A multi-factor analysis of the quality of life of the elderly people in Europe
Keywords:
Quality of life; wellness, SHARE survey; CASP-19; supervised and unsupervised algorithms; decision trees; cluster analysis.Abstract
As it is well known, the increase in life expectancy means that more people reach an age when their physical and mental health can deteriorate. The vulnerability of these people is especially reflected in their health, and in the difficulty to satisfy their vital needs. These facts, along with the development of the welfare state, make it necessary to study the quality of life of these people.
Supervised algorithms have been used on the SHARE survey (the Survey of Health, Aging and Retirement in Europe) to identify the main factors that explain the well-being of people who declare themselves dependent in Europe. The results have been interpreted using Shapley Values.
The main contribution comes from the study of the relationship between health spending and quality of life collected by the CASP-19 indicator. The problem has been approached from a double perspective: qualitative and quantitative. While no relationship can be established between quality of life and European healthcare systems (Beveridge, Bismarck or mixed), the nature of those who provide health services or the degree of centralization, a relationship between the level of public spending on health and the level of perceived well-being has been found.
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Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Taylor & Francis Group. https://doi.org/10.1201/9781315139470
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Han, J., (2020). Distance between Categorical Attributes Ordinal Attributes and Mixed Types. Coursera. https://www.coursera.org/lecture/cluster-analysis/2-4-distance-betweencategorical-attributes-ordinal-attributes-and-mixed-types-KnvRC
Hyde, M., Higgs, P., Wiggins, RD. & Blane, D. (2015). A decade of research using the CASP scale: key findings and future directions. Aging Ment Health, Jul;19(7):571-5. https://doi.org/10.1080/13607863.2015.1018868
Juliá, R. (2016). Validez del indicador general de limitación de la actividad (GALI) para medir funcionamiento en la población. Un análisis comparativo con salud percibida, a través de encuestas de salud de España. Universidad de Alicante. https://rua.ua.es/dspace/bitstream/10045/68918/7/tesis_rocio_julia_sanchis.pdf
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Organización Mundial de la Salud (OMS), (2018). https://www.who.int/es/news-room/fact-sheets/detail/disability-and-health
Pathak, M. (2018). Feature Selection in R with the Boruta R Package. Datacamp. https://www.datacamp.com/community/tutorials/feature-selection-Rboruta
Progress Consulting S.r.l. & Living Prospects Ltd. (2012). The management of health systems in the EU Member States - The role of local and regional authorities. https://doi.org/ 10.2863/83500
Reusova, A., (2018). Hierarchical Clustering on Categorical Data in R. Towards Data Science. https://towardsdatascience.com/hierarchical-clustering-on-categoricaldata-in-r-a27e578f2995
Rodríguez, V., Rodríguez-Mañas, L., Sancho, M. & Díaz, R. (2012). Envejecimiento. La investigación en España y Europa. Revista Española de Geriatría y Gerontología. 47(4):174–179. https://doi.org/10.1016/j.regg.2012.02.005
Soldi, R. (2017). The management of health systems in the EU Member States. The role of local and regional authorities. European Committee of the Regions. European Union. https://op.europa.eu/en/publication-detail/-/publication/239062df-cb4b-11e7-a5d5-01aa75ed71a1/language-en
Teknomo, K. (2015). Normalized Rank Transformation. Revoledu. https://people.revoledu.com/kardi/tutorial/Similarity/Normalized-Rank.html
Tibshirani, R., Walther, G. & Hastie, T. (2000). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society. https://doi.org/10.1111/1467-9868.00293
Tseng, G. (2018). Interpreting complex models with SHAP values. Medium. https://medium.com/@gabrieltseng/interpreting-complex-models-withshap-values-1c187db6ec83c
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