title: Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.
abstract: BACKGROUND
  In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing.
  
  
  METHODS
  As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014.
  
  
  RESULTS
  The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve.
  
  
  CONCLUSIONS
  The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.
authors.affiliation: Department of Preventive Health Care, China-Japan Friendship Hospital, Beijing 100029, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Ye
authors.affiliation: Department of Biochemistry and Molecular Biology, China-Japan Institute of Clinical Medical Science, Beijing 100029, China.
authors.forename: Zhi-Hua
authors.initials: ZH
authors.lastname: Chen
authors.affiliation: Department of Preventive Health Care, China-Japan Friendship Hospital, Beijing 100029, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Preventive Health Care, China-Japan Friendship Hospital, Beijing 100029, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Liu
authors.affiliation: Department of Preventive Health Care, China-Japan Friendship Hospital, Beijing 100029, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Preventive Health Care, China-Japan Friendship Hospital, Beijing 100029, China.
authors.forename: Qin-Ying
authors.initials: QY
authors.lastname: Fan
authors.affiliation: Department of Preventive Health Care, China-Japan Friendship Hospital, Beijing 100029, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Wang
mesh_terms: D000740:Anemia / Q000453:epidemiology*; D000068476:Beijing / Q000453:epidemiology; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D003663:Decision Trees; D005260:Female; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008297:Male; D008962:Models, Theoretical; D012307:Risk Factors