Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-17T15:15:42.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Forecasting Mortality, Different Approaches for Different Cause of Deaths? The Cases of Lung Cancer; Influenza, Pneumonia, and Bronchitis; and Motor Vehicle Accidents

Published online by Cambridge University Press:  10 June 2011

Mariachiara Di Cesare  and
Mike Murphy
Mariachiara Di Cesare
Affiliation:
Department of Social Policy, London School of Economics, Houghton Street, London WC2A 2AE, U.K. Tel: +44-20-7955-7698; E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Most of the methods of mortality forecasting have been assessed using performance on overall mortality, and few studies address the issue of identifying the appropriate forecasting models for specific causes of deaths. This study analyses trends and forecasts mortality rates for three major causes of death — lung cancer, influenza-pneumonia-bronchitis, and motor vehicle accidents — using Lee–Carter, Booth–Maindonald–Smith, Age-Period-Cohort, and Bayesian models, to assess how far different causes of death need different forecasting methods. Using data from the Twentieth and Twenty-First Century Mortality databases for England and Wales, results show major differences among the different forecasting techniques. In particular, when linearity is the main driver of past trends, Lee–Carter-based approaches are preferred due to their straightforward assumptions and limited need for subjective judgment. When a clear cohort pattern is detectable, such as with lung cancer, the Age-Period-Cohort model shows the best outcome. When complete and reliable historical trends are available the Bayesian model does not produce better results than the other models.

Keywords

Cause of DeathsForecasting ModelsLee–CarterBooth–Maindonald-SmithBayesian ModelsLung CancerInfluenza-Pneumonia-BronchitisMotor Vehicle Accidents England & Wales
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 15 , Issue S1 , 2009 , pp. 185 - 211
Copyright
Copyright © Institute and Faculty of Actuaries 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Barbi, E. & Vaupel, J.W. (2005). Comment on Inflammatory Exposure and Historical Changes in Human Life-Spans. Science, 308(5729), 1743a.CrossRefGoogle ScholarPubMed
Booth, H., Maindonald, J. & Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.CrossRefGoogle ScholarPubMed
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(9), 289–310.CrossRefGoogle Scholar
Booth, H. & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. ADSRI Working Paper, No. 3. Available at http://adsri.anu.edu.au/pubs/ADSRIpapers/ADSRIwp-03.pdfCrossRefGoogle Scholar
Brock, A., Griffiths, C. & Rooney, C. (2006). The impact of introducing ICD-10 on analysis of respiratory mortality trends in England and Wales. Health Statistics Quarterly, 29, 9–17.Google Scholar
Carstensen, B. (2007). Age-period-cohort models for the Lexis diagram. Statistics in Medicine, 26, 3018–3045.CrossRefGoogle ScholarPubMed
Catalano, R. & Bruckner, T. (2006). Child mortality and cohort lifespan: a test of diminished entelechy. Internationaljournal of Epidemiology, 35(5), 1264–1269.Google ScholarPubMed
Cleries, R., Martinez, J.M., Valls, J., Pareja, L., Esteban, L., Gispert, R., Moreno, V., Ribes, J. & Borras, J.M. (2009). Life expectancy and age-period-cohort effects: analysis and projections of mortality in Spain between 1977 and 2016. Public Health, 123(2), 156–162.CrossRefGoogle ScholarPubMed
Crimmins, E.M. (1981). The changing pattern of American mortality decline 1940–1977, and its implication for the future. Population and Development Review, 7(2), 229–254.CrossRefGoogle Scholar
Davy, M. (2006). Time and generational trends in smoking among men and women in Great Britain, 1972–2004/5. Health Statistics Quarterly, 32, 35–43.Google Scholar
De Jong, P. & Tickle, L. (2006). Extending Lee-Carter mortality forecasting. Mathematical Population Studies, 13(1), 1–18.CrossRefGoogle Scholar
Finch, C.E. & Crimmins, E.M. (2004). Inflammatory exposure and historical changes in human life-spans. Science, 305, 1736–1739.CrossRefGoogle ScholarPubMed
Girosi, F. & King, G. (2008). Demographic forecasting. Princeton University Press.CrossRefGoogle Scholar
Griffiths, C. & Brock, A. (2003). Twentieth Century mortality trends in England and Wales. Health Statistics Quarterly, 18, 5–17.Google Scholar
Griffiths, C. & Rooney, C. (2006). Trends in mortality from Alzheimer's disease, Parkinson's disease and dementia, England and Wales, 1979–2004. Health Statistics Quarterly, 30, Summer, 6–14.Google Scholar
Heathcote, C. & Higgins, T. (2001). A regression model of mortality, with application to the Netherlands. In: Tabeau, E., Van Den Berg Jeths, A. & Heathcote, C. (eds.). Forecasting mortality in developed countries. Kluwer Academic Publishers.Google Scholar
Hollmann, F.W., Mulder, T.J. & Kallan, J.E. (2000). Methodology and assumptions for the population projections of the United States: 1999 to 2100. Population Division Working Paper No. 38, U.S. Bureau of the Census.Google Scholar
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
Jau-Yih, T., Wen-Chung, L. & Jung-Der, W. (1996). Age-period-cohort analysis of motor vehicle mortality in Taiwan, 1974–1992. Accident Analysis and Prevention, 28(5), 619–626.CrossRefGoogle Scholar
Lee, R.D. & Carter, L.R. (1992). Modelling and forecast U.S. mortality. Journal of the American Statistical Association, 87(419), 659–671.Google Scholar
Lee, R.D. & Miller, T. (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography, 38(4), 537–549.CrossRefGoogle ScholarPubMed
Lutz, W. & Goldstein, J.R., (2004). How to deal with uncertainty in population forecasting? IIASA Reprint Research Report, RR-04–09. Laxenburg, Austria: International Institute for Applied Systems Analysis.Google Scholar
Mason, W.M. & Smith, H.L. (1985). Age-Period-Cohort analysis and the study of deaths from pulmonary tuberculosis. In: Mason, W.M. & Fienberg, S.E. (eds.). Cohort analysis in social research. Springer-Verlag, New York.CrossRefGoogle Scholar
Murphy, M. (2010). Re-examining the dominance of birth cohort effects on mortality. Population and Development Review, 36(2), 365–390.CrossRefGoogle Scholar
OFFICE FOR NATIONAL STATISTICS (2000). 20th century mortality (England & Wales 1901–2000) CD-ROM.Google Scholar
OFFICE FOR NATIONAL STATISTICS (2009). 21st century mortality database. Available at http://www. statistics.gov.uk/STATBASE/ssdataset.asp?vlnk=6922Google Scholar
Pedroza, C. (2006). A Bayesian forecasting model: predicting US male mortality. Biostatistics, 7(4), 530–550.CrossRefGoogle ScholarPubMed
Peto, R., Lopez, A.D., Boreham, J., Thun, M. & Heath, C. (1992). Mortality from tobacco in developed countries: indirect estimation from national vital statistics. Lancet, 339, 1268–1278.CrossRefGoogle ScholarPubMed
Polder, J.J., Barendregt, J.J. & Van Oers, H. (2006). Health care costs in the last year of life — the Dutch experience. Social Science and Medicine, 63(7), 1720–1731.CrossRefGoogle ScholarPubMed
R DEVELOPMENT CORE TEAM (2009). R: A language and environment for statistical computing. Available at http://www.R-project.orgGoogle Scholar
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
Scitovsky, A.A. (1994). The high cost of dying revisited. Milbank Quarterly, 72(4), 561–591.CrossRefGoogle ScholarPubMed
Tabeau, E. (2001). A review of demographic forecasting models for mortality. In: Tabeau, E., Van Den Berg Jeths, A. & Heathcote, C. (eds.). Forecasting mortality in developed countries. Kluwer Academic Publishers.CrossRefGoogle Scholar
Tabeau, E., Ekamper, P., Huisman, C. & Bosch, A. (1999). Improving overall mortality forecasts by analysing cause-of-death, period and cohort effects in trends. European Journal of Population, 15, 153–183.CrossRefGoogle Scholar
Yang, Y., Schulhofer-Wohl, S., Fu, W.J. & Land, K.C. (2008). The intrinsic estimator for Age-Period-Cohort analysis: what it is and how to use it. American Journal of Sociology, 113(6), 1697–1736.CrossRefGoogle Scholar