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. 2024 Jul;50(4):e13291.
doi: 10.1111/cch.13291.

Determinants of stunting among under-five children: Evidence from Cambodian Demographic and Health Survey 2021-2022

Imran Hossain Sumon  1 , Suchana Akter  1 , Md Sazzad Hossan Sujon  1 , Muhammad Khairul Alam  1 , Sabina Yasmin  1 , Sabina Yeasmin  1 , Mohammad Alamgir Kabir  1 , Md Moyazzem Hossain  1
Affiliations

Determinants of stunting among under-five children: Evidence from Cambodian Demographic and Health Survey 2021-2022

Imran Hossain Sumon et al. Child Care Health Dev. 2024 Jul.

Abstract

Background: Epidemiological and nutritional modifications are causing an increase in stunting in many low- and middle-income countries (LMIC), which will eventually result in juvenile diseases and mortality. Therefore, this study aimed to identify the influential factors contributing to stunting among under-five children in Cambodia.

Methods: A secondary dataset consisting of 3268 under-five children was extracted from the latest Cambodian Demographic and Health Survey (CDHS)-2021/2022 dataset. The Chi-square test and Boruta algorithm were used for covariate selection, and logistic regression approaches were used to determine the influence of demographic, socioeconomic and other factors on the presence of stunting.

Results: Findings revealed that about 21% of under-five children were stunted, and the prevalence of stunting was higher in rural areas than in urban areas. The prevalence of child stunting was lower in families with highly educated parents. A child whose father had a secondary education had 0.71 times lower (adjusted odds ratio [AOR]: 0.71, 95% CI: 0.520-0.969) chance of stunting than a child whose father had no education. Findings revealed that Ratnak Kiri, Mondul Kiri, Stung Treng, Pursat and Kampot had a greater prevalence of stunting than other places, ranging from 27.11% to 35.70%, whereas Banteay Meanchey, Phnom Penh and Kandal had the lowest rates, ranging from 12.80% to 16.00%. Results of the Boruta algorithm and logistic regression suggested that under-five stunting is significantly influenced by factors such as the child's age, size at birth, mother's age at first birth, mother's body mass index (BMI), father's educational status, cooking fuel, and wealth index.

Conclusions: It is necessary to take initiatives for reducing the prevalence of stunted children prioritising the identified factors that ultimately help to reduce the burden of child health. The authors believed that the findings of this study will be helpful for policymakers in designing the appropriate policies and actions to achieve the Sustainable Development Goals (SDGs) by reducing stunting among under-five children in Cambodia.

Keywords: Boruta algorithm; logistic regression; stunting; under‐five children.

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References

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