Abstract
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.
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Notes
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).
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Acknowledgements
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.
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From December 2017 Giulio Caperna has been affiliated to the Joint Research Centre of the European Commission. Email: Giulio.CAPERNA@ec.europa.eu.
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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
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DOI: https://doi.org/10.1007/s11205-019-02142-8