Texas Department of Health organizational structure changes: A reflection of public health policy and agency expansion, September 1, 1946--June 30, 1994

The premise of this study is that changes in the agency's organizational structure reflect changes in government public health policy. Based on this premise, this study tracks the changes in the organizational structure and the overall expansion of the Texas Department of Health to understand the evolution of changing public health priorities in state policy from September 1, 1946 through June 30, 1994, a period of growth and new responsibilities. It includes thirty-seven observations of organizational structure as depicted by organizational charts of the agency and/or adapted from public documents. ^ The major questions answered are, what are the changes in the organizational structure, why did they occur and, what are the policy priorities reflected in these changes in and across the various time periods. ^ The analysis of the study included a thorough review of the organizational structure of the agency for the time-span of the study, the formulation of the criteria to be used in ascertaining the changes, the delineation of the changes in the organizational structure and comparison of the observations sequentially to characterize the change, the discovery of reasons for the structural changes (financial, statutory - federal and state, social and political factors), and the determination of policy priorities for each time period and their relation to the expansion and evolution of the agency. ^ The premise that the organizational structure of the agency and the changes over time reflect government public health policy and agency expansion was found to be true. ^

Patients' preferences for a prostate cancer consultation, a discrete choice experiment

Four basic medical decision making models are commonly discussed in the literature in reference to physician-patient interactions. All fall short in their attempt to capture the nuances of physician-patient interactions, and none satisfactorily address patients' preferences for communication and other attributes of care. Prostate cancer consultations are one setting where preferences matter and are likely to vary among patients. Fortunately, discrete choice experiments are capable of casting light on patients' preferences for communication and other attributes of value that make up a consultation before the consultation occurs, which is crucial if patients are to derive the most utility from the process of reaching a decision as well as the decision itself. The results of my dissertation provide strong support to the notion that patients, at least in the hypothetical setting of a DCE, have identifiable preferences for the attributes of a prostate cancer consultation and that those preferences are capable of being elicited before a consultation takes place. Further, patients' willingness-to-pay for the non-cost attributes of the consultation is surprisingly robust to a variety of individual level variables of interest. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^