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INTERNATIONAL CAUSE-SPECIFIC MORTALITY RATES: NEW INSIGHTS FROM A COINTEGRATION ANALYSIS

Published online by Cambridge University Press:  29 December 2015

Séverine Arnold-Gaille  and
Michael Sherris
Séverine Arnold-Gaille*
Affiliation:
Department of Actuarial Science, Faculty of Business and Economics (HEC Lausanne), University of Lausanne, Switzerland
Michael Sherris
Affiliation:
UNSW Business School, University of New South Wales, Australia E-Mail: [email protected]
*
E-Mail: [email protected]
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Abstract

This paper applies cointegration techniques, developed in econometrics to model long-run relationships, to cause-of-death data. We analyze the five main causes of death across five major countries, including USA, Japan, France, England & Wales and Australia. Our analysis provides a better understanding of the long-run equilibrium relationships between the five main causes of death, providing new insights into similarities and differences in trends. The results identify for the first time similarities between countries and genders that are consistent with past studies on the aging processes by biologists and demographers. The insights from biological theory on aging are found to be reflected in the cointegrating relations in all of the countries included in the study.

Keywords

Causes of deathmortality trendscointegrationdependencecommon trendsbiological aging
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 46 , Issue 1 , January 2016 , pp. 9 - 38
Copyright
Copyright © Astin Bulletin 2015 

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References

Adams, J.M. and White, M. (2004) Biological aging: A fundamental, biological link between socio-economic status and health? European Journal of Public Health, 14 (3), 331–334.CrossRefGoogle ScholarPubMed
Anderson, R.N. (1999) US decennial life tables: 1989–91. United States life tables eliminating certain causes of death. DHHS Publication No (PHS) 99-1150-4, 1 (4).Google Scholar
Arnold, S. and Sherris, M. (2013) Forecasting mortality trends allowing for cause-of-death mortality dependence. North American Actuarial Journal, 17 (4), 273–282.CrossRefGoogle Scholar
Arnold, S. and Sherris, M. (2015) Causes-of-death mortality: What do we know on their dependence? North American Actuarial Journal, 19 (2), 116–128.CrossRefGoogle Scholar
Bayo, F. (1968) Life tables: 1959–61. United States life tables by causes of death: 1959–61. Public Health Service Publication No 1252, 1 (6).Google Scholar
Booth, H. and Tickle, L. (2008) Mortality modelling and forecasting: A review of methods. Annals of Actuarial Science, 3 (1–2), 3–43.CrossRefGoogle Scholar
Butler, R.N., Sprott, R., Warner, H., Bland, J., Feuers, R., Forster, M., Fillit, H., Harman, S.M., Hewitt, M., Hyman, M., Johnson, K., Kligman, E., McClearn, G., Nelson, J., Richardson, A., Sonntag, W., Weindruch, R. and Wolf, N. (2004) Biomarkers of aging: From primitive organisms to humans. Journal of Gerontology: Biological Sciences, 59A (6), 560–567.Google Scholar
Carnes, B.A., Holden, L.R., Olshansky, S.J., Witten, T.M. and Siegel, J.S. (2006) Mortality partitions and their relevance to research on senescence. Biogerontology, 7 (4), 183–198.CrossRefGoogle ScholarPubMed
Carnes, B.A. and Olshansky, S.J. (1997) A biologically motivated partitioning of mortality. Experimental Gerontology, 32 (6), 615–631.CrossRefGoogle ScholarPubMed
Carnes, B.A., Olshansky, S.J. and Hayflick, L. (2013) Can human biology allow most of us to become centenarians? Journal of Gerontology: Biological Sciences, 68 (2), 136–142.Google ScholarPubMed
Caselli, G. (1996) Future longevity among the elderly. In Health and Mortality among Elderly Populations (ed. Caselli, G. and Lopez, A.D.), pp. 235–265. Oxford: Clarendon Press.CrossRefGoogle Scholar
Caselli, G., Vallin, J. and Marsili, M. (2006) How useful are the causes of death when extrapolating mortality trends. An update. Social Insurance Studies from the Swedish Social Insurance, 4, 9–36.Google Scholar
Curtin, L.R. and Armstrong, R.J. (1988) US decennial life tables: 1979-81. United States life tables eliminating certain causes of death. DHHS Publication No (PHS) 88-1150-2, 1 (2).Google Scholar
Gaille, S. and Sherris, M. (2011) Modeling mortality with common stochastic long-run trends. The Geneva Papers on Risk and Insurance - Issues and Practice, 36 (4), 595–621.CrossRefGoogle Scholar
Gompertz, B. (1825) On the nature of the function expressive of the law of human mortality and on a new mode of determining life contingencies. Philosophical Transactions of the Royal Society of London, 115, 513–585.Google Scholar
Greville, T.N.E., Bayo, F. and Foster, R.S. (1975) Life tables: 1969–71. United States life tables by causes of death: 1969–71. DHEW Publication No (HRA) 75-1150, 1 (5).Google Scholar
Hamilton, J.D. (1994) Time Series Analysis. Princeton: Princeton University Press.CrossRefGoogle Scholar
Hayflick, L. (2004) “Anti-Aging” is an oxymoron. Journal of Gerontology: Biological Sciences, 59A (6), 573–578.Google Scholar
Holliday, R. (2004) The multiple and irreversible causes of aging. Journal of Gerontology: Biological Sciences, 59A (6), 568–572.Google Scholar
Hougaard, P. (1984) Life table methods for heterogeneous populations: Distributions describing the heterogeneity. Biometrika, 71 (1), 75–83.CrossRefGoogle Scholar
Jin, K. (2010) Modern biological theories of aging. Aging and Disease, 1 (2), 72–74.Google ScholarPubMed
Johansen, S. (1988) Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12 (2–3), 231–254.CrossRefGoogle Scholar
Johansen, S. (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59 (6), 1551–1580.CrossRefGoogle Scholar
Johansen, S. (1994) The role of the constant and linear terms in cointegration analysis of nonstationary variables. Econometric Reviews, 13 (2), 205–229.CrossRefGoogle Scholar
Johansen, S. and Juselius, K. (1992) Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK. Journal of Econometrics, 53 (1–3), 211–244.CrossRefGoogle Scholar
Johansen, S. and Juselius, K. (1994) Identification of the long-run and the short-run structure. An application to the ISLM model. Journal of Econometrics, 63 (1), 7–36.CrossRefGoogle Scholar
Kaishev, V.K., Dimitrova, D.S. and Haberman, S. (2007) Modelling the joint distribution of competing risks survival times using copula functions. Insurance: Mathematics and Economics, 41 (3), 339–361.Google Scholar
Lütkepohl, H. (2005) New Introduction to Multiple Time Series Analysis. Berlin: Springer.CrossRefGoogle Scholar
Makeham, W. (1867) On the law of mortality. Journal of the Institute of Actuaries, 13 (6), 325–358.Google Scholar
Manton, K.G. (1986) Past and future life expectancy increases at later ages: Their implications for the linkage of morbidity, disability, and mortality. Journal of Gerontology, 41 (5), 672–681.CrossRefGoogle ScholarPubMed
Manton, K.G. and Myers, G.C. (1987) Recent trends in multiple-caused mortality 1968 to 1982: Age and cohort components. Population Research and Policy Review, 6, 161–176.CrossRefGoogle Scholar
Manton, K.G., Patrick, C.H. and Stallard, E. (1980a) Mortality model based on delays in progression of chronic diseases: Alternative to cause elimination model. Public Health Reports, 95 (6), 580–588.Google ScholarPubMed
Manton, K.G. and Poss, S.S. (1979) Effects of dependency among causes of death for cause elimination life table strategies. Demography, 16 (2), 313–327.CrossRefGoogle ScholarPubMed
Manton, K.G., Stallard, E. and Poss, S.S. (1980b) Estimates of U.S. multiple cause life tables. Demography, 17 (1), 85–102.CrossRefGoogle ScholarPubMed
Manton, K.G., Stallard, E. and Vaupel, J.W. (1986) Alternative models for the heterogeneity of mortality risks among the aged. Journal of the American Statistical Association, 81 (395), 635–644.CrossRefGoogle ScholarPubMed
Manton, K.G., Tolley, H.D. and Poss, S.S. (1976) Life table techniques for multiple-cause mortality. Demography, 13 (4), 541–564.CrossRefGoogle ScholarPubMed
McNown, R. and Rogers, A. (1992) Forecasting cause-specific mortality using time series methods. International Journal of Forecasting, 8 (3), 413–432.CrossRefGoogle Scholar
Olshansky, S.J. (1987) Simultaneous/multiple cause-delay (SIMCAD): An epidemiological approach to projecting mortality. Journal of Gerontology, 42 (4), 358–365.CrossRefGoogle ScholarPubMed
Olshansky, S.J., Hayflick, L. and Carnes, B.A. (2002) Position statement on human aging. Journal of Gerontology: Biological Sciences, 57A (8), B292–B297.Google Scholar
Olshansky, S.J., Hayflick, L. and Perls, T. (2004) Anti-aging medicine: The hype and reality - I. Journal of Gerontology: Biological Sciences, 59A (6), 000–000.Google Scholar
Robertson, T., Batty, G.D., Der, G., Fenton, C., Shiels, P.G. and Benzeval, M. (2013) Is socioeconomic status associated with biological aging as measured by telomere length? Epidemiologic Review, 35 (1), 98–111.CrossRefGoogle ScholarPubMed
Rosén, M. (2006) Forecasting life expectancy and mortality in Sweden–some comments on methodological problems and potential approaches. Technical Report 4, Social Insurance Studies from the Swedish Social Insurance.Google Scholar
Shryock, H.S., Siegel, J.S. and Associates (1975) The Methods and Materials of Demography, volume 2, Washington, DC: U.S. Dept of Commerce, Bureau of the Census, U.S. Govt. Printing Office.Google Scholar
Strehler, B. (1959) Origin and comparison of the effects of time and high-energy radiations on living systems. The Quarterly Review of Biology, 34, 117–142.CrossRefGoogle ScholarPubMed
Tabeau, E., Ekamper, P., Huisman, C. and Bosch, A. (1999) Improving overall mortality forecasts by analysing cause-of-death, period and cohort effects in trends. European Journal of Population, 15 (2), 153–183.CrossRefGoogle Scholar
Vaupel, J.W. and Yashin, A.I. (1983) The deviant dynamics of death in heterogeneous populations. Technical Report RR-83-001, International Institute for Applied Systems Analysis (IIASA).Google Scholar
Wilmoth, J.R. (1996) Mortality projections for Japan: A comparison of four methods. In Health and Mortality among Elderly Populations (eds. Caselli, G. and Lopez, A.D.), pp. 266–287. Oxford: Clarendon Press.CrossRefGoogle Scholar
Wong-Fupuy, C. and Haberman, S. (2004) Projecting mortality trends: Recent developments in the United Kingdom and the United States. North American Actuarial Journal, 8 (2), 56–83.CrossRefGoogle Scholar
World Health Organization (2012) WHO mortality database. http://www.who.int/whosis/mort/download/en/index.html.Google Scholar