47 research outputs found

    A set of indicators for decomposing the secular increase of life expectancy

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    ABSTRACT: BACKGROUND: The ongoing increase in life expectancy in developed countries is associated with changes in the shape of the survival curve. These changes can be characterized by two main, distinct components: (i) the decline in premature mortality, i.e., the concentration of deaths around some high value of the mean age at death, also termed rectangularization of the survival curve; and (ii) the increase of this mean age at death, i.e., longevity, which directly reflects the reduction of mortality at advanced ages. Several recent observations suggest that both mechanisms are simultaneously taking place. METHODS: We propose a set of indicators aiming to quantify, disentangle, and compare the respective contribution of rectangularization and longevity increase to the secular increase of life expectancy. These indicators, based on a nonparametric approach, are easy to implement. RESULTS: We illustrate the method with the evolution of the Swiss mortality data between 1876 and 2006. Using our approach, we are able to say that the increase in longevity and rectangularization explain each about 50% of the secular increase of life expectancy. CONCLUSION: Our method may provide a useful tool to assess whether the contribution of rectangularization to the secular increase of life expectancy will remain around 50% or whether it will be increasing in the next few years, and thus whether concentration of mortality will eventually take place against some ultimate biological limit

    Revisiting mortality deceleration patterns in a gamma-Gompertz-Makeham framework

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    We calculate life-table aging rates (LARs) for overall mortality by estimating a gamma-Gompertz-Makeham (G GM) model and taking advantage of LAR’s parametric representation by Vaupel and Zhang [34]. For selected HMD countries, we study how the evolution of estimated LAR patterns could explain observed 1) longevity dynamics, and 2) mortality improvement or deterioration at different ages. Surprisingly, the age of mortality deceleration x showed almost no correlation with a number of longevity measures apart from e0. In addition, as mortality concentrates at older ages with time, its characteristic bell-shaped pattern becomes more pronounced. Moreover, in a GGM framework, we identify the impact of senescent mortality on shape of the rate of population aging. We also find evidence for a strong relationship between x and the statistically significant curvilinear changes in the evolution of e0 over time. Finally, model-based LARs appear to be consistent with point b) of the “heterogeneity hypothesis” [12]: mortality deceleration, due to selection effects, should shift to older ages as the level of total adult mortality declines

    Cause-of-Death Contributions to Educational Inequalities in Mortality in Austria between 1981/1982 and 1991/1992: Les contributions des causes de décès aux inégalités de mortalité par niveau d’éducation en Autriche entre 1981/1982 et 1991/1992

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    This article uses census records and deaths records to analyze trends in educational inequalities in mortality for Austrian women and men aged 35–64 years between 1981/1982 and 1991/1992. We find an increasing gradient in mortality by education for circulatory diseases and especially ischaemic heart disease. Respiratory diseases and, in addition for women, cancers showed the opposite trend. Using decomposition analysis, we give evidence that in many cases changes in the age-structure within the 10-year interval had a bigger effect than direct improvements in mortality on the analyzed subpopulations

    Trends in healthy life expectancy in Hong Kong SAR 1996–2008

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    Although Hong Kong has one of the best life expectancy (LE) records in the world, second only to Japan for women, we know very little about the changes in the health status of the older adult population. Our article aims to provide a better understanding of trends in both chronic morbidity and disability for older men and women. The authors compute chronic morbidity-free and disability-free life expectancy and the proportion of both in relation to total LE using the Sullivan method to examine whether Hong Kong older adults are experiencing a compression of morbidity and disability and whether there is any gender difference in relation to mortality and morbidity. The results of this study show that Hong Kong women tend to outlive Hong Kong men but are also more likely to suffer from a ‘double disadvantage’, namely more years of life with more chronic morbidity and disability. There has also been a significant expansion of chronic morbidity, as chronic morbidity-free life expectancy (CMFLE) decreased substantially for both genders from 1996 to 2008. Although disability-free life expectancy (DFLE) increased during this period, it increased at a slower pace compared to LE. The proportion of life without chronic morbidity also declined remarkably during these 12 years. Among the advanced ages, the proportion of remaining life in good health without disability has decreased since 1996, indicating a relative expansion of disability

    Mortality forecasting in Colombia from abridged life tables by sex

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    [EN] BACKGROUND: An adequate forecasting model of mortality that allows an analysis of different population changes is a topic of interest for countries in demographic transition. Phenomena such as the reduction of mortality, ageing, and the increase in life expectancy are extremely useful in the planning of public policies that seek to promote the economic and social development of countries. To our knowledge, this paper is one of the first to evaluate the performance of mortality forecasting models applied to abridged life tables. OBJECTIVE: Select a mortality model that best describes and forecasts the characteristics of mortality in Colombia when only abridged life tables are available. DATA AND METHOD: We used Colombian abridged life tables for the period 1973-2005 with data from the Latin American Human Mortality Database. Different mortality models to deal with modeling and forecasting probability of death are presented in this study. For the comparison of mortality models, two criteria were analyzed: graphical residuals analysis and the hold-out method to evaluate the predictive performance of the models, applying different goodness of fit measures. RESULTS: Only three models did not have convergence problems: Lee-Carter (LC), Lee-Carter with two terms (LC2), and Age-Period-Cohort (APC) models. All models fit better for women, the improvement of LC2 on LC is mostly for central ages for men, and the APC model's fit is worse than the other two. The analysis of the standardized deviance residuals allows us to deduce that the models that reasonably fit the Colombian mortality data are LC and LC2. The major residuals correspond to children's ages and later ages for both sexes. CONCLUSION: The LC and LC2 models present better goodness of fit, identifying the principal characteristics of mortality for Colombia.Mortality forecasting from abridged life tables by sex has clear added value for studying differences between developing countries and convergence/divergence of demographic changes.Support for the research presented in this paper was provided by a grant from the Ministerio de Economía y Competitividad of Spain, project no. MTM2013-45381-P.Diaz-Rojo, G.; Debón Aucejo, AM.; Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus. Journal of Population Sciences (Online). 74(15):1-23. https://doi.org/10.1186/s41118-018-0038-6S1237415Aburto, J.M., & García-Guerrero, V.M. (2015). El modelo aditivo doble multiplicativo. Una aplicacion a la mortalidad mexicaná. Papeles de Población, 21(84), 9–44.Acosta, K., & Romero, J. (2014). Cambios recientes en las principales causas de mortalidad en Colombia. Technical report: Banco de la Banco de la República- Serie Documentos de Trabajo Sobre Economía Regional.Aguilar, E. (2013). Estimación y proyección de la mortalidad para Costa Rica con la aplicación del método Lee-Carter con dos variantes. Población y, Salud en Mesoamérica, 11(1), 3–24.Andreozzi, L (2012). Estimación y pronósticos de la mortalidad de Argentina utilizando el modelo de Lee-Carter. Revista de la Sociedad Argentina de Estadística, 10(1), 21–43.Andreozzi, L, & Blaconá, MT (2011). Estimación y pronóstico de las tasas de mortalidad y la esperanza de vida en la República Argentina. In Proceedings of Anales de las Decimosextas Jornadas Investigaciones en la Facultad de Ciencias Económicas y Estadística. Universidad Nacional de Rosario, Argentina.Andres, V, Millossovich, P, Vladimir, K (2018). StMoMo: Stochastic Mortality Modeling in R. Journal of Statistical Software, 84(3), 1–38.Belliard, M., & Williams, I. (2013). Proyección estocástica de la mortalidad. Una aplicación de Lee-Carter en la Argentina. Revista Latinoamericana de Población, 7(13), 129–148.Bertranou, E. (2008). Tendencias demográficas y protección social en América Latina y el Caribe: CEPAL. http://repositorio.cepal.org/handle/11362/7224 . Accessed 8 Aug 2017.Blaconá, M.T, & Andreozzi, L. (2014). Análisis de la mortalidad por edad y sexo mediante modelos para datos funcionales. Estadística, 66(186–187), 65–89.Blum, A., Kalai, A., Langford, J. (1999). Beating the hold-out: bounds for k-fold and progressive cross-validation. In Proceedings of the twelfth annual Conference on Computational Learning Theory. ACM, (pp. 203–208).Booth, H., Maindonald, J., Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3(1–2), 3–43.Butt, Z., Haberman, S., Shang, H.L. (2014). ilc: Lee-Carter mortality models using iterative fitting algorithms. http://cran.r-project.org/package=ilc .Cairns, A.J., Blake, D., Dowd, K. (2006). A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73(4), 687–718.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Khalaf-Allah, M. (2011). Mortality density forecasts: an analysis of six stochastic mortality models. Insurance: Mathematics and Economics, 48(3), 355–367.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., Balevich, I. (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13(1), 1–35.Canudas-Romo, V. (2008). The modal age at death and the shifting mortality hypothesis. Demographic Research, 19(30), 1179–1204.Carfora, M.F., Cutillo, L., Orlando, A. (2017). A quantitative comparison of stochastic mortality models on Italian population data. Computational Statistics & Data Analysis, 112, 198–214.Currie, I.D., Durban, M., Eilers, P.H.C. (2006). Generalized linear array models with applications to multidimensional smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(2), 259–280.Debón, A., Martínez-Ruiz, F., Montes, F. (2012). Temporal evolution of mortality indicators: application to Spanish data. North American Actuarial Journal, 16(3), 364–377.Debón, A., Montes, F., Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operation Research, 189(3), 624–637.Debón, A., Martínez-Ruiz, F., Montes, F. (2010). A geostatistical approach for dynamic life tables: the effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327–336.Díaz, G., & Debón, A. (2016). Tendencias y comportamiento de la mortalidad en Colombia entre 1973 y 2005. Estadística Española, 58(191), 277–300.García-Guerrero, V.M., & Mellado, M.O. (2012). Proyección estocástica de la mortalidad mexicana por medio del método de Lee-Carter. Estudios Demográficos y Urbanos, 27(2), 409–448.Garfield, R., & Llanten, C. (2004). The public health context of violence in Colombia. Revista Panamericana de Salud Pública, 16(4), 266–271.Haberman, S. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35–55.Holford, T.R. (2006). Approaches to fitting age-period-cohort models with unequal intervals. Statistics in Medicine, 25(6), 977–993.Hunt, A., & Villegas, A.M. (2015). Robustness and convergence in the Lee-Carter model with cohort effects. Insurance: Mathematics and Economics, 64, 186–202.Hyndman, R.J. (2016). Forecast: forecasting functions for time series and linear models. R package version 7.3. https://CRAN.R-project.org/package=forecast .Hyndman, R.J, Booth, H., Tickle, L., Maindonald, J. (2014). Demography: forecasting mortality, fertility, migration and population data. R package version 1.18. https://CRAN.R-project.org/package=demography .Hyndman, R.J., & Ullah, M.S (2007). Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956.Kennes, T. (2017). The convergence and robustness of cohort extensions of mortality models. MaRBLe, 1, 36–53.Lee, R., & Carter, L. (1992). Modelling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–671.Lee, R., & Rofman, R. (1994). Modelación y proyección de la mortalidad en Chile. Notas de Poblacion, 6(59), 183–213.Lee, W. (1997). Characterizing exposure-disease association in human populations using the Lorenz curve and Gini index. Statistics in Medicine, 16(7), 729–739.Levitt, S., & Rubio, M. (2000). Understanding crime in Colombia and what can be done about it. Technical Report 20, FEDESARROLLO.Llorca, J., Prieto, M.D., Alvarez, C.F., Delgado-Rodriguez, M. (1998). Age differential mortality in Spain, 1900–1991. Journal of Epidemiology & Community Health, 52, 259–261.Llorca, J, Prieto, M.D, Delgado-Rodriguez, M (2000). Medición de las desigualdades en la edad de muerte: cálculo del índice de Gini a partir de las tablas de mortalidad. Revista Española de Salud Pública, 74(1), 5–12.Lora, E. (2008). Técnicas de medición económica. Metodología y aplicaciones en Colombia, Ed. Siglo Veintiuno XXI y Fedesarrollo, fourth edition. Bogotá D.C.: Alfaomega Colombiana S.A.Ochoa, C.A. (2015). El modelo Lee-Carter para estimar y pronosticar mortalidad: una aplicación para Colombia. Master’s thesis: Universidad Nacional de Colombia-Sede Medellín.O’hare, C., & Li, Y. (2017). Modelling mortality: are we heading in the right direction?Applied Economics, 49(2), 170–187.Ornelas, A. (2015). La mortalidad y la longevidad en la cuantificación del riesgo actuarial para la población de México. PhD thesis: Universitat de Barcelona.Remund, A., Camarda, C.G., Riffe, T., et al. (2017). A cause-of-death decomposition of the young adult mortality hump. Technical report. Rostock, Germany: Max Planck Institute for Demographic Research.Renshaw, A.E., & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255–272.Renshaw, A.E., & Haberman, S. (2006). A cohort-based extensionto the Lee-Carter model for mortality reduction factors. Insurance: Mathematic and Economics, 38(3), 556–570.Reyes, A.R. (2010). Una aproximación al costo fiscal en pensiones como consecuencia del envejecimiento de la población en Colombia y el efecto de la sobre-mortalidad masculina. Master’s thesis: Universidad Nacional de Colombia.Richards, S. (2008). Detecting year-of-birth mortality patterns with limited data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(1), 279–298.Rodríguez, J. (2007). Desigualdades socioeconómicas entre departamentos y su asociación con indicadores de mortalidad en Colombia en 2000. Rev Panam Salud Publica, 21(2/3), 111–124.Shkolnikov, V.M., Andreev, E.M., Begun, A. (2003). Gini coefficient as a life table function: computation from discrete data, decomposition of differences and empirical examples. Demographic Research, 8, 305–358.Singh, A., Shukla, A., Ram, F., Kumar, K. (2017). Trends in inequality in length of life in India: a decomposition analysis by age and causes of death. Genus, 73(1), 5.Tabeau, E. (2001). A review of demographic forecasting models for mortality. In: Tabeau, E., van den Berg Jeths A., Heathcote C. (Eds.) In Forecasting Mortality in Developed Countries. European Studies of Population, vol 9. Springer, Dordrecht, (pp. 1–32).Turner, H., & Firth, D. (2015). gnm: Generalized nonlinear models in R. R package version 1.0-8. https://CRAN.R-project.org/package=gnm .Urdinola, B.P., & Queiroz, B.L. (2017). Latin American Human Mortality Database. http://www.lamortalidad.org . Accessed 21 Nov 2015

    Youth lost to homicides: disparities in survival in Latin America and the Caribbean

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    Introduction The homicide rates among young men in Latin America and the Caribbean (LAC) are the highest in the world. It is not clear how this has impacted the life expectancy in these countries. This research has two purposes: (1) to quantify the impact of homicides on the mortality gap between LAC and high-income countries over recent years and (2) to assess the changes in homicide impact in overall survival over time. Methods Causes of death data were extracted for 23 countries in the LAC and 15 European countries (average European union-15 [EU-15]), using UN, UNODC, WHO, HMD and IHME databases for the period 2005–2014. The contribution by homicide deaths to the change in life expectancy, over time and as a difference between two populations, was quantified using decomposition methods. Results The contribution by homicide mortality to changes in life expectancy levels differed widely across the examined LAC countries. In Honduras, homicide mortality accounted for 1.75 (95% CI 1.64 to 1.86) and 6.30 (95% CI 6.07 to 6.53) years lower life expectancy than in the EU-15 countries for women and men, respectively. Contrary to this, homicide was just accountable for less than a couple of months of life expectancy differences between Chile and EU-15. Jamaica had the largest reduction in homicides and its impact increased life expectancy over time by almost half a year for men. However, Mexican men and Honduran women have experienced increases in mortality by homicide, which decreased their life expectancy by more than a quarter of a year between 2005 and 2014. Conclusions Excess mortality related to homicides in young people accounted for major changes in life expectancy in the LAC region. Furthermore, reducing excess mortality due to homicides is a crucial goal to further increase longevity towards levels of low-mortality countries. These reductions might prevent homicides spreading to other parts of Latin America. Decision and policy-makers in LAC need to address this immediately, and investing in the young population needs to be given a high priority

    The double-gap life expectancy forecasting model

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    Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we propose a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We present these results for various developed countries. We compare our results with forecasts based on the Lee&ndash;Carter approach and the Cairns&ndash;Blake&ndash;Dowd strategy. We focus on forecasting life expectancy at age 0 and remaining life expectancy at age 65.</p
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