Clustering of chronic malnutrition among Ethiopian children

journal-pone-0170785-g001Hagos S, Hailemariam D, WoldeHanna T, Lindtjørn B (2017) Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model. PLOS ONE 12(2): e0170785. doi: 10.1371/journal.pone.0170785

Background  Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia.

Methods  A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0–59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran’s I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area.

Results  Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child’s age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35–6.58) and among boys (OR 1.28; 95%BCI; 1.12–1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting.

Conclusion  Stunting prevalence may vary across space at different scale. For this, it’s important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.

 

 

Does location matter? A study of malnutrition amongst Ethiopian children

Each month, a paper is selected by one of the Editors of the five Nutrition Society Publications (British Journal of Nutrition, Public Health Nutrition, Nutrition Research Reviews, Proceedings of the Nutrition Society and Journal of Nutritional Science). This month, Seifu Hagos Gebreyesus’ paper on ‘Local spatial clustering of stunting and wasting among children under the age of 5 years: implications for intervention strategies’ was selected.

Seifu wrote on The Nutrition Socienty Blog:

As malnutrition is a major public health problem in Ethiopia, we aimed to find out how the acute and chronic forms of undernutrition occur in the districts and kebeles (a kebele is the smallest administrative unit in Ethiopia). Such knowledge could be helpful in improving our understanding of the distribution of undernutrition on a local scale, as well as designing targeted nutrition intervention programmes.

For this purpose, we surveyed children aged less than five years, who were found in 1744 households. We measured children’s height, weight, and the geographic locations (latitudes and longitudes) of households. Using data from 2371 children aged less than five years of age, we evaluated how malnutrition is distributed within a district and kebeles.

Although many believe that undernutrition is equally distributed within an area, we found that children living in locations within a district are more susceptible to undernutrition than children in other locations but living in the same district. For example, children living in these locations were 1.5 times more likely to be stunted and 1.7 times more likely to be severely stunted than children living in other locations within the district. Similarly, in some kebeles, children living in some small areas experience more acute malnutrition (wasting and severe wasting).

Our finding has important implications to nutritional intervention strategies. Stunting and wasting are not equally distributed in an area, suggesting that planning of nutrition interventions may need to consider the variations in the vulnerability.

To help accelerate the reduction of malnutrition, it could be important to consider targeting locations where more susceptible children live. The approach would help reach children who are most likely to benefit from intervention programmes.

We recommend that this research needs to be repeated in other areas of Ethiopia and other developing countries. We also would like to recommend further study possibly using an implementation research approach to evaluate the feasibility, advantages and effectiveness of targeting nutritional interventions.