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Viral respiratory tract diseases pose serious public health problems. Our ability to predict and thus, be able to prepare for outbreaks is strained by the complex factors driving the prevalence and severity of these diseases. The abundance of diseases and transmission dynamics of strains are not only affected by external factors, such as weather, but also driven by interactions among viruses mediated by human behavior and immunity. To untangle the complex out-of-phase annual and biennial pattern of three common paramyxoviruses. Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus (HPIV), and Human Metapneumovirus (hMPV), we adopt a theoretical approach that integrates ecological and immunological mechanisms of disease interactions. By estimating parameters from multiyear time series of laboratory-confirmed cases from the intermountain west region of the United States and using statistical inference, we show that models of immune-mediated interactions better explain the data than those based on ecological competition by convalescence. The strength of cross-protective immunity among viruses is correlated with their genetic distance in the phylogenetic tree of the paramyxovirus family. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BACKGROUND. Influenza causes significant morbidity among children. Previous studies used indirect case ascertainment methods with little cost data. We sought to measure the burden of laboratory-confirmed influenza from hospitalized children. METHODS. We conducted a retrospective cohort study during 3 viral seasons at Primary Children's Medical Center (Salt Lake City, UT). Children ≤18 years of age who were hospitalized with laboratory-confirmed influenza infection were included. Outcomes included hospitalization rates, complications including intensive care unit stays, mechanical ventilation, length of stay, and total hospital costs. RESULTS. A total of 325 children had hospitalizations attributable to influenza over 3 viral seasons: 28% <6 months of age, 33% between 6 and 23 months of age; and 39% >2 years of age; 37% had high-risk medical conditions. Population-based rates of hospitalization for Salt Lake County residents ranged from 6.3 to 252.7 per 100 000 children. The highest rates were in children younger than 6 months, and rates decreased with increasing age. Forty-nine (15%) children had an ICU stay; 27 required mechanical ventilation, and half of these patients were >2 years of age. Total hospital cost for the cohort was $2 million; 55% was accounted for by children >2 years of age. Length of stay and total hospital costs were significantly higher in all children >2 years of age compared with children <6 months of age and were comparable to all children 6 to 23 months of age. CONCLUSIONS. Proven influenza infection in children results in substantial hospital resource utilization and morbidity. Nationwide, the median hospital costs may total $55 million. Our data support the Advisory Committee on Immunization's recommendations to expand the use of influenza vaccine to children >2 years of age. [ABSTRACT FROM AUTHOR]

Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data. [ABSTRACT FROM AUTHOR]

The article focuses on the role of health information technology implementation in improving medical care workflow. There are potential dangers of computerized physician order entry (CPOE) implementation, including the workflow changes that were necessary to use CPOE functions for ordering, verification and dispensing medication. The huge potential for confounding and the lack of substantiated causes, the crude mortality risk difference between study periods was real and unfortunate. Computer-assisted interventions have helped process outcomes such as medication errors.

Purpose Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases. Methods A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years). Results The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. Conclusions Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models. [ABSTRACT FROM AUTHOR]

Context. Poison control centers (PCCs) and emergency departments (EDs) rely upon telephone communication to collaborate. PCCs and EDs each create electronic records for the same patient during the course of collaboration, but those electronic records are not shared. Objective. The purpose of this study was to describe the current, telephone based process of PCC-ED communication as the basis for potential process improvement. Materials and methods. This study was conducted at one PCC and two tertiary care EDs. We developed workflow diagrams to depict clinician descriptions of the current process, descriptions obtained through interviews of key informants. We also analyzed transcripts of phone calls between emergency departments and the poison control center, corresponding to a random sample of 120 PCC cases occurring January 1-December 31, 2011. Results. Collaboration between the ED and PCC takes place during multiple telephone calls, and the process is unsupported by shared documentation. The process occurs in three phases: notification, collaborative care, and ongoing consultation. In the ED, multiple care providers may communicate with the PCC, but only one ED care provider communicates with the poison control center specialist at a time. Handoffs occur for both ED and PCC. Collaborative care planning is common and most cases involve some type of request for information, whether vital signs, laboratory results, or verification that a treatment was administered. We found evidence of inefficiencies and safety vulnerabilities, including the inability of PCC specialists to reach ED care providers, telephone calls routed through multiple ED staff members in an attempt to reach the appropriate care provider, and exchange of clinical information with non-clinical staff. In 55% of cases, the patient was discharged prior to any synchronous telephone communication between the ED care provider and a PCC specialist. Ambiguous communication of information was observed in 22% of cases. In 12% of cases, a PCC specialist was unable to obtain requested information from the ED. Discussion and conclusion. Inefficiencies and vulnerabilities occur in telephone-based PCC-ED communication. Prudence begs consideration of alternative processes and models of ED-PCC communication and information sharing, including a process that supports collaboration with health information exchange. [ABSTRACT FROM AUTHOR]

Context. The US emergency departments and poison control centers use telephone communication to exchange information about poison exposed patients. Electronically exchanged patient information could better support care for poisoned patients by improving information availability for decision making and by decreasing unnecessary emergency department telephone interruptions. As federal initiatives push to increase clinical health information exchange (HIE), it is essential to assess the readiness of US poison control centers. We conducted a nationwide Delphi study to determine consensus on legal, operational, and clinical considerations that are important for electronic information exchange between emergency departments and poison control centers. Materials and methods. A national panel of US experts (n = 71) in emergency medicine and poison control participated in a Delphi study, September-December 2010. Panelists rated statements describing concepts related to implementation, adoption, or potential outcomes of electronic information exchange between emergency departments and poison control centers. The statements reflected panelist responses to initial open-ended questions and literature-based concepts. Results. A total of 71 panelists agreed to participate. The response rate for each round ranged from 0.73 to 0.77. Most (114/121) statements reached consensus. Seven statements failed to reach consensus. Panelists indicated that user involvement in the design of systems and tools is important. Workflow integration, safety, evidence of benefit, and outcomes are high-importance issues. Discussion/conclusions. Future research and development related to electronic information exchange should address high-importance issues: safety, patient outcomes, workflow integration, and evidence of benefit. It should also address key barriers: initial and ongoing costs associated with electronic information exchange, the absence of software and tools to facilitate exchange, and the need for training. Users should be involved in the design of an electronic information exchange process, and the process should support, not replace, verbal communication. [ABSTRACT FROM AUTHOR]

In this retrospective cohort study, a clinical and administrative database of children hospitalized at Primary Children''s Medical Center, Salt Lake City, Utah, between January 1, 2002, and December 31, 2006, was used to identify those with laboratory-confirmed rotavirus infections and at least one seizure. In all, 59 children were identified, 34 of whom (58%) had no other potential medical explanation for their seizures. Of these 34 children, 23 (68%) were afebrile at seizure onset and 11 were febrile. Electroencephalography was performed for 21 of the 34 children (62%); all findings were normal, except for a child with slowing related to cerebral edema. Twenty-six of the 34 children (76%) had neuroimaging studies; all findings were normal, except for the child with cerebral edema and a child with an incidental arachnoid cyst. Twenty of the 34 children (59%) had a lumbar puncture; again, all findings were normal. All 34 children recovered uneventfully, including the 6 children who spent at least 1 day in an intensive care unit. Follow-up data on 27 of these children identified 2 children (7%) who required chronic anticonvulsant therapy. The results indicate that seizures associated with rotavirus infection are a relatively benign neurologic condition in young children. With few exceptions, neurodiagnostic studies do not influence management or outcome. [Copyright &y& Elsevier]

Background: Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks. Methods: Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008. Results: NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley. Conclusions: We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years. [ABSTRACT FROM AUTHOR]

The article examines the severity of the seasonal influenza epidemic in Utah from 2016 to 2018. It provides important health data from the U.S. National Center for Health Statistics such as health visits due to flu, number of people who had the illness and population of patients who were hospitalized because of influenza through time. It discusses the partnership of the Centers for Disease Control and Prevention (CDC) and the Utah Department of Health to address the medical situation.

Objectives: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. Methods: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients’ diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. Results: Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution’s cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. Conclusion: We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser. [ABSTRACT FROM AUTHOR]

Background: Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence.Methods: We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead.Results: Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic.Conclusions: Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations. [ABSTRACT FROM AUTHOR]

Please cite this paper as: Allison et al. (2010) Feasibility of elementary school children’s use of hand gel and facemasks during influenza season. Influenza and Other Respiratory Viruses 4(4), 223–229. Background The feasibility of non-pharmacologic interventions to prevent influenza’s spread in schools is not well known. Objectives To determine the acceptability of, adherence with, and barriers to the use of hand gel and facemasks in elementary schools. Patients and Methods Intervention: We provided hand gel and facemasks to 20 teachers and their students over 4 weeks. Gel use was promoted for the first 2 weeks; mask use was promoted for the second 2 weeks. Outcomes: Acceptability, adherence, and barriers were measured by teachers’ responses on weekly surveys. Mask use was also measured by observation. Results The weekly survey response rate ranged from 70% to 100%. Averaged over 2 weeks, 89% of teachers thought gel use was not disruptive (week 1 – 17/20, week 2 – 16/17), 95% would use gel next winter (week 1 – 19/20, week 2 – 16/17), and 97% would use gel in a pandemic (week 1 – 20/20, week 2 – 16/17). Averaged over 2 weeks, 39% thought mask use was not disruptive (week 1 – 6/17, week 2 – 6/14), 35% would use masks next winter (week 1 – 5/17, week 2 – 6/14), and 97% would use masks in a pandemic (week 1 – 16/17, week 2 – 14/14). About 70% estimated that their students used hand gel ≥4×/day for both weeks (week 1 – 14/20, week 2 – 13/17). Students’ mask use declined over time with 59% of teachers (10/17) estimating regular mask use during week 1 and 29% (4/14) during week 2. By observation, 30% of students wore masks in week 1, while 15% wore masks in week 2. Few barriers to gel use were identified; barriers to mask use were difficulty reading facial expressions and physical discomfort. Conclusions Hand gel use is a feasible strategy in elementary schools. Acceptability and adherence with facemasks was low, but some students and teachers did use facemasks for 2 weeks, and most teachers would use masks in their classroom in a pandemic. [ABSTRACT FROM AUTHOR]

OBJECTIVES. The goals were (1) to identify evidence-based clinical process measures that are appropriate, feasible, and reliable for assessing the quality of inpatient asthma care for children and (2) to evaluate provider compliance with these measures. METHODS. Key asthma quality measures were identified by using a modified Rand appropriateness method, combining a literature review of asthma care evidence with a consensus panel. The feasibility and reliability of obtaining these measures were determined through manual chart review. Provider compliance with these measures was evaluated through retrospective manual chart review of data for 252 children between 2 and 17 years of age who were admitted to a tertiary care children's hospital in 2005 because of asthma exacerbations. RESULTS. Nine appropriate, feasible, reliable, clinical process measures of inpatient asthma care were identified. Provider compliance with these measures was as follows: acute asthma severity assessment at admission, 39%; use of systemic corticosteroid therapy, 98%; use of oral (not intravenous) systemic corticosteroid therapy, 87%; use of ipratropium bromide restricted to <24 hours after admission, 71%; use of albuterol delivered with a metered-dose inhaler (not nebulizer) for children >5 years of age, 20%; documented chronic asthma severity assessment, 22%; parental participation in an asthma education class, 33%; written asthma action plan, 5%; scheduled follow-up appointment with the primary care provider at discharge, 22%. CONCLUSIONS. Nine appropriate, feasible, reliable, clinical process measures of inpatient asthma care were identified. Provider compliance across these measures was highly variable but generally low. Our study highlights opportunities for improvement in the provision of asthma care for hospitalized children. Future studies are needed to confirm these findings in other inpatient settings. [ABSTRACT FROM AUTHOR]