title: Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models.
abstract: BACKGROUND
  Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI.
  
  
  METHODS
  The health insurance claims data during 2004-2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM.
  
  
  RESULTS
  The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods.
  
  
  CONCLUSIONS
  The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.
authors.affiliation: Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, 115 Nankang, Taipei, Taiwan. dachianpig@gmail.com.
authors.forename: Ta-Chien
authors.initials: TC
authors.lastname: Chan
authors.affiliation: Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, 115 Nankang, Taipei, Taiwan. twseventh@gmail.com.
authors.forename: Yung-Chu
authors.initials: YC
authors.lastname: Teng
authors.affiliation: Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, 115 Nankang, Taipei, Taiwan. hwang@sinica.edu.tw.
authors.forename: Jing-Shiang
authors.initials: JS
authors.lastname: Hwang
mesh_terms: D016208:Databases, Factual; D004196:Disease Outbreaks; D004636:Emergency Service, Hospital; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000302:isolation & purification*; D007251:Influenza, Human / Q000453:epidemiology*; D015233:Models, Statistical; D012044:Regression Analysis; D012621:Seasons; D013624:Taiwan