Mind the gap: What explains the poor-non-poor inequalities in severe wasting among under-five children in low- and
Adeniyi Francis Fagbamigbe1,2, Ngianga-Bakwin Kandala3, Olalekan A Uthman2
1Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Insights
Severe wasting (SW) disproportionately affects children in poor households in low- and middle-income countries (LMICs). Addressing wealth inequalities in SW requires multi-sectoral interventions targeting factors like neighborhood socioeconomic status and maternal education.
Area of Science:
- Global Health
- Child Nutrition
- Socioeconomic Determinants of Health
Background:
- Severe wasting (SW) is a critical health challenge for vulnerable children in low- and middle-income countries (LMICs).
- Understanding wealth-related disparities in SW is crucial for effective public health interventions.
- Limited research exists on factors driving wealth inequalities in SW distribution within LMICs.
Purpose of the Study:
- To quantify the contributions of demographic, contextual, and proximate factors to the poor-non-poor gap in SW in LMICs.
- To identify key determinants of wealth inequalities in SW across diverse LMICs.
- To inform targeted strategies for reducing SW prevalence among disadvantaged populations.
Main Methods:
- Utilized pooled secondary data from Demographic and Health Surveys (2010-2018) across 51 LMICs.
- Included 532,680 under-five children and 55,823 neighborhoods.
- Applied Oaxaca-Blinder decomposition to analyze the poor-non-poor gap in SW determinants.
Main Results:
- Overall SW prevalence was 5.3% in poor households versus 4.2% in non-poor households.
- Twenty-one countries showed significant pro-poor inequality in SW.
- Key contributors to wealth inequalities included neighborhood socioeconomic status, media access, maternal age, and education.
Conclusions:
- Significant wealth inequalities in SW exist, predominantly favoring non-poor children in LMICs.
- Socioeconomic factors, particularly neighborhood characteristics and maternal attributes, significantly widen the SW gap.
- Multi-sectoral, country-specific interventions reducing wealth disparities are recommended to alleviate the burden of SW.
Abstract:
A good understanding of the poor-non-poor gap in childhood development of severe wasting (SW) is a must in tackling the age-long critical challenge to health outcomes of vulnerable children in low- and middle-income countries (LMICs). There is a dearth of information about the factors explaining differentials in wealth inequalities in the distribution of SW in LMICs. This study is aimed at quantifying the contributions of demographic, contextual and proximate factors in explaining the poor-non-poor gap in SW in LMICs. We pooled successive secondary data from the Demographic and Health Survey conducted between 2010 and 2018 in LMICs. The final data consist of 532,680 under-five children nested within 55,823 neighbourhoods from 51 LMICs. Our outcome variable is having SW or not among under-five children. Oaxaca-Blinder decomposition was used to decipher poor-non-poor gap in the determinants of SW. Children from poor households ranged from 37.5% in Egypt to 52.1% in Myanmar. The overall prevalence of SW among children from poor households was 5.3% compared with 4.2% among those from non-poor households. Twenty-one countries had statistically significant pro-poor inequality (i.e. SW concentrated among children from poor households) while only three countries showed statistically significant pro-non-poor inequality. There were variations in the important factors responsible for the wealth inequalities across the countries. The major contributors to wealth inequalities in SW include neighbourhood socioeconomic status, media access, as well as maternal age and education. Socio-economic factors created the widest gaps in the inequalities between the children from poor and non-poor households in developing SW. A potential strategy to alleviate the burden of SW is to reduce wealth inequalities among mothers in the low- and middle-income countries through multi-sectoral and country-specific interventions with considerations for the factors identified in this study.
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