The Emerging Science of Very Early Detection of Disease Outbreaks

Wagner, Michael M.; Tsui, Fu-Chiang; Espino, Jeremy U.; Dato, Virginia; Sittig, Dean F.; Caruana, Richard A.; McGinnis, Laura; Deerfield, David W.; Drużdżel, Marek J.; Fridsma, Douglas B.

doi:10.1097/00124784-200107060-00006

Cited by 181 publications

(125 citation statements)

References 29 publications

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“…These include methods for analysis of data from healthcare facilities, as well as reports to health departments of unusual cases. Many of these methods involve intensive, automated statistical analysis of large amounts of data and intensive use of informatics techniques to gather data for analysis and to communicate among physicians and public health officials [WTE01]. Some of these systems go beyond health care data to include nonhealth data such as over-the-counter (OTC) pharmaceutical sales and absenteeism that might indicate people with symptoms who have not sought health care [Hen04].…”

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confidence: 99%

Evaluating Statistical Methods for Syndromic Surveillance

Stoto

Fricker

Jain

et al. 2006

Statistical Methods in Counterterrorism

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Since the terrorist attacks on September 11, 2001, many state and local health departments around the United States have started to develop syndromic surveillance systems. Syndromic surveillance -a new concept in epidemiology -is the statistical analyses of data on individuals seeking care in emergency rooms (ER) or other health care settings with preidentified sets of symptoms thought to be related to the precursors of diseases. Making use of existing health care or other data, often already in electronic form, these systems are intended to give early warnings of bioterrorist attacks or other emerging health conditions. By focusing on symptoms rather than confirmed diagnoses, syndromic surveillance aims to detect bioevents earlier than would be possible with traditional surveillance systems. Because potential bioterrorist agents such as anthrax, plague, brucellosis, tularemia, Q-fever, glanders, smallpox, and viral hemorrhagic fevers initially exhibit symptoms ("present" 142 Michael A. Stoto et al. in medical terminology) of a flulike illness, data suggesting a sudden increase of individuals with fever, headache, muscle pain, and malaise might be the first indication of a bioterrorist attack or natural disease outbreak. Syndromic surveillance is also thought to be useful for early detection of natural disease outbreaks [Hen04].Research groups based at universities, health departments, private firms, and other organizations have proposed and are developing and promoting a variety of surveillance systems purported to meet public health needs. These include methods for analysis of data from healthcare facilities, as well as reports to health departments of unusual cases. Many of these methods involve intensive, automated statistical analysis of large amounts of data and intensive use of informatics techniques to gather data for analysis and to communicate among physicians and public health officials [WTE01]. Some of these systems go beyond health care data to include nonhealth data such as over-the-counter (OTC) pharmaceutical sales and absenteeism that might indicate people with symptoms who have not sought health care [Hen04].There are a number of technological, logistical, and legal constraints to obtaining appropriate data and effective operation of syndromic surveillance systems [Bue04]. However, even with access to the requisite data and perfect organizational coordination and cooperation, the statistical challenges in reliably and accurately detecting a bioevent are formidable. The object of these surveillance systems, of course, is to analyze a stream of data in realtime and determine whether there is an anomaly suggesting that an incident has occurred. All data streams, however, have some degree of natural variability. These include seasonal or weekly patterns, a flu season that appears at a different time each winter or perhaps not at all, differences in coding practices, sales promotions for OTC medications, and random fluctuations due to small numbers of individuals with particular symptoms. Fur...

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confidence: 99%

Evaluating Statistical Methods for Syndromic Surveillance

Stoto

Fricker

Jain

et al. 2006

Statistical Methods in Counterterrorism

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“…The usefulness of each method of analysis depends on the ability to confidently detect anomalies at a time 117 and scale (eg, single hospital per month) at which effective interventions can be designed and implemented. This translates to a range of applications in healthcare.…”

Section: Appropriate Statistical Methods For Analysis Must Be Appliedmentioning

confidence: 99%

Catheter‐related bloodstream infections in hematology

Worth

Slavin

Brown

et al. 2007

Cancer

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THE NEED FOR SURVEILLANCE AND LIMITATIONS OF CURRENT METHODSTunneled and nontunneled central venous catheters (CVCs) are necessary to facilitate treatment of many hematologic disorders, but catheter-related bloodstream infections (CR-BSIs) are an important cause of morbidity and mortality, and may lead to interruptions in planned therapy for malignancy or increases in length of hospital stay. In immunocompromised populations with neoplastic disease, risks for infection are not uniform, 1 and increased risk for bloodstream infections in patients with hematological malignancy may be related to the underlying malignancy, chemotherapy-induced granulocytopenia, breaches in skin or mucosal integrity, or invasive procedures. 2,3Standardized surveillance measures have not been used to assess interventions with potential impact on the epidemiology of CR-BSIs. For example, widespread use of antibiotic lock therapy,

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“…Syndromic surveillance (also referred to in the literature as early detection of disease outbreaks, pre-diagnosis surveillance, non-traditional surveillance, enhanced surveillance, non-traditional surveillance, and disease early warning systems) has received substantial interest recently, especially after Sept. 11, 2001 [3,5,9,12,13,14,15].…”

Section: Background and Related Workmentioning

confidence: 99%

EPI-SPIRE: A Bio-Surveillance Prototype

Campbell¹

2005

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503 AGENCY USE ONLY (Leave blank)2. REPORT DATE MAY 2005 REPORT TYPE AND DATES COVEREDFinal Aug 01 -Nov 04 TITLE AND SUBTITLE EPI-SPIRE: A BIO-SURVEILLANCE PROTOTYPE AUTHOR(S)Murray Campbell FUNDING NUMBERSC -F30602-01-C-0184 PE -62301E PR -BIOS TA -00 WU -01 The objective of this project was to develop prototype technologies to detect disease outbreak resulting from bioterrorism (biosurveillance) through the analysis of non-traditional data sources. The areas of focus for IBM were: 1. Develop methodologies for evaluating the usefulness of data sources for biosurveillance 2. Identify the most promising data sources for biosurveillance 3. Investigate detection algorithms that can identify early signs of disease outbreak 4. Develop methodologies to evaluate the detection algorithms 5. Develop technologies for protecting privacy of data 6. Investigate site-based biosurveillance We worked with Greg Glass and his team at the Johns Hopkins School of Public Health. The areas of focus for JHU were: PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)IBM1. Evaluate the impact of air travel on the dispersion of communicable agents 2. Evaluate selected strategies for early identification of disease outbreaks 3. Develop methods to identify permissive environmental conditions for outbreaks of zoonotic diseases in human populations This report will give overview coverage for all of these areas, and give pointers to the included documents that explore the areas in greater depth. The report will also include a listing of all other documentation for this project, including: PI meeting documents, site visit documents, quarterly reports, and a publication list. EXECUTIVE SUMMARYThe objective of this project was to develop prototype technologies that can detect the outbreak of disease resulting from bioterrorism through the analysis of non-traditional data sources (biosurveillance). There were six specific areas of focus for the IBM team's effort: 1. Develop methodologies for evaluating the usefulness of non-traditional data sources for biosurveillance 2. Apply the above methodologies to a wide variety of data sources and identify the most promising 3. Investigate detection algorithms that can identify early signs of disease outbreak in non-traditional data sources 4. Develop methodologies to evaluate the detection algorithms with respect to timeliness a...

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The Emerging Science of Very Early Detection of Disease Outbreaks

Cited by 181 publications

References 29 publications

Evaluating Statistical Methods for Syndromic Surveillance

Evaluating Statistical Methods for Syndromic Surveillance

Catheter‐related bloodstream infections in hematology

EPI-SPIRE: A Bio-Surveillance Prototype

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