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

BackgroundNowadays, 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.ObjectivesThe 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.Materials and MethodsThe 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.ResultsThe 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).ConclusionsIt 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.

Childhood stunting and wasting in Myanmar: Key drivers and implications for policies and programmes

“…Consistent with findings from other DHS surveys, we did not find an association between the prevalence of wasting and household wealth quintile (Assaf & Pullum, ). Previous research has suggested that immediate factors, such as child feeding and care practices and prevalence of disease and inflammation, are drivers of child wasting and are mediating factors between socio‐economic status and child wasting (Fernandez, Himes, & de Onis, ; Zayeri et al, ). Agroecological zone was significantly associated with child stunting in Myanmar, with children in the mountainous upland states most likely to be stunted and children in the coastal zone more likely to be wasted, a finding that is in line with previous national and subnational surveys (LIFT, ; Ministry of National Planning and Economic Development & Ministry of Health, ).…”