The evolution and assessment of pharmaceutical care: Structure, processes, and outcomes

The evolution of pharmaceutical care is identified through a complete review of the literature published in the American Journal of Health-System Pharmacy, the sole comprehensive publication of institutional pharmacy practice. The evolution is categorized according to characteristics of structure (organizational structure, the role of the pharmacist), process (drug delivery systems, formulary management, acquiring drug products, methods to impact drug therapy decisions), and outcomes (cost of drug delivery, cost of drug acquisition and use, improved safety, improved health outcomes) recorded from the 1950s through the 1990s. While significant progress has been made in implementing basic drug distribution systems, levels of pharmacy involvement with direct patient care is still limited.^ A new practice framework suggests enhanced direct patient care involvement through increase in the efficiency and effectiveness of traditional pharmacy services. Recommendations advance internal and external organizational structure relationships that position pharmacists to fully use their unique skills and knowledge to impact drug therapy decisions and outcomes. Specific strategies facilitate expansion of the breadth and scope of each process component in order to expand the depth of integration of pharmacy and pharmaceutical care within the broad healthcare environment. Economic evaluation methods formally evaluate the impact of both operational and clinical interventions.^ Outcome measurements include specific recommendations and methods to increase efficiency of drug acquisition, emphasizing pharmacists' roles that impact physician prescribing decisions. Effectiveness measures include those that improve safety of drug distribution systems, decrease the potential of adverse drug therapy events, and demonstrate that pharmaceutical care can significantly contribute to improvement in overall health status.^ The implementation of the new framework is modeled on a case study at the M.D. Anderson Cancer Center. The implementation of several new drug distribution methods facilitated the redeployment of personnel from distributive functions to direct patient care activities with significant personnel and drug cost reduction. A cost-benefit analysis illustrates that framework process enhancements produced a benefit-to-cost ratio of 7.9. In addition, measures of effectiveness demonstrated significant levels of safety and enhanced drug therapy outcomes. ^

Models of DNA sequence evolution

Models of DNA sequence evolution and methods for estimating evolutionary distances are needed for studying the rate and pattern of molecular evolution and for inferring the evolutionary relationships of organisms or genes. In this dissertation, several new models and methods are developed.^ The rate variation among nucleotide sites: To obtain unbiased estimates of evolutionary distances, the rate heterogeneity among nucleotide sites of a gene should be considered. Commonly, it is assumed that the substitution rate varies among sites according to a gamma distribution (gamma model) or, more generally, an invariant+gamma model which includes some invariable sites. A maximum likelihood (ML) approach was developed for estimating the shape parameter of the gamma distribution $(\alpha)$ and/or the proportion of invariable sites $(\theta).$ Computer simulation showed that (1) under the gamma model, $\alpha$ can be well estimated from 3 or 4 sequences if the sequence length is long; and (2) the distance estimate is unbiased and robust against violations of the assumptions of the invariant+gamma model.^ However, this ML method requires a huge amount of computational time and is useful only for less than 6 sequences. Therefore, I developed a fast method for estimating $\alpha,$ which is easy to implement and requires no knowledge of tree. A computer program was developed for estimating $\alpha$ and evolutionary distances, which can handle the number of sequences as large as 30.^ Evolutionary distances under the stationary, time-reversible (SR) model: The SR model is a general model of nucleotide substitution, which assumes (i) stationary nucleotide frequencies and (ii) time-reversibility. It can be extended to SRV model which allows rate variation among sites. I developed a method for estimating the distance under the SR or SRV model, as well as the variance-covariance matrix of distances. Computer simulation showed that the SR method is better than a simpler method when the sequence length $L>1,000$ bp and is robust against deviations from time-reversibility. As expected, when the rate varies among sites, the SRV method is much better than the SR method.^ The evolutionary distances under nonstationary nucleotide frequencies: The statistical properties of the paralinear and LogDet distances under nonstationary nucleotide frequencies were studied. First, I developed formulas for correcting the estimation biases of the paralinear and LogDet distances. The performances of these formulas and the formulas for sampling variances were examined by computer simulation. Second, I developed a method for estimating the variance-covariance matrix of the paralinear distance, so that statistical tests of phylogenies can be conducted when the nucleotide frequencies are nonstationary. Third, a new method for testing the molecular clock hypothesis was developed in the nonstationary case. ^

Mouse models and quantitative trait loci of susceptibility to bleomycin- and radiation-induced pulmonary fibrosis

Radiotherapy involving the thoracic cavity and chemotherapy with the drug bleomycin are both dose limited by the development of pulmonary fibrosis. From evidence that there is variation in the population in susceptibility to pulmonary fibrosis, and animal data, it was hypothesized that individual variation in susceptibility to bleomycin-induced, or radiation-induced, pulmonary fibrosis is, in part, genetically controlled. In this thesis a three generation mouse genetic model of C57BL/6J (fibrosis prone) and C3Hf/Kam (fibrosis resistant) mouse strains and F1 and F2 (F1 intercross) progeny derived from the parental strains was developed to investigate the genetic basis of susceptibility to fibrosis. In the bleomycin studies the mice received 100 mg/kg (125 for females) of bleomycin, via mini osmotic pump. The animals were sacrificed at eight weeks following treatment or when their breathing rate indicated respiratory distress. In the radiation studies the mice were given a single dose of 14 or 16 Gy (Co$\sp{60})$ to the whole thorax and were sacrificed when moribund. The phenotype was defined as the percent of fibrosis area in the left lung as quantified with image analysis of histological sections. Quantitative trait loci (QTL) mapping was used to identify the chromosomal location of genes which contribute to susceptibility to bleomycin-induced pulmonary fibrosis in C57BL/6J mice compared to C3Hf/Kam mice and to determine if the QTL's which influence susceptibility to bleomycin-induced lung fibrosis in these progenitor strains could be implicated in susceptibility to radiation-induced lung fibrosis. For bleomycin, a genome wide scan revealed QTL's on chromosome 17, at the MHC, (LOD = 11.7 for males and 7.2 for females) accounting for approximately 21% of the phenotypic variance, and on chromosome 11 (LOD = 4.9), in male mice only, adding 8% of phenotypic variance. The bleomycin QTL on chromosome 17 was also implicated for susceptibility to radiation-induced fibrosis (LOD = 5.0) and contributes 7% of the phenotypic variance in the radiation study. In conclusion, susceptibility to both bleomycin-induced and radiation-induced pulmonary fibrosis are heritable traits, and are influenced by a genetic factor which maps to a genomic region containing the MHC. ^

Comparing the factorial structure, invariance, and predictive validity of transtheoretical model constructs for alcohol use across restricted and unrestricted settings

With substance abuse treatment expanding in prisons and jails, understanding how behavior change interacts with a restricted setting becomes more essential. The Transtheoretical Model (TTM) has been used to understand intentional behavior change in unrestricted settings, however, evidence indicates restrictive settings can affect the measurement and structure of the TTM constructs. The present study examined data from problem drinkers at baseline and end-of-treatment from three studies: (1) Project CARE (n = 187) recruited inmates from a large county jail; (2) Project Check-In (n = 116) recruited inmates from a state prison; (3) Project MATCH, a large multi-site alcohol study had two recruitment arms, aftercare (n = 724 pre-treatment and 650 post-treatment) and outpatient (n = 912 pre-treatment and 844 post-treatment). The analyses were conducted using cross-sectional data to test for non-invariance of measures of the TTM constructs: readiness, confidence, temptation, and processes of change (Structural Equation Modeling, SEM) across restricted and unrestricted settings. Two restricted (jail and aftercare) and one unrestricted group (outpatient) entering treatment and one restricted (prison) and two unrestricted groups (aftercare and outpatient) at end-of-treatment were contrasted. In addition TTM end-of-treatment profiles were tested as predictors of 12 month drinking outcomes (Profile Analysis). Although SEM did not indicate structural differences in the overall TTM construct model across setting types, there were factor structure differences on the confidence and temptation constructs at pre-treatment and in the factor structure of the behavioral processes at the end-of-treatment. For pre-treatment temptation and confidence, differences were found in the social situations factor loadings and in the variance for the confidence and temptation latent factors. For the end-of-treatment behavioral processes, differences across the restricted and unrestricted settings were identified in the counter-conditioning and stimulus control factor loadings. The TTM end-of-treatment profiles were not predictive of drinking outcomes in the prison sample. Both pre and post-treatment differences in structure across setting types involved constructs operationalized with behaviors that are limited for those in restricted settings. These studies suggest the TTM is a viable model for explicating addictive behavior change in restricted settings but calls for modification of subscale items that refer to specific behaviors and caution in interpreting the mean differences across setting types for problem drinkers. ^

A computerized statistical framework for coalescent analysis

Coalescent theory represents the most significant progress in theoretical population genetics in the past three decades. The coalescent theory states that all genes or alleles in a given population are ultimately inherited from a single ancestor shared by all members of the population, known as the most recent common ancestor. It is now widely recognized as a cornerstone for rigorous statistical analyses of molecular data from population [1]. The scientists have developed a large number of coalescent models and methods[2,3,4,5,6], which are not only applied in coalescent analysis and process, but also in today’s population genetics and genome studies, even public health. The thesis aims at completing a statistical framework based on computers for coalescent analysis. This framework provides a large number of coalescent models and statistic methods to assist students and researchers in coalescent analysis, whose results are presented in various formats as texts, graphics and printed pages. In particular, it also supports to create new coalescent models and statistical methods. ^

TECHNOLOGY ASSESSMENT: SMALL-VOLUME INFUSION PUMPS

"Technology assessment is a comprehensive form of policy research that examines the short- and long-term social consequences of the application or use of technology" (US Congress 1967).^ This study explored a research methodology appropriate for technology assessment (TA) within the health industry. The case studied was utilization of external Small-Volume Infusion Pumps (SVIP) at a cancer treatment and research center. Primary and secondary data were collected in three project phases. In Phase I, hospital prescription records (N = 14,979) represented SVIP adoption and utilization for the years 1982-1984. The Candidate Adoption-Use (CA-U) diffusion paradigm developed for this study was germane. Compared to classic and unorthodox curves, CA-U more accurately simulated empiric experience. The hospital SVIP 1983-1984 trends denoted assurance in prescribing chemotherapy and concomitant balloon SVIP efficacy and efficiency. Abandonment of battery pumps was predicted while exponential demand for balloon SVIP was forecast for 1985-1987. In Phase II, patients using SVIP (N = 117) were prospectively surveyed from July to October 1984; the data represented a single episode of therapy. The questionnaire and indices, specifically designed to measure the impact of SVIP, evinced face validity. Compeer group data were from pre-SVIP case reviews rather than from an inpatient sample. Statistically significant results indicated that outpatients using SVIP interacted socially more than inpatients using the alternative technology. Additionally, the hospital's education program effectively taught clients to discriminate between self care and professional SVIP services. In these contexts, there was sufficient evidence that the alternative technology restricted patients activity whereas SVIP permitted patients to function more independently and in a social lifestyle, thus adding quality to life. In Phase III, diffusion forecast and patient survey findings were combined with direct observation of clinic services to profile some economic dimensions of SVIP. These three project phases provide a foundation for executing: (1) cost effectiveness analysis of external versus internal infusors, (2) institutional resource allocation, and (3) technology deployment to epidemiology-significant communities. The models and methods tested in this research of clinical technology assessment are innovative and do assess biotechnology. ^

Using spatial linear models with SAR and CAR structure to examine Texas lung cancer incidence rates

Scholars have found that socioeconomic status was one of the key factors that influenced early-stage lung cancer incidence rates in a variety of regions. This thesis examined the association between median household income and lung cancer incidence rates in Texas counties. A total of 254 individual counties in Texas with corresponding lung cancer incidence rates from 2004 to 2008 and median household incomes in 2006 were collected from the National Cancer Institute Surveillance System. A simple linear model and spatial linear models with two structures, Simultaneous Autoregressive Structure (SAR) and Conditional Autoregressive Structure (CAR), were used to link median household income and lung cancer incidence rates in Texas. The residuals of the spatial linear models were analyzed with Moran's I and Geary's C statistics, and the statistical results were used to detect similar lung cancer incidence rate clusters and disease patterns in Texas.^