Suchergebnisse

In a health context dependency is defined as lack of autonomy in performing basic activities of daily living that require the care of another person or significant help. However, this contingency, if present, changes throughout the lifetime. In fact, empirical evidence shows that, once this situation occurs, it is almost impossible to return to the previous state and in most cases the intensity increases. In this article, the evolution of the intensity in this situation is studied for the Spanish population affected by this contingency. Evolution in dependency can be seen as sparsely observed functional data, where for each individual we get a curve only observed at those points in which changes in the condition of his/her dependency occur. We use functional data analysis techniques such as curve registration, functional data depth or distance-based clustering to analyse this kind of data. This approach proves to be useful in this context since it takes into account the dynamics of the dependency process and provides more meaningful conclusions than simple pointwise or multivariate analysis. The database analysed comes from the Survey about Disabilities, Personal Autonomy and Dependency Situations, EDAD 2008, (Spanish National Institute of Statistics, 2008). The evaluation of the dependency situation for each person is ruled in Spain by the Royal Decree 504/2007 that passes the scale for assessment of the situation set by Act 39/2006. In this article, the scale value for each individual included in EDAD 2008 has been calculated according to this legislation. Differences between sex, ages and first appearance time have been considered and prediction of future evolution of dependency is obtained ; The research of Ana Arribas-Gil was supported by grants ECO2011-25706 and MTM2010-17323 of the Spanish Ministry of Science and Innovation

In a health context, dependence is defined as a lack of autonomy in performing the basic activities of daily living and requiring care giving or significant help from another person. However, this contingency, if present, changes over one's lifetime. Empirical evidence shows that, once this situation occurs, it is almost impossible to return to the previous state and in most cases increases in intensity. In the paper, the evolution of the intensity of this situation is studied for the Spanish population affected by this contingency. Evolution in dependence can be seen as sparsely observed functional data, where we obtain a curve for each individual that is observed at only those points where changes in his or her condition of dependence occur. We use functional data analysis techniques, such as curve registration, functional data depth and distance-based clustering, to analyse this type of data. This approach proves to be useful in this context because it considers the dynamics of the dependence process and provides more meaningful conclusions than simple pointwise or multivariate analysis. We use the sample statistics obtained to predict the future evolution of dependence. The database analysed originates from the 'Survey on disability, personal autonomy and dependence situations' in Spain in 2008. The survey is the largest and most complete survey to be made available in Europe for the study of disability. In addition, the Spanish legislation is one of the most recent in Europe and provides a detailed quantitative scale to assess dependence. In the paper, the scale value according to this legislation has been calculated for each individual included in the survey. Differences between sex, age and the time of first appearance were considered, and a prediction of the future evolution of dependence is obtained.

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.

We propose a semiparametric nonlinear mixed-effects model (SNMM) using penalized splines to classify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of pregnancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies on the basis of alternative formulations of SNMMs depending on the characteristics of the data set under consideration. For our motivating example, we address the classification problem using a particular case of the SNMM in which the parameter space has a finite dimensional component (fixed effects and variance components) and an infinite dimensional component (unknown function) that need to be estimated. The nonparametric component of the model is estimated using penalized splines. For the parametric component, we compare the advantages of using random effects versus direct modeling of the correlation structure of the errors. Numerical studies show that our approach improves over other existing methods for the analysis of this type of data. Furthermore, the results obtained using our method support the idea that explicit modeling of the serial correlation of the error term improves the prediction accuracy with respect to a model with random effects, but independent errors. ; The first author was supported by Proyecto DI Regular 37.0/2016 VRIEA PUCV; FONDECYT grants 1120739, 1130233, and 1141256; Ministerio de Economía y Competitividad and FEDER, Spain, grants MTM2013-40941-P and MTM2016-74931-P; FONDAP grant 15130011; and grant 16-MATH-03 SIDRE from CONICYT MATH-AmSud Program. The third author was supported by FONDECYT grant 1141256; and grant 16-MATH-03 SIDRE from CONICYT MATH-AmSud Program. The fourth author was supported by the Basque Government through the BERC 360 2014-2017 and the Department of Education, Language Policy and Culture of the Basque Government IT-620-13 programs and Basque Government Industry Department under the ELKARTEK Program, by the Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation SEV-2013-0323 and MTM2014-52184-P, and by the RISE Horizon 2020 European Project GEAGAM (644602). The fifth author was funded by grants MTM2014-56535-R and ECO2015-66593-P of the Ministerio de Economía y Competitividad, Spain. Contributions of the second, fourth, and fifth authors were partially supported by grants FONDECYT1120739 and 1141256 while visiting PUCV and CIMFAV-UV.