title: Gastrointestinal disease outbreak detection using multiple data streams from electronic medical records.
abstract: BACKGROUND
  Passive 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.
  
  
  METHODS
  Zip 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.
  
  
  RESULTS
  Potential 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.
  
  
  CONCLUSIONS
  GI 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.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215-3920, USA. Sharon_Greene@harvardpilgrim.org
authors.forename: Sharon K
authors.initials: SK
authors.lastname: Greene
authors.forename: Jie
authors.initials: J
authors.lastname: Huang
authors.forename: Allyson M
authors.initials: AM
authors.lastname: Abrams
authors.forename: Debra
authors.initials: D
authors.lastname: Gilliss
authors.forename: Mary
authors.initials: M
authors.lastname: Reed
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
authors.forename: Susan S
authors.initials: SS
authors.lastname: Huang
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
mesh_terms: D000930:Antidiarrheals / Q000627:therapeutic use; D002140:California / Q000453:epidemiology; D016000:Cluster Analysis; D003191:Comprehensive Health Care; D003458:Cryptosporidium / Q000145:classification / Q000302:isolation & purification; D004196:Disease Outbreaks; D057286:Electronic Health Records; D005243:Feces / Q000382:microbiology / Q000469:parasitology; D005517:Foodborne Diseases / Q000188:drug therapy / Q000453:epidemiology / Q000382:microbiology / Q000469:parasitology; D005759:Gastroenteritis / Q000188:drug therapy / Q000453:epidemiology* / Q000382:microbiology / Q000469:parasitology; D006090:Gram-Negative Bacteria / Q000145:classification / Q000302:isolation & purification; D006801:Humans; D015999:Multivariate Analysis; D011159:Population Surveillance / Q000379:methods*; D012475:Salmonella / Q000145:classification / Q000302:isolation & purification; D012703:Serotyping
chemical_list: D000930:Antidiarrheals