Research Article: Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model

Date Published: February 7, 2017

Publisher: Public Library of Science

Author(s): Seifu Hagos, Damen Hailemariam, Tasew WoldeHanna, Bernt Lindtjørn, Kebede Deribe.

http://doi.org/10.1371/journal.pone.0170785

Abstract

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.

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.

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.

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.

Partial Text

Child undernutrition, including macronutrient and micronutrient deficiencies, contributed around 45% of child deaths in 2011 [1]. Stunting, a measure of chronic undernutrition, is the most prevalent form of child undernutrition in developing countries. Thus, in 2011, about 165 million stunted children were estimated in developing countries[2]. Globally, the prevalence of stunting among children under age five has decreased. At regional level, as compared to other regions, very little decline in the prevalence of stunting is documented in Africa[2]. In 2012, the WHO sets a global target to reduce the number of stunted children by 40% from the baseline 171 million in 2010 to 100 million by 2025[2].

The study involved a total of 4,094 children 0–59 months; 104(2.6%) of them had missing information on age, sex, or height or the values collected on age or height were not plausible. Fifteen (0.35%) households were not surveyed after repeated visits because of unavailability, which gives a final analytical sample size of 3975.

In the south of Ethiopia, household food insecurity was identified as a major risk factor for stunting. Other factors such as child’s age, sex, and place of delivery and mother’s education also seem to contribute to the risk of stunting. We also found out a clear spatial pattern on the distribution of stunting at district level which would need further attention. This aggregation might be driven by environmental, anthropological and biological factors that have not been considered in this study. Additionally, our study also demonstrates the relevance of accounting for spatial dependency when exploring risk factors of stunting.

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. Thus, geographically targeted nutritional interventions might be more efficient and cost-effective in southern Ethiopia and, by extension, in similar settings. 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.

 

Source:

http://doi.org/10.1371/journal.pone.0170785