Associations between comorbidity-related functional limitations and pneumonia outcomes.

BACKGROUND: Underlying comorbidities are common in children with pneumonia. OBJECTIVE: To determine associations between comorbidity-related functional limitations and risk for severe pneumonia outcomes. DESIGN, SETTING, AND PARTICIPANTS: We prospectively enrolled children <18 years with and without comorbidities presenting to the emergency department with clinical and radiographic pneumonia at two institutions. Comorbidities included chronic conditions requiring daily medications, frequent healthcare visits, or which limited age-appropriate activities. Among children with comorbidities, functional limitations were defined as none or mild, moderate, and severe. MAIN OUTCOMES AND MEASURES: Outcomes included an ordinal severity outcome, categorized as very severe (mechanical ventilation, shock, or death), severe (intensive care without very severe features), moderate (hospitalization without severe features), or mild (discharged home), and length of stay (LOS). Multivariable ordinal logistic regression was used to examine associations between comorbidity-related functional limitations and outcomes, while accounting for relevant covariates. RESULTS: A cohort of 1116 children, including 452 (40.5%) with comorbidities; 200 (44.2%) had none or mild functional limitations, 93 (20.6%) moderate, and 159 (35.2%) had severe limitations. In multivariable analysis, comorbidity-related functional limitations were associated with the ordinal severity outcome and LOS (p < .001 for both). Children with severe functional limitations had tripling of the odds of a more severe ordinal (adjusted odds ratio [aOR]: 3.01, 95% confidence interval [2.05, 4.43]) and quadrupling of the odds for longer LOS (aOR: 4.72 [3.33, 6.70]) as compared to children without comorbidities. CONCLUSION: Comorbidity-related functional limitations are important predictors of disease outcomes in children with pneumonia. Consideration of functional limitations, rather than the presence of comorbidity alone, is critical when assessing risk of severe outcomes.

Association Between Procalcitonin and Antibiotics in Children With Community-Acquired Pneumonia.

OBJECTIVE: To determine whether empirical antibiotic initiation and selection for children with pneumonia was associated with procalcitonin (PCT) levels when results were blinded to clinicians. METHODS: We enrolled children <18 years with radiographically confirmed pneumonia at 2 children's hospitals from 2014 to 2019. Blood for PCT was collected at enrollment (blinded to clinicians). We modeled associations between PCT and (1) antibiotic initiation and (2) antibiotic selection (narrow versus broad-spectrum) using multivariable logistic regression models. To quantify potential stewardship opportunities, we calculated proportions of noncritically ill children receiving antibiotics who also had a low likelihood of bacterial etiology (PCT <0.25 ng/mL) and those receiving broad-spectrum therapy, regardless of PCT level. RESULTS: We enrolled 488 children (median PCT, 0.37 ng/mL; interquartile range [IQR], 0.11-2.38); 85 (17%) received no antibiotics (median PCT, 0.32; IQR, 0.09-1.33). Among the 403 children receiving antibiotics, 95 (24%) received narrow-spectrum therapy (median PCT, 0.24; IQR, 0.08-2.52) and 308 (76%) received broad-spectrum (median PCT, 0.46; IQR, 0.12-2.83). In adjusted analyses, PCT values were not associated with antibiotic initiation (odds ratio [OR], 1.02, 95% confidence interval [CI], 0.97%-1.06%) or empirical antibiotic selection (OR 1.07; 95% CI, 0.97%-1.17%). Of those with noncritical illness, 246 (69%) were identified as potential targets for antibiotic stewardship interventions. CONCLUSION: Neither antibiotic initiation nor empirical antibiotic selection were associated with PCT values. Whereas other factors may inform antibiotic treatment decisions, the observed discordance between objective likelihood of bacterial etiology and antibiotic use suggests important opportunities for stewardship.

Pneumonia Severity in Children: Utility of Procalcitonin in Risk Stratification.

OBJECTIVES: To determine if serum procalcitonin, an indicator of bacterial etiology in pneumonia in all ages and a predictor of severe pneumonia in adults, is associated with disease severity in children with community-acquired pneumonia. METHODS: We prospectively enrolled children 2 months to <18 years with clinical and radiographic pneumonia at 2 children's hospitals (2014-2019). Procalcitonin samples were obtained at presentation. An ordinal outcome scale of pneumonia severity was defined: very severe (intubation, shock, or death), severe (intensive care admission without very severe features and/or high-flow nasal cannula), moderate (hospitalization without severe or very severe features), and mild (discharge). Hospital length of stay (LOS) was also examined. Ordinal logistic regression was used to model associations between procalcitonin and outcomes. We estimated adjusted odds ratios (aORs) for a variety of cut points of procalcitonin ranging from 0.25 to 3.5 ng/mL. RESULTS: The study included 488 children with pneumonia; 30 (6%) were classified as very severe, 106 (22%) as severe, 327 (67%) as moderate, and 25 (5%) as mild. Median procalcitonin in the very severe group was 5.06 (interquartile range [IQR] 0.90-16.83), 0.38 (IQR 0.11-2.11) in the severe group, 0.29 (IQR 0.09-1.90) in the moderate group, and 0.21 (IQR 0.12-1.2) in the mild group. Increasing procalcitonin was associated with increasing severity (range of aORs: 1.03-1.25) and increased LOS (range of aORs: 1.04-1.36). All comparisons were statistically significant. CONCLUSIONS: Higher procalcitonin was associated with increased severity and LOS. Procalcitonin may be useful in helping clinicians evaluate pneumonia severity.

A Bayesian approach for detecting a disease that is not being modeled.

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.

Estimating the Incidence of Influenza at the State Level - Utah, 2016-17 and 2017-18 Influenza Seasons.

The 2017-18 U.S. influenza season was notable for its high severity, with approximately 45 million illnesses and 810,000 influenza-associated hospitalizations throughout the United States (1). The purpose of the investigation reported here was to create a state-level estimate of the number of persons in Utah who became ill with influenza disease during this severe national seasonal influenza epidemic and to create a sustainable system for making timely updates in future influenza seasons. Knowing the extent of influenza-associated illness can help public health officials, policymakers, and clinicians tailor influenza messaging, planning, and responses for seasonal influenza epidemics or during pandemics. Using national methods and existing influenza surveillance and testing data, the influenza burden (number of influenza illnesses, medical visits for influenza, and influenza-associated hospitalizations) in Utah during the 2016-17 and 2017-18 influenza seasons was estimated. During the 2016-17 season, an estimated 265,000 symptomatic illnesses affecting 9% of Utah residents occurred, resulting in 125,000 medically attended illnesses and 2,700 hospitalizations. During the 2017-18 season, an estimated 338,000 symptomatic illnesses affecting 11% of Utah residents occurred, resulting in 160,000 medically attended illnesses and 3,900 hospitalizations. Other state or county health departments could adapt similar methods in their jurisdictions to estimate the burden of influenza locally and support prompt public health activities.

Isolated Increased Intracranial Pressure and Unilateral Papilledema in an Infant With Traumatic Brain Injury and Nondepressed Basilar Skull Fracture.

Traumatic brain injury is one of the most common pediatric injuries; totaling more than 500,000 emergency department visits per year. When the injury involves a skull fracture, sinus venous thrombosis and the risk of resultant increased intracranial pressure (ICP) are a concern. We describe a previously healthy 11-month-old female infant with nondepressed skull fracture who developed increased ICP in the absence of intracranial changes on imaging. Funduscopic examination revealed unilateral papilledema, and opening pressure on lumbar puncture was elevated at 35 cm of H2O. Computed tomography scan demonstrated a nondepressed occipital bone fracture. However, further imaging, including magnetic resonance imaging with angiogram/venogram, did not reveal any intracranial abnormalities. In particular, there was no evidence of sinus venous thrombosis. Given her presentation and signs of increased ICP, she was started on acetazolamide and improved dramatically. A thorough literature search was completed but yielded no information on infants with increased ICP after nondepressed skull fracture in the absence of radiographic findings to suggest a cause for the increase in pressure. Trauma alone can lead to increased ICP secondary to several processes, although this is expected in moderate to severe head trauma. Our case demonstrates that increased ICP can be present in infants with mild traumatic brain injury in the absence of intracranial pathology. This should be considered in patients who present with persistent vomiting that is refractory to antiemetics.

The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza.

The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and explicitly model non-influenza influenza-like illnesses.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

BACKGROUND: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. OBJECTIVE: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. METHODS: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. RESULTS: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. CONCLUSIONS: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection.

RATIONALE: Nearly 60% of U.S. children live in counties with particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5) concentrations above air quality standards. Understanding the relationship between ambient air pollution exposure and health outcomes informs actions to reduce exposure and disease risk. OBJECTIVES: To evaluate the association between ambient PM2.5 levels and healthcare encounters for acute lower respiratory infection (ALRI). METHODS: Using an observational case-crossover design, subjects (n = 146,397) were studied if they had an ALRI diagnosis and resided on Utah's Wasatch Front. PM2.5 air pollution concentrations were measured using community-based air quality monitors between 1999 and 2016. Odds ratios for ALRI healthcare encounters were calculated after stratification by ages 0-2, 3-17, and 18 or more years. MEASUREMENTS AND MAIN RESULTS: Approximately 77% (n = 112,467) of subjects were 0-2 years of age. The odds of ALRI encounter for these young children increased within 1 week of elevated PM2.5 and peaked after 3 weeks with a cumulative 28-day odds ratio of 1.15 per +10 µg/m3 (95% confidence interval, 1.12-1.19). ALRI encounters with diagnosed and laboratory-confirmed respiratory syncytial virus and influenza increased following elevated ambient PM2.5 levels. Similar elevated odds for ALRI were also observed for older children, although the number of events and precision of estimates were much lower. CONCLUSIONS: In this large sample of urban/suburban patients, short-term exposure to elevated PM2.5 air pollution was associated with greater healthcare use for ALRI in young children, older children, and adults. Further exploration is needed of causal interactions between PM2.5 and ALRI.

A Bayesian system to detect and characterize overlapping outbreaks.

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising.

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

OBJECTIVES: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance. METHODS: We independently developed two NLP parsers, one at Intermountain Healthcare (IH) in Utah and the other at University of Pittsburgh Medical Center (UPMC) using local clinical notes from emergency department (ED) encounters of influenza. We measured NLP parser performance for the presence and absence of 70 clinical findings indicative of influenza. We then developed Bayesian network models from NLP processed reports and tested their ability to discriminate among cases of (1) influenza, (2) non-influenza influenza-like illness (NI-ILI), and (3) 'other' diagnosis. RESULTS: On Intermountain Healthcare reports, recall and precision of the IH NLP parser were 0.71 and 0.75, respectively, and UPMC NLP parser, 0.67 and 0.79. On University of Pittsburgh Medical Center reports, recall and precision of the UPMC NLP parser were 0.73 and 0.80, respectively, and IH NLP parser, 0.53 and 0.80. Bayesian case-detection performance measured by AUROC for influenza versus non-influenza on Intermountain Healthcare cases was 0.93 (using IH NLP parser) and 0.93 (using UPMC NLP parser). Case-detection on University of Pittsburgh Medical Center cases was 0.95 (using UPMC NLP parser) and 0.83 (using IH NLP parser). For influenza versus NI-ILI on Intermountain Healthcare cases performance was 0.70 (using IH NLP parser) and 0.76 (using UPMC NLP parser). On University of Pisstburgh Medical Center cases, 0.76 (using UPMC NLP parser) and 0.65 (using IH NLP parser). CONCLUSION: In all but one instance (influenza versus NI-ILI using IH cases), local parsers were more effective at supporting case-detection although performances of non-local parsers were reasonable.

A study of the transferability of influenza case detection systems between two large healthcare systems.

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.

Predicting asthma control deterioration in children.

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.

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses.

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.

A systematic review of predictive modeling for bronchiolitis.

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.

Inefficiencies and vulnerabilities of telephone-based communication between U. S. poison control centers and emergency departments.

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.

Electronic information exchange between emergency departments and poison control centers: a Delphi study.

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.

The EpiCanvas infectious disease weather map: an interactive visual exploration of temporal and spatial correlations.

Advances in surveillance science have supported public health agencies in tracking and responding to disease outbreaks. Increasingly, epidemiologists have been tasked with interpreting multiple streams of heterogeneous data arising from varied surveillance systems. As a result public health personnel have experienced an overload of plots and charts as information visualization techniques have not kept pace with the rapid expansion in data availability. This study sought to advance the science of public health surveillance data visualization by conceptualizing a visual paradigm that provides an 'epidemiological canvas' for detection, monitoring, exploration and discovery of regional infectious disease activity and developing a software prototype of an 'infectious disease weather map'. Design objectives were elucidated and the conceptual model was developed using cognitive task analysis with public health epidemiologists. The software prototype was pilot tested using retrospective data from a large, regional pediatric hospital, and gastrointestinal and respiratory disease outbreaks were re-created as a proof of concept.

Clinical Value of an Ambulatory-Based Antibiogram for Uropathogens in Children.

Unnecessarily broad-spectrum antibiotic prescribing for ambulatory pediatric urinary tract infection may result from clinicians not having antibiograms specific to this population. Comparing an existing hospital-based with a proposed ambulatory uropathogen antibiogram for children in Utah, Escherichia coli accounted for a larger percentage and was more susceptible to narrower-spectrum antibiotics, demonstrating the potential need for ambulatory pediatric antibiograms.

Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics.

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.