DISCHARGE PLANNING: A PROCESS MODEL

As the requirements for health care hospitalization have become more demanding, so has the discharge planning process become a more important part of the health services system. A thorough understanding of hospital discharge planning can, then, contribute to our understanding of the health services system. This study involved the development of a process model of discharge planning from hospitals. Model building involved the identification of factors used by discharge planners to develop aftercare plans, and the specification of the roles of these factors in the development of the discharge plan. The factors in the model were concatenated in 16 discrete decision sequences, each of which produced an aftercare plan.^ The sample for this study comprised 407 inpatients admitted to the M. D. Anderson Hospital and Tumor Institution at Houston, Texas, who were discharged to any site within Texas during a 15 day period. Allogeneic bone marrow donors were excluded from the sample. The factors considered in the development of discharge plans were recorded by discharge planners and were used to develop the model. Data analysis consisted of sorting the discharge plans using the plan development factors until for some combination and sequence of factors all patients were discharged to a single site. The arrangement of factors that led to that aftercare plan became a decision sequence in the model.^ The model constructs the same discharge plans as those developed by hospital staff for every patient in the study. Tests of the validity of the model should be extended to other patients at the MDAH, to other cancer hospitals, and to other inpatient services. Revisions of the model based on these tests should be of value in the management of discharge planning services and in the design and development of comprehensive community health services.^

An examination of the relationship between maternal and neighborhood-level characteristics and intrauterine growth retardation in Harris County, Texas, 1999--2001

The persistence of low birth weight and intrauterine growth retardation (IUGR) in the United States has puzzled researchers for decades. Much of the work that has been conducted on adverse birth outcomes has focused on low birth weight in general and not on IUGR. Studies that have examined IUGR specifically thus far have focused primarily on individual-level maternal risk factors. These risk factors have only been able to explain a small portion of the variance in IUGR. Therefore, recent work has begun to focus on community-level risk factors in addition to the individual-level maternal characteristics. This study uses Social Ecology to examine the relationship of individual and community-level risk factors and IUGR. Logistic regression was used to establish an individual-level model based on 155, 856 births recorded in Harris County, TX during 1999-2001. IUGR was characterized using a fetal growth ratio method with race/ethnic and sex specific mean birth weights calculated from national vital records. The spatial distributions of 114,460 birth records spatially located within the City of Houston were examined using choropleth, probability and density maps. Census tracts with higher than expected rates of IUGR and high levels of neighborhood disadvantage were highlighted. Neighborhood disadvantage was constructed using socioeconomic variables from the 2000 U.S. Census. Factor analysis was used to create a unified single measure. Lastly, a random coefficients model was used to examine the relationship between varying levels of community disadvantage, given the set of individual-level risk factors for 152,997 birth records spatially located within Harris County, TX. Neighborhood disadvantage was measured using three different indices adapted from previous work. The findings show that pregnancy-induced hypertension, previous preterm infant, tobacco use and insufficient weight gain have the highest association with IUGR. Neighborhood disadvantage only slightly further increases the risk of IUGR (OR 1.12 to 1.23). Although community level disadvantage only helped to explain a small proportion of the variance of IUGR, it did have a significant impact. This finding suggests that community level risk factors should be included in future work with IUGR and that more work needs to be conducted. ^

A model for use by local public health departments to evaluate pandemic influenza plans

The events of the 1990's and early 2000's demonstrated the need for effective planning and response to natural and man-made disasters. One of those potential natural disasters is pandemic flu. Once defined, the CDC stated that program, or plan, effectiveness is improved through the process of program evaluation. (Centers for Disease Control and Prevention, 1999) Program evaluation should be accomplished not only periodically, but in the course of routine administration of the program. (Centers for Disease Control and Prevention, 1999) Accomplishing this task for a "rare, but significant event" is challenging. (Herbold, John R., PhD., 2008) To address this challenge, the RAND Corporation (under contract to the CDC) developed the "Facilitated Look-Backs" approach that was tested and validated at the state level. (Aledort et al., 2006).^ Nevertheless, no comprehensive and generally applicable pandemic influenza program evaluation tool or model is readily found for use at the local public health department level. This project developed such a model based on the "Facilitated Look-Backs" approach developed by RAND Corporation. (Aledort et al., 2006) Modifications to the RAND model included stakeholder additions, inclusion of all six CDC program evaluation steps, and suggestions for incorporating pandemic flu response plans in seasonal flu management implementation. Feedback on the model was then obtained from three LPHD's—one rural, one suburban, and one urban. These recommendations were incorporated into the final model. Feedback from the sites also supported the assumption that this model promotes the effective and efficient evaluation of both pandemic flu and seasonal flu response by reducing redundant evaluations of pandemic flu plans, seasonal flu plans, and funding requirement accountability. Site feedback also demonstrated that the model is comprehensive and flexible, so it can be adapted and applied to different LPHD needs and settings. It also stimulates evaluation of the major issues associated with pandemic flu planning. ^ The next phase in evaluating this model should be to apply it in a program evaluation of one or more LPHD's seasonal flu response that incorporates pandemic flu response plans.^

A qualitative analysis of leadership styles and the selection of quality improvement in primary care practice

In the Practice Change Model, physicians act as key stakeholders, people who have both an investment in the practice and the capacity to influence how the practice performs. This leadership role is critical to the development and change of the practice. Leadership roles and effectiveness are an important factor in quality improvement in primary care practices.^ The study conducted involved a comparative case study analysis to identify leadership roles and the relationship between leadership roles and the number and type of quality improvement strategies adopted during a Practice Change Model-based intervention study. The research utilized secondary data from four primary care practices with various leadership styles. The practices are located in the San Antonio region and serve a large Hispanic population. The data was collected by two ABC Project Facilitators from each practice during a 12-month period including Key Informant Interviews (all staff members), MAP (Multi-method Assessment Process), and Practice Facilitation field notes. This data was used to evaluate leadership styles, management within the practice, and intervention tools that were implemented. The chief steps will be (1) to analyze if the leader-member relations contribute to the type of quality improvement strategy or strategies selected (2) to investigate if leader-position power contributes to the number of strategies selected and the type of strategy selected (3) and to explore whether the task structure varies across the four primary care practices.^ The research found that involving more members of the clinic staff in decision-making, building bridges between organizational staff and clinical staff, and task structure are all associated with the direct influence on the number and type of quality improvement strategies implemented in primary care practice.^ Although this research only investigated leadership styles of four different practices, it will offer future guidance on how to establish the priorities and implementation of quality improvement strategies that will have the greatest impact on patient care improvement. ^

Sexual initiation of U.S. Latino and Colombian high school students: Toward a new theory of adolescent sexuality and implications for sexual health prevention and promotion

The general research question for this dissertation was: do the data on adolescent sexual experiences and sexual initiation support the explicit or implicit adolescent sexuality theories informing the sexual health interventions currently designed for youth? To respond to this inquiry, three different studies were conducted. The first study included a conceptual and historical analysis of the notion of adolescence introduced by Stanley Hall, the development of an alternative model based on a positive view of adolescent sexuality, and the rationale for introducing to adolescent sexual health prevention programs the new definitions of sexual health and the social determinants of health approach. The second one was a quantitative study aimed at surveying not only adolescents' risky sexual behaviors but also sexual experiences associated with desire/pleasure which have been systematically neglected when investigating the sexual and reproductive health of the youth. This study was conducted with a representative sample of the adolescents attending public high schools in the State of Caldas in the Republic of Colombia. The third study was a qualitative analysis of 22 interviews conducted with male and female U.S. Latino adolescents on the reasons for having had or having not had vaginal sex. The more relevant results were: most current adolescent sexual health prevention programs are still framed in a negative approach to adolescent sexuality developed a century ago by Stanley Hall and Sigmund Freud which do not accept the adolescent sexual experience and propose its sublimation. In contrast, the Colombian study indicates that, although there are gender differences, adolescence is for males and females a normal period of sexual initiation not limited to coital activity, in which sexual desire/pleasure is strongly associated with sexual behavior. By the same token, the study about the reasons for having had or not had initiated heterosexual intercourse indicated that curiosity, sexual desire/pleasure, and love are basic motivations for deciding to have vaginal sexual intercourse for the first time and that during adolescence, young women and men reach the cognitive development necessary for taking conscious decisions about their sexual acts. The findings underline the importance of asking pertinent questions about desire/pleasure when studying adolescent sexuality and adopting an evidence-based approach to sexual health interventions.^

HPV vaccine coverage in Texas counties: An application of multilevel, small area estimation

Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^