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Early detection of rotavirus gastrointestinal illness outbreaks by multiple data sources and detection algorithms at
James E Levin1, Sivakumaran Raman
1Clinical Informatics, Information Technology Services, Children's Hospitals and Clinics of Minnesota, Minneapolis/Saint Paul, Minnesota, USA.
Insights
Early detection of rotavirus outbreaks in children is possible using categorized "reason for visit" text. This method identified outbreaks faster than laboratory tests or diagnosis codes.
Area of Science:
- Public Health
- Infectious Disease Epidemiology
- Health Informatics
Background:
- Rotavirus is a leading cause of severe diarrheal disease in children globally.
- Timely detection of rotavirus outbreaks is crucial for implementing control measures and reducing disease burden.
- Traditional surveillance methods can have significant delays in outbreak detection.
Purpose of the Study:
- To evaluate the effectiveness of different data sources for early detection of rotavirus outbreaks.
- To compare the timeliness of outbreak detection using laboratory data, diagnosis codes, and categorized free-text clinical notes.
- To assess the utility of free-text clinical data for real-time disease surveillance.
Main Methods:
- Analysis of over 450,000 pediatric encounters.
- Comparison of three data sources: laboratory studies, diagnosis codes, and free-text 'reason for visit' strings.
- Categorization of free-text strings using a support vector machine classifier for Gastrointestinal syndrome.
- Application of simple control charts for outbreak detection analysis.
Main Results:
- Categorized free-text data detected rotavirus outbreaks within 10 days of onset.
- Laboratory studies detected outbreaks with an average delay of 14 days.
- Diagnosis codes showed the longest delay, averaging 20 days for outbreak detection.
- Free-text analysis proved to be the most timely surveillance method.
Conclusions:
- Categorized free-text clinical notes offer a valuable tool for real-time disease outbreak detection.
- This informatics approach can significantly improve the speed of rotavirus surveillance.
- Real-time analysis of clinical text data enhances public health response capabilities.
Abstract:
Using data from over 450,000 pediatric encounters three data sources were evaluated for their ability to support early detection of a yearly outbreak of rotavirus disease: 1) Laboratory studies ordered, 2) Diagnosis codes, and 3) Free text "reason for visit" strings categorized as Gastrointestinal syndrome by a support vector machine software classifier. We found that in this setting the categorized free text analyzed through simple control charts detected each outbreak within 10 days of their beginning as determined by laboratory detection of rotavirus antigen (the gold standard). Outbreak detection by laboratory studies was delayed an average of 14 days and by diagnosis codes by an average of 20 days. We conclude that categorized text may provide a valuable basis for real-time detection of disease outbreaks.
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