10 resultados para modeling trends and data

em DigitalCommons@The Texas Medical Center


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This study describes the patterns of occurrence of amyotrophic lateral sclerosis (ALS) and parkinsonism-dementia complex (PDC) of Guam during 1950-1989. Both ALS and PDC occur with high frequency among the indigenous Chamorro population, first recognized in the early 1950's. Reports in the early 1980's indicated that both ALS and PDC were disappearing, due to a purported reduction in exposure to harmful environmental factors as a result of the dramatic changes in lifestyle that took place after World War II. However, this study provides compelling evidence that ALS and PDC have not disappeared on Guam and that rates for both are higher during 1980-1989 than previously reported.^ The patterns of occurrence for both ALS and PDC overlap in most respects: (1) incidence and mortality are decreasing; (2) median age at onset is increasing; (3) males are at increased risk for developing disease; (4) risk is higher for those residing in the south compared to the non-south; and (5) age-specific incidence is decreasing over time except in the oldest age groups.^ Age-specific incidence of ALS and PDC, separately and together, is generally higher for cohorts born before 1920 than for those born after 1920. A significant birth cohort effect on the incidence of PDC for the 1906-1915 birth cohort was found, but not for ALS and for ALS and PDC together. Whether or not a cohort effect, period effect, or both are associated with incidence of ALS and PDC cannot be determined from the data currently available and will require additional follow-up of individuals born after 1920.^ The epidemiological data amassed over this 40-year period provide evidence that supports an environmental exposure model for disease occurrence as opposed to a simple genetic or infectious disease model. Whether neurodegenerative disease in this population occurs as a consequence of a single exposure or is explained by a multifactorial model such as a genetic predisposition with some environmental interaction is yet to be determined. However, descriptive studies such as this can provide clues concerning timing and location of potential adverse exposures but cannot determine etiology, underscoring the urgent need for analytic studies of ALS and PDC to further investigate existing etiologic hypotheses and to test new hypotheses. ^

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Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

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Hodgkin's disease (HD) is a cancer of the lymphatic system. Survivors of HD face varieties of consequent adverse effects, in which secondary primary tumors (SPT) is one of the most serious consequences. This dissertation is aimed to model time-to-SPT in the presence of death and HD relapses during follow-up.^ The model is designed to handle a mixture phenomenon of SPT and the influence of death. Relapses of HD are adjusted as a covariate. Proportional hazards framework is used to define SPT intensity function, which includes an exponential term to estimate explanatory variables. Death as a competing risk is considered according to different scenarios, depending on which terminal event comes first. Newton-Raphson method is used to estimate the parameter estimates in the end.^ The proposed method is applied to a real data set containing a group of HD patients. Several risk factors for the development of SPT are identified and the findings are noteworthy in the development of healthcare guidelines that may lead to the early detection or prevention of SPT.^

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People often use tools to search for information. In order to improve the quality of an information search, it is important to understand how internal information, which is stored in user’s mind, and external information, represented by the interface of tools interact with each other. How information is distributed between internal and external representations significantly affects information search performance. However, few studies have examined the relationship between types of interface and types of search task in the context of information search. For a distributed information search task, how data are distributed, represented, and formatted significantly affects the user search performance in terms of response time and accuracy. Guided by UFuRT (User, Function, Representation, Task), a human-centered process, I propose a search model, task taxonomy. The model defines its relationship with other existing information models. The taxonomy clarifies the legitimate operations for each type of search task of relation data. Based on the model and taxonomy, I have also developed prototypes of interface for the search tasks of relational data. These prototypes were used for experiments. The experiments described in this study are of a within-subject design with a sample of 24 participants recruited from the graduate schools located in the Texas Medical Center. Participants performed one-dimensional nominal search tasks over nominal, ordinal, and ratio displays, and searched one-dimensional nominal, ordinal, interval, and ratio tasks over table and graph displays. Participants also performed the same task and display combination for twodimensional searches. Distributed cognition theory has been adopted as a theoretical framework for analyzing and predicting the search performance of relational data. It has been shown that the representation dimensions and data scales, as well as the search task types, are main factors in determining search efficiency and effectiveness. In particular, the more external representations used, the better search task performance, and the results suggest the ideal search performance occurs when the question type and corresponding data scale representation match. The implications of the study lie in contributing to the effective design of search interface for relational data, especially laboratory results, which are often used in healthcare activities.

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OBJECTIVE: Because studies suggest that ultraviolet (UV) radiation modulates the myositis phenotype and Mi-2 autoantigen expression, we conducted a retrospective investigation to determine whether UV radiation may influence the relative prevalence of dermatomyositis and anti-Mi-2 autoantibodies in the US. METHODS: We assessed the relationship between surface UV radiation intensity in the state of residence at the time of onset with the relative prevalence of dermatomyositis and myositis autoantibodies in 380 patients with myositis from referral centers in the US. Myositis autoantibodies were detected by validated immunoprecipitation assays. Surface UV radiation intensity was estimated from UV Index data collected by the US National Weather Service. RESULTS: UV radiation intensity was associated with the relative proportion of patients with dermatomyositis (odds ratio [OR] 2.3, 95% confidence interval [95% CI] 0.9-5.8) and with the proportion of patients expressing anti-Mi-2 autoantibodies (OR 6.0, 95% CI 1.1-34.1). Modeling of these data showed that these associations were confined to women (OR 3.8, 95% CI 1.3-11.0 and OR 17.3, 95% CI 1.8-162.4, respectively) and suggests that sex influences the effects of UV radiation on autoimmune disorders. Significant associations were not observed in men, nor were UV radiation levels related to the presence of antisynthetase or anti-signal recognition particle autoantibodies. CONCLUSION: This first study of the distribution of myositis phenotypes and UV radiation exposure in the US showed that UV radiation may modulate the clinical and immunologic expression of autoimmune disease in women. Further investigation of the mechanisms by which these effects are produced may provide insights into pathogenesis and suggest therapeutic or preventative strategies.

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Background: The US has higher rates of teen births and sexually transmitted infections (STI) than other developed countries. Texas youth are disproportionately impacted. Purpose: To review local, state, and national data on teens’ engagement in sexual risk behaviors to inform policy and practice related to teen sexual health. Methods: 2009 middle school and high school Youth Risk Behavior Survey (YRBS) data, and data from All About Youth, a middle school study conducted in a large urban school district in Texas, were analyzed to assess the prevalence of sexual initiation, including the initiation of non-coital sex, and the prevalence of sexual risk behaviors among Texas and US youth. Results: A substantial proportion of middle and high school students are having sex. Sexual initiation begins as early as 6th grade and increases steadily through 12th grade with almost two-thirds of high school seniors being sexually experienced. Many teens are not protecting themselves from unintended pregnancy or STIs – nationally, 80% and 39% of high school students did not use birth control pills or a condom respectively the last time they had sex. Many middle and high school students are engaging in oral and anal sex, two behaviors which increase the risk of contracting an STI and HIV. In Texas, an estimated 689,512 out of 1,327,815 public high school students are sexually experienced – over half (52%) of the total high school population. Texas students surpass their US peers in several sexual risk behaviors including number of lifetime sexual partners, being currently sexually active, and not using effective methods of birth control or dual protection when having sex. They are also less likely to receive HIV/AIDS education in school. Conclusion: Changes in policy and practice, including implementation of evidence-based sex education programs in middle and high schools and increased access to integrated, teen-friendly sexual and reproductive health services, are urgently needed at the state and national levels to address these issues effectively.

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Recently it has been proposed that the evaluation of effects of pollutants on aquatic organisms can provide an early warning system of potential environmental and human health risks (NRC 1991). Unfortunately there are few methods available to aquatic biologists to conduct assessments of the effects of pollutants on aquatic animal community health. The primary goal of this research was to develop and evaluate the feasibility of such a method. Specifically, the primary objective of this study was to develop a prototype rapid bioassessment technique similar to the Index of Biotic Integrity (IBI) for the upper Texas and Northwestern Gulf of Mexico coastal tributaries. The IBI consists of a series of "metrics" which describes specific attributes of the aquatic community. Each of these metrics are given a score which is then subtotaled to derive a total assessment of the "health" of the aquatic community. This IBI procedure may provide an additional assessment tool for professionals in water quality management.^ The experimental design consisted primarily of compiling previously collected data from monitoring conducted by the Texas Natural Resource Conservation Commission (TNRCC) at five bayous classified according to potential for anthropogenic impact and salinity regime. Standardized hydrological, chemical, and biological monitoring had been conducted in each of these watersheds. The identification and evaluation of candidate metrics for inclusion in the estuarine IBI was conducted through the use of correlation analysis, cluster analysis, stepwise and normal discriminant analysis, and evaluation of cumulative distribution frequencies. Scores of each included metric were determined based on exceedances of specific percentiles. Individual scores were summed and a total IBI score and rank for the community computed.^ Results of these analyses yielded the proposed metrics and rankings listed in this report. Based on the results of this study, incorporation of an estuarine IBI method as a water quality assessment tool is warranted. Adopted metrics were correlated to seasonal trends and less so to salinity gradients observed during the study (0-25 ppt). Further refinement of this method is needed using a larger more inclusive data set which includes additional habitat types, salinity ranges, and temporal variation. ^

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The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

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Child overweight and obesity reaches across ethnic, cultural, socioeconomic and regional barriers. It must be assessed, diagnosed and treated to help families make sustainable behavior changes. Treatment recommendations have been made to address pediatric overweight and obesity. They include screening for risk factors, monitoring BMI trends and educating patients and families to make small sustainable changes. Health insurance companies can play a meaningful role in supporting and facilitating provider education and behavior change to diagnose, treat and prevent pediatric overweight and obesity.^ The aims of this thesis were: (1) Describe a disease management program that uses evidence-based practices to create provider behavior change related to pediatric obesity screening, diagnosis and treatment. (2) Identify ways to improve the implementation of the program based on the perceptions of participating clinicians.^ A literature review was completed to evaluate current recommendations for screening and treating pediatric obesity using the Ovid data base. The evidenced-based recommendations were compared against the practices of the Healthy Lifestyles Program (HeLP). The literature confirmed that HeLP is following evidence-based recommendations for assessment, diagnosis and treatment of pediatric obesity.^ A Children’s Mercy Family Health Partners focus group was convened to create a provider survey. The goals of the survey were to assess providers’ perception of the Healthy Lifestyles Program (HeLP). The survey was sent out through email using Survey Monkey. All survey responses were anonymous. The survey was sent to a total of 80 providers who had completed HeLP. Twenty-five percent responded. The survey results were evaluated to make recommendations for HeLP.^ Results of the survey included motivating factors for participation in HeLP. Concern about the increasing prevalence of pediatric obesity was a frequent motivator for participation. Provider barriers to obesity diagnosis were evaluated. Lack of time during clinic visits a frequent barrier to obesity diagnosis. ^ In conclusion several recommendations for the HeLP were made based on survey results. It is recommended that the program evaluate methods and tools for facilitating effective weight management follow up visits. Promotional materials should highlight the increasing prevalence of pediatric obesity when advertising HeLP. These recommendations will be used to refine the current Healthy Lifestyles Program.^

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Background. Research has demonstrated associations between sociodemographic characteristics and illness perceptions; however, the impact of cancer exposure through personal or family diagnoses is not well-studied. The purposes of this study were to examine the prevalence of different cancer beliefs and the disparity in cancer beliefs across groups of individuals with distinct cancer histories; and to identify whether, when adjusted for sociodemographic characteristics, cancer history predicts a set of cancer beliefs.^ Methods. Using Leventhal’s Common Sense Model and data from the 2007 Health Information National Trends Survey (N=7172), we constructed multivariable logistic regressions to evaluate the effect of different stimuli, including cancer experience, on cancer perceptions (e.g., risk, worry, causation, outcome).^ Results. Findings indicate significant associations between cancer history and cancer perceptions. Individuals with family and personal cancer histories were more likely than individuals without any cancer history to worry about getting cancer (OR=3.55, P<0.01), agree they will develop cancer in the future (OR=8.81, P<0.01), and disagree that cancer is most often caused by a person’s behavior or lifestyle (OR=1.24, P=0.03). Additionally, results support education’s role in forming cancer perceptions. Individuals with high levels of education were more likely to endorse cancer prevention (OR=1.68, P<0.01) and higher 5-year survival rates (OR=1.41, P<0.01). ^ Conclusions. Results indicate cancer history affects cancer perceptions throughout the cancer continuum. Additionally, cancer history may influence coping behaviors and outcomes related to cancer.^ Impact. Cancer education and survivorship programs should assess important variables (e.g., cancer history) to more effectively tailor services and monitor evolving needs throughout cancer care.^