Back pain is the 6th most costly condition in the United States and is responsible for the most workdays lost. Approximately 33 million American adults suffered from back and neck problems in 2005. The societal cost of chronic back pain (CBP) has not been calculated from a US perspective. Longitudinal data files from Panels 12, 13, and 14 of the Medical Expenditure Panel Survey (MEPS) were used to estimate excess direct (ambulatory visits, inpatient admissions, emergency room visits, and prescription medication) costs and indirect (lost productivity) costs for persons 18 years and older reporting CBP compared to those not reporting back pain. Persons were included in the CBP group if they reported back pain (ICD-9-CM codes 720, 721, 722, 723, 724, 737, 805, 806, 839, 846, 847) in at least 3 consecutive interview rounds. The complex sampling design of MEPS was taken into account to get accurate national estimates. All costs were adjusted to 2011 using Consumer Price Indices. All mean costs were computed using age-stratified regression models, after adjusting for demographic and clinical covariates. Utilization of provider-based complementary and alternative medicine (CAM) among CBP patients was studied, and differences in costs between CAM users and non-users examined. Based on this analysis, the prevalence of CBP in the adult US population was estimated to be 3.76%. Total all-cause costs for CBP patients were estimated to be $187 billion over 2 years (direct costs = $176 billion, indirect cost = $11 billion). Overall estimates of excess costs of CBP over 2 years per person for direct medical costs were $37,129 ($25,273 vs. $48,984; p<0.001). This breaks down to $11,711 ($14,929 vs. $3,219; p<0.001) for ambulatory visits; $3,560 ($6,514 vs. $2,914; p<0.001) for inpatient admissions; $300 ($690 vs. $390; p<0.001) for emergency department visits; and $19,849 ($23,873 vs. $4,024; p<0.001) for prescription medications. Excess indirect costs for CBP patients were $1,668 ($2,329 vs. $661; p<0.001). Thirty-seven percent of CBP patients reported at least one CAM visit. There was no significant difference in overall costs between CAM users and non-users. The high cost of chronic back pain in the US population has potential implications for prioritizing policy, and in attempting to improve care and outcomes for these patients. / text

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.

Objective: To estimate the national prevalence and direct incremental expenditures of musculoskeletal disorders (MSD's) using the 2007 Medical Expenditure Panel Survey data. <br>Methods: A retrospective database analysis was conducted and individuals with MSD's (ICD-9-CM codes 274.00; 710.00-738.00) were identified. Dependent variables were total health care and other service category expenditures. The study utilized descriptive and regression analyses. <br>Results: In 2007, the national prevalence of MSD's was 33 million with incremental costs of $886.49 per person. The inpatient expenditures ($33,461.85) were the highest cost component in MSD's and the predictors of total health care expenditures were age, marital status, and presence of the disease condition. <br>Conclusion: The systematic assessment of MSD's and their associated incremental costs to the society is essential in increasing the awareness of decision makers to implement intervention strategies that are effective in lowering the disease incidence and in reducing the overall cost of disease management. / Mylan School of Pharmacy and the Graduate School of Pharmaceutical Sciences / Pharmacy Administration / MS / Thesis

No description available.

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.

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.

No description available.

The UK government has pledged to end child poverty by 2020. It is not known how far the measure of child poverty used by the government corresponds to differences in children’s experiences. Qualitative research on poverty has not generally been informed by the insights of dynamic research, which investigates duration, timing and transitions, among other temporal topics. Qualitative and quantitative methods have not generally been combined in social policy research on poverty, which limits the explanatory power of both. The thesis presents an analysis of the correspondence or lack of correspondence between qualitative and quantitative research on child poverty as a temporal experience. Semi-structured life history interviews were conducted with 15-21 year olds in Britain with experience of child poverty in the period 1997-2001. These were analysed alongside secondary analysis of the British Household Panel Survey (1991-2005). The qualitative respondents lived in households which took part in the survey, so there is a direct link between the two methods. The assumptions, methods and findings of dynamic poverty research are in general found to be a simplified and decontexualised version, rather than a misrepresentation of, the qualitative findings. Time formed an important part of the experience of poverty for children. It was not possible to fully match together exits from poverty with perceived improvements in circumstances, and entries into poverty with perceived deteriorations in circumstances, though this was partly due to limited recall and lack of contemporaneous knowledge. Nor were these changes clearly placed in time by respondents, in terms of duration and timing. Although most respondents did not explicitly engage with the idea of poverty as a personal experience, poverty-like accounts of disadvantage and difference were found in the accounts of all respondents. Thus, there is evidence for and against the way child poverty is currently measured.

In Healthy People 2010, mental health is listed as a major public health concern as evidenced by an alarming increase in the number of individuals who suffer from mental disorders. Mental disorders are a treatable public health condition. However, health disparities in the treatment of mental disorders are evident. The purpose of this study was to examine factors that affected health outcomes of persons with mental disorders. Two specific aims were addressed: Aim 1: to examine the relationships of population characteristics (predisposing factors and enabling resources), health behaviors (health services use and health practice); and health outcomes (physical health status and mental health status); Aim 2: to determine the differences in the usual source of care and health outcomes between individuals with self-reported mental disorders and individuals without mental disorders. This study was a secondary analysis of existing data collected from 2006 Medical Expenditure Panel Survey Household Component Consolidated file. A sample of U.S. civilian non-institutionalized adults (N=622) was grouped according to three self-reported health conditions: mental disorders (MD), physical illnesses (PI) and co-morbid mental disorders and physical illnesses (CM). This sample was predominantly male, White non-Hispanic and married; had a high school diploma, middle to high income, and private insurance; and preferred office-based clinics as the usual source of care, F(2,29)=5.94, p = .007. No statistically significant differences between groups in usual source of care (p=.069) and physical health status (p=.490) but there was a significant difference in mental health status (p=.001). Participants with CM had a poorer mental health status than those with PI and MD, F (2,619) =21.8, p= .000. The mental health status of individuals with PI was significantly better than that of participants with MD. Awareness of disparities in the usual source of care, health services use, and health outcomes among individuals with mental health conditions is imperative if barriers to care are to be eliminated. Innovative interventions pertinent to decreasing barriers to accessing health care and improving the health outcomes among individuals with MD must be tested. Advocating for mental health care policies that reduce health care services disparities among individuals with self-reported MD must be encouraged.

As medical care becomes an increasingly large share of Gross Domestic Product, understanding the mechanisms for how and why medical care spending is rising becomes increasingly important. Such an evaluation should consider the productivity relationship between medical care and health. An evaluation of medical productivity involves the measurement of medical care input prices, disease treatment output prices, and the productive relationship between medical care inputs and disease treatment health outcomes. Medical care price measurement is complicated by the heterogeneity of services, the role of insurance in negotiating prices, rapid technological advancements in medical care and limited availability of transaction price data. Health outcome prices are difficult to construct because of the difficulty in measuring health outcomes, the heterogeneity of health outcomes, and the messy relationship between consumption goods and health. Finally, in addition to accurate input and output price measurement, a productivity assessment requires a measurable causal relationship between medical care services and health outcomes. To date, all of these requirements have been insurmountable hurdles to assessing the productivity of medical care for the entire United States economy. This dissertation uses the Medical care Expenditure Panel Survey to address the necessary requirements for evaluating the productivity of medical care. The second chapter constructs regional medical care price indices using transaction prices that control for service type heterogeneity. The data employed in the analysis associates the observed medical care spending with the diseases the spending is used to treat. This association is exploited in the third chapter, which constructs medical care treatment prices for twelve of the major health conditions in the United States. The fourth chapter compares the productivity of medical care services used to produce disease treatment health outcomes across insurance types. / text