A Multivariate Functional Analysis of Mortality Trends in Europe

• Autorzy: Justyna Majewska
• Języki publikacji: EN

Abstrakty

EN

ortality data provide information on the current demographic situation of the population and explain its demographic future. In addition to their role in demographic accounting, mortality data serve as important indicators of progress or of socio-economic and health problems. They provide an overview of pro- gress in one of the areas of greatest human concern - life expectancy and the prevention of premature death. Mortality data show consistent patterns of risk in specific populations and trends in specific causes of death over time. They are also sensitive indicators of differences within the population and can help to identify target groups for spe- cial programmes in health care and development. The analysis of trends is still important in the forecasting of mortality. The trends observed in the past will determine the method and the historical period to be used. A variety of mortality measures are commonly used to monitor trends and explore patterns within and between populations. There is a lack of a single per- spective for understanding and interpreting mortality trends. Primary measures are changes over time and absolute and relative differences between countries and groups (geographical, gender, ethnic, socio-economic). The analysis of the evolution of mortality in one or more populations in- volves a choice between a wide range of mortality indicators and a focus either on global mortality or on a specific component. The literature in this area is vast. In most cases, analysis of mortality trends is carried out with a focus on sum- mary measures (e.g. life expectancy at birth, life expectancy gap) [Vaupel, Zhang and van Raalte, 2011; van Raalte, Sasson and Martikainen, 2018; Amin and Steinmetz, 2019]. In other cases, researchers focus on specific components of mortality without considering the global pattern [Medford et al., 2019; Kan- nisto, 2001; Zanotto, Canudas-Romo and Mazzuco, 2020].However, there is little work that looks at mortality trends using a multidi- mensional approach. Research tends to focus on cluster analysis. Meslé, Vallin and Andreyev [2002] have already tested a clustering solution for several Euro- pean countries based on their age-specific probability of dying and have found significant differences in the life expectancies and age structures of eastern and western countries. Debón et al. [2017] grouped EU countries using fuzzy c-means cluster analysis of mortality surfaces, with similar results. Léger and Mazzuco [2021] used functional data analysis to identify the role played by all mortality components, and analyzed whether there were different patterns of mortality decline among low-mortality countries.(fragment of text)

Słowa kluczowe

EN

Mortality; Mortality statistics; Functional analysis; Demography; Population; Social problems

PL

Umieralność; Statystyka umieralności; Analiza funkcjonalna; Demografia; Ludność; Problemy społeczne

• Czasopismo: Scientific Publications / University of Economics in Katowice
• Rocznik: 2023
• Tom: Multidimensional Data Modelling and Risk Analysis
• Strony: 70--90

Twórcy

autor

Justyna Majewska

• Uniwersytet Ekonomiczny w Katowicach

Bibliografia

• Abraham C., Cornillon P.A., Matzner-Løber E., Molinari N. (2003), Unsupervised Curve Clustering Using b-splines, "Scandinavian Journal of Statistics", Vol. 30(3), pp. 581-595.
• Amin R.W., Steinmetz J. (2019), Spatial Clusters of Life Expectancy and Association with Cardiovascular Disease Mortality and Cancer Mortality in the Contiguous United States: 1980-2014, "Geospatial Health", Vol. 14, No. 1, pp. 139-145.
• Booth H., Maindonald J., Smith L. (2002), Applying Lee-Carter under Conditions of Variable Mortality Decline, "Population Studies", Vol. 56(3), pp. 325-336.
• Bouveyron C., Jacques J. (2011), Model-based Clustering of Time Series in Group- specific Functional Subspaces, "Advances in Data Analysis and Classification", Vol. 5(4), pp. 281-300.
• Camarda C.G. (2012), Mortalitysmooth: An R Package for Smoothing Poisson Counts with P-splines, "Journal of Statistical Software", Vol. 50(1), pp. 1-24.
• Caselli G., Mesle F., Vallin J. (2002), Epidemiologic Transition Theory Exceptions, "Genus", Vol. 58, pp. 9-52.
• De Beer J., Janssen F. (2016), A New Parametric Model to Assess Delay and Compression of Mortality, "Population Health Metrics", Vol. 14(46).
• Debón A., Chaves L., Haberman S., Villa F. (2017), Characterization of Between-Group Inequality of Longevity in European Union Countries, "Insurance: Mathematics and Economics", Vol. 75, pp. 151-165.
• Ferraty F., Vieu P. (2006), Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, Berlin.
• Human Mortality Database (2023), University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany), www.mortality.org.
• Hyndman R.J., Ullah M.S. (2007), Robust Forecasting of Mortality and Fertility Rates: A Functional Data Approach, "Computational Statistics & Data Analysis", Vol. 51(10), pp. 4942-4956.
• Hyndman R.J., Booth H., Yasmeen F. (2013), Coherent Mortality Forecasting: The Product-Ratio Method with Functional Time Series Models, "Demography", Vol. 50(1), pp. 261-283.
• Jacques J., Preda C. (2014), Functional Data Clustering: A Survey, "Advances in Data Analysis and Classification", Vol. 8(3), pp. 231-255.
• James G.M., Sugar C.A. (2003), Clustering for Sparsely Sampled Functional Data, "Journal of the American Statistical Association", Vol. 98(462), pp. 397-408.
• Kannisto V. (2001), Mode and Dispersion of the Length of Life, "Population: An English Selection", Vol. 13(1), pp. 159-171.
• Lee R.D., Carter L.R. (1992), Modeling and Forecasting U.S. Mortality, "Journal of the American Statistical Association", Vol. 87(41), pp. 659-671.
• Léger A.E., Mazzuco S. (2021), What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database, "European Journal of Population", Vol. 37, pp. 769-798.
• Li N., Lee R. (2005), Coherent Mortality Forecasts for a Group of Population: An Extension of the Lee-Carter Method, "Demography", Vol. 42, pp. 575-94.
• Medford A., Christensen K., Skytthe A., Vaupel J.W. (2019), A Cohort Comparison of Lifespan after Age 100 in Denmark and Sweden: Are Only the Oldest Getting Older? "Demography", Vol. 56(2), pp. 665-677.
• Meslé F., Vallin J., Andreyev Z. (2002), Mortality in Europe: The Divergence between East and West, "Population", Vol. 57(1), pp. 157-197.
• Ramsay J.O., Hooker G., Graves S. (2010), Functional Data Analysis with R and Matlab, Springer, New York.
• Renshaw A.E., Haberman S. (2006), A Cohort-based Extension to the Lee-Carter Model for Mortality Reduction Factors, "Insurance: Mathematics and Economics", Vol. 38(3), pp. 556-570.
• Riley J.C. (2005), Estimates of Regional and Global Life Expectancy, 1800-2001, "Population and Development Review", Vol. 31(3), pp. 537-543.
• Saarela J., Finnäs F. (2008), Cause-specific Mortality at Young Ages: Lessons from Finland, "Health & Place", Vol. 14(2), pp. 265-274.
• Thatcher A.R., Cheung S.L.K., Horiuchi S., Robine J.M. (2010), The Compression of Deaths above the Mode, "Demographic Research", Vol. 22(17), pp. 505-538.
• United Nations (2011), Department of Economic and Social Affairs, Population Division, Changing Levels and Trends in Mortality: The role of patterns of death by cause (United Nations Publication, ST/ESA/SER.A/318).
• United Nations (2022), Department of Economic and Social Affairs, Population Division, World Population Prospects 2022, Online Edition.
• Van Raalte A.A., Sasson I., Martikainen P. (2018), The Case for Monitoring Life-spanInequality, "Science", Vol. 362(6418), pp. 1002-1004.
• Vaupel J.W., Zhang Z., van Raalte A.A. (2011), Life Expectancy and Disparity: An International Comparison of Life Table Data, BMJ Open.
• Zanotto L., Canudas-Romo V., Mazzuco S. (2020), A Mixture-function Mortality Model: Illustration of the Evolution of Premature Mortality, "European Journal of Population", Vol. 37(7), pp. 1-27.
• (www 1) https://population.un.org/wpp/Download/Standard/MostUsed/ (access: 15.06.2022).

Identyfikatory

DOI

10.22367/uekat.9788378758686