The RODS Open Source Project
Open Source Outbreak and Disease Surveillance Software

Untitled Document Publications

Please visit the RODS Laboratory site for an up to date publication list.

J. U. Espino, M. M. Wagner, et al (2003). "The RODS Open Source Project: Removing a Barrier to Syndromic Surveillance." (Submitted to MEDINFO 2004) pdf

The goal of the Real-time Outbreak and Disease Surveillance (RODS) Open Source Project is to accelerate deployment of computer-based syndromic surveillance. To this end, the project has released the RODS software under the GNU General Public License and created an organizational structure to catalyze its development. This paper describes the design of the software, requested extensions, and the structure of the development effort.

J. U. Espino, M. M. Wagner, et al (2003). "Removing a Barrier to Syndromic Surveillance: Open Sourcing the RODS System." (Submitted to the National Syndromic Surveillance Conference 2003)

The Real-time Outbreak and Disease Surveillance (RODS) software for syndromic surveillance has been under continuous development and use since 1999, first in Pennsylvania and then in Utah with additional deployments underway in Georgia, New Jersey, and Ohio. The rate of adoption of syndromic technology, although rapid, is still not commensurate with the threat posed by bioterrorism, emerging infections, and common outbreaks of disease. This threat warrants even faster deployment, which in turn suggests the need for identifying and removing existing barriers to adoption. A key barrier—the one addressed by the RODS Open Source Project—is the availability of high quality, well-supported, and affordable syndromic surveillance software.

Tsui, F. C., J. U. Espino, et al. (2003). "Technical Description of RODS: A Real-time Public Health Surveillance System." J Am Med Inform Assoc. www.jamia.org

This paper describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol. The data are sent in real time. RODS automatically classifies the registration chief complaint from the visit into one of seven syndrome categories using Bayesian classifiers. It stores the data in a relational database; aggregates the data for analysis using data warehousing techniques; applies univariate and multivariate statistical detection algorithms to the data, and alerts users of when the algorithms identify anomalous patterns in the syndrome counts. RODS also has a Web-based user interface that supports temporal and spatial analyses. RODS processes sales of over-the-counter healthcare products in a similar manner, but receives such data in batch mode on a daily basis. RODS was used during the Winter 2002 Olympics and currently operates in two states-Pennsylvania and Utah. It has been and continues to be a resource for implementing, evaluating, and applying new methods of public health surveillance.

Gesteland, P. H., R. M. Gardner, et al. (2003). "Automated Syndromic Surveillance for the 2002 Winter Olympics." J Am Med Inform Assoc. www.jamia.org

The 2002 Olympic Winter Games were held in Utah from February 8th to March 16, 2002. Following the terrorist attacks on September 11, 2001 and the Anthrax release in October 2001, the need for bioterrorism surveillance during the Games was paramount. A team of informaticists and public health specialists from Utah and Pittsburgh implemented the RODS system in Utah for the Games in just seven weeks. The strategies and challenges of implementing such a system in such a short time are discussed. The motivation and cooperation inspired by the 2002 Olympic Winter Games was a powerful driver in overcoming the organizational issues. Over 114,000 acute care encounters were monitored between the February 8th and March 31st, 2002. No outbreaks of public health significance were detected. The system was successfully implemented and operational for the 2002 Olympic Winter Games and remains operational today.

Wagner, M. M., J. M. Robinson, et al. (2003). "Design of a National Retail Data Monitor for Public Health Surveillance." J Am Med Inform Assoc. www.jamia.org

The National Retail Data Monitor receives data daily from 10,000 stores that sell healthcare products, including pharmacies. These stores belong to national chains that process sales data centrally, and utilize Universal Product Codes and scanners to collect sales information at the cash register. The high degree of retail sales data automation enabled the monitor to collect information from thousands of store locations in near to real time for use in public health surveillance. The monitor provides user interfaces that display summary sales data on timelines and maps. Algorithms monitor the data automatically on a daily basis to detect unusual patterns of sales. The project provides the resulting data and analyses, free of charge, to health departments nationwide. Future plans include continued enrollment and support of health departments, developing methods to make the service financially self-supporting, and further refinement of the data collection system to reduce the time latency of data receipt and analysis.

Hogan, W. R., F. C. Tsui, et al. (2003). "Detection of Pediatric Respiratory and Diarrheal Outbreaks from Sales of Over-the-counter Electrolyte Products." J Am Med Inform Assoc. www.jamia.org

OBJECTIVE To determine whether sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal disease in children, and if so, how much earlier a signal relative to hospital diagnoses. DESIGN Retrospective analysis of sales of electrolyte products and hospital diagnoses for six urban regions in three states for the period 1998-2001 inclusive. MEASUREMENTS Presence of signal was ascertained by measuring correlation between electrolyte sales and hospital diagnoses and the temporal relationship that maximized correlation. Earliness was the difference between the date that the exponentially weighted moving average (EWMA) method first detected an outbreak from sales and the date it first detected the outbreak from diagnoses. The coefficient of determination (r(2)) measured how much variance in earliness resulted from differences in sales and diagnoses signal strengths. RESULTS The correlation between electrolyte sales and hospital diagnoses was 0.90 (95% C.I. 0.87, 0.93) at a time offset of 1.7 weeks (95% C.I. 0.50-2.9), meaning that sales preceded diagnoses by 1.7 weeks. EWMA with a nine-sigma threshold detected the 18 outbreaks on average 2.4 weeks (95% C.I., 0.1-4.8 weeks) earlier from sales than from diagnoses. Twelve outbreaks were first detected from sales, four were first detected from diagnoses, and two were detected simultaneously. Only 26% of variance in earliness was explained by the relative strength of the sales and diagnoses signals (r(2))=0.26). CONCLUSION Sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal diseases in children, and are usually an earlier signal than hospital diagnoses.

Tsui, F. C., J. U. Espino, et al. (2002). "Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System." Proc AMIA Symp: 815-9.

Given the post September 11th climate of possible bioterrorist attacks and the high profile 2002 Winter Olympics in the Salt Lake City, Utah, we challenged ourselves to deploy a computer-based real-time automated biosurveillance system for Utah, the Utah Real-time Outbreak and Disease Surveillance system (Utah RODS), in six weeks using our existing Real-time Outbreak and Disease Surveillance (RODS) architecture. During the Olympics, Utah RODS received real-time HL-7 admission messages from 10 emergency departments and 20 walk-in clinics. It collected free-text chief complaints, categorized them into one of seven prodromes classes using natural language processing, and provided a web interface for real-time display of time series graphs, geographic information system output, outbreak algorithm alerts, and details of the cases. The system detected two possible outbreaks that were dismissed as the natural result of increasing rates of Influenza. Utah RODS allowed us to further understand the complexities underlying the rapid deployment of a RODS-like system.

Gesteland, P. H., M. M. Wagner, et al. (2002). "Rapid deployment of an electronic disease surveillance system in the state of Utah for the 2002 Olympic Winter Games." Proc AMIA Symp: 285-9.

The key to minimizing the effects of an intentionally caused disease outbreak is early detection of the attack and rapid identification of the affected individuals. The Bush administration's leadership in advocating for biosurveillance systems capable of monitoring for bioterrorism attacks suggests that we should move quickly to establish a nationwide early warning biosurveillance system as a defense against this threat. The spirit of collaboration and unity inspired by the events of 9-11 and the 2002 Olympic Winter Games in Salt Lake City provided the opportunity to demonstrate how a prototypic biosurveillance system could be rapidly deployed. In seven weeks we were able to implement an automated, real-time disease outbreak detection system in the State of Utah and monitored 80,684 acute care visits occurring during a 28-day period spanning the Olympics. No trends of immediate public health concern were identified.

Ivanov, O., M. M. Wagner, et al. (2002). "Accuracy of three classifiers of acute gastrointestinal syndrome for syndromic surveillance." Proc AMIA Symp: 345-9.

ICD-9-coded emergency department (ED) diagnoses and free-text triage diagnoses are routinely collected data elements that have potential value for public health surveillance and early detection of epidemics. We constructed and measured performance of three classifiers for the detection of cases of acute gastrointestinal syndrome of public health significance: one used ICD-9-coded ED diagnosis as input data; the other two used free-text triage diagnosis. We measured the performance of these classifiers against the expert classification of cases based on review of ED reports. The sensitivity of the ICD-9-code classifier was 0.32, and the specificity was 0.99. The sensitivity of a naive Bayes classifier using triage diagnoses was 0.63, the specificity was 0.94, and the area under the ROC curve was 0.82. A bigram Bayes classifier had sensitivity 0.38, specificity 0.94, and area under the ROC of 0.69. We conclude that a naive Bayes classifier of free-text triage diagnosis data provides more sensitive and earlier detection of cases of acute gastrointestinal syndrome than either a bigram Bayes classifier or an ICD-9 code classifier. The sensitivity achieved should be sufficient for syndromic surveillance system designed to detect moderate to large epidemics.

Lober, W. B., B. T. Karras, et al. (2002). "Roundtable on bioterrorism detection: information system-based surveillance." J Am Med Inform Assoc 9(2): 105-15. www.jamia.org

During the 2001 AMIA Annual Symposium, the Anesthesia, Critical Care, and Emergency Medicine Working Group hosted the Roundtable on Bioterrorism Detection. Sixty-four people attended the roundtable discussion, during which several researchers discussed public health surveillance systems designed to enhance early detection of bioterrorism events. These systems make secondary use of existing clinical, laboratory, paramedical, and pharmacy data or facilitate electronic case reporting by clinicians. This paper combines case reports of six existing systems with discussion of some common techniques and approaches. The purpose of the roundtable discussion was to foster communication among researchers and promote progress by 1) sharing information about systems, including origins, current capabilities, stages of deployment, and architectures; 2) sharing lessons learned during the development and implementation of systems; and 3) exploring cooperation projects, including the sharing of software and data. A mailing list server for these ongoing efforts may be found at http://bt.cirg.washington.edu.

 

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