Among 10,384 rural Colorado female patients who received MDC 14 (obstetric services) from 2000 to 2003, 6,615 (63.7%) were admitted to their local rural hospitals; 1,654 (15.9%) were admitted to other rural hospitals; and 2,115 (20.4%) traveled to urban hospitals for inpatient services. This study is to examine how network participation, service scopes, and market competition influences rural women's choice of hospital for their obstetric care. A conditional logistic regression analysis was used. The network participation (p < 0.01), the number of services offered (p < 0.05), and the hospital market competition had a positive and significant relationship with patients' choice to receive obstetric care. That is, rural patients prefer to receive care from a hospital that participates in a network, that provides more number of services, and that has a greater market share (i.e., a lower level of market competition) in their locality. Rural hospitals could actively increase their competitiveness and market share by increasing the number of health care services provided and seeking to network with other hospitals.

The bypassing of rural hospitals increased in Colorado's rural communities during the 1990s. To understand this phenomenon, this study explores why rural Medicare patients in Colorado bypassed their local rural hospitals when they could have received health care services at their nearest local hospital. To identify both individual factors and institutional variables associated with hospital choice behavior, the conditional logistic regression model analyzes 4,099 rural Medicare patients who received heart failure and shock procedures. This study determines that both institutional variables (ownership type, number of beds, number of services, accreditation, and distance between the hospital and a patient's residence) and patient variables (age, length of stay, race, and total charge) are significant in patients' hospital choice. This study suggests that rural hospitals could build cooperative relationships with other large rural and urban hospitals.

No description available.

Classifying individuals into binary disease categories can be challenging due to complex relationships across different exposures of interest. In this thesis, we investigate three different approaches for disease classification using multiple biomarkers. First, we consider combining information from literature reviews and INTERHEART data set to identify the threshold of ApoB, ApoA1 and the ratio of these two biomarkers to classify individuals at risk of developing myocardial infarction. We develop a Bayesian estimation procedure for this purpose that utilizes the conditional probability distribution of these biomarkers. This method is flexible compared to standard logistic regression approach and allows us to identify a precise threshold of these biomarkers. Second, we consider the problem of disease classification using two dependent biomarkers. An independently identified threshold for this purpose usually leads to a conflicting classification for some individuals. We develop and describe a method of determining the joint threshold of two dependent biomarkers for a disease classification, based on the joint probability distribution function constructed through copulas. This method will allow researchers uniquely classify individuals at risk of developing the disease. Third, we consider the problem of classifying an outcome using a gene and miRNA expression data sets. Linear principal component analysis (PCA) is a widely used approach to reduce the dimension of such data sets and subsequently use it for classification, but many authors suggest using kernel PCA for this purpose. Using real and simulated data sets, we compare these two approaches and assess the performance of components towards genetic data integration for an outcome classification. We conclude that reducing dimensions using linear PCA followed by a logistic regression model for classification seems to be acceptable for this purpose. We also observe that integrating information from multiple data sets using either of these approaches leads to a better performance of an outcome classification. / Thesis / Doctor of Philosophy (PhD)

Semiparametric modeling is a combination of the parametric and nonparametric models in which some functions follow a known form and some others follow an unknown form. In this dissertation we made contributions to semiparametric modeling for matched case-crossover data. In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. Any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. However, some matching covariates such as time, and/or spatial location often play an important role as an effect modification. Failure to include them makes incorrect statistical estimation, prediction and inference. Hence in this dissertation, we propose several approaches that will allow the inclusion of time and spatial location as well as other effect modifications such as heterogeneous subpopulations among the data. To address modification due to time, three methods are developed: the first is a parametric approach, the second is a semiparametric penalized approach and the third is a semiparametric Bayesian approach. We demonstrate the advantage of the one stage semiparametric approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. To address modifications due to time and spatial location, two methods are developed: the first one is a semiparametric spatial-temporal varying coefficient model for a small number of locations. The second method is a semiparametric spatial-temporal varying coefficient model, and is appropriate when the number of locations among the subjects is medium to large. We demonstrate the accuracy of these approaches by using simulation studies, and when appropriate, an epidemiological example of a 1-4 bi-directional case-crossover study. Finally, to explore further effect modifications by heterogeneous subpopulations among strata we propose a nonparametric Bayesian approach constructed with Dirichlet process priors, which clusters subpopulations and assesses heterogeneity. We demonstrate the accuracy of our approach using a simulation study, as well a an example of a 1-4 bi-directional case-crossover study. / Ph. D.

Today biodiversity is rapidly decreasing and an increasing number of threatened species live in modified and human dominated landscapes. Therefore it is essential to learn more about how species cope with the changes of their habitat. The focus of this study lies on a primate species, the critically endangered Javan slow loris (Nycticebus javanicus), endemic to the densely populated island of Java, Indonesia.  In cooperation with the Little fire face project in West Java, I used a step selection function (SSF) framework, to understand how landscape structure affects the movement of Javan slow lorises within a fragmented mountain-agroforest landscape of Cipaganti, West Java. To investigate the movement and fine scale habitat selection of slow lorises I used one hour locations of 6 radio-collared slow lorises. The habitat and vegetation of observed and random steps was investigated in multiple variables such as presence of food trees and signs of human disturbance. For the analysis I paired observed steps (1h relocations) with 3 random habitat locations and used a conditional logistic regression to parameterize the SSF, which represents the probability of a focal slow loris to select a given step as a function of the habitat and vegetation factors surveyed. In average the slow lorises travelled about 450 m each night and most frequently they used a step length of about 0 – 50 m. My result reveals that slow lorises fine scale habitat selection is positively influenced by the presence of trees and tree trunk cover (indirect increasing the canopy cover and connectivity). They are also to a high extent positively affected by the presence of a feeding tree species, Calliandra calothyrsus. Surprisingly slow lorises selected steps associated with a higher number of fields (fields may indicate an increased biodiversity within the location). The results also indicate that slow lorises are limited in their movement by the presence of fields or rivers, which indicates that slow lorises are negatively influenced in their movement by a declining ability to move and forage within Cipaganti. I found no significant differences between sexes in their distance travelled. The recommendation for future conservation of slow lorises in Cipaganti is to prevent further habitat loss and fragmentation through activities that protect or maintain the present suitable slow loris habitat. Further research is needed to increase the knowledge of these primates’ abilities to live in this modified landscape.

No description available.

Graduation date: 2006 / Habitat loss causes a reduction in available resources for wildlife, alters the configuration of remaining habitat, and may isolate wildlife populations. White-breasted nuthatches (Sitta carolinensis) are experiencing long-term population declines in the Willamette Valley of Oregon, where they are historically associated with oak woodlands. As secondary cavity-nesters, white-breasted nuthatches may be limited by the availability of existing cavities for nesting and roosting. Oak vegetation in the Willamette Valley has changed since European-American settlement times from vast areas of open oak savanna to isolated closed-canopy stands separated by agricultural fields. We examined nuthatch density, nest cavity selection, and nest success in relation to oak woodland structure and landscape context. We conducted point transect surveys in 3 strata: woodland interiors, large woodland edges, and small woodlands. We located and monitored nuthatch nests and sampled vegetation at nest locations and matching random locations around each nest. Woodland structure and edge density were measured at a 178-m radius (home range) scale, and landscape context was measured using vegetation cover within a 1-km radius around point transects and nests. We used program DISTANCE to fit detection functions and calculate nuthatch densities. We used conditional logistic regression to compare nest locations with random locations, and analyzed nest success with Mayfield logistic regression. White-breasted nuthatch density was significantly higher in small woodlands than in edges of large woodlands, which had higher nuthatch density than woodland interiors. Density of nuthatches increased with a combination of oak cover within a 1-km radius of the point, edge density within a 178-m radius, and number of oak trees >50 cm diameter at breast height (dbh) within a 100-m radius. Nest cavities were situated in oak trees containing more cavities than random oak trees that had cavities, and oak trees used as nest trees had a larger dbh than oak trees within random plots. Local woodland structure at nest locations was characterized by larger trees, measured by greater mean dbh, canopy cover, and basal area of oaks than random locations within the home range. Nest success in natural cavities was 71% and was not predicted by attributes of nest cavities, nest trees, local woodland structure at nests, woodland structure at the home range scale, or landscape context. These results suggest that the most suitable habitat for white-breasted nuthatches in the Willamette Valley includes oak woodlands in close proximity to one another with a high proportion of edge and mature oak trees. Managers should preserve trees containing cavities and large oak trees whenever possible. Thinning of small oaks and removal of conifers in oak woodlands to create more open, savanna-like conditions may also promote the development of larger oaks with more spreading branches, providing more opportunities for cavities to form and more foraging surface area for nuthatches.