Abstract
Aim
The main aim of this study was to uncover the risk factors associated with wasting status of under-5 children residing in urban areas of Bangladesh.
Subjects and methods
For analysis purposes, the necessary information was extracted from the Bangladesh Urban Health Survey (BUHS) 2013. The outcome measure was wasting. Chi-square analysis was performed to assess the association between outcome variables and selected factors. Multilevel logistic regression models with a random intercept at each of the individual and regional levels were considered to identify the risk factors of wasting.
Results
A total of 10,511 urban children aged 0 to less than 60 months were included in this study. The overall prevalence of wasting was 17.2%. In the bivariate setup, all the selected covariates except child age were found to be significant for wasting status of children (p < 0.05). According to the two-level logistic regression model, the odds of wasting status of children increase with the age of the child and decrease with the increasing level of mother’s education. Compared to children from poor income families, the odds of being wasted were 18% and 47% lower for children coming from middle and rich income families. Among the domains, the odds of having wasted children was higher in the city-corporation slum compared to the city-corporation non-slum. Mothers receiving ANC services had lower odds of having a wasted child. Moreover, a mother who regularly breastfed her baby had 57% lower odds of having a wasted child, compared to a mother who never breastfed her baby.
Conclusion
The prevalence of wasting among under-5 children in Bangladesh is still high, and the risk was examined at various multilevel factors. Therefore, top priority should be given to reducing the rate of wasting as a major public health intervention.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data for this study are available through the MEASURE Evaluation Dataverse website (https://goo.gl/TixL9h).
References
Bangladesh Nutrition Priorities (2016) Child malnutrition in Bangladesh. Copenhagen Consensus Center. https://www.copenhagenconsensus.com/sites/default/files/brief_child_malnutrition_in_bangladeshr_ccc.pdf
Bhutta ZA, Das JK, Rizvi A, Gaffey MF, Walker N, Horton S et al (2013) Evidence-based interventions for improvement of maternal and child nutrition: what can be done and at what cost? Lancet. 382(9890):452–477. https://doi.org/10.1016/S0140-6736
Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M et al (2013) Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 382(9890):427–451. https://doi.org/10.1016/S0140-6736(13)60937-X
Das S, Gulshan J (2017) Different forms of malnutrition among under five children in Bangladesh: a cross sectional study on prevalence and determinants. BMC Nutr 3:1
Harding KL, Aguayo VM, Webb P (2018) Factors associated with wasting among children under five years old in South Asia: implications for action. PLoS One 13(7):e0198749. https://doi.org/10.1371/journal.pone.0198749
International Institute for Population Sciences (IIPS), Macro International (2007) National Family Health Survey (NFHS-3), 2005–06: India: volume I. IIPS, Mumbai
International Institute for Population Sciences (IIPS), Macro International (2017) National Family Health Survey (NFHS-4, 2015–16: India. IIPS, Mumbai, p 2017
Khara T, Dolan C (2014) Technical briefing paper: the relationship between wasting and stunting, policy, programming and research implications. Emergency Nutrition Network (ENN), Kidlington UK
Menon P, Stoltzfus RJ (2012) Building convergence in science, programs, and policy actions on child undernutrition: symposium rationale and overview. Adv Nutr 3(2):224–226. https://doi.org/10.3945/an.111.001115
Martorell R, Young MF (2012) Patterns of stunting and wasting: potential explanatory factors. Adv Nutr 3(2):227–233. https://doi.org/10.3945/an.111.001107
Mahgoub SEO, Nnyepi M, Bandeke T (2006) Factors affecting prevalence of malnutrition among children under three years of age in Botswana. Afr J Food Agric Nutr Dev 6(1):15
Nel ED (2016) Severe acute malnutrition. Curr Opin Clin Nutr Metab Care 19(3):234–238. https://doi.org/10.1097/MCO.0000000000000269
National Institute of Population Research and Training (NIPORT), Mitra and Associates, Macro International (2009) Bangladesh Demographic and Health Survey 2007. NIPORT, Dhaka, Bangladesh; Mitra and Associates, Dhaka, Bangladesh; Macro International, Calverton MD, USA
National Institute of Population Research and Training (NIPORT), Mitra and Associates, Macro International (2016) Bangladesh Demographic and Health Survey 2014. NIPORT, Dhaka, Bangladesh; Mitra and Associates, Dhaka, Bangladesh; Macro International, Calverton MD, USA
National Institute of Population Research and Training (NIPORT), Measure Evaluation, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), (2015) Bangladesh Urban Health Survey 2013 Final Report. NIPORT, Dhaka, Bangladesh; Measure Evaluation, Chapel Hill NC, USA; icddr,b, Dhaka, Bangladesh
Park S, Lake ET (2005) Multilevel modeling of a clustered continuous outcome: nurses’ work hours and burnout. Nurs Res 54:406–413
Scrimshaw NS (2003) Historical concepts of interactions, synergism and antagonism between nutrition and infection. J Nutr 133(1):316S–321S. https://doi.org/10.1093/jn/133.1.316S
Tickell KD, Pavlinac PB, John-Stewart GC, Denno DM, Richardson BA, Naulikha JM et al (2017) Impact of childhood nutritional status on pathogen prevalence and severity of acute diarrhea. Am J Trop Med Hyg 97(5):1337–1344. https://doi.org/10.4269/ajtmh.17-0139
UNICEF, WHO, World Bank (2017) Joint Child Malnutrition Estimates Database. http://www.who.int/nutgrowthdb/estimates2016/en/
WHO (2011) Anthro for Personal Computers, Version 3.2.2, 2011: Software for assessing growth and development of the World’S children. World Health Organization, Geneva. Available online: http://www.who.int/childgrowth/software/en/
WHO (2014) Global Nutrition Targets 2025: Policy Brief Series (WHO/NMH/NHD/14.2). World Health Organization, Geneva
WHO Multicenter Growth Reference Study Group (2006) WHO Child Growth Standards based on length/ height, weight and age. Acta Paediatr Suppl 450:76–85
World Health Organization (2011) World Health Statistics 2011. World Health Organization, Geneva
Acknowledgements
Data for this article was supplied by MEASURE Evaluation, a project of the United States Agency for International Development. I also thank the reviewers and editor for their valuable comments and suggestions that helped me to improve the previous version of this paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval and consent to participate
All participants gave informed consent before taking part in the survey. Additional approval was not necessary as the datasets are available in the public domain in anonymized form.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Supplementary Table 1
(DOCX 13 kb)
Rights and permissions
About this article
Cite this article
Talukder, A. Risk factors associated with wasting among under-5 children residing in urban areas of Bangladesh: a multilevel modelling approach. J Public Health (Berl.) 29, 525–531 (2021). https://doi.org/10.1007/s10389-019-01163-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10389-019-01163-4