Abstract
The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.
Highlights
The most common anthropometric measurements to assess infant physical growth are body weight and length [1]
Infants born alive at Adare General Hospital between April to August 2016 were followed with six visit periods: at birth, 1.5, 3, 6, 9 and 12 months
The main objective of this study was to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model
Summary
The most common anthropometric measurements to assess infant physical growth are body weight and length [1]. Alternative growth models have been widely applied to longitudinal data settings to capture and map theoretical and practical meanings of individual and population level growth variation, [3,4,5,6]. These modelling approaches are popular methodological tools due to their flexibility in simultaneously analyzing both within-person effects and between-person effects [5,6]. Growth curve models are applied to the repeated measurement data in the form of hierarchical linear models due to their computational flexibility and solving computational difficulties with advanced estimations methods in the Bayesian framework [13,14,15,16]. This form of modelling framework, the approach is capable to produce results that are simple to interpret in an intuitive and direct way by summarizing the posterior
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