Factors determining the use of universal precautions by emergency department nurses

The Health Belief Model (HBM) provided the theoretical framework for examining Universal Precautions (UP) compliance factors by Emergency Department nurses. A random sample of Emergency Nurses Association (ENA) clinical nurses (n = 900) from five states (New York, New Jersey, California, Texas, and Florida), were surveyed to explore the factors related to their decision to comply with UP. Five-hundred-ninety-eight (598) useable questionnaires were analyzed. The responders were primarily female (84.9%), hospital based (94.6%), staff nurses (66.6%) who had a mean 8.5 years of emergency nursing experience. The nurses represented all levels of hospitals from rural (4.5%) to urban trauma centers (23.7%). The mean UP training hours was 3.0 (range 0-38 hours). Linear regression was used to analyze the four hypotheses. The first hypothesis evaluating perceived susceptibility and seriousness with reported UP use was not significant (p = $>$.05). Hypothesis 2 tested perceived benefits with internal and external barriers. Both perceived benefits and internal barriers as well as the overall regression were significant (F = 26.03, p = $<$0.001). Hypothesis 3 which tested modifying factors, cues to action, select demographic variables, and the main effects of the HBM with self reported UP compliance, was also significant (F = 12.39, p = $<$0.001). The additive effects were tested by use of a stepwise regression that assessed the contribution of each of the significant variables. The regression was significant (F = 12.39, p = $<$0.001) and explained 18% of the total variance. In descending order of contribution, the significant variables related to compliance were: internal barriers (t = $-$6.267; p = $<$0.001) such as the perception that because of the nature of the emergency care environment there is sometimes inadequate time to put on UP; cues to action (t = 3.195; p = 0.001) such as posted reminder signs or verbal reminders from peers; the number of Universal Precautions training hours (t = 3.667; p = $<$0.001) meaning that as the number of training hours increase so does compliance; perceived benefits (t = 3.466; p = 0.001) such as believing that UP will provide adequate barrier protection; and perceived susceptibility (t = 2.880; p = 0.004) such as feeling that they are at risk of exposure. ^

Variance of familial dietary intake

Although many family-based genetic studies have collected dietary data, very few have used the dietary information in published findings. No single solution has been presented or discussed in the literature to deal with the problem of using factor analyses for the analyses of dietary data from several related individuals from a given household. The standard statistical approach of factor analysis cannot be applied to the VIVA LA FAMILIA Study diet data to ascertain dietary patterns since this population consists of three children from each family, thus the dietary patterns of the related children may be correlated and non-independent. Addressing this problem in this project will enable us to describe the dietary patterns in Hispanic families and to explore the relationships between dietary patterns and childhood obesity. ^ In the VIVA LA FAMILIA Study, an overweight child was first identified and then his/her siblings and parents were brought in for data collection which included 24 hour recalls and food frequency questionnaire (FFQ). Dietary intake data were collected using FFQ and 24 hour recalls on 1030 Hispanic children from 319 families. ^ The design of the VIVA LA FAMILIA Study has important and unique statistical considerations since its participants are related to each other, the majority form distinct nuclear families. Thus, the standard approach of factor analysis cannot be applied to these diet data to ascertain dietary patterns. In this project we propose to investigate whether the determinants of the correlation matrix of each family unit will allow us to adjust the original correlation matrix of the dietary intake data prior to ascertaining dietary intake patterns. If these methods are appropriate, then in the future the dietary patterns among related individuals could be assessed by standard orthogonal principal component factor analysis.^

Effects of air pollution on asthma hospitalization rates in different age groups in metropolitan cities of Korea

Many studies have shown relationships between air pollution and the rate of hospital admissions for asthma. A few studies have controlled for age-specific effects by adding separate smoothing functions for each age group. However, it has not yet been reported whether air pollution effects are significantly different for different age groups. This lack of information is the motivation for this study, which tests the hypothesis that air pollution effects on asthmatic hospital admissions are significantly different by age groups. Each air pollutant's effect on asthmatic hospital admissions by age groups was estimated separately. In this study, daily time-series data for hospital admission rates from seven cities in Korea from June 1999 through 2003 were analyzed. The outcome variable, daily hospital admission rates for asthma, was related to five air pollutants which were used as the independent variables, namely particulate matter <10 micrometers (μm) in aerodynamic diameter (PM10), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). Meteorological variables were considered as confounders. Admission data were divided into three age groups: children (<15 years of age), adults (ages 15-64), and elderly (≥ 65 years of age). The adult age group was considered to be the reference group for each city. In order to estimate age-specific air pollution effects, the analysis was separated into two stages. In the first stage, Generalized Additive Models (GAMs) with cubic spline for smoothing were applied to estimate the age-city-specific air pollution effects on asthmatic hospital admission rates by city and age group. In the second stage, the Bayesian Hierarchical Model with non-informative prior which has large variance was used to combine city-specific effects by age groups. The hypothesis test showed that the effects of PM10, CO and NO2 were significantly different by age groups. Assuming that the air pollution effect for adults is zero as a reference, age-specific air pollution effects were: -0.00154 (95% confidence interval(CI)= (-0.0030,-0.0001)) for children and 0.00126 (95% CI = (0.0006, 0.0019)) for the elderly for PM 10; -0.0195 (95% CI = (-0.0386,-0.0004)) for children for CO; and 0.00494 (95% CI = (0.0028, 0.0071)) for the elderly for NO2. Relative rates (RRs) were 1.008 (95% CI = (1.000-1.017)) in adults and 1.021 (95% CI = (1.012-1.030)) in the elderly for every 10 μg/m3 increase of PM10 , 1.019 (95% CI = (1.005-1.033)) in adults and 1.022 (95% CI = (1.012-1.033)) in the elderly for every 0.1 part per million (ppm) increase of CO; 1.006 (95%CI = (1.002-1.009)) and 1.019 (95%CI = (1.007-1.032)) in the elderly for every 1 part per billion (ppb) increase of NO2 and SO2, respectively. Asthma hospital admissions were significantly increased for PM10 and CO in adults, and for PM10, CO, NO2 and SO2 in the elderly.^

An exploratory analysis of variance and volatility in epileptic electroencephalograms

The electroencephalogram (EEG) is a physiological time series that measures electrical activity at different locations in the brain, and plays an important role in epilepsy research. Exploring the variance and/or volatility may yield insights for seizure prediction, seizure detection and seizure propagation/dynamics.^ Maximal Overlap Discrete Wavelet Transforms (MODWTs) and ARMA-GARCH models were used to determine variance and volatility characteristics of 66 channels for different states of an epileptic EEG – sleep, awake, sleep-to-awake and seizure. The wavelet variances, changes in wavelet variances and volatility half-lives for the four states were compared for possible differences between seizure and non-seizure channels.^ The half-lives of two of the three seizure channels were found to be shorter than all of the non-seizure channels, based on 95% CIs for the pre-seizure and awake signals. No discernible patterns were found the wavelet variances of the change points for the different signals. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Use of universal precautions in the workplace: A comparison of two fire /EMS services

The Health Belief Model (HBM) provided the theoretical framework for examining Universal Precautions (UP) compliance factors by Firefighter, EMTs and Paramedics (prehospital care providers). A convenient sample of prehospital care providers (n = 4000) from two cities (Houston and Washington DC), were surveyed to explore the factors related to their decision to comply with Universal Precautions. Eight hundred and sixty-five useable questionnaires were analyzed. The responders were primarily male (95.7%) eight hundred and twenty-eight and thirty-seven were female, prehospital based (100%), EMTs (60.0%) and paramedics (12.8%) who had a mean 13 years of prehospital care experience. ^ Linear regression was used to evaluate the four hypotheses. The first hypothesis evaluating perceived susceptibility and seriousness with reported UP use was statistically significant (p = < .05). Perceived susceptibility, when considered independently, did not make a significant contribution (t = −4.2852; p = 0.0000) to the stated use of Universal precautions. The hypothesis is not supported as stated. The data indicates the opposite effect. Supported is the premise that as perceived susceptibility and perceived seriousness increase the use of Universal Precautions decreases. Hypothesis two tested perceived benefits with internal and external barriers. Both perceived benefits and internal and external barriers as well as the overall regression were significant (F = 112.6, p = 0.0000). The contribution of internal and external barriers was statistically significant (t = 0.0175; p = 0.0000) and (t = 0.0128; p = 0.0000). Hypothesis three which tested modifying factors, cues to action, select demographic variables, and the main effects of the HBM with self reported UP compliance overall was significant. The variables gender, birth, education, job type, EMS certification, years of service, years of experience providing patient care, Universal Precautions training hours, type of apparatus assigned to and the number of EMS related incidents responded to in a month were found to have a significant contribution to the stated use of Universal Precautions. ^ The additive effects were tested by use of a stepwise regression that assessed the contribution of each of the significant variables. Three variables in the equation were statistically significant. Internal barriers (t = −8.5507; p = 0.0000), external barriers (t = −6.2862; p = 0.000) and job type 2 & 3. Job type two (t = −2.8464; p = 0.0045 is titled Engineer/Operator. Job type three (t = −2.5730; p = 0.0103) is titled captain. The overall regression was significant (F = 24.06; p = 0.000). The Hypothesis is supported in the certain demographic variables do influence the stated use of Universal precautions and that as internal and external barriers are decreased, there is an increase in the stated use of Universal Precautions. ^ In summary, this study demonstrated that internal and external barriers have a significant impact on the stated use of Universal Precautions. Internal barriers are those factors within the individual that require an internal change (i.e., forgetfulness, freedom, perception of the urgency of the patient's needs etc.) and external barriers are things in the environment that can be altered (i.e., equipment design, availability of equipment, ease of use). These two model variables explained 23%–30% of the variance. ^