Development of a rapid bioassessment method and index of biotic integrity for estuaries located along the northwest Gulf of Mexico

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. ^

A focused ethnographic study of community social capital

Social capital, a relatively new public health concept, represents the intangible resources embedded in social relationships that facilitate collective action. Current interest in the concept stems from empirical studies linking social capital with health outcomes. However, in order for social capital to function as a meaningful research variable, conceptual development aimed at refining the domains, attributes, and boundaries of the concept are needed. An existing framework of social capital (Uphoff, 2000), developed from studies in India, was selected for congruence with the inductive analysis of pilot data from a community that was unsuccessful at mobilizing collective action. This framework provided the underpinnings for a formal ethnographic research study designed to examine the components of social capital in a community that had successfully mobilized collective action. The specific aim of the ethnographic study was to examine the fittingness of Uphoff's framework in the contrasting American community. A contrasting context was purposefully selected to distinguish essential attributes of social capital from those that were specific to one community. Ethnographic data collection methods included participant observation, formal interviews, and public documents. Data was originally analyzed according to codes developed from Uphoff's theoretical framework. The results from this analysis were only partially satisfactory, indicating that the theoretical framework required refinement. The refinement of the coding system resulted in the emergence of an explanatory theory of social capital that was tested with the data collected from formal fieldwork. Although Uphoff's framework was useful, the refinement of the framework revealed, (1) trust as the dominant attribute of social capital, (2) efficacy of mutually beneficial collective action as the outcome indicator, (3) cognitive and structural domains more appropriately defined as the cultural norms of the community and group, and (4) a definition of social capital as the combination of the cognitive norms of the community and the structural norms of the group that are either constructive or destructive to the development of trust and the efficacy of mutually beneficial collective action. This explanatory framework holds increased pragmatic utility for public health practice and research. ^

Evaluation of a pilot diabetes educational intervention using the American Diabetes Association's Risk Assessment form with the patrons of The Health Museum, Houston Texas

Diabetes Mellitus is not a disease, but a group of diseases. Common to all types of diabetes is high levels of blood glucose produced from a variety of causes. In 2006, the American Diabetes Association ranked diabetes as the fifth leading cause of death in the United States. The complications and consequences are serious and include nephropathy, retinopathy, neuropathy, heart disease, amputations, pregnancy complications, sexual dysfunction, biochemical imbalances, susceptibility and sensitivity to many other diseases and in some cases death. ^ The serious nature of diabetes mellitus and its complications has compelled researchers to devise new strategies to reach population segments at high risk. Various avenues of outreach have been attempted. This pilot program is not unique in using a health museum as a point of outreach. However health museums have not been a major source of interventions, either. Little information was available regarding health museum visitor demographics, visitation patterns, companion status and museum trust levels prior to this pilot intervention. This visitor information will improve planning for further interventions and studies. ^ This thesis also examined prevalence data in a temporal context, the populations at risk for diabetes, the collecting agencies, and other relevant collected data. The prevalence of diabetes has been rapidly increasing. The increase is partially explained by refinement of the definition of diabetes as the etiology has become better understood. Increasing obesity and sedentary lifestyles have contributed to the increase, as well as the burdensome increase on minority populations. ^ Treatment options are complex and have had limited effectiveness. This would lead one to conclude that prevention and early diagnosis are preferable. However, the general public has insufficient awareness and education regarding diabetes symptoms and the serious risks and complications the disease can cause. Reaching high risk, high prevalence, populations is challenging for any intervention. During its “free family Thursdays” The Health Museum (Houston, Texas) has attracted a variety of ethnic patrons; similar to the Houston and Harris County demographics. This research project explored the effectiveness of a pilot diabetes educational intervention in a health museum setting where people chose to visit. ^

IMPROVING QUANTITATIVE TREATMENT RESPONSE MONITORING WITH DEFORMABLE IMAGE REGISTRATION

Quantitative imaging with 18F-FDG PET/CT has the potential to provide an in vivo assessment of response to radiotherapy (RT). However, comparing tissue tracer uptake in longitudinal studies is often confounded by variations in patient setup and potential treatment induced gross anatomic changes. These variations make true response monitoring for the same anatomic volume a challenge, not only for tumors, but also for normal organs-at-risk (OAR). The central hypothesis of this study is that more accurate image registration will lead to improved quantitation of tissue response to RT with 18F-FDG PET/CT. Employing an in-house developed “demons” based deformable image registration algorithm, pre-RT tumor and parotid gland volumes can be more accurately mapped to serial functional images. To test the hypothesis, specific aim 1 was designed to analyze whether deformably mapping tumor volumes rather than aligning to bony structures leads to superior tumor response assessment. We found that deformable mapping of the most metabolically avid regions improved response prediction (P<0.05). The positive predictive power for residual disease was 63% compared to 50% for contrast enhanced post-RT CT. Specific aim 2 was designed to use parotid gland standardized uptake value (SUV) as an objective imaging biomarker for salivary toxicity. We found that relative change in parotid gland SUV correlated strongly with salivary toxicity as defined by the RTOG/EORTC late effects analytic scale (Spearman’s ρ = -0.96, P<0.01). Finally, the goal of specific aim 3 was to create a phenomenological dose-SUV response model for the human parotid glands. Utilizing only baseline metabolic function and the planned dose distribution, predicting parotid SUV change or salivary toxicity, based upon specific aim 2, became possible. We found that the predicted and observed parotid SUV relative changes were significantly correlated (Spearman’s ρ = 0.94, P<0.01). The application of deformable image registration to quantitative treatment response monitoring with 18F-FDG PET/CT could have a profound impact on patient management. Accurate and early identification of residual disease may allow for more timely intervention, while the ability to quantify and predict toxicity of normal OAR might permit individualized refinement of radiation treatment plan designs.

Logistic regression with incompletely observed binary

Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.^ Two methods were developed to estimate logistic regression coefficients for mixed dichotomous and continuous covariates including partially observed binary covariates. The data were assumed missing at random (MAR). One method (PD) used predictive distribution as weight to calculate the average of the logistic regressions performing on all possible values of missing observations, and the second method (RS) used a variant of resampling technique. Additional seven methods were compared with these two approaches in a simulation study. They are: (1) Analysis based on only the complete cases, (2) Substituting the mean of the observed values for the missing value, (3) An imputation technique based on the proportions of observed data, (4) Regressing the partially observed covariates on the remaining continuous covariates, (5) Regressing the partially observed covariates on the remaining continuous covariates conditional on response variable, (6) Regressing the partially observed covariates on the remaining continuous covariates and response variable, and (7) EM algorithm. Both proposed methods showed smaller standard errors (s.e.) for the coefficient involving the partially observed covariate and for the other coefficients as well. However, both methods, especially PD, are computationally demanding; thus for analysis of large data sets with partially observed covariates, further refinement of these approaches is needed. ^