A Su Salud Injury Prevention expansion: Analysis of injury prevention perceptions in a Hispanic community

Unintentional injury is the leading cause of death for American ages one to 44 and is ranked in the top ten causes of death for all age groups (CDC, 2006a). A Su Salud Injury Prevention was developed to address injury prevention awareness and education. The program is a mass media education campaign that uses role models, mass media, and community outreach to prevent injury. In 2009, University Health System (UHS) expanded the program. Baseline data were collected from 426 residents in targeted neighborhoods northwest of downtown San Antonio to support the expansion. The purpose of this study was to explore injury perceptions, knowledge, and behaviors of adults living in the expansion area, and define the predominant factors associated with these perceptions. A secondary aim was to assess community awareness and willingness to participate in the program.^ Survey results showed motor vehicle crashes (MVC), falls, drinking and driving, and guns and assaults were considered the most serious injures for adults. The most serious child injuries were MVC, abuse and neglect, falls, and head injuries. Residents were knowledgeable of state seatbelt policy, and over 90% responded as compliant for seatbelt and child car seat use. Most were knowledgeable about drinking and driving state policy and negative outcomes. However, 70% of those reporting driving under the influence of alcohol within the last year engaged in repeat high risk behavior. Men and residents under the age of 55 were more likely to engage in repeat drinking and driving (OR= 3.6, 7.0 respectively). Residents consider injury prevention an important issue, and have interest in a local injury prevention program. Younger women are the most likely to participate in a local program as potential role models and volunteers.^ Results from the study are summarized into an injury prevention and demographic profile of the community that will be used to develop tailored injury prevention messages to create a more effective program, and support program coordinators in effective community engagement. Results will also be used as a comparative basis for future evaluation of a behavioral injury prevention program focused on a predominantly Mexican-American community.^

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

External validation study of a clinical prediction rule for ambulation outcomes after traumatic spinal cord injury

The main objective of this study was to determine the external validity of a clinical prediction rule developed by the European Multicenter Study on Human Spinal Cord Injury (EM-SCI) to predict the ambulation outcomes 12 months after traumatic spinal cord injury. Data from the North American Clinical Trials Network (NACTN) data registry with approximately 500 SCI cases were used for this validity study. The predictive accuracy of the EM-SCI prognostic model was evaluated using calibration and discrimination based on 231 NACTN cases. The area under the receiver-operating-characteristics curve (ROC) curve was 0.927 (95% CI 0.894 – 0.959) for the EM-SCI model when applied to NACTN population. This is lower than the AUC of 0.956 (95% CI 0.936 – 0.976) reported for the EM-SCI population, but suggests that the EM-SCI clinical prediction rule distinguished well between those patients in the NACTN population who were able to achieve independent ambulation and those who did not achieve independent ambulation. The calibration curve suggests that higher the prediction score is, the better the probability of walking with the best prediction for AIS D patients. In conclusion, the EM-SCI clinical prediction rule was determined to be generalizable to the adult NACTN SCI population.^