A planning model for needs analysis for subacute care

The purpose of this study is to produce a model to be used by state regulating agencies to assess demand for subacute care. In accomplishing this goal, the study refines the definition of subacute care, demonstrates a method for bed need assessment, and measures the effectiveness of this new level of care. This was the largest study of subacute care to date. Research focused on 19 subacute units in 16 states, each of which provides high-intensity rehabilitative and/or restorative care carried out in a high-tech unit. Each of the facilities was based in a nursing home, but utilized separate staff, equipment, and services. Because these facilities are under local control, it was possible to study regional differences in subacute care demand.^ Using this data, a model for predicting demand for subacute care services was created, building on earlier models submitted by John Whitman for the American Hospital Association and Robin E. MacStravic. The Broderick model uses the "bootstrapping" method and takes advantage of high technology: computers and software, databases in business and government, publicly available databases from providers or commercial vendors, professional organizations, and other information sources. Using newly available sources of information, this new model addresses the problems and needs of health care planners as they approach the challenges of the 21st century. ^

Essays on international macroeconomics and conflict analysis

This dissertation addressed two broad problems in international macroeconomics and conflict analysis. The first problem in the first chapter looked at the behavior of exchange rate and its interaction with industry-level tradable goods prices for three countries, USA, UK and Japan. This question has important monetary policy implications. Here, I computed to what extent changes in exchange rate affected prices of consumer, producer, and export goods. I also studied the timing of these changes in these prices. My results, based on thirty-four industrial prices for USA, UK and Japan, supported the view that changes in exchange rates significantly affect prices of industrial and consumer goods. It also provided an insight to the underlying economic process that led to changes in relative prices. ^ In the second chapter, I explored the predictability of future inflation by incorporating shocks to exchange rates and clearly specified the transmission mechanisms that link exchange rates to industry-level consumer and producer prices. Employing a variety of linear and state-of-the-art nonlinear models, I also predicted growth rates of future prices. Comparing levels of inflation obtained from the above approaches showed superiority of the structural model incorporating the exchange rate pass-through effect. ^ The second broad issue addressed in the third chapter of the dissertation investigated the economic motives for conflict, manifested by rebellion and civil war for seventeen Latin American countries. Based on the analytical framework of Garfinkel, Skaperdas and Syropoulos (2004), I employed ordinal regressions and Markov switching for a panel of seventeen countries to identify trade and openness factors responsible for conflict occurrence and intensity. The results suggested that increased trade openness reduced high intensity domestic conflicts but overdependence on agricultural exports, along with a lack of income earning opportunities lead to more conflicts. Thereafter, using the Cox Proportional Hazard model I studied “conflict duration” and found that over-reliance on agricultural exports explained a major part of the length of conflicts in addition to various socio-political factors. ^

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

An assessment of natural and human disturbance effects on Mexican ecosystems: current trends and research gaps

Mexico harbors more than 10% of the planet’s endemic species. However, the integrity and biodiversity of many ecosystems is experiencing rapid transformation under the influence of a wide array of human and natural disturbances. In order to disentangle the effects of human and natural disturbance regimes at different spatial and temporal scales, we selected six terrestrial (temperate montane forests, montane cloud forests, tropical rain forests, tropical semi-deciduous forests, tropical dry forests, and deserts) and four aquatic (coral reefs, mangrove forests, kelp forests and saline lakes) ecosystems. We used semiquantitative statistical methods to assess (1) the most important agents of disturbance affecting the ecosystems, (2) the vulnerability of each ecosystem to anthropogenic and natural disturbance, and (3) the differences in ecosystem disturbance regimes and their resilience. Our analysis indicates a significant variation in ecological responses, recovery capacity, and resilience among ecosystems. The constant and widespread presence of human impacts on both terrestrial and aquatic ecosystems is reflected either in reduced area coverage for most systems, or reduced productivity and biodiversity, particularly in the case of fragile ecosystems (e.g., rain forests, coral reefs). In all cases, the interaction between historical human impacts and episodic high intensity natural disturbance (e.g., hurricanes, fires) has triggered a reduction in species diversity and induced significant changes in habitat distribution or species dominance. The lack of monitoring programs assessing before/after effects of major disturbances in Mexico is one of the major limitations to quantifying the commonalities and differences of disturbance effects on ecosystem properties.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

A planning model for needs analysis for subacute care

The purpose of this study is to produce a model to be used by state regulating agencies to assess demand for subacute care. In accomplishing this goal, the study refines the definition of subacute care, demonstrates a method for bed need assessment, and measures the effectiveness of this new level of care. This was the largest study of subacute care to date. Research focused on 19 subacute units in 16 states, each of which provides high-intensity rehabilitative and/or restorative care carried out in a high-tech unit. Each of the facilities was based in a nursing home, but utilized separate staff, equipment, and services. Because these facilities are under local control, it was possible to study regional differences in subacute care demand. Using this data, a model for predicting demand for subacute care services was created, building on earlier models submitted by John Whitman for the American Hospital Association and Robin E. MacStravic. The Broderick model uses the "bootstrapping" method and takes advantage of high technology: computers and software, databases in business and government, publicly available databases from providers or commercial vendors, professional organizations, and other information sources. Using newly available sources of information, this new model addresses the problems and needs of health care planners as they approach the challenges of the 21st century.