
  42. Stoeldraijer, L., van Duin, C., van Wissen, L., & Janssen, F. (2013). Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands. Demographic Research, 29(13), 323–354.CrossRefGoogle Scholar
  43. Torri, T., & Vaupel, J. W. (2012). Forecasting life expectancy in an international context. International Journal of Forecasting, 28, 519–531.CrossRefGoogle Scholar
  44. University of California, Berkeley (USA), & Max Planck Institute for DemographicResearch, Rostock, (Germany). (2014). Human mortality database. Available at
  45. Vaupel, J. W., Gambill, B. A., & Yashin, A. I. (1985). Contour maps of population surfaces. Technical report, International Institute for Applied Systems Analysis (IIASA) (Working Paper WP-85-047).Google Scholar
  46. Wang, H., & Preston, S. H. (2009). Forecasting United States mortality using cohort smoking histories. PNAS, 106(2), 393–398.CrossRefGoogle Scholar
  47. White, K. M. (2002). Longevity advances in high-income countries, 1955–96. Population and Development Review, 28(1), 59–76.CrossRefGoogle Scholar
  48. World Health Organization. (2000). Obesity: preventing and managing the global epidemic (Report of a WHO Consultation. WHO Technical Report Series 894)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Sociology and DemographyUniversity of RostockRostockGermany

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