title: [Case classification in measles surveillance system under the Two-level Logistic Model].
abstract: OBJECTIVE
  To study the prevalence rates of Rash and Febrile Illnesses (RFIs) including measles, rubella, scarlet fever, exanthema subitum and the differences among measles and other RFIs to tentatively formulate the logistic regression model through clinical manifestation.
  
  
  METHODS
  All the suspected cases of measles, rubella, scarlet fever, exanthema subitum reported by the county/prefecture lever hospitals at four counties were collected during March 2011 to February 2012. When setting laboratory confirmed measles as dependent variable and existed symptoms as independent variable, a logistic regression model was formulated and optimal operational point (OOP)chosen, according to the ROC curve.
  
  
  RESULTS
  A total number of 551 cases were collected but the consistency of measles diagnosis between clinical and laboratory was not satisfied, with Kappa value = 0.349, same to the diagnosis of rubella. As for the result from the two-lever logistic regression model, symptoms that related to the confirmation of measles would include cough (OR = 5.75), conjunctivitis (OR = 3.00), Koplik spot (OR = 7.52), lymphadenectasis(OR = 0.07), rash after fever (OR = 0.07). The area under ROC curve was 0.97 and the optimal operational point was 0.249.
  
  
  CONCLUSIONS
  A logistic regression model was formulated using the clinical symptoms which was resulted in better performance on prediction. As the sample size of this survey was small, the expansion on the scale of investigation and laboratory testings were needed before the types and components of measles-related RFIs be clarified.
authors.affiliation: Department of Epidemiology and Biostatistics, West School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Handong
authors.initials: H
authors.lastname: Li
authors.affiliation: Sichuan Provincial Center for Disease Control and Prevention.
authors.forename: Rui
authors.initials: R
authors.lastname: Ao
authors.affiliation: Department of Epidemiology and Biostatistics, West School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Peng
authors.affiliation: Department of Epidemiology and Biostatistics, West School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Di
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Biostatistics, West School of Public Health, Sichuan University, Chengdu 610041, China. Email: juying109@163.com.
authors.forename: Juying
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D002681:China / Q000453:epidemiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D008457:Measles / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control; D011159:Population Surveillance / Q000379:methods*; D012409:Rubella / Q000145:classification / Q000175:diagnosis; D012541:Scarlet Fever / Q000139:chemically induced / Q000145:classification