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Research Article Open access CC BY 4.0

Statistical Analysis of Weight Gain of Children Under-five Years in the Kintampo Municipality, Ghana

Isaac Zingure, Abukari Alhassan, Jakperik Dioggban

Journal of Advances in Mathematics and Computer Science · pp. 1–11 · Published 26 Nov 2016

10.9734/BJMCS/2016/29302

Abstract

Aims/Objectives: The study aims at using statistical models to assess the change in weight of children over time and to investigate whether some maternal and baby’s characteristics directly influence those changes. Also to determine if there is significant difference in weight gain between male and female children under-five years. Study Design: The study design is a repeated measures case study. Place and Duration of Study: The key area of the study involved Ghana Health Service, Department of Public Health, (Child Health Unit), in the Kintampo municipality, between July 2014 to March 2016. Methodology: The monthly repeated measurement data was collected from one hundred and fifteen (115) children less than five years (56 male and 59 female), in the Kintampo municipality. Multivariate Analysis of Variance and Profile Analysis were employed to ascertain the significant factors influencing weight gain. Results: The study revealed that, the mean birth weight for both male and female children were 3.06kg and 2.96kg respectively, which were higher than the World Health Organization standard birth weight of 2.5kg. The multivariate analysis of variance disclosed that feeding type and parity were statistically significant, (P<0.05), in determining weight gain of children less than five years. The test of main effects of feeding and sex were significant. However, their interaction effect was not significant (P >0.05), suggesting that male and female children do not differ significantly in terms of weight gain. Conclusion: The effect of feeding on weight gain follows the same pattern for both male and female children. Moreover, feeding type and parity are important factors influencing weight gain.

Weight parity covariance profile sex

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