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The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in the literature. The aim of this work is to predict the impact of the financial catastrophe a household will face as a result of out-of-pocket payments in long-term care in Spain. These predictions were made using machine learning techniques such as LASSO (Least Absolute Shrinkage and Selection Operator) penalized regression and elastic-net, as well as algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees and SVM (Support Vector Machine). The results reveal that all the classification methods performed well, with the complex models performing better than the simpler ones and showing no evidence of overfitting. Detecting and defining the profiles of individuals and families most likely to suffer from financial catastrophe is crucial in enabling the design of financial policies aimed at protecting vulnerable groups. Full article

Background: Out-of-pocket (OOP) payments are configured as an important source of financing long-term care (LTC). However, very few studies have analyzed the risk of impoverishment and catastrophic effects of OOP in LTC. To estimate the contribution of users to the financing of LTC and to analyze the economic consequences for households in terms of impoverishment and catastrophism after financial crisis in Spain. Methods: The database that was used is the 2008 Spanish Disability and Dependency Survey, projected to 2012. We analyze the OOP payments effect associated to the impoverishment of households comparing volume and financial situation before and after OOP payment. At the same time, the extent to which OOP payment had led to catastrophism was analyzed using different thresholds. Results: The results show that contribution of dependent people to the financing of the services they receive exceeds by 50% the costs of these services. This expenditure entails an increase in the number of households that live below the poverty. In terms of catastrophism, more than 80% of households dedicate more than 10% of their income to dependency OOP payments. In annual terms, the catastrophe gap generated by devoting more than 10% of the household income to dependent care OOP payment reached €3955, 1 million (0.38% of GDP). Conclusion: This article informs about consequences of OOP in LCT and supplements previous research that focus on health. Our results should serve to develop strategic for protection against the financial risk resulting from facing the costs of a situation of dependence. Full article