Analysis of Spatiotemporal Patterns of Undernutrition among Children below Five Years of Age in Uganda

Vallence Ngabo Maniragaba (), Leonard K. Atuhaire and Pierre Claver Rutayisire
Additional contact information
Vallence Ngabo Maniragaba: African Center of Excellence in Data Science, College of Business and Economic, University of Rwanda, Kigali P.O. Box 3248, Rwanda
Leonard K. Atuhaire: School of Statistical Methods, College of Management Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
Pierre Claver Rutayisire: African Center of Excellence in Data Science, College of Business and Economic, University of Rwanda, Kigali P.O. Box 3248, Rwanda

Sustainability, 2023, vol. 15, issue 20, 1-19

Abstract: Background: This study aimed at examining the prevalence and variations in undernutrition among children below five years of age in Uganda while considering the influence of space and time factors. Various studies conducted in Uganda analyzed the undernutrition phenomenon among children below five years of age with a focus on the risk factors and spatial variations; however, no study has ever integrated the elements of time in examining the problem of undernutrition in Uganda. The approach of spatial and spatiotemporal analysis is essential in identifying cluster patterns, hotspots, trends, and emerging hotspots, which is crucial in making timely and location-specific interventions. Methods: Data from the six Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used, with the main outcome variable being undernutrition among children below five years of age. A Composite Index of Anthropometric Failure was derived from the three undernutrition outcomes and subsequently used as a proxy of undernutrition in this study. All data that were relevant to this study were retrieved from the survey datasets and combined with the 2014 shape files of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, was used to test the hypothesis of no spatial autocorrelation, while the Getis–Ord (Gi*) statistic was used to examine hot and cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across the country. Moreover, emerging hot spot analysis was done to help in identifying the patterns of undernutrition over time. Results: The national prevalence of undernutrition among children below five years of age was 31.96 percent, with significant spatial variations both in space across Uganda and in the time since 1989. The index of spatial autocorrelation (Moran’s I) confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely, the Karamoja, the Sebei, the West Nile, and the Toro regions, were significantly evident. Most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi, and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive, and intensifying hot spots, West Nile region was identified as a sporadic hotspot area, while the Toro region was identified with both sporadic and emerging hotspots. In conclusions , undernutrition is a silent pandemic that calls for immediate and stringent measures. At 31.96 percent, the prevalence is still very high and unpleasant. To reduce the prevalence of undernutrition and to achieve SDG goal 2, policymakers, as well as implementers, should consider the spatial effects and spatial and spatiotemporal variations across the country and prioritize interventions to hot spot areas. This would ensure efficient, timely, and region-specific interventions.

Keywords: undernutrition; spatial autocorrelation; hotspots; geographically weighted regression; emerging hotspots; under-fives; Uganda (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/20/14872/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/20/14872/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:20:p:14872-:d:1259530

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().