Survey of safety climate in public hospitals in Costa Rica in 1997

In response to growing concern for occupational health and safety in the public hospital system in Costa Rica, a research program was initiated in 1995 to evaluate and improve the safety climate in the national healthcare system through regional training programs, and to develop the capacity of the occupational health commissions in these settings to improve the identification and mitigation of workplace risks. A cross-sectional survey of 1000 hospital-based healthcare workers was conducted in 1997 to collect baseline data that will be used to develop appropriate worker training programs in occupational health. The objectives of this survey were to: (1) describe the safety climate within the national hospital system, (2) identify factors associated with safety climate focusing on individual and organizational variables, and (3) to evaluate the relationship between safety climate and workplace injuries and safety practices of employees. Individual factors evaluated included the demographic variables of age, gender, education and profession. Organizational factors evaluated included training, psychosocial work environment, job-task demands, availability of protective equipment and administrative controls. Work-related injuries and safety practices of employees included the type and frequency of injuries experienced and reported, and compliance with established safety practices. Multivariate regression analyses demonstrated that training and administrative controls were the two most significant predictors of safety climate. None of the demographic variables were significant predictors of safety climate. Safety climate was inversely and significantly associated with workplace injuries and positively and significantly associated with safety practices. These results suggest that training and administrative controls should be included in future training efforts and that improving safety climate will decrease workplace injuries and increase safety practices. ^

Predictors of weight stability in women and men: The aerobic center longitudinal study, 1987--1995

It is estimated that more than half the U.S. adult population is overweight or obese as classified by a body mass index of 25.0–29.9 or ≥30 kg/m 2, respectively. Since the current treatment approaches for long-term maintenance of weight loss are lacking, the National Institutes of Health state that an effective approach may be to focus on weight gain prevention. There is a limited body of literature describing how adults maintain a stable weight as they age. It is hypothesized that weight stability is the result of a balance between energy consumption and energy expenditure as influenced by diet, lifestyle, behavior, genetics and environment. The purpose of this research was to examine the dietary intake and behaviors, lifestyle habits, and risk factors for weight change that predict weight stability in a cohort of 2101 men and 389 women aged 20 to 8 7 years in the Aerobic Center Longitudinal Study regardless of body weight at baseline. At baseline, participants completed a maximal exercise treadmill test to determine cardiorespiratory fitness, a medical history questionnaire, which included self-reported measures of weight, dietary behaviors, lifestyle habits, and risk factors for weight change, a three-day diet record, and a mail-back version of the medical history questionnaire in 1990 or 1995. All analyses were performed separately for men and women. Results from multivariate regression analyses indicated that the strongest predictor of follow-up weight for men and women was previous weight, accounting for 87.0% and 81.9% of the variance, respectively. Age, length of follow-up and eating habits were also significant predictors of follow-up weight in men, though these variables only explained 3% of the variance. For women, length of follow-up and currently being on a diet were significantly associated with follow-up weight but these variables explained only an additional 2% of the variance. Understanding the factors that influence weight change has tremendous public health importance for developing effective methods to prevent weight gain. Since current weight was the strongest predictor of previous weight, preventing initial weight gain by maintaining a stable weight may be the most effective method to combat the increasing prevalence of overweight and obesity. ^

Case-control study of factors associated with virologic failure in HIV-infected children on HAART in Botswana, a developing African country

According to the United Nations Program on HIV/AIDS (UNAIDS, 2008), in 2007 about 67 per cent of all HIV-infected patients in the world were in Sub-Saharan Africa, with 35% of new infections and 38% of the AIDS deaths occurring in Southern Africa. Globally, the number of children younger than 15 years of age infected with HIV increased from 1.6 million in 2001 to 2.0 million in 2007 and almost 90% of these were in Sub-Saharan Africa. (UNAIDS, 2008).^ Both clinical and laboratory monitoring of children on Highly Active Anti-Retroviral Therapy (HAART) are important and necessary to optimize outcomes. Laboratory monitoring of HIV viral load and genotype resistance testing, which are important in patient follow-up to optimize treatment success, are both generally expensive and beyond the healthcare budgets of most developing countries. This is especially true for the impoverished Sub-Saharan African nations. It is therefore important to identify those factors that are associated with virologic failure in HIV-infected Sub-Saharan African children. This will inform practitioners in these countries so that they can predict which patients are more likely to develop virologic failure and therefore target the limited laboratory monitoring budgets towards these at-risk patients. The objective of this study was to examine those factors that are associated with virologic failure in HIV-infected children taking Highly Active Anti-retroviral Therapy in Botswana, a developing Sub-Saharan African country. We examined these factors in a Case-Control study using medical records of HIV-infected children and adolescents on HAART at the Botswana-Baylor Children's Clinical Center of Excellence (BBCCCOE) in Gaborone, Botswana. Univariate and Multivariate Regression Analyses were performed to identify predictors of virologic failure in these children.^ The study population comprised of 197 cases (those with virologic failure) and 544 controls (those with virologic success) with ages ranging from 3 months to 16 years at baseline. Poor adherence (pill count <95% on at least 3 consecutive occasions) was the strongest independent predictor of virologic failure (adjusted OR = 269.97, 95% CI = 104.13 to 699.92; P < 0.001). Other independent predictors of virologic failure identified were: First Line NNRTI with Nevirapine (OR = 2.99, 95% CI = 1.19 to7.54; P = 0.020), Baseline HIV-1 Viral Load >750,000/ml (OR = 257, 95% CI = 1.47 to 8.63; P = 0.005), Positive History of PMTCT (OR = 11.65, 95% CI = 3.04-44.57; P < 0.001), Multiple Care-givers (>=3) (OR = 2.56, 95% CI = 1.06 to 6.19; P = 0.036) and Residence in a Village (OR = 2.85, 95% CI = 1.36 to 5.97; P = 0.005).^ The results of this study may help to improve virologic outcomes and reduce the costs of caring for HIV-infected children in resource-limited settings. ^ Keywords: Virologic Failure, Highly Active Anti-Retroviral Therapy, Sub-Saharan Africa, Children, Adherence.^

Ethnic differences in tumor characteristics, treatment and survival in glioma patients

Purpose. A descriptive analysis of glioma patients by race was carried out in order to better elucidate potential differences between races in demographics, treatment, characteristics, prognosis and survival. ^ Patients and Methods. Among 1,967 patients ≥ 18 years diagnosed with glioma seen between July 2000 and September 2006 at The University of Texas M.D. Anderson Cancer Center (UTMDACC). Data were collated from the UTMDACC Patient History Database (PHDB) and the UTMDACC Tumor Registry Database (TRDB). Chi-square analysis, uni- /multivariate Cox proportional hazards modeling and survival analysis were used to analyze differences by race. ^ Results. Demographic, treatment and histologic differences exist between races. Though risk differences were seen between races, race was not found to be a significant predictor in multivariate regression analysis after accounting for age, surgery, chemotherapy, radiation, tumor type as stratified by WHO tumor grade. Age was the most consistent predictor in risk for death. Overall survival by race was significantly different (p=0.0049) only in low-grade gliomas after adjustment for age although survival differences were very slight. ^ Conclusion. Among this cohort of glioma patients, age was the strongest predictor for survival. It is likely that survival is more influenced by age, time to treatment, tumor grade and surgical expertise rather than racial differences. However, age at diagnosis, gender ratios, histology and history of cancer differed significantly between race and genetic differences to this effect cannot be excluded. ^

The association between mental health and hypertension in the 2005 Department of Defense Population Survey

Major objectives within Healthy People 2010 include improving hypertension and mental health management of the American population. Both mental health issues and hypertension exist in the military which may decrease the health status of military personnel and diminish the ability to complete assigned missions. Some cases may be incompatible with military service even with optimum treatment. In the interest of maintaining a fit fighting force, the Department of Defense regularly conducts a survey of health related behaviors among active duty military personnel. The 2005 DoD Survey was conducted to obtain information regarding health and behavioral readiness among active duty military personnel to assess progress toward selected Healthy People 2010 objectives. ^ This study is a cross-sectional prevalence design looking at the association of hypertension treatment with mental health issues (either treatment or perceived need for treatment) within the military population sampled in the 2005 DoD Survey. There were 16,946 military personnel in the final cross-sectional sample representing 1.3 million active duty service members. The question is whether there is a significant association between the self-reported occurrence of hypertension and the self-reported occurrence of mental health issues in the 2005 DoD Survey. In addition to these variables, this survey examined the contribution of various sociodemographic, occupational, and behavioral covariates. An analysis of the demographic composition of the study variables was followed by logistic analysis, comparing outcome variables with each of the independent variables. Following univariate regression analysis, multivariate regression was performed with adjustment (for those variables with an unadjusted alpha level less than or equal to 0.25). ^ All the mental health related indicators were associated with hypertension treatment. The same relationship was maintained after multivariate adjustment. The covariates remaining as significant (p < 0.05) in the final model included gender, age, race/ethnicity and obesity. There is a need to recognize and treat co-morbid medical diagnoses among mental health patients and to improve quality of life outcomes, whether in the military population or the general population. Optimum health of the individual can be facilitated through discovery of treatable cases, to minimize disruptions of military missions, and even allow for continued military service. ^

Genetic variations in the PI3K-AKT-mTOR pathway and bladder cancer susceptibility and clinical outcomes

Bladder cancer is the fourth most common cancer in men in the United States. There is compelling evidence supporting that genetic variations contribute to the risk and outcomes of bladder cancer. The PI3K-AKT-mTOR pathway is a major cellular pathway involved in proliferation, invasion, inflammation, tumorigenesis, and drug response. Somatic aberrations of PI3K-AKT-mTOR pathway are frequent events in several cancers including bladder cancer; however, no studies have investigated the role of germline genetic variations in this pathway in bladder cancer. In this project, we used a large case control study to evaluate the associations of a comprehensive catalogue of SNPs in this pathway with bladder cancer risk and outcomes. Three SNPs in RAPTOR were significantly associated with susceptibility: rs11653499 (OR: 1.79, 95%CI: 1.24–2.60), rs7211818 (OR: 2.13, 95%CI: 1.35–3.36), and rs7212142 (OR: 1.57, 95%CI: 1.19–2.07). Two haplotypes constructed from these 3 SNPs were also associated with bladder cancer risk. In combined analysis, a significant trend was observed for increased risk with an increase in the number of unfavorable genotypes (P for trend<0.001). Classification and regression tree analysis identified potential gene-environment interactions between RPS6KA5 rs11653499 and smoking. In superficial bladder cancer, we found that PTEN rs1234219 and rs11202600, TSC1 rs7040593, RAPTOR rs901065, and PIK3R1 rs251404 were significantly associated with recurrence in patients receiving BCG. In muscle invasive and metastatic bladder cancer, AKT2 rs3730050, PIK3R1 rs10515074, and RAPTOR rs9906827 were associated with survival. Survival tree analysis revealed potential gene-gene interactions: patients carrying the unfavorable genotypes of PTEN rs1234219 and TSC1 rs704059 exhibited a 5.24-fold (95% CI: 2.44–11.24) increased risk of recurrence. In combined analysis, with the increasing number of unfavorable genotypes, there was a significant trend of higher risk of recurrence and death (P for trend<0.001) in Cox proportional hazard regression analysis, and shorter event (recurrence and death) free survival in Kaplan-Meier estimates (P log rank<0.001). This study strongly suggests that genetic variations in PI3K-AKT-mTOR pathway play an important role in bladder cancer development. The identified SNPs, if validated in further studies, may become valuable biomarkers in assessing an individual's cancer risk, predicting prognosis and treatment response, and facilitating physicians to make individualized treatment decisions. ^

Racial differences in heart failure with preserved ejection fraction in the ambulatory heart failure population

Racial differences in heart failure with preserved ejection fraction (HFpEF) have rarely been studied in an ambulatory, financially "equal access" cohort, although the majority of such patients are treated as outpatients. ^ Retrospective data was collected from 2,526 patients (2,240 Whites, 286 African American) with HFpEF treated at 153 VA clinics, as part of the VA External Peer Review Program (EPRP) between October 2000 and September 2002. Kaplan Meier curves (stratified by race) were created for time to first heart failure (HF) hospitalization, all cause hospitalization and death and Cox proportional multivariate regression models were constructed to evaluate the effect of race on these outcomes. ^ African American patients were younger (67.7 ± 11.3 vs. 71.2 ± 9.8 years; p < 0.001), had lower prevalence of atrial fibrillation (24.5 % vs. 37%; p <0.001), chronic obstructive pulmonary disease (23.4 % vs. 36.9%, p <0.001), but had higher blood pressure (systolic blood pressure > 120 mm Hg 77.6% vs. 67.8%; p < 0.01), glomerular filtration rate (67.9 ± 31.0 vs. 61.6 ± 22.6 mL/min/1.73 m2; p < 0.001), anemia (56.6% vs. 41.7%; p <0.001) as compared to whites. African Americans were found to have higher risk adjusted rate of HF hospitalization (HR 1.52, 95% CI 1.1 - 2.11; p = 0.01), with no difference in risk-adjusted all cause hospitalization (p = 0.80) and death (p= 0.21). ^ In a financially "equal access" setting of the VA, among ambulatory patients with HFpEF, African Americans have similar rates of mortality and all cause hospitalization but have an increased risk of HF hospitalizations compared to whites.^

Ascertainment, meta-analysis and reporting of adverse reactions in clinical trials of antibiotics

The ascertainment and analysis of adverse reactions to investigational agents presents a significant challenge because of the infrequency of these events, their subjective nature and the low priority of safety evaluations in many clinical trials. A one year review of antibiotic trials published in medical journals demonstrates the lack of standards in identifying and reporting these potentially fatal conditions. This review also illustrates the low probability of observing and detecting rare events in typical clinical trials which include fewer than 300 subjects. Uniform standards for ascertainment and reporting are suggested which include operational definitions of study subjects. Meta-analysis of selected antibiotic trials using multivariate regression analysis indicates that meaningful conclusions may be drawn from data from multiple studies which are pooled in a scientifically rigorous manner. ^

A QUANTITATIVE INVESTIGATION OF SOURCE AND ELEMENTAL COMPOSITION OF INDOOR AND OUTDOOR FINE PARTICULATE AIR POLLUTION IN HOUSTON (TEXAS)

An investigation was undertaken to determine the chemical characterization of inhalable particulate matter in the Houston area, with special emphasis on source identification and apportionment of outdoor and indoor atmospheric aerosols using multivariate statistical analyses.^ Fine (<2.5 (mu)m) particle aerosol samples were collected by means of dichotomous samplers at two fixed site (Clear Lake and Sunnyside) ambient monitoring stations and one mobile monitoring van in the Houston area during June-October 1981 as part of the Houston Asthma Study. The mobile van allowed particulate sampling to take place both inside and outside of twelve homes.^ The samples collected for 12-h sampling on a 7 AM-7 PM and 7 PM-7 AM (CDT) schedule were analyzed for mass, trace elements, and two anions. Mass was determined gravimetrically. An energy-dispersive X-ray fluorescence (XRF) spectrometer was used for determination of elemental composition. Ion chromatography (IC) was used to determine sulfate and nitrate.^ Average chemical compositions of fine aerosol at each site were presented. Sulfate was found to be the largest single component in the fine fraction mass, comprising approximately 30% of the fine mass outdoors and 12% indoors, respectively.^ Principal components analysis (PCA) was applied to identify sources of aerosols and to assess the role of meteorological factors on the variation in particulate samples. The results suggested that meteorological parameters were not associated with sources of aerosol samples collected at these Houston sites.^ Source factor contributions to fine mass were calculated using a combination of PCA and stepwise multivariate regression analysis. It was found that much of the total fine mass was apparently contributed by sulfate-related aerosols. The average contributions to the fine mass coming from the sulfate-related aerosols were 56% of the Houston outdoor ambient fine particulate matter and 26% of the indoor fine particulate matter.^ Characterization of indoor aerosol in residential environments was compared with the results for outdoor aerosols. It was suggested that much of the indoor aerosol may be due to outdoor sources, but there may be important contributions from common indoor sources in the home environment such as smoking and gas cooking. ^

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.^

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

Genetic variants in the mammalian target of rapamycin (mTOR) signaling pathway as predictors of survival and clinical response in women with ovarian cancer

Background. The mTOR pathway is commonly altered in human tumors and promotes cell survival and proliferation. Preliminary evidence suggests this pathway's involvement in chemoresistance to platinum and taxanes, first line therapy for epithelial ovarian cancer. A pathway-based approach was used to identify individual germline single nucleotide polymorphisms (SNPs) and cumulative effects of multiple genetic variants in mTOR pathway genes and their association with clinical outcome in women with ovarian cancer. ^ Methods. The case-series was restricted to 319 non-Hispanic white women with high grade ovarian cancer treated with surgery and platinum-based chemotherapy. 135 SNPs in 20 representative genes in the mTOR pathway were genotyped. Hazard ratios (HRs) for death and Odds ratios (ORs) for failure to respond to primary therapy were estimated for each SNP using the multivariate Cox proportional hazards model and multivariate logistic regression model, respectively, while adjusting for age, stage, histology and treatment sequence. A survival tree analysis of SNPs with a statistically significant association (p<0.05) was performed to identify higher order gene-gene interactions and their association with overall survival. ^ Results. There was no statistically significant difference in survival by tumor histology or treatment regimen. The median survival for the cohort was 48.3 months. Seven SNPs were significantly associated with decreased survival. Compared to those with no unfavorable genotypes, the HR for death increased significantly with the increasing number of unfavorable genotypes and women in the highest risk category had HR of 4.06 (95% CI 2.29–7.21). The survival tree analysis also identified patients with different survival patterns based on their genetic profiles. 13 SNPs on five different genes were found to be significantly associated with a treatment response, defined as no evidence of disease after completion of primary therapy. Rare homozygous genotype of SNP rs6973428 showed a 5.5-fold increased risk compared to the wild type carrying genotypes. In the cumulative effect analysis, the highest risk group (individuals with ≥8 unfavorable genotypes) was significantly less likely to respond to chemotherapy (OR=8.40, 95% CI 3.10–22.75) compared to the low risk group (≤4 unfavorable genotypes). ^ Conclusions. A pathway-based approach can demonstrate cumulative effects of multiple genetic variants on clinical response to chemotherapy and survival. Therapy targeting the mTOR pathway may modify outcome in select patients.^