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HOW FUNCTIONAL DATA CAN ENHANCE THE ESTIMATION OF HEALTH EXPECTANCY: THE CASE OF DISABLED SPANISH POPULATION

Part of: Statistics Multivariate analysis Applications

Published online by Cambridge University Press:  22 November 2018

Irene Albarrán ,
Pablo J. Alonso-González ,
Ana Arribas-Gil  and
Aurea Grané Open the ORCID record for Aurea Grané [Opens in a new window]
Irene Albarrán
Affiliation:
Statistics Department, Universidad Carlos III de Madrid, C/Madrid 126, 28903 Getafe, Spain E-Mail: [email protected]
Pablo J. Alonso-González
Affiliation:
Economics Department, Universidad de Alcalá, Plaza de la Victoria 2, 28802 Alcalá de Henares, Spain E-Mail: [email protected]
Ana Arribas-Gil
Affiliation:
UC3M-BS Institute of Financial Big Data, C/Madrid 135, 28903 Getafe, Spain E-Mail: [email protected]
Aurea Grané*
Affiliation:
Statistics Department, Universidad Carlos III de Madrid, C/Madrid 126, 28903 Getafe, Spain E-Mail: [email protected]
*
E-Mail: [email protected]
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Abstract

The aging of population is perhaps the most important problem that developed countries must face in the near future. Dependency can be seen as a consequence of the process of gradual aging. In a health context, this contingency is defined as a lack of autonomy in performing basic activities of daily living that requires the care of another person or significant help. In Europe in general and in Spain in particular, this phenomena represents a problem with economic, political, social and demographic implications. The prevalence of dependency in the population, as well as its intensity and evolution over the course of a person’s life are issues of greatest importance that should be addressed. The aim of this work is the estimation of life expectancy free of dependency (LEFD) based on functional trajectories to enhance the regular estimation of health expectancy. Using information from the Spanish survey EDAD 2008, we estimate the number of years spent free of dependency for disabled people according to gender, dependency degree (moderate, severe, major) and the earlier or later onset of dependency compared to a central trend. The main findings are as follows: first, we show evidence that to estimate LEFD ignoring the information provided by the functional trajectories may lead to non-representative LEFD estimates; second, in general, dependency-free life expectancy is higher for women than for men. However, its intensity is higher in women with later onset on dependency; Third, the loss of autonomy is higher (and more abrupt) in men than in women. Finally, the diversity of patterns observed at later onset of dependency tends to a dependency extreme-pattern in both genders.

Keywords

ADLcox regressiondependencydisabilityfunctional data

MSC classification

Primary: 62-07: Data analysis
Secondary: 62-09: Graphical methods 62H20: Measures of association (correlation, canonical correlation, etc.) 62H99: None of the above, but in this section 62P05: Applications to actuarial sciences and financial mathematics
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 49 , Issue 1 , January 2019 , pp. 57 - 84
Copyright
Copyright © Astin Bulletin 2018 

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References

Albarrán, I. and Alonso, P. (2009) La población dependiente en España: estimación del número y coste global asociado a su cuidado. Estudios de Economa, 36(2), 127–163.Google Scholar
Albarrán, I., Alonso, P., Arribas-Gil, A. and Grané, A. (2014) Can personal dependency paths help to estimate life expectancy free of dependency? In Mathematical and Statistical Methods for Actuarial Sciences and Finance (eds. Sibilio, M. and Perna, C.), pp. 1–5. Cham, Switzerland: Springer.Google Scholar
Albarrán, I., Alonso, P. and Grané, A. (2015) Profile identification via weighted related metric scaling: An application to dependent Spanish children. Journal of the Royal Statistical Society A, 178, 1–26.CrossRefGoogle Scholar
Albarrán-Lozano, I., Alonso-González, P. and Arribas-Gil, A. (2017) Dependence evolution in the Spanish disabled population: A functional data analysis approach. Journal of the Royal Statistical Society A, 180(2), 657–677.CrossRefGoogle Scholar
Arribas-Gil, A. and Romo, J. (2012) Robust depth-based estimation in the time warping model. Biostatistics, 13, 398–414.CrossRefGoogle ScholarPubMed
Biessy, G. (2017). Continuous-time semi-Markov inference of biometric laws associated with a long-term care insurance portfolio. Astin Bulletin, 47, 527–561.CrossRefGoogle Scholar
Cox, D. (1972) Regression models and life tables. Journal of the Royal Statistical Society B, 34, 187–220.Google Scholar
Cox, D.R. and Oakes, D. (1984) Analysis of Survival Data. London: Chapman & Hall.Google Scholar
Czado, C. and Rudolph, F. (2002) Application of survival analysis methods to long-term care insurance. Insurance: Mathematics and Economics, 31, 395–413.Google Scholar
Dupuy, J., Loubes, J. and Maza, E. (2011) Non parametric estimation of the structural expectation of a stochastic increasing function. Statistics and Computing, 21(1), 121–136.CrossRefGoogle Scholar
Eurostat (2009) Health statistics and atlas on mortality in the European Union. Eurostat, Luxembourg.Google Scholar
Fong, J., Sherris, M. and Yap, J. (2017) Forecasting disability: Application of a frailty model. Scandinavian Actuarial Journal, 2017(2), 125–147.CrossRefGoogle Scholar
Fries, J. (1983) The compression of morbidity. The Milbank Memorial Fund Quarterly, 61, 397–419.CrossRefGoogle ScholarPubMed
Gasser, T. and Kneip, A. (1995) Searching for structure in curve samples. Journal of the American Statistical Association, 90, 1179–1188.Google Scholar
Gasser, T., Müller, H., Köhler, W., Molinari, L. and Prader, A. (1984) Nonparametric regression analysis of growth curves. The Annals of Statistics, 12, 210–229.CrossRefGoogle Scholar
INE (2010) Encuesta sobre Discapacidad, Autonoma personal y Situaciones de Dependencia (EDAD), Metodologa. Ed. Subdirección General de Estadsticas Sociales Sectoriales (INE), Madrid, España.Google Scholar
Kamette, F. (2011) Dependency care in the EU: A comparative analysis. Foundation Robert Schumann. Social Issues European Issue, No. 196.Google Scholar
Kneip, A. and Gasser, T. (1992) Statistical tools to analyze data representing a sample of curves. The Annals of Statistics, 16, 82–112.CrossRefGoogle Scholar
Levantesi, S. and Menzietti, M. (2018) Natural hedging in long-term care insurance. Astin Bulletin, 48(1), 233–274.CrossRefGoogle Scholar
Liu, X. and Müller, H. (2004) Functional convex averaging and synchronization for time-warped random curves. Journal of the American Statistical Association, 99(467), 687–699.CrossRefGoogle Scholar
Lloyd-Sherlock, P., McKee, M., Ebrahim, S., Gorman, M., Greegross, S., Prince, M., Pruchno, R., Gutman, G., Kirwood, T., O’neill, D., Ferrucci, L., Kritchewski, S. and Vellas, B. (2012) Population ageing and health. The Lancet, 379, 1295–1296.CrossRefGoogle ScholarPubMed
López-Pintado, S. and Romo, J. (2009) On the concept of depth for functional data. Journal of the American Statistical Association, 114, 486–503.Google Scholar
Martel, L. and Bélanger, A. (2000) Regression models and life tables. Canadian Social Trends-Statistics Canada Autumn, 2000, 26–29.Google Scholar
Orden TAS/4054/2005 (2005) BOE num. 316, 28 de diciembre de 2005. Ministerio de Trabajo y Asuntos Sociales.Google Scholar
Ramsay, J. and Silverman, B. (2005) Functional Data Analysis, 2nd ed. New York: Springer Series in Statistics.CrossRefGoogle Scholar
Robine, J. and Ritche, K. (1991) Healthy life expectancy: Evaluation of global indicator of change in population health. BMJ, 302, 457–460.CrossRefGoogle ScholarPubMed
Robine, J., Jagger, C., Mathers, C., Krimmins, E. and Suzman, R. (2003) Determining Health Expectancies. Chichester: Wiley.Google Scholar
Sanderson, W. and Scherbov, S. (2010) Remeasuring aging. Science, 329, 1287–1288.CrossRefGoogle ScholarPubMed
Tarabelloni, N., Arribas-Gil, A., Ieva, F., Paganoni, A. and Romo, J. (2016) roahd: Robust analysis of high dimensional data. Package on CRAN. Version 1.0.4. Published 2016-07-06.Google Scholar
Wang, K. and Gasser, T. (1997) Alignment of curves by dynamic time warping. The Annals of Statistics, 25, 1251–1276.Google Scholar
WHO (2011a) Global health and aging. WHO (World Health Organization). US National Institute of Aging.Google Scholar
WHO (2011b) Global report on disability. WHO (World Health Organization). US National Institute of Aging.Google Scholar