title: Application of Bayesian Hierarchical Model for Detecting Effective Factors on Growth Failure of Infants Less Than Two Years of Age in a Multicenter Longitudinal Study.
abstract: BACKGROUND
  Nowadays, one of the major public health problems among children is growth failure. It can be characterized in terms of either inadequate growth or the inability to maintain growth.
  
  
  OBJECTIVE
  The main objective of this study was to examine the effects of some factors on growth failure among a sample of infants less than two years old.
  
  
  METHODS
  The present longitudinal archival study relied on data gathered from health files from February 2007 to July 2010 for 1,358 children under two years of age, selected from eight health centers in the east and northeast parts of Tehran, Iran. In the present study, growth failure refers to at least a 50 g decrease in an infant's weight as recorded at each attendance in comparison to the previous measurement. The impacts of risk indicators were assessed using the Bayesian hierarchical logistic regression modeling technique.
  
  
  RESULTS
  The highest and lowest percentage of growth failure was 5.8% and 0.1%, respectively, in the eleventh and the first month after birth. The obtained results from the Bayesian hierarchical modeling revealed that diarrhea (95% credible interval (CrI): 0.70 - 3.31), discontinuation of breastfeeding (95% CrI: 0.77 - 5.96), and respiratory infections (95% CrI: 2.07 - 4.61) were significant risk factors for growth failure. The random term at the child level was significant (95% CrI: 0.74 - 7.82), while the variation in centers was extremely small (95% CrI: 0.004 - 4.22).
  
  
  CONCLUSIONS
  It was noted that a relatively high prevalence of growth failure was observed in the study sample. For minimizing the impact of significant risk factors on growth failure, the early detection of growth failure and its risk indicators is of great importance. In addition, when the focus of the analysis is on the different nested sources of variability and the data has a hierarchical structure, using a hierarchical modeling approach is recommended to achieve more accurate results.
authors.affiliation: Department of Biostatistics, Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
authors.forename: Farid
authors.initials: F
authors.lastname: Zayeri
authors.affiliation: Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran.
authors.forename: Maedeh
authors.initials: M
authors.lastname: Amini
authors.affiliation: Department of Biostatistics and Epidemiology, Modeling of Noncommunicable Disease Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Moghimbeigi
authors.affiliation: Department of Biostatistics and Epidemiology, Modeling of Noncommunicable Disease Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran.
authors.forename: Ali Reza
authors.initials: AR
authors.lastname: Soltanian
authors.affiliation: Department of Health and Social Medicine, Shahed University Faculty of Medicine, Tehran, IR Iran.
authors.forename: Nahid
authors.initials: N
authors.lastname: Kholdi
authors.affiliation: Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, IR Iran.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Gholami-Fesharaki
keywords: Bayesian Method; Growth Failure; Longitudinal Study, Hierarchical Model; Multicenter; Risk Factors