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Journal ArticleDOI

Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments

19 Sep 2007-BMC Public Health (BioMed Central)-Vol. 7, Iss: 1, pp 250-250
TL;DR: Monitoring time series of ED visits clinically diagnosed with influenza could potentially provide three days early warning compared with surveillance of laboratory-confirmed influenza.
Abstract: Background Although syndromic surveillance systems are gaining acceptance as useful tools in public health, doubts remain about whether the anticipated early warning benefits exist. Many assessments of this question do not adequately account for the confounding effects of autocorrelation and trend when comparing surveillance time series and few compare the syndromic data stream against a continuous laboratory-based standard. We used time series methods to assess whether monitoring of daily counts of Emergency Department (ED) visits assigned a clinical diagnosis of influenza could offer earlier warning of increased incidence of viral influenza in the population compared with surveillance of daily counts of positive influenza test results from laboratories.

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Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors present a biostatistical introduction of the Time Series, a time series for time series, and a Biostatistic Introduction of time series.
Abstract: (1992). Time Series—A Biostatistical Introduction. Technometrics: Vol. 34, No. 2, pp. 229-230.

551 citations

Journal ArticleDOI
TL;DR: GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated, and current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection.
Abstract: The goal of influenza-like illness (ILI) surveillance is to determine the timing, location and magnitude of outbreaks by monitoring the frequency and progression of clinical case incidence. Advances in computational and information technology have allowed for automated collection of higher volumes of electronic data and more timely analyses than previously possible. Novel surveillance systems, including those based on internet search query data like Google Flu Trends (GFT), are being used as surrogates for clinically-based reporting of influenza-like-illness (ILI). We investigated the reliability of GFT during the last decade (2003 to 2013), and compared weekly public health surveillance with search query data to characterize the timing and intensity of seasonal and pandemic influenza at the national (United States), regional (Mid-Atlantic) and local (New York City) levels. We identified substantial flaws in the original and updated GFT models at all three geographic scales, including completely missing the first wave of the 2009 influenza A/H1N1 pandemic, and greatly overestimating the intensity of the A/H3N2 epidemic during the 2012/2013 season. These results were obtained for both the original (2008) and the updated (2009) GFT algorithms. The performance of both models was problematic, perhaps because of changes in internet search behavior and differences in the seasonality, geographical heterogeneity and age-distribution of the epidemics between the periods of GFT model-fitting and prospective use. We conclude that GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated. Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection. New generation surveillance systems such as GFT should incorporate the use of near-real time electronic health data and computational methods for continued model-fitting and ongoing evaluation and improvement.

310 citations

Journal ArticleDOI
TL;DR: City-level GFT shows strong correlation with influenza cases and ED ILI visits, validating its use as an ED surveillance tool.
Abstract: 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 5 0.876; pediatric ED, r 5 0.718) and number of ED patients presenting with ILI (adult ED, r 5 0.885; pediatric ED, r 5 0.652). Pediatric but not adult crowding measures, such as total ED volume (r 5 0.649) and leaving without being seen (r 5 0.641), also had good correlation with GFT. Adult crowding measures for low-acuity patients, such as waiting room time (r 5 0.421) and length of stay for discharged patients (r 5 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.

208 citations

Journal ArticleDOI
TL;DR: Influenza appears to have had a much larger effect on ED visits than was captured by clinical diagnoses of influenza or ILI, and was strongly associated with excess respiratory complaints during the 2009 pandemic.
Abstract: Surveillance of influenza activity in a jurisdiction involves many components. Influenza-like illness (ILI) consultation rates collected through networks of primary care physicians have provided an important indication of influenza activity and are still a major component of influenza surveillance alongside virologic surveillance and other measures.1 However, only a small portion of influenza cases are clinically diagnosed as influenza or ILI and ILI only has a modest specificity in predicting an influenza infection.2 For this reason, the full burden of influenza on mortality and hospitalization has been estimated statistically for many years3–6 and thresholds are routinely applied to ILI consultation rates to identify periods of influenza activity.7 In general, little is known about the full effect of influenza on the emergency department (ED),8 and significant deficits in preparedness for pandemic influenza and other disease outbreaks have been identified for EDs in the United States.9 Attempts to assess the relationship between ILI consultation rates and the full burden of influenza are just emerging.10–13 The recent availability of an administrative database of ED visits14 using standardized coding based on the International Classification of Diseases (ICD)15,16 provides an opportunity to estimate the full effect of influenza on the operations of the ED, as well as to more fully assess ILI consultation rates as an indicator of this burden. The primary objective of this study was to estimate the number and rate of ED visits attributable to seasonal and pandemic influenza and describe the effect of influenza by diagnostic category groupings, age, and discharge disposition, with the aim of guiding the planning for and management of the ED during periods of high influenza activity. In addition, front-line health care providers use various real-time indicators, such as weekly time series of influenza activity provided by influenza surveillance systems1 for resource planning.17 As guidance on the interpretation of these indicators for resource planning is currently limited, a secondary objective was to assess the weekly time series of ILI ED visits as an indicator of the real effect of influenza on the number of ED visits.

91 citations

Journal ArticleDOI
13 Sep 2013-PLOS ONE
TL;DR: Syndromic surveillance for influenza and ILI from the Emergency Department is becoming more prevalent as a measure of yearly influenza outbreaks and two very large surveillance networks, the North American DiSTRIBuTE network and the European Triple S system have collected large-scale Emergency Department-based influenza andILI syndromic Surveillance data.
Abstract: The science of surveillance is rapidly evolving due to changes in public health information and preparedness as national security issues, new information technologies and health reform. As the Emergency Department has become a much more utilized venue for acute care, it has also become a more attractive data source for disease surveillance. In recent years, influenza surveillance from the Emergency Department has increased in scope and breadth and has resulted in innovative and increasingly accepted methods of surveillance for influenza and influenza-like-illness (ILI). We undertook a systematic review of published Emergency Department-based influenza and ILI syndromic surveillance systems. A PubMed search using the keywords “syndromic”, “surveillance”, “influenza” and “emergency” was performed. Manuscripts were included in the analysis if they described (1) data from an Emergency Department (2) surveillance of influenza or ILI and (3) syndromic or clinical data. Meeting abstracts were excluded. The references of included manuscripts were examined for additional studies. A total of 38 manuscripts met the inclusion criteria, describing 24 discrete syndromic surveillance systems. Emergency Department-based influenza syndromic surveillance has been described worldwide. A wide variety of clinical data was used for surveillance, including chief complaint/presentation, preliminary or discharge diagnosis, free text analysis of the entire medical record, Google flu trends, calls to teletriage and help lines, ambulance dispatch calls, case reports of H1N1 in the media, markers of ED crowding, admission and Left Without Being Seen rates. Syndromes used to capture influenza rates were nearly always related to ILI (i.e. fever +/− a respiratory or constitutional complaint), however, other syndromes used for surveillance included fever alone, “respiratory complaint” and seizure. Two very large surveillance networks, the North American DiSTRIBuTE network and the European Triple S system have collected large-scale Emergency Department-based influenza and ILI syndromic surveillance data. Syndromic surveillance for influenza and ILI from the Emergency Department is becoming more prevalent as a measure of yearly influenza outbreaks.

71 citations


Cites background or methods from "Potential for early warning of vira..."

  • ...The most frequently used data were chief complaint or ED presentation [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41] and preliminary or discharge diagnosis codes [8,9,11,16,17,18,22,23,26,27,32,33,38,39,41,42]....

    [...]

  • ...The observed syndromic cases based on ED data were in some cases linked to objective, confirmatory data, such as culture and other laboratory results, [8,10,11,12,14,15,16,17,18,19,20,21,24,25,27,30,31,32,33,34,35,37,41] and in some cases to traditional regional and national surveillance databases [9,13,16,17,22,25,26,27,29,40,43], historic data [44] and pneumonia and influenza weekly mortality data [12,14]....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: In this paper, the overall test for lack of fit in autoregressive-moving average models proposed by Box & Pierce (1970) is considered, and it is shown that a substantially improved approximation results from a simple modification of this test.
Abstract: SUMMARY The overall test for lack of fit in autoregressive-moving average models proposed by Box & Pierce (1970) is considered. It is shown that a substantially improved approximation results from a simple modification of this test. Some consideration is given to the power of such tests and their robustness when the innovations are nonnormal. Similar modifications in the overall tests used for transfer function-noise models are proposed.

6,008 citations

Book
01 Jan 2000
TL;DR: Characteristics of Time Series * Time Series Regression and ARIMA Models * Dynamic Linear Models and Kalman Filtering * Spectral Analysis and Its Applications.
Abstract: Characteristics of Time Series * Time Series Regression and ARIMA Models * Dynamic Linear Models and Kalman Filtering * Spectral Analysis and Its Applications.

1,812 citations

Journal ArticleDOI
21 Apr 2006-Science
TL;DR: Influenza-related mortality data is used to analyze the between-state progression of interpandemic influenza in the United States over the past 30 years and a simple epidemiological model captures the observed increase of influenza spatial synchrony with transmissibility.
Abstract: Quantifying long-range dissemination of infectious diseases is a key issue in their dynamics and control. Here, we use influenza-related mortality data to analyze the between-state progression of interpandemic influenza in the United States over the past 30 years. Outbreaks show hierarchical spatial spread evidenced by higher pairwise synchrony between more populous states. Seasons with higher influenza mortality are associated with higher disease transmission and more rapid spread than are mild ones. The regional spread of infection correlates more closely with rates of movement of people to and from their workplaces (workflows) than with geographical distance. Workflows are described in turn by a gravity model, with a rapid decay of commuting up to around 100 km and a long tail of rare longer range flow. A simple epidemiological model, based on the gravity formulation, captures the observed increase of influenza spatial synchrony with transmissibility; high transmission allows influenza to spread rapidly beyond local spatial constraints.

815 citations

Book
01 Jan 1990
TL;DR: In this article, the authors describe simple descriptive methods of analysis for bivariate time-series analysis and present an approach for fitting autoregressive moving average processes to data. But they do not discuss the application of these processes to forecasting.
Abstract: Introduction 1 Simple descriptive methods of analysis 2 Theory of stationery processes 3 Spectral analysis 4 Repeated measurements 5 Fitting autoregressive moving average processes to data 6 Forecasting 7 Elements of bivariate time-series analysis References Appendix A, B & C

658 citations

Journal ArticleDOI

579 citations


"Potential for early warning of vira..." refers background in this paper

  • ...This technique is likely to miss the effect of influenza because the timing and severity of seasonal influenza epidemics are marked by excess pneumonia and influenza mortality over and above the seasonal background [40-42]....

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