COMPARATIVE STUDY OF INFORMAL HEALTH CARE NETWORKS AND ELDERLY HEALTH STATUS IN ARGENTINA AND CUBA

This study will explore familial and friend support networks and living arrangements among elderly individuals in Latin America and the impact that this type of support has on the health of the elderly individuals in the countries of interest. Using data from the Survey on Health and Well-Being of Elders (SABE) from 1999-2000, I will explore which type of support has a larger impact on overall health. I will also measure differences in unmet needs for certain health services. This topic is particularly interesting because it will help to uncover what policies are best for aiding in the healthcare of the elderly in aging population. Lastly, the investigation of this topic will allow me to draw conclusions about the most effective means of social and public policy for the elderly community and provide me with information about the role of both informal provisions of support from family and friends, and formal provisions of support from the government. My primary focus will be on Argentina, using Buenos Aires as the sample city, and Cuba, using Havana as the sample city. These two countries have increasingly aging populations, poorer resources and vast inequalities, but, extremely different political, economic and cultural situations. Comparing the two countries will further allow me to determine correlations between health and the existence of support networks, as well as provide me with information to make more general claims that may be of use in the United States. Argentina is particularly interesting to me because of my abroad experience and homestay experience with an older Argentine woman who lived alone but depended upon her family for many healthcare needs, doctors’ visits and general well-being. In Argentina, I experienced a different form of living than I am used to in the United States, where many older individuals or couples live in nursing homes or assisted living facilities rather than alone or with family. The changing economic climate of the two countries coupled with labor patterns of women returning to work at rapid rates indicates that policies cannot just rely on either the formal or informal sector but require a combination of the two sectors working together.This paper will first give background on the difference in the economies and the health care systems in Argentina and Cuba and will show why it interesting to study and compare these two countries. I will then discuss the health status of the elderly in each population as well as discuss the informal care networks and the role of family in each country. This section will then be followed by a description of the data and methods used. I will end by drawing conclusions about the study and the outcomes, and then I will attempt to make suggestions about effective health care policies for the elderly.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.