Spatial Variation of Child Stunting and Maternal Malnutrition after Controlling for Known Risk Factors in a Drought-Prone Rural Community in Southern Ethiopia

Epidemiological study of pediatric nutritional deficiencies: an analysis from the global burden of disease study 2019

BACKGROUND

Nutritional deficiencies (ND) continue to threaten the lives of millions of people around the world, with children being the worst hit. Nevertheless, no systematic study of the epidemiological features of child ND has been conducted so far. Therefore, we aimed to comprehensively assess the burden of pediatric ND.

METHODS

We analyzed data on pediatric ND between 1990 and 2019 from the Global Burden of Disease study (GBD) 2019 at the global, regional, and national levels. In addition, joinpoint regression models were used to assess temporal trends.

RESULTS

In 2019, the number of prevalent cases of childhood malnutrition increased to 435,071,628 globally. The global age-standardized incidence, prevalence, and DALY rates showed an increasing trend between 1990 and 2019. Meanwhile, the burden of child malnutrition was negatively correlated with sociodemographic index (SDI). Asia and Africa still carried the heaviest burden. The burden and trends of child malnutrition varied considerably across countries and regions. At the age level, we found that malnutrition was significantly more prevalent among children < 5 years of age.

CONCLUSION

Pediatric ND remains a major public health challenge, especially in areas with low SDI. Therefore, primary healthcare services in developing countries should be improved, and effective measures, such as enhanced pre-school education, strengthened nutritional support, and early and aggressive treatment, need to be developed.

Spatial trends and projections of chronic malnutrition among children under 5 years of age in Ethiopia from 2011 to 2019: a geographically weighted regression analysis

Introduction

Undernutrition is a serious global health issue, and stunting is a key indicator of children's nutritional status which results from long-term deprivation of basic needs. Ethiopia, the largest and most populous country in Sub-Saharan Africa, has the greatest rate of stunting among children under the age of five, yet the problem is unevenly distributed across the country. Thus, we investigate spatial heterogeneity and explore spatial projection of stunting among under-five children. Further, spatial predictors of stunting were assessed using geospatial regression models.

Methods

The Ethiopia Demographic and Health Surveys (EDHS) data from 2011, 2016, and 2019 were examined using a geostatistical technique that took into account spatial autocorrelation. Ordinary kriging was used to interpolate stunting data, and Kulldorff spatial scan statistics were used to identify spatial clusters with high and low stunting prevalence. In spatial regression modeling, the ordinary least square (OLS) model was employed to investigate spatial predictors of stunting and to examine local spatial variations geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models were employed.

Results

Overall, stunting prevalence was decreased from 44.42% [95%, CI: 0.425–0.444] in 2011 to 36.77% [95%, CI: 0.349–0.375] in 2019. Across three waves of EDHS, clusters with a high prevalence of stunting in children under 5 years were consistently observed in northern Ethiopia stretching in Tigray, Amhara, Afar, and Benishangul-Gumuz. Another area of very high stunting incidence was observed in the Southern parts of Ethiopia and the Somali region of Ethiopia. Our spatial regression analysis revealed that the observed geographical variation of under-five stunting significantly correlated with poor sanitation, poor wealth index, inadequate diet, residency, and mothers' education.

Conclusions

In Ethiopia, substantial progress has been made in decreasing stunting among children under the age of 5 years; although disparities varied in some areas and districts between surveys, the pattern generally remained constant over time. These findings suggest a need for region and district-specific policies where priority should be given to children in areas where most likely to exhibit high-risk stunting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41043-022-00309-7.

Magnitude of under-nutrition among under five children in Ethiopia based on 2019 Mini-Ethiopia Demographic and Health Survey: Generalized Linear Mixed Model (GLMM)

INTRODUCTION

Malnutrition is a major public health problem that is experienced by many developing countries, like Ethiopia. Though some studies were conducted to identify the magnitude and determinants of acute malnutrition among under-five children, there is a lack of evidence that is representative of all children in Ethiopia. Hence, this national-level data could be representative of all targets and provide us with updated information on the nation-wide magnitude of nutritional status among children under the age of five in Ethiopia.

METHODS

This study used data from the 2019 Mini-Ethiopia Demographic and Health Survey (EDHS). Children aged 0-59 months with anthropometry data were included. Data processing and analysis were performed using STATA 15 software. Cross-tabulations and summary statistics were done to describe the study population. Generalized Linear Mixed Models (GLMMs) were used to estimate the association between nutritional status and explanatory variables and were expressed as an odds ratio with a 95% confidence interval (CI). Model comparison was done based on Akaike and Bayesian information criteria (AIC and BIC).

RESULTS

The magnitude of stunting was 37.71% [95%CI: 36.35-39.08], while the magnitude of wasting was 7.14% [95%CI: 6.52-7.91]. Living in Tigray [AOR = 2.90, 95%CI: 2.05-4.11], Amhara [AOR = 1.98, 95%CI: 1.41-2.79], having a child aged 24-35 [AOR = 3.79, 95%CI: 3.07-4.68], and being a rural resident were all significantly associated with stunting. Being born in Tigray [AOR = 1.75, 95% CI: 1.02-3.01], being born into the richest family [AOR = 0.74, 95% CI: 0.27-0.80], and being born from mothers aged 25-29 [AOR = 0.73, 95% CI: 0.55-0.96] were all significantly associated with wasting.

CONCLUSION

The magnitude of stunting and wasting is relatively high in Ethiopia. Region, place of residence, and age of the child were significantly associated with stunting, and region, wealth index, and age of the child were significantly associated with wasting. This result provides a clue to give due consideration to under-five children to mitigate the risks of malnutrition through various techniques.

Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors

This study presents the determinants of childhood stunting as the consequence of child malnutrition. We checked two groups of factors-the socio-economic situation and climate vulnerability-using disaggregated sub-regional data in the spatial context. Data related to the percentage of stunted children in Pakistan for 2017 were retrieved from MICS 2017-18 along with other features. We used three quantitative models: ordinary least squares regression (OLS) to examine the linear relationships among the selected features, spatial regression (SDEM) to identify and capture the spatial spillover effect, and the Extreme Gradient Boosting machine learning algorithm (XGBoost) to analyse the importance of spatial lag and generate predictions. The results showed a high degree of spatial clustering in childhood stunting at the sub-regional level. We found that a 1 percentage point (p.p.) increase in multi-dimensional poverty may translate into a 0.18 p.p. increase in childhood stunting. Furthermore, high climate vulnerability and common marriages before age 15 each exacerbated childhood stunting by another 1 p.p. On the contrary, high female literacy and their high exposure to mass media, together with low climate vulnerability, may reduce childhood stunting. Model diagnostics showed that the SDEM outperformed the OLS model, as AIC_OLS = 766 > AIC_SDEM = 760. Furthermore, XGBoost generated the most accurate predictions in comparison to OLS and SDEM, having the lowest root-mean-square error (RMSE).