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Quantile regression approach to estimating prevalence and determinants of child malnutrition

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Abstract

Aim

Child malnutrition is also associated with higher incidence of morbidity and mortality. Therefore, it is imperative to have knowledge of its correlates and determinants. The objective of this paper is to examine the association between demographic and socioeconomic factors and child nutritional status in Bangladesh.

Subject and methods

A secondary data analysis was conducted using the 2014 Bangladesh Demographic and Health Survey data. The surveys used a stratified two-stage cluster sampling. In the first stage, 600 enumeration areas (EAs) were selected with probability proportional to the EA size. In the second stage of selection, a fixed number of 30 households per cluster will be selected with an equal probability systematic selection from the household listing. The sample constitutes 17,886 ever-married women age 15–49, with 34.38% and 65.62% from urban and rural areas respectively. The anthropometric indicators height-for-age, weight-for-age and weight-for-height z-scores were used as the primary and secondary outcome measures.

Results

Results show that age and birth order of child is negatively associated with height-for-age, weight-for-age, and weight-for-height z-scores. However, the size of the child at birth is positively allied with the three anthropometric indicators. Mothers’ BMI and educational level are positively connected with the nutrition z-scores, but these factors have differential effects at different points of the conditional distribution of the anthropometric z-scores. Moreover, the economic status of a family is an essential factor in determining the z-score of height-for-age, weight-for-age, and weight-for-height of a child.

Conclusion

The age, size of child at birth, mother’s BMI and educational status, and wealth index are very important determinants of the z-score of the anthropometric indicators of a child. In order to improve the nutritional status of children in Bangladesh, the authors suggest that a joint effort by the government, non-governmental organizations, and the community is absolutely essential.

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Acknowledgements

The authors are grateful to ICF International, Rockville, Maryland, USA, for providing the Bangladesh DHS data sets for this analysis. The access link of data set is http://dhsprogram.com/data/available-datasets.cfm. We would also like to sincerely thank the two anonymous reviewers and the Editor for their valuable comments which were used to improve the manuscript.

Contributors

AR and MMH were involved in the conception and design of this study. MMH carried out the analysis and drafted the manuscript under the guideline of AR. AR provided data analysis and interpretation advice, and revised and edited the final manuscript. All the authors read and approved the manuscript. AR and MMH both were involved in revising the manuscript.

Data sharing

Data used in this research were freely accessible at the web-link: http://dhsprogram.com/data/available-datasets.cfm

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Data Science Research Unit, School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, NSW, 2678, Australia

    Azizur Rahman

  2. School of Mathematics, Statistics and Physics, Newcastle University, Newcastle, UK

    Md. Moyazzem Hossain

  3. Department of Statistics, Jahangirnagar University, Dhaka, Savar, Bangladesh

    Md. Moyazzem Hossain

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  1. Azizur Rahman
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Correspondence to Azizur Rahman.

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None declared.

Ethics approval

This study was based on an analysis of existing public domain survey data sets that is freely available online with all identifier information removed. The survey was approved by the Ethics Committee of the ICF Macro at Calverton in the USA and by the Ethics Committee in Bangladesh.

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Rahman, A., Hossain, M.M. Quantile regression approach to estimating prevalence and determinants of child malnutrition. J Public Health (Berl.) 30, 323–339 (2022). https://doi.org/10.1007/s10389-020-01277-0

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