A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

Risk of Second Malignant Neoplasms Following Proton Arc Therapy and Volumetric Modulated Arc Therapy for Prostate Cancer

The risk of second malignant neoplasms (SMNs) following prostate radiotherapy is a concern due to the large population of survivors and decreasing age at diagnosis. It is known that parallel-opposed beam proton therapy carries a lower risk than photon IMRT. However, a comparison of SMN risk following proton and photon arc therapies has not previously been reported. The purpose of this study was to predict the ratio of excess relative risk (RRR) of SMN incidence following proton arc therapy to that after volumetric modulated arc therapy (VMAT). Additionally, we investigated the impact of margin size and the effect of risk-minimized proton beam weighting on predicted RRR. Physician-approved treatment plans were created for both modalities for three patients. Therapeutic dose was obtained with differential dose-volume histograms from the treatment planning system, and stray dose was estimated from the literature or calculated with Monte Carlo simulations. Then, various risk models were applied to the total dose. Additional treatment plans were also investigated with varying margin size and risk-minimized proton beam weighting. The mean RRR ranged from 0.74 to 0.99, depending on risk model. The additional treatment plans revealed that the RRR remained approximately constant with varying margin size, and that the predicted RRR was reduced by 12% using a risk-minimized proton arc therapy planning technique. In conclusion, proton arc therapy was found to provide an advantage over VMAT in regard to predicted risk of SMN following prostate radiotherapy. This advantage was independent of margin size and was amplified with risk-optimized proton beam weighting.

SEVERE HEAD TRAUMA, EMERGENCY TRANSPORT, AND PATIENT OUTCOME

Trauma and severe head injuries are important issues because they are prevalent, because they occur predominantly in the young, and because variations in clinical management may matter. Trauma is the leading cause of death for those under age 40. The focus of this head injury study is to determine if variations in time from the scene of accident to a trauma center hospital makes a difference in patient outcomes.^ A trauma registry is maintained in the Houston-Galveston area and includes all patients admitted to any one of three trauma center hospitals with mild or severe head injuries. A study cohort, derived from the Registry, includes 254 severe head injury cases, for 1980, with a Glasgow Coma Score of 8 or less.^ Multiple influences relate to patient outcomes from severe head injury. Two primary variables and four confounding variables are identified, including time to emergency room, time to intubation, patient age, severity of injury, type of injury and mode of transport to the emergency room. Regression analysis, analysis of variance, and chi-square analysis were the principal statistical methods utilized.^ Analysis indicates that within an urban setting, with a four-hour time span, variations in time to emergency room do not provide any strong influence or predictive value to patient outcome. However, data are suggestive that at longer time periods there is a negative influence on outcomes. Age is influential only when the older group (55-64) is included. Mode of transport (helicopter or ambulance) did not indicate any significant difference in outcome.^ In a multivariate regression model, outcomes are influenced primarily by severity of injury and age which explain 36% (R('2)) of variance. Inclusion of time to emergency room, time to intubation, transport mode and type injury add only 4% (R('2)) additional contribution to explaining variation in patient outcome.^ The research concludes that since the group most at risk to head trauma is the young adult male involved in automobile/motorcycle accidents, more may be gained by modifying driving habits and other preventive measures. Continuous clinical and evaluative research are required to provide updated clinical wisdom in patient management and trauma treatment protocols. A National Institute of Trauma may be required to develop a national public policy and evaluate the many medical, behavioral and social changes required to cope with the country's number 3 killer and the primary killer of young adults.^

Effect of primary care availability and health insurance coverage on risk and cost of Preventable Hospitalizations in Harris County

Preventable Hospitalizations (PHs) are hospitalizations that can be avoided with appropriate and timely care in the ambulatory setting and hence are closely associated with primary care access in a community. Increased primary care availability and health insurance coverage may increase primary care access, and consequently may be significantly associated with risks and costs of PHs. Objective. To estimate the risk and cost of preventable hospitalizations (PHs); to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs, first alone and then simultaneously; and finally, to estimate the impact of expansions in primary care availability and health insurance coverage on the burden of PHs among non-elderly adult residents of Harris County. Methods. The study population was residents of Harris County, age 18 to 64, who had at least one hospital discharge in a Texas hospital in 2008. The primary independent variables were availability of primary care physicians, availability of primary care safety net clinics and health insurance coverage. The primary dependent variables were PHs and associated hospitalization costs. The Texas Health Care Information Collection (THCIC) Inpatient Discharge data was used to obtain information on the number and costs of PHs in the study population. Risk of PHs in the study population, as well as average and total costs of PHs were calculated. Multivariable logistic regression models and two-step Heckman regression models with log-transformed costs were used to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs respectively, while controlling for individual predisposing, enabling and need characteristics. Predicted PH risk and cost were used to calculate the predicted burden of PHs in the study population and the impact of expansions in primary care availability and health insurance coverage on the predicted burden. Results. In 2008, hospitalized non-elderly adults in Harris County had 11,313 PHs and a corresponding PH risk of 8.02%. Congestive heart failure was the most common PH. PHs imposed a total economic burden of $84 billion at an average of $7,449 per PH. Higher primary care safety net availability was significantly associated with the lower risk of PHs in the final risk model, but only in the uninsured. A unit increase in safety net availability led to a 23% decline in PH odds in the uninsured, compared to only a 4% decline in the insured. Higher primary care physician availability was associated with increased PH costs in the final cost model (β=0.0020; p<0.05). Lack of health insurance coverage increased the risk of PH, with the uninsured having 30% higher odds of PHs (OR=1.299; p<0.05), but reduced the cost of a PH by 7% (β=-0.0668; p<0.05). Expansions in primary care availability and health insurance coverage were associated with a reduction of about $1.6 million in PH burden at the highest level of expansion. Conclusions. Availability of primary care resources and health insurance coverage in hospitalized non-elderly adults in Harris County are significantly associated with the risk and costs of PHs. Expansions in these primary care access factors can be expected to produce significant reductions in the burden of PHs in Harris County.^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Proton therapy versus intensity modulated x-ray therapy in the treatment of prostate cancer: Estimating secondary cancer risks

External beam radiation therapy is used to treat nearly half of the more than 200,000 new cases of prostate cancer diagnosed in the United States each year. During a radiation therapy treatment, healthy tissues in the path of the therapeutic beam are exposed to high doses. In addition, the whole body is exposed to a low-dose bath of unwanted scatter radiation from the pelvis and leakage radiation from the treatment unit. As a result, survivors of radiation therapy for prostate cancer face an elevated risk of developing a radiogenic second cancer. Recently, proton therapy has been shown to reduce the dose delivered by the therapeutic beam to normal tissues during treatment compared to intensity modulated x-ray therapy (IMXT, the current standard of care). However, the magnitude of stray radiation doses from proton therapy, and their impact on this incidence of radiogenic second cancers, was not known. ^ The risk of a radiogenic second cancer following proton therapy for prostate cancer relative to IMXT was determined for 3 patients of large, median, and small anatomical stature. Doses delivered to healthy tissues from the therapeutic beam were obtained from treatment planning system calculations. Stray doses from IMXT were taken from the literature, while stray doses from proton therapy were simulated using a Monte Carlo model of a passive scattering treatment unit and an anthropomorphic phantom. Baseline risk models were taken from the Biological Effects of Ionizing Radiation VII report. A sensitivity analysis was conducted to characterize the uncertainty of risk calculations to uncertainties in the risk model, the relative biological effectiveness (RBE) of neutrons for carcinogenesis, and inter-patient anatomical variations. ^ The risk projections revealed that proton therapy carries a lower risk for radiogenic second cancer incidence following prostate irradiation compared to IMXT. The sensitivity analysis revealed that the results of the risk analysis depended only weakly on uncertainties in the risk model and inter-patient variations. Second cancer risks were sensitive to changes in the RBE of neutrons. However, the findings of the study were qualitatively consistent for all patient sizes and risk models considered, and for all neutron RBE values less than 100. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Depressive symptoms and sexual risk behaviors in adolescents

The purpose of this study was to determine whether depression is a factor in explaining the difference in sex behaviors among adolescents with different ethnic backgrounds, family and school contexts. We hypothesize that adolescents with a higher number of depressive symptoms are more likely to engage in sexual risk behaviors than adolescents with fewer depressive symptoms. Further, adolescent depression and sexual behaviors are mediated or moderated by individual characteristics, family and school contexts. ^ Background. large ethnic disparities exist in adolescent engagement in risky sexual behaviors, yet, there is little in the literature that explains these disparities. Studies of sexual behavior of youths abound; yet, there is little literature on the prevalence and correlates of depression or the association between depression and sexual behaviors among different ethnic groups. Objectives. (1) To determine ethnic differences in the prevalence of depressive symptoms using data collected through the National Longitudinal Study of Adolescent Health (Add Health). (2) To determine predictors of sex risk behaviors among adolescents, including the role of depression. (3) To identify predictors of depression among these adolescents. Methods. Add Health data from wave 1 and wave 2 interviews of 7th–12th graders were analyzed using multivariate models constructed with both depression and sexual behavior as outcome variables. Logistic regression models determined whether and to what extent the independent variables, including depression, sex behaviors, demographic factors, individual and family characteristics, and school context were related to the probability of engaging in risky sexual behaviors. Results. Ethnic differences in depressive symptoms did not persist after demographic and contextual variables were included in the model. Sex behaviors all shared the hypothesized relationship with depressive symptoms. The odds of risky sex behaviors increased as number of depressive symptoms increased. Depression was predicted by marijuana use and having a serious argument with father for males at Wave 1 and by age and future orientation for females. Wave 2 depression was predicted by Wave 1 depression. ^

Prevalence and risk factors for human immunodefiency virus and hepatitis C virus co-infection among drug users in innercity neighborhoods in Houston, Texas

Background. There are 200,000 HIV/HCV co-infected people in the US and IDUs are at highest risk of exposure. Between 52-92% of HIV infected IDUs are chronically infected with HCV. African Americans and Hispanics bear the largest burden of co-infections. Furthermore HIV/HCV co-infection is associated with high morbidity and mortality if not treated. The present study investigates the demographic, sexual and drug related risk factors for HIV/HCV co-infection among predominantly African American injecting and non-injecting drug users living in two innercity neighborhoods in Houston, Texas. ^ Methods. This secondary analysis used data collected between February 2004 and June 2005 from 1,889 drug users. Three case-comparison analyses were conducted to investigate the risk factors for HIV/HCV co-infection. HIV mono-infection, HCV mono-infection and non-infection were compared to HIV/HCV co-infection to build multivariate logistic regression models. Race/ethnicity and age were forced into each model regardless of significance in the univariate analysis. ^ Results. The overall prevalence of HIV/HCV co-infection was 3.9% while 39.8% of HIV infected drug users were co-infected with HCV and 10.7% of HCV infected drug users were co-infected with HIV. Among HIV infected IDUs the prevalence of HCV was 71.7% and among HIV infected NIDUs the prevalence of HCV was 24%. In the multivariate analysis, HIV/HCV co-infection was associated with injecting drug use when compared to HIV mono-infection, with MSM when compared to HCV mono-infection and with injecting drug use as well as MSM when compared to non-infection. ^ Conclusion. HIV/HCV co-infection was associated with a combination of sexual and risky injecting practices. More data on the prevalence and risk factors for co-infection among minority populations is urgently needed to support the development of targeted interventions and treatment options. Additionally there should be a focus on promoting safer sex and injecting practices among drug users as well as the expansion of routine testing for HIV and HCV infections in this high risk population.^

Risk factors for encephalitis and death from West Nile virus infection: An analysis of hospitalized cases in Houston, Texas from 2002--2008

We conducted a nested case-control study to determine the significant risk factors for developing encephalitis from West Nile virus (WNV) infection. The purpose of this research project was to expand the previously published Houston study of 2002–2004 patients to include data on Houston patients from four additional years (2005–2008) to determine if there were any differences in risk factors shown to be associated with developing the more severe outcomes of WNV infection, encephalitis and death, by having this larger sample size. A re-analysis of the risk factors for encephalitis and death was conducted on all of the patients from 2002–2008 and was the focus of this proposed research. This analysis allowed for the determination to be made that there are differences in the outcome in the risk factors for encephalitis and death with an increased sample size. Retrospective medical chart reviews were completed for the 265 confirmed WNV hospitalized patients; 153 patients had encephalitis (WNE), 112 had either viral syndrome with fever (WNF) or meningitis (WNM); a total of 22 patients died. Univariate logistic regression analyses on demographic, comorbidities, and social risk factors was conducted in a similar manner as in the previously conducted study to determine the risk factors for developing encephalitis from WNV. A multivariate model was developed by using model building strategies for the multivariate logistic regression analysis. The hypothesis of this study was that there would be additional risk factors shown to be significant with the increase in sample size of the dataset. This analysis with a greater sample size and increased power supports the hypothesis in that there were additional risk factors shown to be statistically associated with the more severe outcomes of WNV infection (WNE or death). Based on univariate logistic regression results, these data showed that even though age of 20–44 years was statistically significant as a protecting effect for developing WNE in the original study, the expanded sample lacked significance. This study showed a significant WNE risk factor to be chronic alcohol abuse, when it was not significant in the original analysis. Other WNE risk factors identified in this analysis that showed to be significant but were not significant in the original analysis were cancer not in remission > 5 years, history of stroke, and chronic renal disease. When comparing the two analyses with death as an outcome, two risk factors that were shown to be significant in the original analysis but not in the expanded dataset analysis were diabetes mellitus and immunosuppression. Three risk factors shown to be significant in this expanded analysis but were not significant in the original study were illicit drug use, heroin or opiate use, and injection drug use. However, with the multiple logistic regression models, the same independent risk factors for developing encephalitis of age and history of hypertension including drug induced hypertension were consistent in both studies.^

Association of part-time employment and sexual risk-taking behaviors among middle school students

Background. Previous research shows inconsistent results as to the association between part-time employment and sexual behavior among younger teens. Studies of older teens cannot be generalized to younger teens because of the wide differences in types of work performed, nature of work environments, and work intensity. Objective. Examine the relationship between part-time employment and sexual behavior in a cross-sectional sample of public middle school students in Houston, Texas. Methods . The study presents a secondary analysis of data from the It’s Your Game…Keep it Real baseline data collection (11/2004–1/2005). It’s Your Game… is an intervention program for middle school students designed to prevent Sexually Transmitted Infections. Statistical analysis. Univariate and multivariate logistic regression analyses were conducted to examine the association between part-time employment and vaginal intercourse: (a) ever had sex; and (b) current sexual activity. Results. Overall, 13.2% of students worked for pay; male students were 1.5 times as likely as females to be working. Of all the students, 11.0% had had sexual intercourse; students who worked were 3 times more likely to be sexually experienced than those who did not. Among students who were sexually experienced, 67.0% were currently sexually active. After adjusting for the other covariates, Hispanic students were almost 3.6 times more likely to report current sexual activity compared to students in other racial/ethnic groups. In univariate analysis, students who worked 1-5 hrs/week were more likely to be sexually experienced than those not currently employed, and the likelihood increased with number of hours worked. There is a similar pattern in the multivariate model, but the odds ratios are too close for the evidence to be more than suggestive. Of sexually experienced students, students working 1-5 hrs/week were 2.7 times more likely to report current sexual intercourse than those not working; those working >5 hrs/week were 4.7 times more likely. The multivariate model showed a similar increase in likelihood, and adjustment for covariates increased these associations: students who worked 1-5 hrs/week were 3.6 times more likely to report current sexual intercourse, and students who worked >5 hrs/week were 4.5 times more likely, than students not currently employed.^

Exploring race/ethnicity as a risk factor for mortality in newborns following congenital heart surgery at a center of excellence

The purpose of this study was to determine if race/ethnicity was a significant risk factor for hospital mortality in children following congenital heart surgery in a contemporary sample of newborns with congenital heart disease. Unlike previous studies that utilized administrative databases, this study utilized clinical data collected at the point of care to examine racial/ethnic outcome differences in the context of the patients' clinical condition and their overall perioperative experience. A retrospective cohort design was used. The study sample consisted of 316 newborns (<31 days of age) who underwent congenital heart surgery between January 2007 through December 2009. A multivariate logistic regression model was used to determine the impact of race/ethnicity, insurance status, presence of a spatial anomaly, prenatal diagnosis, postoperative sepsis, cardiac arrest, respiratory failure, unplanned reoperation, and total length of stay in the intensive care unit on outcomes following congenital heart surgery in newborns. The study findings showed that the strongest predictors of hospital mortality following congenital heart surgery in this cohort were postoperative cardiac arrest, postoperative respiratory failure, having a spatial anomaly, and total ICU LOS. Race/ethnicity and insurance status were not significant risk factors. The institution where this study was conducted is designated as a center of excellence for congenital heart disease. These centers have state-of-the-art facilities, extensive experience in caring for children with congenital heart disease, and superior outcomes. This study suggests that optimal care delivery for newborns requiring congenital heart surgery at a center of excellence portends exceptional outcomes and this benefit is conferred upon the entire patient population despite the race/ethnicity of the patients. From a public health and health services view, this study also contributes to the overall body of knowledge on racial/ethnic disparities in children with congenital heart defects and puts forward the possibility of a relationship between quality of care and racial/ethnic disparities. Further study is required to examine the impact of race/ethnicity on the long-term outcomes of these children as they encounter the disparate components of the health care delivery system. There is also opportunity to study the role of race/ethnicity on the hospital morbidity in these patients considering current expectations for hospital survival are very high, and much of the current focus for quality improvement rests in minimizing the development of patient morbidities.^

Disease risk factors and humoral antibody response to periodontal-associated pathogens: A model to evaluate periodontal disease in older adults

Periodontal diseases (PD) are infectious, inflammatory, and tissue destructive events which affect the periodontal ligament that surround and support the teeth. Periodontal diseases are the major cause of tooth loss after age 35, with gingivitis and periodontitis affecting 75% of the adult population. A select group of bacterial organisms are associated with periodontal pathogenesis. There is a direct association between oral hygiene and prevention of PD. The importance of genetic differences and host immune response capabilities in determining host, susceptibility or resistance to PD has not been established. This study examined the risk factors and serum (humoral) immune response to periodontal diseased-associated pathogens in a 55 to 80+ year old South Texas study sample with PD. This study sample was described by: age, sex, ethnicity, the socioeconomic factors marital status, income and occupation, IgG, IgA, IgM immunoglobulin status, and the autoimmune response markers rheumatoid factor (RF) and antinuclear antibody (ANA). These variables were used to determine the risk factors associated with development of PD. Serum IgG, IgA, IgM antibodies to bacterial antigens provided evidence for disease exposure.^ A causal model for PD was constructed from associations for risk factors (ethnicity, marital status, income, and occupation) with dental exam and periodontitis. The multiple correlation between PD and ethnicity, income and dental exam was significant. Hispanics of low income were least likely to have had a dental exam in the last year and most likely to have PD. The etiologic agents for PD, as evidenced by elevated humoral antibody responses, were the Gram negative microorganisms Bacteroides gingivalis, serotypes FDC381 and SUNYaBA7A1-28, and Wolinella recta. Recommendation for a PD prevention and control program are provided. ^

RESPIRATORY COMPENSATION MECHANISMS IN THE UPPER AND LOWER RESPIRATORY TRACTS OF AN ANIMAL MODEL AFTER SUBCHRONIC INHALATION EXPOSURE TO FORMALDEHYDE: IMPLICATIONS FOR TOXICOLOGY RISK ASSESSMENT (DEPOSITION, DOSE RECEIVED, RESPONSE)

The potential for significant human populations to experience long-term inhalation of formaldehyde and reports of symptomatology due to this exposure has led to a considerable interest in the toxicologic assessment of risk from subchronic formaldehyde exposures using animal models. Since formaldehyde inhalation depresses certain respiratory parameters in addition to its other forms of toxicity, there is a potential for the alteration of the actual dose received by the exposed individual (and the resulting toxicity) due to this respiratory effect. The respiratory responses to formaldehyde inhalation and the subsequent pattern of deposition were therefore investigated in animals that had received subchronic exposure to the compound, and the potential for changes in the formaldehyde dose received due to long-term inhalation evaluated. Male Sprague-Dawley rats were exposed to either 0, 0.5, 3, or 15 ppm formaldehyde for 6 hours/day, 5 days/week for up to 6 months. The patterns of respiratory response, deposition and the compensation mechanisms involved were then determined in a series of formaldehyde test challenges to both the upper and to the lower respiratory tracts in separate groups of subchronically exposed animals and age-specific controls (four concentration groups, two time points). In both the control and pre-exposed animals, there was a characteristic recovery of respiratory parameters initially depressed by formaldehyde inhalation to at or approaching pre-exposure levels within 10 minutes of the initiation of exposure. Also, formaldehyde deposition was found to remain very high in the upper and lower tracts after long-term exposure. Therefore, there was probably little subsequent effect on the dose received by the exposed individual that was attributable to the repeated exposures. There was a diminished initial minute volume response in test challenges of both the upper and lower tracts of animals that had received at least 16 weeks of exposure to 15 ppm, with compensatory increases in tidal volume in the upper tract and respiratory rate in the lower tract. However, this dose-related effect was probably not relevant to human risk estimation because this formaldehyde dose is in excess of that experienced by human populations. ^

ACCURACY OF THE BRCAPRO RISK ASSESSMENT MODEL IN MALES PRESENTING TO MD ANDERSON FOR BRCA TESTING

ACCURACY OF THE BRCAPRO RISK ASSESSMENT MODEL IN MALES PRESENTING TO MD ANDERSON FOR BRCA TESTING Publication No. _______ Carolyn A. Garby, B.S. Supervisory Professor: Banu Arun, M.D. Hereditary Breast and Ovarian Cancer (HBOC) syndrome is due to mutations in BRCA1 and BRCA2 genes. Women with HBOC have high risks to develop breast and ovarian cancers. Males with HBOC are commonly overlooked because male breast cancer is rare and other male cancer risks such as prostate and pancreatic cancers are relatively low. BRCA genetic testing is indicated for men as it is currently estimated that 4-40% of male breast cancers result from a BRCA1 or BRCA2 mutation (Ottini, 2010) and management recommendations can be made based on genetic test results. Risk assessment models are available to provide the individualized likelihood to have a BRCA mutation. Only one study has been conducted to date to evaluate the accuracy of BRCAPro in males and was based on a cohort of Italian males and utilized an older version of BRCAPro. The objective of this study is to determine if BRCAPro5.1 is a valid risk assessment model for males who present to MD Anderson Cancer Center for BRCA genetic testing. BRCAPro has been previously validated for determining the probability of carrying a BRCA mutation, however has not been further examined particularly in males. The total cohort consisted of 152 males who had undergone BRCA genetic testing. The cohort was stratified by indication for genetic counseling. Indications included having a known familial BRCA mutation, having a personal diagnosis of a BRCA-related cancer, or having a family history suggestive of HBOC. Overall there were 22 (14.47%) BRCA1+ males and 25 (16.45%) BRCA2+ males. Receiver operating characteristic curves were constructed for the cohort overall, for each particular indication, as well as for each cancer subtype. Our findings revealed that the BRCAPro5.1 model had perfect discriminating ability at a threshold of 56.2 for males with breast cancer, however only 2 (4.35%) of 46 were found to have BRCA2 mutations. These results are significantly lower than the high approximation (40%) reported in previous literature. BRCAPro does perform well in certain situations for men. Future investigation of male breast cancer and men at risk for BRCA mutations is necessary to provide a more accurate risk assessment.