1. Gastrointestinal Disease Outbreak Detection Using Multiple Data Streams from Electronic Medical Records
- Author
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Greene, Sharon K, Huang, Jie, Abrams, Allyson M, Gilliss, Debra, Reed, Mary, Platt, Richard, Huang, Susan S, and Kulldorff, Martin
- Subjects
Agricultural ,Veterinary and Food Sciences ,Food Sciences ,Vaccine Related ,Emerging Infectious Diseases ,Prevention ,Clinical Research ,Digestive Diseases ,Biodefense ,Health Services ,Infectious Diseases ,4.1 Discovery and preclinical testing of markers and technologies ,Detection ,screening and diagnosis ,Infection ,Good Health and Well Being ,Antidiarrheals ,California ,Cluster Analysis ,Comprehensive Health Care ,Cryptosporidium ,Disease Outbreaks ,Electronic Health Records ,Feces ,Foodborne Diseases ,Gastroenteritis ,Gram-Negative Bacteria ,Humans ,Multivariate Analysis ,Population Surveillance ,Salmonella ,Serotyping ,antidiarrheal agent ,article ,day care ,electronic medical record ,feces culture ,gastrointestinal disease ,human ,laboratory test ,microbiological examination ,priority journal ,serotype ,Biological Sciences ,Engineering ,Medical and Health Sciences ,Microbiology ,Food sciences - Abstract
BackgroundPassive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection.MethodsZip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.ResultsPotential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection.ConclusionsGI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection.
- Published
- 2012