Emergency Department Diagnosis and Managment of Influenza

Introduction: Diagnosing influenza in the emergency department (ED) remains a challenge as physicians have no reliable tools to accurately and rapidly diagnose influenza; however, rapid diagnosis is crucial to begin antiviral therapy in patients with complications or at risk of complications from influenza. Centers for Disease Control and Prevention (CDC) Guidelines recommend prompt antiviral treatment for patients who are hospitalized, at extremes of age (65 years old), or have a chronic disease or conditions putting them at increased risk of complications. Methods: First, we determined compliance with CDC antiviral guidelines via a retrospective evaluation of ED patients with confirmed influenza. Then, we created a prospective cohort of ED patients who met CDC criteria for recommended antiviral treatment who were evaluated for influenza by 3 means: clinical diagnosis, a new molecular-based rapid test, and a Polymerase Chain Reaction (PCR) test. Comparing the clinical diagnosis and rapid influenza test to the standard PCR assay allowed for a performance evaluation of both clinician diagnosis, and the new molecular-based rapid test. Finally, a cost-effectiveness analysis was performed to compare influenza testing and treatment strategies. Results: ED providers have poor compliance with CDC guidelines regarding antiviral treatment with only 41% of patients recommended to receive antiviral treatment being treated in the ED. Provider diagnosis for influenza has a poor sensitivity of 36%, especially compared to the molecular-based rapid influenza test which has 95% sensitivity in the same population. Finally, the most cost-effective testing and treatment strategy depends on influenza prevalence with rapid testing as the most cost-effective treatment at low influenza prevalence, and treating all patients without testing as the most cost-effective strategy at high prevalence. Conclusions: The challenges of making a clinical diagnosis of influenza in the ED, and current lack of a rapid sensitive influenza test, likely contribute to poor compliance with current CDC guidelines regarding antiviral administration. Integrating a new highly sensitive molecular-based rapid influenza test into ED clinical care, could improve compliance with CDC guidelines and is cost effective at low influenza prevalence

The Frequency of Influenza and Bacterial Co-infection: A Systematic Review and Meta-Analysis.

AIM: Co-infecting bacterial pathogens are a major cause of morbidity and mortality in influenza. However, there remains a paucity of literature on the magnitude of co-infection in influenza patients. METHOD: A systematic search of MeSH, Cochrane Library, Web of Science, SCOPUS, EMBASE, and PubMed was performed. Studies of humans in which all individuals had laboratory confirmed influenza, and all individuals were tested for an array of common bacterial species, met inclusion criteria. RESULTS: Twenty-seven studies including 3,215 participants met all inclusion criteria. Common etiologies were defined from a subset of eight articles. There was high heterogeneity in the results (I(2) = 95%), with reported co-infection rates ranging from 2% to 65%. Though only a subset of papers were responsible for observed heterogeneity, subanalyses and meta-regression analysis found no study characteristic that was significantly associated with co-infection. The most common co-infecting species were Streptococcus pneumoniae and Staphylococcus aureus, which accounted for 35% (95% CI, 14%-56%) and 28% (95% CI, 16%-40%) of infections, respectively; a wide range of other pathogens caused the remaining infections. An assessment of bias suggested that lack of small-study publications may have biased the results. CONCLUSIONS: The frequency of co-infection in the published studies included in this review suggests that though providers should consider possible bacterial co-infection in all patients hospitalized with influenza, they should not assume all patients are co-infected and be sure to properly treat underlying viral processes. Further, high heterogeneity suggests additional large-scale studies are needed to better understand the etiology of influenza bacterial co-infection. This article is protected by copyright. All rights reserved

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits

Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits.

BACKGROUND: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. OBJECTIVE: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. METHODS: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. RESULTS: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P CONCLUSIONS: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness

Influenza Forecasting with Google Flu Trends

BACKGROUND: We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. METHODS: Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004–2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. RESULTS: A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. CONCLUSIONS: Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases

Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits

Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness

Capability of adding an exogenous covariate to forecast.

<p>The number of confirmed Emergency Department (ED) influenza cases compared to the base 3^rd order Negative Binomial Generalized Autoregressive Poisson (GARMA) model. Δ indicates the change of the indicated variable between the prior and current week. Forecast Global Deviance indicates the sum of each forecast global deviance for all 7 leave-one-out validation models. Forecast Confidence indicates the average of confidences from all 7 leave-one-out validation models. Forecast confidence is the percentage of forecast values, during an influenza peak, that are within seven influenza cases of the actual data.</p

Input variables over study timeframe.

<p>Input variables over study timeframe.</p

Forecast of Emergency Department Influenza Cases.

<p>Capability of Generalized Autoregressive Negative Binomial (GARMA) and univariate generalized linear models (GLM) to forecast the number of confirmed Emergency Department (ED) influenza cases. Δ indicates the change of the indicated variable between the prior and current week. Forecast Global Deviance indicates the sum of each forecast global deviance for all 7 leave-one-out validation models. Forecast Confidence indicates the average of confidences from all 7 leave-one-out validation models. Forecast confidence is the percentage of forecast values, during an influenza peak, that are within seven influenza cases of the actual data.</p