Environmental, health and safety cost benefit analysis in the United States chemical industry

The purpose of this research project is to determine whether there is a cost/benefit to allocating financial and other company-related resources to improve environmental, health and safety performance beyond that which is required by law. The issue of whether a company benefits from spending dollars to achieve environmental, health and safety performance beyond legal compliance is an important issue to the chemical manufacturing industry in the United States because of the voluminous and complex legal requirements impacting environmental, health and safety expenditures. The cost/benefit issue has practical significance because many U.S. chemical manufacturing companies base their environmental, health and safety management strategies on just achieving and maintaining compliance with legal requirements when in reality this strategy may actually be a higher cost way of managing environmental, health and safety practices. This difference in environmental, health and safety management strategy is being investigated to determine if managing environmental, health and safety to achieve performance beyond that which is required by law results in a greater benefit to companies in the U.S. chemical manufacturing sector.

Cost-benefit analysis: The ``gold standard'' re-examined

The dissertation reviews the recommendations of the Panel on Cost Effectiveness in Health and Medicine (Panel) convened by the US Public Health Service in 1993 in four areas: aggregation of costs and benefits, methods of estimating resources used, definition of population impacted and perspective used in cost benefit analysis. Financial data from a clinical trial was used to test whether different approaches in each of the above four areas would change the net benefit resulting from a cost benefit analysis. Differences in aggregation of cost and benefit resulted in the same net benefit, but not the same cost/benefit ratios. Differences in resource use estimation methods, population subgroups definitions and perspectives all produced different net benefits. Difference in perspective resulted in different and often opposing decisions as to whether the proposed intervention from the clinical trial should be implemented. ^

Health care policy analysis of Medicaid's early and periodic screening, diagnosis, and treatment (EPSDT) benefit in Colorado

The purpose of this thesis is to identify "best practice" recommendations for successful implementation of the EPSDT outreach program at Memorial Health System's Hospital for Children in Colorado Springs through a policy analysis of Medicaid EPSDT services in Colorado. A successful program at Memorial will increase education and awareness of EPSDT services, enrollment, and access to and utilization of health care services for eligible children. Methodology utilized in this study included questionnaires designed for the EPSDT contract administrator and outreach coordinators/workers; analysis of current federal and state policies; and studies conducted at the federal and state level, and by various advocacy groups. The need for this analysis of EPSDT came about in part through an awareness of increasingly high numbers of children in poverty and who are uninsured. Though the percentage of children living in poverty in Colorado is slightly below the national average (see Table 2), according to data analyzed by The Annie E. Casey Foundation, the percentage of children (0-18) living in poverty in Colorado increased from 10% in 2000 to 16% in 2006, a dramatic increase of 60% surpassed by only one other state in the nation (The Annie E. Casey Foundation, 2008). By comparison, the U.S. percentage of children in poverty during the same time frame rose from 17% to 18% (The Annie E. Casey Foundation, 2008). What kind of health care services are available to this vulnerable and growing group of Coloradans, and what are the barriers that affect their enrollment in, access to and utilization of these health care services? Barriers identified included difficulty with the application process; system and process issues; a lack of providers; and a lack of awareness and knowledge of EPSDT. Fiscal restraints and legislation at the federal and state level are also barriers to increasing enrollment and access to services. Outreach services are a critical component of providing EPSDT services, and there were several recommendations regarding outreach and case management that will benefit the program in the future. Through this analysis and identification of a broad range of barriers, a clearer picture emerged of current challenges within the EPSDT program as well as a broad range of strategies and recommendations to address these challenges. Through increased education and advocacy for EPSDT and the services it encompasses; stronger collaboration and cooperation between all groups involved, including providing a Medical Home for all eligible children; and new legislation putting more money and focus on comprehensive health care for low-income uninsured children; enrollment, access to and utilization of developmentally appropriate and quality health care services can be achieved. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Physician knowledge and beliefs toward hospice and the effect on hospice referral in El Paso, Texas

Hospice care has existed in the United States for over 20 years yet referral rates to hospice services are still well under the 180 days allowed by the Medicare Hospice Benefit. The average length of stay in El Paso is 56.8. ^ The aim of this study was to ascertain physician’s knowledge and attitudes towards hospice referral in the El Paso County. Particular issues to be addressed were: Physician’s knowledge of patient’s eligibility criteria and perception of the type of services provided by hospice. Other issues included, physician’s comfort level and willingness to determine terminal diagnosis and to discuss hospice services. Furthermore, physician’s perceptions of barriers to hospice referrals and how those perceptions differ between physicians who refer as compared to those who do not refer. ^ There were seven hypothesis tested to determine physicians knowledge and perceptions of hospice services. Using a mail-survey developed by Ogle, Mavis and Wang, this study surveyed 165 cardiologists, pediatric cardiologists, gastroenterologists, pulmonologists, neurologists, nephrologists, family practice, internists, oncologists, and pediatric oncologists. A t-test was used to test a comparison of means of categorical associations for all hypotheses. The data in the current study however, did not support the hypotheses tested. ^ Results indicated that physicians (52%) are knowledgeable with the eligibility criteria for hospice and that 95% are knowledgeable of the services hospice offers. Research findings appear to indicate physicians are not the hindering factor when making referrals to hospice. Physicians (46%) felt that one of the strongest barriers to hospice referrals is the patient/family unwillingness to accept hospice services. This offers an opportunity for future research in patients/families behavioral attitudes and beliefs toward death and dying issues and their perception of hospice services. ^

Using small area estimation and geographic information systems technology to target health services for the uninsured

The three articles that comprise this dissertation describe how small area estimation and geographic information systems (GIS) technologies can be integrated to provide useful information about the number of uninsured and where they are located. Comprehensive data about the numbers and characteristics of the uninsured are typically only available from surveys. Utilization and administrative data are poor proxies from which to develop this information. Those who cannot access services are unlikely to be fully captured, either by health care provider utilization data or by state and local administrative data. In the absence of direct measures, a well-developed estimation of the local uninsured count or rate can prove valuable when assessing the unmet health service needs of this population. However, the fact that these are “estimates” increases the chances that results will be rejected or, at best, treated with suspicion. The visual impact and spatial analysis capabilities afforded by geographic information systems (GIS) technology can strengthen the likelihood of acceptance of area estimates by those most likely to benefit from the information, including health planners and policy makers. ^ The first article describes how uninsured estimates are currently being performed in the Houston metropolitan region. It details the synthetic model used to calculate numbers and percentages of uninsured, and how the resulting estimates are integrated into a GIS. The second article compares the estimation method of the first article with one currently used by the Texas State Data Center to estimate numbers of uninsured for all Texas counties. Estimates are developed for census tracts in Harris County, using both models with the same data sets. The results are statistically compared. The third article describes a new, revised synthetic method that is being tested to provide uninsured estimates at sub-county levels for eight counties in the Houston metropolitan area. It is being designed to replicate the same categorical results provided by a current U.S. Census Bureau estimation method. The estimates calculated by this revised model are compared to the most recent U.S. Census Bureau estimates, using the same areas and population categories. ^

A descriptive study of comprehensive and categorical services for pregnant and parenting adolescents in West Dallas

This study evaluates the effectiveness of the Children and Youth Projects' Adolescent Family Life Program, a comprehensive program serving pregnant and parenting adolescents in the economically disadvantaged area of West Dallas. The underlying question asked is what are the relative contributions of the comprehensive, school-linked Adolescent Family Life (AFL) Program compared with the Maternal Health and Family Planning Program (MHFPP), a categorical provider of family planning and reproductive services, towards meeting the immediate and intermediate term needs of adolescent mothers. Also addressed are the protective effects of participation in the Dallas Independent School District Health Special Program, a segregated school for pregnant adolescents.^ A cohort of 339 West Dallas adolescent mothers who delivered babies during a two-year period, 1986 through 1987, are monitored by linking records from Parkland Hospital, the primary provider to hospital services to indigent women in Dallas, the Dallas Independent School District, and the prenatal care providers, the AFL and MHFP Programs. Information is collected on each teen describing her demographic, fertility, service utilization and educational characteristics.^ The study tests the hypothesis that adolescents receiving services from the comprehensive AFL program will be less likely to have a repeat birth and to discontinue school during the 24 month study period, compared with categorical provider clients. Although the study finds that there are no statistically significant differences in repeat deliveries, using survival analysis, or in school continuation between programs, important findings are revealed about the ethnic differences. Black and Hispanic fertility and educational behaviors are compared, and their implications for program design and evaluation discussed. ^

A categorical data analysis approach to estimation of demographic characteristics for Texas county populations

The need for timely population data for health planning and Indicators of need has Increased the demand for population estimates. The data required to produce estimates is difficult to obtain and the process is time consuming. Estimation methods that require less effort and fewer data are needed. The structure preserving estimator (SPREE) is a promising technique not previously used to estimate county population characteristics. This study first uses traditional regression estimation techniques to produce estimates of county population totals. Then the structure preserving estimator, using the results produced in the first phase as constraints, is evaluated.^ Regression methods are among the most frequently used demographic methods for estimating populations. These methods use symptomatic indicators to predict population change. This research evaluates three regression methods to determine which will produce the best estimates based on the 1970 to 1980 indicators of population change. Strategies for stratifying data to improve the ability of the methods to predict change were tested. Difference-correlation using PMSA strata produced the equation which fit the data the best. Regression diagnostics were used to evaluate the residuals.^ The second phase of this study is to evaluate use of the structure preserving estimator in making estimates of population characteristics. The SPREE estimation approach uses existing data (the association structure) to establish the relationship between the variable of interest and the associated variable(s) at the county level. Marginals at the state level (the allocation structure) supply the current relationship between the variables. The full allocation structure model uses current estimates of county population totals to limit the magnitude of county estimates. The limited full allocation structure model has no constraints on county size. The 1970 county census age - gender population provides the association structure, the allocation structure is the 1980 state age - gender distribution.^ The full allocation model produces good estimates of the 1980 county age - gender populations. An unanticipated finding of this research is that the limited full allocation model produces estimates of county population totals that are superior to those produced by the regression methods. The full allocation model is used to produce estimates of 1986 county population characteristics. ^

BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

Policy analysis of iron micronutrient programs at USAID: The FANTA program in East Africa and the micronutrient initiative in Bihar region of India, using comparative cost effective and cost benefit analysis

It is the aim of this paper to examine iron supplementation programs which receive funding from United States Agency for International Development (USAID) but approach combating iron deficiency anemia in two vastly different ways. A brief literature review and background information on iron deficiencies and the differences between supplementation programs and micronutrient fortification were reviewed. Two non-governmental organizations (NGO's) were examined for this paper: the Food and Nutrition Technical Assistance II (FANTA) and the MicroNutrient Initiative. The FANTA program included an educational component to their supplementation program while the MicroNutrient Initiative solely used supplementation of micronutrients to their population. Methods used were cost-benefit analysis and cost-effectiveness analysis to determine the overall effectiveness of each program in reducing iron deficiency anemia in each population, if the added costs of the incentives in the FANTA program changed the cost-effectiveness of the program compared to the MicroNutrient Initiative program and to determine which program imparted the greatest benefit to each population by reducing the disease burden in Disability Adjusted Life Years (DALY). Results showed that the unit cost of the FANTA program per person was higher than the MicroNutrient Initiative program due to the educational component. The FANTA program reduced iron deficiency anemia less overall but cost less for each percentage point of anemia decreased in their respective populations. The MicroNutrient Initiative program had a better benefit cost ratio for the populations it served. The MicroNutrient Initiative's large scale program imparted many advantages by reducing unit cost per person and decreasing iron deficiency anemia. The FANTA program was more effective at decreasing iron deficiency anemia with less money: $5,660 per 1% decrease in iron deficiency anemia versus $18,450 per 1% decrease in iron deficiency anemia for the MicroNutrient Initiative program. ^ In conclusion, economic analysis cannot measure all of the benefits associated with programs that contain an educational component or large scale supplementation. More information needs to be gathered by NGOs and reported to USAID, such as detailed prevalence rates of iron deficiency anemia among the populations served. Further research is needed to determine the effects an educational supplementation program has on compliance rates of participants and motivation to participate in supplementation programs whose aim is to decrease iron deficiency anemia in a targeted population.^

Evaluation of Knowledge Regarding Diagnostic Strategies for Genetic Diseases in Select Residents

Genetics education for physicians has been a popular publication topic in the United States and in Europe for over 20 years. Decreasing numbers of medical genetics professionals and an increasing volume of genetic information has created a dire need for increased genetics training in medical school and in clinical practice. This study aimed to assess how well pediatrics-focused primary care physicians apply their general genetics knowledge to clinical genetic testing using scenario-based questions. We chose to specifically focus on knowledge of the diagnostic applicability of Chromosomal Microarray (CMA) technology in pediatrics because of its recent recommendation by the International Standard Cytogenomic Array (ISCA) Consortium as a first-tier genetic test for individuals with developmental disabilities and/or congenital anomalies. Proficiency in ordering baseline genetic testing was evaluated for eighty-one respondents from four pediatrics-focused residencies (categorical pediatrics, pediatric neurology, internal medicine/pediatrics, and family practice) at two large residency programs in Houston, Texas. Similar to other studies, we found an overall deficit of genetic testing knowledge, especially among family practice residents. Interestingly, residents who elected to complete a genetics rotation in medical school scored significantly better than expected, as well as better than residents who did not elect to complete a genetics rotation. We suspect that the insufficient knowledge among physicians regarding a baseline genetics work-up is leading to redundant (i.e. concurrent karyotype and CMA) and incorrect (i.e. ordering CMA to detect achondroplasia) genetic testing and is contributing to rising health care costs in the United States. Our results provide specific teaching points upon which medical schools can focus education about clinical genetic testing and suggest that increased collaboration between primary care physicians and genetics professionals could benefit patient health care overall.

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^