Geolocalization of Influenza Outbreak Within an Acute Care Population: A Layered-Surveillance Approach.

STUDY OBJECTIVE: We seek to use a novel layered-surveillance approach to localize influenza clusters within an acute care population. The first layer of this system is a syndromic surveillance screen to guide rapid polymerase chain reaction testing. The second layer is geolocalization and cluster analysis of these patients. We posit that any identified clusters could represent at-risk populations who could serve as high-yield targets for preventive medical interventions. METHODS: This was a prospective observational surveillance study. Patients were screened with a previously derived clinical decision guideline that has a 90% sensitivity and 30% specificity for influenza. Patients received points for the following signs and symptoms within the past 7 days: cough (2 points), headache (1 point), subjective fever (1 point), and documented fever at triage (temperature >38°C [100.4°F]) (1 point). Patients scoring 3 points or higher were indicated for influenza testing. Patients were tested with Xpert Flu (Cepheid, Sunnyvale, CA), a rapid polymerase chain reaction test. Positive results were mapped with ArcGIS (ESRI, Redlands, CA) and analyzed with kernel density estimation to create heat maps. RESULTS: There were 1,360 patients tested with Xpert Flu with retrievable addresses within the greater Phoenix metro area. One hundred sixty-seven (12%) of them tested positive for influenza A and 23 (2%) tested positive for influenza B. The influenza A virus exhibited a clear cluster pattern within this patient population. The densest cluster was located in an approximately 1-square-mile region southeast of our hospital. CONCLUSION: Our layered-surveillance approach was effective in localizing a cluster of influenza A outbreak. This region may house a high-yield target population for public health intervention. Further collaborative efforts will be made between our hospital and the Maricopa County Department of Public Health to perform a series of community vaccination events before the next influenza season. We hope these efforts will ultimately serve to reduce the burden of this disease on our patient population, and that this system will serve as a framework for future investigations locating at-risk populations.

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<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. 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.

An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes.

BACKGROUND: Patient triage is necessary to manage excessive patient volumes and identify those with critical conditions. The most common triage system used today, Emergency Severity Index (ESI), focuses on resources utilized and critical outcomes. OBJECTIVE: This study derives and validates a computer-based electronic triage system (ETS) to improve patient acuity distribution based on serious patient outcomes. METHODS: This cross-sectional study of 25,198 (97 million weighted) adult emergency department visits from the 2009 National Hospital Ambulatory Medical Care Survey. The ETS distributes patients by using a composite outcome based on the estimated probability of mortality, intensive care unit admission, or transfer to operating room or catheterization suite. We compared the ETS with the ESI based on the differentiation of patients, outcomes, inpatient hospitalization, and resource utilization. RESULTS: Of the patients included, 3.3% had the composite outcome and 14% were admitted, and 2.52 resources/patient were used. Of the 90% triaged to low-acuity levels, ETS distributed patients evenly (Level 3: 30%; Level 4: 30%, and Level 5: 29%) compared to ESI (46%, 34%, and 7%, respectively). The ETS better-identified patients with the composite outcome present in 40% of ETS Level 1 vs. 17% for ESI and the ETS area under the receiver operating characteristic curve (AUC) was 0.83 vs. ESI 0.73. Similar results were found for hospital admission (ETS AUC = 0.83 vs. ESI AUC = 0.72). The ETS demonstrated slight improvements in discriminating patient resource utilization. CONCLUSIONS: The ETS is a triage system based on the frequency of critical outcomes that demonstrate improved differentiation of patients compared to the current standard ESI.

Cost-utility of rapid polymerase chain reaction-based influenza testing for high-risk emergency department patients.

STUDY OBJECTIVE: We evaluate the cost-effectiveness of polymerase chain reaction (PCR)-based rapid influenza testing and treatment for influenza in adult emergency department (ED) patients who are at high risk for or have evidence of influenza-related complications. METHODS: We developed a cost-utility decision analysis model that assessed adult patients presenting to the ED with symptoms of an acute respiratory infection, who met the Centers for Disease Control and Prevention criteria for recommended antiviral treatment. Analysis was performed from the societal perspective, with incremental comparisons of 4 influenza testing and treatment strategies: treat none, treat according to provider judgment, treat according to results of a PCR-based rapid diagnostic test, and treat all. RESULTS: Treating no patients with antivirals was dominated by all other strategies that increased in both cost and benefit in the following order: treat according to provider judgment, treat according to results of a PCR-based rapid diagnostic test, and treat all. As influenza prevalence increases, treating all patients eventually dominated all other options. CONCLUSION: The economic benefit of incorporating use of rapid PCR-based influenza testing for ED patients at risk of developing influenza-related complications depends on influenza prevalence; treatment guided by physician diagnosis or rapid testing, and treatment of all patients is more effective and less costly than no treatment.

Revitalizing a vital sign: improving detection of tachypnea at primary triage.

STUDY OBJECTIVE: This study evaluates the accuracy of emergency department (ED) triage respiratory rate measurement using the usual care method and a new electronic respiratory rate sensor (BioHarness, Zephyr Technology Corp.), both compared to a criterion standard measurement. METHODS: This is a cross-sectional study with convenience sampling conducted in an urban academic adult ED, including 3 separate respiratory rate measurements performed at ED triage: usual care measurement, electronic BioHarness measurement, and criterion standard measurement. The criterion standard measurement used was defined by the World Health Organization as manual observation or auscultation of respirations for 60 seconds. The resultant usual care and BioHarness measurements were compared with the criterion standard, evaluating accuracy (sensitivity and specificity) for detecting tachypnea, as well as potential systematic biases of usual care and BioHarness measurements using a Bland Altman analysis. RESULTS: Of 191 analyzed patients, 44 presented with tachypnea (>20 breaths/min). Relative to criterion standard measurement, usual care measurement had a sensitivity of 23% (95% confidence interval [CI] 12% to 37%) and specificity of 99% (95% CI 97% to 100%) for tachypnea, whereas BioHarness had a sensitivity of 91% (95% CI 80% to 97%) and specificity of 97% (95% CI 93% to 99%) for tachypnea. Usual care measurements clustered around respiratory rates of 16 and 18 breaths/min (n=144), with poor agreement with criterion standard measurement. Conversely, BioHarness measurement closely tracked criterion standard values over the range of respiratory rates. CONCLUSION: Current methods of respiratory rate measurement at ED triage are inaccurate. A new electronic respiratory rate sensor, BioHarness, has significantly greater sensitivity for detecting tachypnea versus usual care method of measurement.

Google Flu Trends: correlation with emergency department influenza rates and crowding metrics.

BACKGROUND: Google Flu Trends (GFT) is a novel Internet-based influenza surveillance system that uses search engine query data to estimate influenza activity and is available in near real time. This study assesses the temporal correlation of city GFT data to cases of influenza and standard crowding indices from an inner-city emergency department (ED). METHODS: This study was performed during a 21-month period (from January 2009 through October 2010) at an urban academic hospital with physically and administratively separate adult and pediatric EDs. We collected weekly data from GFT for Baltimore, Maryland; ED Centers for Disease Control and Prevention-reported standardized influenzalike illness (ILI) data; laboratory-confirmed influenza data; and ED crowding indices (patient volume, number of patients who left without being seen, waiting room time, and length of stay for admitted and discharged patients). Pediatric and adult data were analyzed separately using cross-correlation with GFT. RESULTS: GFT correlated with both number of positive influenza test results (adult ED, r = 0.876; pediatric ED, r = 0.718) and number of ED patients presenting with ILI (adult ED, r = 0.885; pediatric ED, r = 0.652). Pediatric but not adult crowding measures, such as total ED volume (r = 0.649) and leaving without being seen (r = 0.641), also had good correlation with GFT. Adult crowding measures for low-acuity patients, such as waiting room time (r = 0.421) and length of stay for discharged patients (r = 0.548), had moderate correlation with GFT. CONCLUSIONS: City-level GFT shows strong correlation with influenza cases and ED ILI visits, validating its use as an ED surveillance tool. GFT correlated with several pediatric ED crowding measures and those for low-acuity adult patients.

Improving Emergency Providers' Attitudes Toward Sickle Cell Patients in Pain.

BACKGROUND: Provider biases and negative attitudes are recognized barriers to optimal pain management in sickle cell disease, particularly in the emergency department (ED). MEASURES: This prospective cohort measures preintervention and postintervention providers' attitudes toward patients with sickle pain crises using a validated survey instrument. INTERVENTION: ED providers viewed an eight-minute online video that illustrated challenges in sickle cell pain management, perspectives of patients and providers, as well as misconceptions and stereotypes of which to be wary. OUTCOMES: Ninety-six ED providers were enrolled. Negative attitude scoring decreased, with a mean difference -11.5 from baseline, and positive attitudes improved, with a mean difference +10. Endorsement of red-flag behaviors similarly decreased (mean difference -12.8). Results were statistically significant and sustained on repeat testing three months after intervention. CONCLUSIONS/LESSONS LEARNED: Brief video-based educational interventions can improve emergency providers' attitudes toward patients with sickle pain crises, potentially curtailing pain crises early, improving health outcomes and patient satisfaction scores.

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.

Interventions to mitigate emergency department and hospital crowding during an infectious respiratory disease outbreak: results from an expert panel.

OBJECTIVE: To identify and prioritize potential Emergency Department (ED) and hospital-based interventions which could mitigate the impact of crowding during patient surge from a widespread infectious respiratory disease outbreak and determine potential data sources that may be useful for triggering decisions to implement these high priority interventions. DESIGN: Expert panel utilizing Nominal Group Technique to identify and prioritize interventions, and in addition, determine appropriate "triggers" for implementation of the high priority interventions in the context of four different infectious respiratory disease scenarios that vary by patient volumes (high versus low) and illness severity (high versus low). SETTING: One day in-person conference held November, 2011. PARTICIPANTS: Regional and national experts representing the fields of public health, disease surveillance, clinical medicine, ED operations, and hospital operations. MAIN OUTCOME MEASURE: Prioritized list of potential interventions to reduce ED and hospital crowding, respectively. In addition, we created a prioritized list of potential data sources which could be useful to trigger interventions. RESULTS: High priority interventions to mitigate ED surge included standardizing admission and discharge criteria and instituting infection control measures. To mitigate hospital crowding, panelists prioritized mandatory vaccination and an algorithm for antiviral use. Data sources identified for triggering implementation of these interventions were most commonly ED and hospital utilization metrics. CONCLUSIONS: We developed a prioritized list of potentially useful interventions to mitigate ED and hospital crowding in various outbreak scenarios. The data sources identified to "trigger" the implementation of these high priority interventions consist mainly of sources available at the local, institutional level.

Prevalence and characteristics of nonlactate and lactate expressors in septic shock.

PURPOSE: The study's objective was to determine the proportion and patient characteristics of patients in vasopressor-dependent septic shock who presented without lactatemia. METHODS: A retrospective review of patients presenting to an urban tertiary-care emergency department between December 2007 and September 2008 was conducted. Patients with a final diagnosis of septic shock requiring vasopressors were divided, based on initial lactate, to nonlactate expressors (0-2.4 mmol/L), intermediate (2.5-3.9 mmol/L), and high (>4.0 mmol/L) lactate groups. RESULTS: Among 123 patients with vasopressor-dependent septic shock, 55 (45%) were nonlactate expressors (lactate ≤ 2.4 mmol/L). Acute liver injury, history of liver disease, and presence of bacteremia were associated with elevated lactate. CONCLUSION: Almost one-half of patients with vasopressor-dependent septic shock did not express lactate on presentation, although a high mortality rate remains in this population. We found a significant association between lactate expressors and liver disease and between lactate expressors and positive blood cultures. The use of lactatemia as the sole indicator of need for additional intravenous fluid or an end point of resuscitation in septic shock may be inadequate.