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Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold-based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT-selected intervention periods performed better than the observed hospital site periods (2004/2005-2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off-season incidence. [ABSTRACT FROM AUTHOR]

The Study to Explore Early Development (SEED) is a multi-site case-control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) to classify children with ASD, (2) examine psychometric properties of different ASD classification methods, including the SEED method that incorporates rules for resolving ADI-R and ADOS discordance, and (3) determine whether restricted interests and repetitive behaviors were noted for children who had instrument discordance resolved using ADI-R social and communication scores. Results support the utility of SEED criteria when well-defined groups of children are an important clinical or research outcome. [ABSTRACT FROM AUTHOR]

Takeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this situation, the Federal Aviation Administration has developed a precipitous terrain classification as a way to impose more restrictive terrain clearances in the vicinity of complex terrain and to mitigate possible altimeter errors and pilot control problems experienced while executing instrument approach procedures. The current precipitous point value (PPV) algorithm relies on the terrain characteristics within a local area of 2 n mi (3.7 km) in radius and is therefore static in time. In this work, we investigate the role of meteorological effects leading to potential aviation hazards over complex terrain, namely, turbulence, altimeter-setting errors, and density-altitude deviations. To that end, we combine observations with high-resolution numerical weather forecasts within a 28 3 28 region over the Rocky Mountains in Colorado containing three airports that are surrounded by precipitous terrain. Both available turbulence reports and model's turbulence forecasts show little correlation with the PPV algorithm for the region analyzed, indicating that the static terrain characteristics cannot generally be used to reliably capture hazardous low-level turbulence events. Altimeter-setting errors and densityaltitude effects are also found to be only very weakly correlated with the PPV algorithm. Altimeter-setting errors contribute to hazardous conditions mainly during cold seasons, driven by synoptic weather systems, whereas density-altitude effects are on the contrary predominantly present during the spring and summer months and follow a very well-marked diurnal evolution modulated by surface radiative effects. These findings demonstrate the effectiveness of high-resolution weather forecast information in determining aviation-relevant hazardous conditions over complex terrain. [ABSTRACT FROM AUTHOR]

Introduction: The Risk Identification Unit (RIU) of the US Dept. of Agriculture’s Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. As part of an initiative to increase the number of species, health issues, and data sources monitored, CEAH epidemiologists are building a surveillance system based on weekly syndromic counts of laboratory test orders in consultation with Colorado State University laboratorians and statistical analysts from the Johns Hopkins University Applied Physics Laboratory. Initial efforts focused on 12 years of equine test records from three state labs. Trial syndrome groups were formed based on RIU experience and published literature. Exploratory analysis, stakeholder input, and laboratory workflow details were needed to modify these groups and filter the corresponding data to eliminate alerting bias. Customized statistical detection methods were sought for effective monitoring based on specialized laboratory information characteristics and on the likely presentation and animal health significance of diseases associated with each syndrome. Methods: Data transformation and syndrome formation focused on test battery type, test name, submitter source organization, and specimen type. We analyzed time series of weekly counts of tests included in candidate syndrome groups and conducted an iterative process of data analysis and veterinary consultation for syndrome refinement and record filters. This process produced a rule set in which records were directly classified into syndromes using only test name when possible, and otherwise, the specimen type or related body system was used with test name to determine the syndrome. Test orders associated with government regulatory programs, veterinary teaching hospital testing protocols, or research projects, rather than clinical concerns, were excluded. We constructed a testbed for sets of 1000 statistical trials and applied a stochastic injection process assuming lognormally distributed incubation periods to choose an alerting algorithm with the syndrome-required sensitivity and an alert rate within the specified acceptable range for each resulting syndrome. Alerting performance of the EARS C3 algorithm traditionally used by CEAH was compared to modified C2, CuSUM, and EWMA methods, with and without outlier removal and adjustments for the total weekly number of non-mandatory tests. Results: The equine syndrome groups adopted for monitoring were abortion/reproductive, diarrhea/GI, necropsy, neurological, respiratory, systemic fungal, and tickborne. Data scales, seasonality, and variance differed widely among the weekly time series. Removal of mandatory and regulatory tests reduced weekly observed counts significantly—by >80% for diarrhea/GI syndrome. The RIU group studied outcomes associated with each syndrome and called for detection of single-week signals for most syndromes with expected false-alert intervals >8 and <52 weeks, 8-week signals for neurological and tickborne monitoring (requiring enhanced sensitivity), 6-week signals for respiratory, and 4-week signals for systemic fungal. From the test-bed trials, recommended methods, settings and thresholds were derived. Conclusions: Understanding of laboratory submission sources, laboratory workflow, and of syndrome-related outcomes are crucial to form syndrome groups for routine monitoring without artifactual alerting. Choices of methods, parameters, and thresholds varied by syndrome and depended strongly on veterinary epidemiologist-specified performance requirements. [ABSTRACT FROM AUTHOR]

Thunderstorms that produce surface hail accumulations, sometimes as large as 60 cm in depth, have significantly affected the residents of the Front Range and High Plains of Colorado and Wyoming by creating hazardous road conditions and endangering lives and property. To date, surface hail accumulation is not part of a routine forecasting or monitoring system. Extensive coordinated hail accumulation reports and operational products designed to identify deep hail accumulating storms in real time are lacking. Kalina et al. used dual-polarization WSR-88D radar observations to calculate hail depth and hail accumulations but never validated the algorithm. This study shows how 20 quality-controlled hail depth reports from the hail depth database built by the Colorado Hail Accumulation from Thunderstorms (CHAT) project are being used to validate the Kalina et al. radar-based hail accumulation algorithm for operational application. The validated algorithm shows increased correlations between radar-derived and reported accumulations for hail depth reports not included in the validation. Furthermore, increases in computational efficiency have allowed the improved algorithm to be used operationally. With an improved hail accumulation algorithm, thunderstorms that produce hail accumulations are more frequently detected than previously reported. [ABSTRACT FROM AUTHOR]

Objective: Clinical evaluation of the efficacy of a new hearing aid binaural beamforming algorithm; specifically, comparing the aided speech recognition of individuals with hearing loss to that of omnidirectional processing, and a second group of participants with normal hearing. Design: Speech recognition in noise was assessed at two different sites using the Oldenburger Satztest (Site 1) and the Hearing In Noise Test (Site 2). The target sentence was presented from 0°, and the competing speech material (uncorrelated sentences of the same speech test) was presented from seven loudspeakers surrounding the participant. Study Sample: The hearing impaired individuals all had bilaterally symmetrical mild-to-moderate downward sloping sensory/neural hearing loss (Site 1: n=29, mean age 68.8; Site 2: n=14, mean age 65.8). A second group of participants were individuals with normal hearing (Site 1: n=15, mean age 64.5; Site 2: n=14; mean age 58.1). Results: Statistical analysis revealed a significant advantage (average SNR improvement of 5.0 dB) for the beamforming algorithm when compared to omnidirectional processing. When the beamforming algorithm was used by the hearing impaired participants, there also was a significant difference between groups at both sites, with better speech recognition performance for the hearing impaired groups fitted with the binaural beamforming hearing aids. Cohen's d revealed a large effect size for this latter finding for both sites. [ABSTRACT FROM AUTHOR]