title: [Analysis of effect on infectious diseases outbreak detection performance by classifying provinces for moving percentile method].
abstract: OBJECTIVE
  Providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) via analyzing the outbreak detection performance of Moving Percentile Method (MPM) by optimizing thresholds in different provinces.
  
  
  METHODS
  We collected the amount of MPM signals, response results of signals in CIDARS, cases data in nationwide Notifiable Infectious Diseases Reporting Information System, and outbreaks data in Public Health Emergency Reporting System of 16 infectious diseases in 31 provinces in Chinese mainland from January 2011 to October 2013. The threshold with the optimal sensitivity, the shortest time to detect outbreak and the least number of signals was considered as the best threshold of each disease in Chinese mainland and in each province.
  
  
  RESULTS
  Among all the 16 diseases, the optimal thresholds of 10 diseases, including dysentery, dengue, hepatitis A, typhoid and paratyphoid, meningococcal meningitis, Japanese encephalitis, scarlet fever, leptospirosis, hepatitis, typhus in country level were the 90(th) percentile (P90), which was the same as provincial level for those diseases.For the other 6 diseases, including other infectious diarrhea, influenza, acute hemorrhagic conjunctivitis, mumps, rubella and epidemic hemorrhagic fever, the nationwide optimal thresholds were the 80th percentile (P80), which was different from that by provinces for each disease. For these 6 diseases, the number of signals generated by MPM with the optimal threshold for each province was decreased by 23.71% (45 557), 15.59% (6 124), 14.07% (1 870), 9.44% (13 881), 8.65% (1 294) and 6.03% (313) respectively, comparing to the national optimal threshold, while the sensitivity and time to detection of CIDARS were still the same.
  
  
  CONCLUSIONS
  Optimizing the threshold by different diseases and provinces for MPM in CIDARS could reduce the number of signals while maintaining the same sensitivity and time to detection.
authors: Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
authors: Honglong
authors: H
authors: Zhang
authors: Qiao
authors: Q
authors: Sun
authors: Shengjie
authors: S
authors: Lai
authors: Xiang
authors: X
authors: Ren
authors: Dinglun
authors: D
authors: Zhou
authors: Xianfei
authors: X
authors: Ye
authors: Lingjia
authors: L
authors: Zeng
authors: Jianxing
authors: J
authors: Yu
authors: Liping
authors: L
authors: Wang
authors: Hongjie
authors: H
authors: Yu
authors: Zhongjie
authors: Z
authors: Li
authors: Email: lw6851@163.com.
authors: Wei
authors: W
authors: Lyu
authors: Email: lanyajia@sina.com.
authors: Yajia
authors: Y
authors: Lan
authors: Weizhong
authors: W
authors: Yang
mesh_terms: D002681:China; D003141:Communicable Diseases; D018563:Disease Notification; D004196:Disease Outbreaks / Q000517:prevention & control*; D006801:Humans; D011159:Population Surveillance / Q000379:methods*