Schizophrenia is a chronic and debilitating disease that affects approximately one percent of the US population and exerts a disproportionately high financial burden on the society. The objective of this study was to estimate the direct and indirect costs of schizophrenia among community-dwelling US residents and identify patient characteristics associated with high schizophrenia-related direct costs. Patients with a diagnosis of schizophrenia (ICD-9 code 295) or other non-organic psychoses (ICD-9 code 298) between January 1, 2005 and December 31, 2008 were identified from the Medical Expenditure Panel Survey (MEPS). To estimate direct costs, the following cost categories were identified: inpatient hospitalizations, outpatient visits, emergency department visits, office-based physician visits, home healthcare visits, and prescription medications. The following cost categories were identified to estimate indirect costs: caregivers’ costs and cost of lost productivity due to missed work days, reduced employment, and suicide. Logistic regression was used to compare patients belonging to the high-cost group and to the low-cost group. All analyses were carried out using SAS version 9.2 (SAS Institute Inc., Cary, North Carolina). The weighted average number of patients with schizophrenia identified for each year was 757,893. The annual direct and indirect costs were estimated at $3.96 billion and $15.35 billion, respectively. The mean annual direct medical schizophrenia-related cost per patient was $5,586. For each one-year increase in age, patients were 5.7% less likely to be in the high-cost group. Patients with a spouse were 77.7% less likely than patients without a spouse to be in the high-cost group. Healthcare providers and policymakers can use these cost estimates to better understand the economic burden of schizophrenia and identify services and subgroups of patients associated with the highest costs. This would help in the provision of healthcare services to patients with schizophrenia and in the optimization of patient outcomes. / text

Thesis (Ph. D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 169-177)

No description available.

Abstract Background and Objectives: Since 1894, open appendectomy (OA) has been the treatment of choice for acute appendicitis. In 1981 Semm performed the first laparoscopic appendectomy (LA). More than 2 decades later, the benefits of LA are still controversial. The goal of the present investigation was to compare the effectiveness of LA and OA based on a large administrative (The Bureau of National Health Insurance, BNHI) Research Database. The source of data analyzed was the administrative claims data from the BNHI Research Database. Methods: The objective of this retrospective study was based on the ICD-9-CM procedure code of 4701 (Laparoscopic appendectomy, LA) and 4709 (Open appendectomy, OA) respectively from a database of 20 million insurance population, Separate analyses were performed for uncomplicated (ICD-9-CM, 540.9) and complicated (presence of appendiceal perforation or abscess; ICD-9-CM 540.0 and 540.1) appendicitis. Exclusive criteria were: (1) Average length of stay exceeds 3 S.D. (n=1,262). (2) Gender unmentioned (n=243). All these data will analyze in multiple dimensions including length of hospital stay, in-hospital complications, in-hospital mortality, and rate of routine discharge between laparoscopic (LA) and open appendectomy (OA) based on The Bureau of National Health Insurance (BNHI) database. Results: We enrolled 11,118 patients underwent LA and 47,725 patients underwent OA during 2004 to 2007. The prevalence of LA increases gradually from 6.97 per 100,000 populations in 2004 to 21 per 100,000 populations in 2007. The prevalence of OA decreases gradually from 57.5 per 100,000 populations in 2004 to 44.86 per 100,000 populations in 2007. Patients underwent LA (3.25¡Ó1.51day) have significant lower length of hospital stay than OA (3.57¡Ó1.49 day) (p<0.001). We also found the trend that the annual medical expenditure of LA increases gradually but OA decreases gradually. In general, LA spends higher medical expenditure than OA. With respect to medical expenditure, higher length of hospital stay and co-morbidity are associated with more medical expenditure significantly. Conclusions: LA is the current developing trend of surgical treatments for appendicitis. LA can reduce length of hospital stay significantly. OA can reduce the medical expenditure in Taiwan. In our opinion, the results represent the native data in Taiwan and are very important for a good administration of public resources distribution.

Not available / text

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

abstract: This work is driven by two facts. First, the majority of households in the U.S. obtain health insurance through their employer. Second, around 20% of working age households choose not to purchase health insurance. The link between employment and health insurance has potentially large implications for household selection into employment and participation in public health insurance programs. In these two essays, I address the role of public and private provisions of health insurance on household employment and insurance decisions, the distribution of welfare, and the aggregate economy. In the first essay, I quantify the effects of key parts of the 2010 health care reform legislation. I construct a lifecycle incomplete markets model with an endogenous choice of health insurance coverage and calibrate it to U.S. data. I find that the reform decreases the fraction of uninsured households by 94% and increases ex-ante household welfare by 2.3% in consumption equivalence. The main driving force behind the reduction in the uninsured population is the health insurance mandate, although I find no significant welfare loss associated with the elimination of the mandatory health insurance provision. In the second essay, I provide a quantitative analysis of the role of medical expenditure risk in the employment and insurance decisions of households approaching retirement. I construct a dynamic general equilibrium model of the household that allows for self-selection into employment and health insurance coverage. I find that the welfare cost of medical expenditure risk is large at 5% of lifetime consumption equivalence for the non-institutionalized population. In addition, the provision of health insurance through the employer accounts for 20% of hours worked for households ages 60-64. Finally, I provide an quantitative analysis of changes in Medicare minimum eligibility age in a series of policy experiments. / Dissertation/Thesis / Ph.D. Economics 2011

No description available.

In this research, we apply data mining techniques to a nationally-representative expenditure data from the US to predict very high-cost patients in the top 5 cost percentiles, among the general population. Samples are derived from the Medical Expenditure Panel Survey’s Household Component data for 2006-2008 including 98,175 records. After pre-processing, partitioning and balancing the data, the final MEPS dataset with 31,704 records is modeled by Decision Trees (including C5.0 and CHAID), Neural Networks. Multiple predictive models are built and their performances are analyzed using various measures including correctness accuracy, G-mean, and Area under ROC Curve. We conclude that the CHAID tree returns the best G-mean and AUC measures for top performing predictive models ranging from 76% to 85%, and 0.812 to 0.942 units, respectively. Among a primary set of 66 attributes, the best predictors to estimate the top 5% high-cost population include individual’s overall health perception, history of blood cholesterol check, history of physical/sensory/mental limitations, age, and history of colonic prevention measures. It is worthy to note that we do not consider number of visits to care providers as a predictor since it has a high correlation with the expenditure, and does not offer a new insight to the data (i.e. it is a trivial predictor). We predict high-cost patients without knowing how many times the patient was visited by doctors or hospitalized. Consequently, the results from this study can be used by policy makers, health planners, and insurers to plan and improve delivery of health services.

This study explores healthcare coverage disparity using a quantitative analysis on a large dataset from the United States. One of the objectives is to build supervised models including decision tree and neural network to study the efficient factors in healthcare coverage. We also discover groups of people with health coverage problems and inconsistencies by employing unsupervised modeling including K-Means clustering algorithm. Our modeling is based on the dataset retrieved from Medical Expenditure Panel Survey with 98,175 records in the original dataset. After pre-processing the data, including binning, cleaning, dealing with missing values, and balancing, it contains 26,932 records and 23 variables. We build 50 classification models in IBM SPSS Modeler employing decision tree and neural networks. The accuracy of the models varies between 76% and 81%. The models can predict the healthcare coverage for a new sample based on its significant attributes. We demonstrate that the decision tree models provide higher accuracy that the models based on neural networks. Also, having extensively analyzed the results, we discover the most efficient factors in healthcare coverage to be: access to care, age, poverty level of family, and race/ethnicity.