Determination of Background Uranium Concentration in the Snake River Plain Aquifer under the Idaho National Engineering and Environmental Laboratory's Radioactive Waste Management Complex

Uranium occurs naturally in the environment and is also a contaminant that is disposed of at the Radioactive Waste Management Complex (RWMC) at the Idaho National Engineering and Environmental Laboratory. To determine whether uranium concentrations in the Snake River Plain Aquifer, which underlies the laboratory, are elevated as a result of migration of anthropogenic uranium from the Subsurface Disposal Area in the RWMC, uranium background concentrations are necessary. Guideline values are calculated for total uranium, 234U, 235U, and 238U from analytical results from up to five datasets. Three of the datasets include results of samples analyzed using isotope dilution thermal ionization mass spectrometry (ID-TIMS) and two of the datasets include results obtained using alpha spectrometry. All samples included in the statistical testing were collected from aquifer monitoring wells located within 10 miles of the RWMC. Results from ID-TIMS and alpha spectrometry are combined when the data are not statistically different. Guideline values for total uranium were calculated using four of the datasets, while guideline values for 234U were calculated using only the alpha spectrometry results (2 datasets). Data from all five datasets were used to calculate 238U guideline values. No limit is calculated for 235U because the ID-TIMS results are not useful for comparison with routine monitoring data, and the alpha spectrometry results are too close to the detection limit to be deemed accurate or reliable for calculating a 235U guideline value. All guideline values presented represent the upper 95% coverage 95% confidence tolerance limits for background concentration. If a future monitoring result is above this guideline, then the exceedance will be noted in the quarterly monitoring report and assessed with respect to other aquifer information. The guidelines (tolerance limits) for total U, 234U, and 238U are 2.75 pCi/L, 1.92 pCi/L, and 0.90 pCi/L, respectively

A Piece of the Public Health Surveillance Puzzle: Social Contacts among School-Aged Children

OBJECTIVE: To enhance public health surveillance and response for acute respiratory infectious diseases by understanding social contacts among school-aged children INTRODUCTION: Timely and effective public health decision-making for control and prevention of acute respiratory infectious diseases relies on early disease detection, pathogen properties, and information on contact behavior affecting transmission. However, data on contact behavior are currently limited, and when available are commonly obtained from traditional self-reported contact surveys [1, 2]. Information for contacts among school-aged children is especially limited, even though children frequently have higher attack rates than adults, and school-related transmission is commonly predictive of subsequent community-wide outbreaks, especially for pandemic influenza. Within this context, high-quality data are needed about social contacts. Precise contact estimates can be used in mathematical models to understand infectious disease transmission [3] and better target surveillance efforts. Here we report preliminary data from an ongoing 2-year study to collect social contact data on school-aged children and examine the transmission dynamics of an influenza pandemic. METHODS: Our aim is to capture mixing patterns and contact rates of school-aged children in 24 schools and other non-school-related venues. We used a stratified design to ensure coverage of urban, suburban, and rural school districts, as well as climatically different areas (mountains and desert) in Utah. Elementary, middle, and high schools were chosen in each stratum. We defined a self-reported contact as anyone with whom the participant talked to face-to-face, played with, or touched. Contact logs collected subjective information (age, location, and duration) on self-reported contacts during a 2-day period. Objective contact data were collected by using proximity sensors [4] that recorded signals from other sensors within approximately 3–4 feet. Mixing patterns during school and non-school-related activities were summarized for participating school-aged children. We developed contact networks using proximity sensor data, providing visualizations of contact patterns as well as numeric contact measures. Contact networks were characterized with respect to degree distribution, and density. The degree for each person was calculated as the number of unique contacts. The density for a network was calculated as the number of observed contacts divided by the number of possible contacts. RESULTS: Two elementary schools, four summer camps, and one club participated in the study between May and August, 2012. Data were processed for the two schools and one camp. The mean degrees for the two schools were 28 and 29, with network sizes 109 and 129, respectively. The mean degree from camp was 43, whose network size was 141. The density of contacts was 0.26 and 0.22 for the schools and 0.31 for the camp. The density within classrooms at the two schools ranged from 0.78 to 0.98. School-aged children typically underreported contacts using the contact log compared with objective proximity sensor data; this difference was statistically significant. CONCLUSIONS: The variability in these and other contact network characteristics represent factors that could impact influenza transmission. Quantifying these factors improves our understanding of influenza transmission dynamics, which in turn can be used to adapt surveillance methods and control and prevention strategies. Almost all contact among students in our two elementary schools occurs within the classroom and the contact patterns differ by classroom, due to desk arrangement or other characteristics. Thus, during an elementary school outbreak it may be beneficial to focus on classroom-specific surveillance and control strategies. The study is ongoing and we expect the variability in contact rates and mixing patterns will be even greater for middle and high schools where students switch classrooms and classmates each period. These schools could benefit from alternative surveillance and control strategies that account for the heightened overall mixing of the student body

A Piece of the Public Health Surveillance Puzzle: Social Contacts among School-Aged Children

Estimates of contact rates and mixing patterns among school-aged children may be informative for acute respiratory infectious disease surveillance as well as prevention and control activities. We collected contact data from children at school and non-school settings using objective proximity sensors and self-report surveys and logs. Contact rates for school-aged children are variable across settings and among classrooms within schools. Quantifying this variability can be beneficial in better understanding transmission dynamics of acute respiratory diseases and lead to improved surveillance, as well as control and prevention strategies

An Improved Model for Spatially Correlated Binary Responses

In this paper we use covariates and an indication of sampling effort in an autologistic model (Besag, 1974) to improve predictions of probability of presence for lattice data. The model is applied to sampled data where only a small proportion of the available sites have been observed. We adopt a Bayesian set-up and develop a Gibbs sampling estimation procedure. In four examples based on simulated data, we show that the autologistic model with covariates improves predictions as compared to the simple logistic regression model and the basic autologistic model (without covariates). Software to implement the methodology is available at no cost from StatLib. Keywords: Autologistic model, Bayesian estimation, Gibbs sampling, Markov random field. Jennifer Hoeting is Assistant Professor, Molly Leecaster is a Doctoral Candidate, and David Bowden is Professor at Department of Statistics, Colorado State University. Address correspondence to Jennifer Hoeting, Colorado State University, F..

Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics

Abstract Background Seasonal respiratory syncytial virus (RSV) epidemics occur annually in temperate climates and result in significant pediatric morbidity and increased health care costs. Although RSV epidemics generally occur between October and April, the size and timing vary across epidemic seasons and are difficult to predict accurately. Prediction of epidemic characteristics would support management of resources and treatment. Methods The goals of this research were to examine the empirical relationships among early exponential growth rate, total epidemic size, and timing, and the utility of specific parameters in compartmental models of transmission in accounting for variation among seasonal RSV epidemic curves. RSV testing data from Primary Children's Medical Center were collected on children under two years of age (July 2001-June 2008). Simple linear regression was used explore the relationship between three epidemic characteristics (final epidemic size, days to peak, and epidemic length) and exponential growth calculated from four weeks of daily case data. A compartmental model of transmission was fit to the data and parameter estimated used to help describe the variation among seasonal RSV epidemic curves. Results The regression results indicated that exponential growth was correlated to epidemic characteristics. The transmission modeling results indicated that start time for the epidemic and the transmission parameter co-varied with the epidemic season. Conclusions The conclusions were that exponential growth was somewhat empirically related to seasonal epidemic characteristics and that variation in epidemic start date as well as the transmission parameter over epidemic years could explain variation in seasonal epidemic size. These relationships are useful for public health, health care providers, and infectious disease researchers.</p

SaTScan on a Cloud: On-Demand Large Scale Spatial Analysis of Epidemics

By using cloud computing it is possible to provision on- demand resources for epidemic analysis using computer intensive applications like SaTScan. Using 15 virtual machines (VM) on the Nimbus cloud we were able to reduce the total execution time for the same ensemble run from 8896 seconds in a single machine to 842 seconds in the cloud. Using the caBIG tools and our iterative software development methodology the time required to complete the implementation of the SaTScan cloud system took approximately 200 man-hours, which represents an effort that can be secured within the resources available at State Health Departments. The approach proposed here is technically advantageous and practically possible

Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data

<div><p>Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that “sensor-detectable”, proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than “self-reportable” talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.</p></div

Does universal active MRSA surveillance influence anti-MRSA antibiotic use? A retrospective analysis of the treatment of patients admitted with suspicion of infection at Veterans Affairs Medical Centers between 2005 and 2010

After the implementation of an active surveillance programme for MRSA in US Veterans Affairs (VA) Medical Centers, there was an increase in vancomycin use. We investigated whether positive MRSA admission surveillance tests were associated with MRSA-positive clinical admission cultures and whether the availability of surveillance tests influenced prescribers' ability to match initial anti-MRSA antibiotic use with anticipated MRSA results from clinical admission cultures