Population aging, which is common in developed countries, highlights issues related to the health and social assistance of frail individuals. The Italian Chronic Disease Program launched by the Ministry of Health proposes the implementation of tools that stratify the population based on health and care needs and enhances the integration of existing administrative health data flows. This work has its roots in the need to identify frail individuals conveyed by an Italian Local Health Unit (LHU) of the Veneto region and organize an efficient service of care and prevention. We propose an indicator of frailty, computed using the poset approach, comprising only eight variables available in administrative health data at the LHU level. The proposed indicator associates a value of frailty between 0 and 1 to every individual of the population aged 65 years and older, thereby allowing stratification of the population by the risk level. By validating and analyzing the frailty indicator, we show that this approach provides an effective stratification of older people.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Source: own elaboration based on ISTAT data (Survey: Everyday life aspects).
Classification and regression trees are machine-learning methods to construct prediction models from data. Thus, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values (Loh 2011).
Boccuzzo, G., & Caperna, G. (2017). Evaluation of life satisfaction in Italy: Proposal of a synthetic measure based on poset theory. In F. Maggino (Ed.), Complexity in society: From indicators construction to their synthesis (pp. 291–321). Cham: Springer. https://doi.org/10.1007/978-3-319-60595-1_12.
Brüggemann, R., & Carlsen, L. (2011). An improved estimation of averaged ranks of partial orders. MATCH Communications in Mathematical and in Computer Chemistry,65, 383–414.
Brüggemann, R., & Patil, G. P. (2011). Ranking and prioritization with multiple indicators—introduction to partial order applications. New York: Springer. https://doi.org/10.1007/s10651-010-0164-6.
Caperna, G. (2016). Partial order theory for synthetic indicators. Doctoral dissertation, University of Padova, Italy.
Caperna, G. (2019). Approximation of Average Rank by means of a formula (Version v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.2565699.
Caperna, G., & Boccuzzo, G. (2018). Use of poset theory with big datasets: A new proposal applied to the analysis of life satisfaction in Italy. Social Indicators Research,136(3), 1071–1088. https://doi.org/10.1007/s11205-016-1482-3.
Charlson, M. E., Pompei, P., Ales, K. L., & Mackenzie, C. R. C. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases,40, 373–383. https://doi.org/10.1016/0021-9681(87)90171-8.
Corrao, G., Rea, F., Di Martino, M., De Palma, R., Scondotto, S., Fusco, D., et al. (2017). Developing and validating a novel multisource comorbidity score from administrative data: A large population-based cohort study from Italy. British Medical Journal Open. https://doi.org/10.1136/bmjopen-2017-019503.
Davey, B. A., & Priestley, H. A. (2002). Introduction to lattices and order. New York: Cambridge University Press. https://doi.org/10.1163/_q3_SIM_00374.
de Groot, V., Beckerman, H., Lankhorst, G. J., & Bouter, L. M. (2003). How to measure comorbidity: A critical review of available methods. Journal of Clinical Epidemiology,56, 221–229. https://doi.org/10.1016/S0895-4356(02)00585-1.
De Loof, K., De Baets, B., & De Meyer, H. (2011). Approximation of average ranks in posets. MATCH Communications in Mathematical and in Computer Chemistry,66, 219–229.
Deyo, R. A., Cherkin, D. C., & Ciol, M. A. (1993). Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: A response. Journal of Clinical Epidemiology,45, 613–619. https://doi.org/10.1016/0895-4356(92)90133-8.
Eurostat. (2018). Population structure and ageing. http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_structure_and_ageing. Last Accessed: 20 Aug 2018.
Falasca, P., Berardo, A., & Di Tommaso, F. (2011). Development and validation of predictive MoSaiCo (Modello Statistico Combinato) on emergency admissions: Can it also identify patients at high risk of frailty? Annali dell’Istituto Superiore di Sanità,47, 220–228. https://doi.org/10.4415/ANN-11-02-15.
Fattore, M. (2016). Partially ordered sets and the measurement of multidimensional ordinal deprivation. Social Indicators Research,128(2), 835–858. https://doi.org/10.1007/s11205-015-1059-6.
Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences,59, 255–263. https://doi.org/10.1093/gerona/59.3.M255.
Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., et al. (2001). Frailty in older adults: Evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences,56, 46–56. https://doi.org/10.1093/gerona/56.3.M146.
Gobbens, R. J. J., Luijkx Katrien, G., Wijnen-Sponselee, M. T., & Schols, J. M. G. A. (2010). In search of an integral conceptual definition of frailty: Opinions of experts. Journal of the American Medical Directors Association,11, 338–343. https://doi.org/10.1016/j.jamda.2009.09.015.
Hogan, D. B. (2018). Chapter 3—models, definitions, and criteria for frailty. In J. L. Ram & P. M. Conn (Eds.), Conn’s handbook of models for human aging (2nd ed., pp. 35–44). New York: Academic Press. https://doi.org/10.1016/B978-0-12-811353-0.00003-8; ISBN: 9780128113530.
Huang, Y., Gou, R., Diao, Y., Yin, Q., Fan, W., & Liang, Y. (2014). Charlson comorbidity index helps predict the risk of mortality for patients with type 2 diabetic nephropathy. Journal of Biomedicine and Biotechnology,15, 58–66. https://doi.org/10.1631/jzus.B1300109.
Ilinca, S., & Calciolari, S. (2015). The patterns of health care utilization by elderly Europeans: Frailty and its implications for health systems. Health Services Research,50(1), 306–320.
ISTAT. (2018). Il futuro demografico del Paese. https://www.istat.it/it/files//2018/05/previsioni_demografiche.pdf. Last Accessed: 20 Aug 2018.
Lerche, D., & Sorensen, P. (2003). Evaluation of the ranking probabilities for partial orders based on random linear extensions. Chemosphere,53, 981–992. https://doi.org/10.1016/S0045-6535(03)00558-7.
Loh, W. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,1, 14–23. https://doi.org/10.1002/widm.8.
Louis, D. Z., Robeson, M., McAna, J., Maio, V., Keith, S. W., Liu, M., et al. (2014). Predicting risk of hospitalisation or death: A retrospective population-based analysis. British Medical Journal Open,4, e005223. https://doi.org/10.1136/bmjopen-2014-005223.
Marcon, A., Accorsi, A., Di Tommaso, F., Falasca, P., Berardo, A., & Quargnolo, E. (2010). La fragilità nella popolazione anziana: Analisi della letteratura dal 1983 al 2009. G. Gerontology,58, 179–183.
Ministero della Salute. (2016). Piano Nazionale della Cronicità. http://www.salute.gov.it/imgs/C_17_pubblicazioni_2584_allegato.pdf. Last Accessed: 10 Aug 2018.
Mitnitski, A. B., Mogilner, A. J., MacKnight, C., & Rockwood, K. (2002). The mortality rate as a function of accumulated deficits in a frailty index. Mechanisms of Ageing and Development,123, 1457–1460. https://doi.org/10.1016/S0047-6374(02)00082-9.
Pandolfi, P., Collina, N., Marzaroli, P., Stivanello, E., Musti, M. A., Giansante, C., et al. (2016). Development of a predictive model of death or urgent hospitalization to identify frail elderly. Epidemiologia e Prevenzione,40(6), 395–403. https://doi.org/10.19191/EP16.6.P395.119. (in Italian).
Razzanelli, M., Profili, F., Mossello, E., Bandinelli, S., Corridori, C., Salvioni, A., et al. (2013). A screening and comprehensive assessment programme aimed at secondary prevention of disability in community-dwelling frail older subjects: A pilot study. Epidemiologia e Prevenzione,37, 271–278.
Rockwood, K., & Mitnitski, A. (2007). Frailty in relation to the accumulation of deficits. Journals of Gerontology Series A, Biological Sciences and Medical Sciences,62, 722–727. https://doi.org/10.1093/gerona/62.7.722.
Sternberg, S. A., Schwartz, A. W., Karunananthan, S., Bergman, H., & Clarfield, M. A. (2011). The identification of frailty: A systematic literature review. Journal of the American Geriatrics Society,59, 2129–2138. https://doi.org/10.1111/j.1532.
Tennstedt, S. L., & McKinlay, J. B. (1994). Frailty and its consequences: Introduction. Social Science and Medicine,38, 863–865. https://doi.org/10.1016/0277-9536(94)90419-7.
Wallace, E., Stuart, E., Vaughan, N., Bennett, K., Fahey, T., & Smith, S. M. (2014). Risk prediction models to predict emergency hospital admission in community-dwelling adults. A systematic review. Medical Care,52, 751–765.
The Authors thank Dott. Armando Olivieri and Dott. Luca Benacchio (Local Health Unit 15 “Alta Padovana”—today Local Health Unit 6 “Euganea”—of the Veneto region) for their useful suggestions. The data used for the research are part of the administrative datasets of Local Health Unit 15. They have been managed following a formal agreement between Local Health Unit 15 and the Department of Statistical Science of the University of Padova.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
From December 2017 Giulio Caperna has been affiliated to the Joint Research Centre of the European Commission. Email: Giulio.CAPERNA@ec.europa.eu.
About this article
Cite this article
Silan, M., Caperna, G. & Boccuzzo, G. Quantifying Frailty in Older People at an Italian Local Health Unit: A Proposal Based on Partially Ordered Sets. Soc Indic Res 146, 757–782 (2019). https://doi.org/10.1007/s11205-019-02142-8