Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden

Vestine Uwiringiyimana; Frank Osei; Sherif Amer; Antonie Veldkamp

doi:10.1186/s12889-022-12552-y

Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden

BMC Public Health. 2022 Jan 24;22(1):159. doi: 10.1186/s12889-022-12552-y.

Authors

Vestine Uwiringiyimana^{1

2}, Frank Osei¹, Sherif Amer³, Antonie Veldkamp¹

Affiliations

¹ Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands.
² Department of Food Science and Technology, College of Agriculture Animal Science and Veterinary Medicine, University of Rwanda, PO Box 3900, Kigali, Rwanda.
³ Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands. s.amer@utwente.nl.

Abstract

Background: Stunting remains a significant public health issue in Rwanda and its prevalence exhibits considerable geographical variation. We apply Bayesian geostatistical modelling to study the spatial pattern of stunting in children less than five years considering anthropometric, socioeconomic and demographic risk factors in Rwanda. In addition, we predict the spatial residuals effects to quantify the burden of stunting not accounted for by our geostatistical model.

Methods: We used the data from the 2015 Rwanda Demographic and Health Survey. We fitted two spatial logistic models with similar structures, only differentiated by the inclusion or exclusion of spatially structured random effects.

Results: The risk factors of stunting identified in the geostatistical model were being male (OR = 1.32, 95% CI: 1.16, 1.47), lower birthweight (kg) (OR = 0.96, 95% CI: 0.95, 0.97), non-exclusive breastfeeding (OR = 1.24, 95% CI: 1.04, 1.45), occurrence of diarrhoea in the last two weeks (OR = 1.18, 95% CI: 1.02, 1.37), a lower proportion of mothers with overweight (BMI ≥ 25) (OR = 0.82, 95% CI: 0.71, 0.95), a higher proportion of mothers with no or only primary education (OR = 1.14, 95% CI: 0.99, 1.36). Also, a higher probability of living in a house with poor flooring material (OR = 1.22, 95% CI: 1.06, 1.41), reliance on a non-improved water source (OR = 1.13, 95% CI: 1.00, 1.27), and a low wealth index were identified as risk factors of stunting. Mapping of the spatial residuals effects showed that, in particular, the Northern and Western regions, followed by the Southern region of Rwanda, still exhibit a higher risk of stunting even after accounting for all the covariates in the spatial model.

Conclusions: Further studies are needed to identify the still unknown spatially explicit factors associated with higher risk of stunting. Finally, given the spatial heterogeneity of stunting, interventions to reduce stunting should be geographically targeted.

Keywords: Bayesian modelling; Rwanda; Spatial pattern; Spatial residuals; Stunting.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Child
Female
Growth Disorders* / epidemiology
Growth Disorders* / etiology
Humans
Infant
Male
Prevalence
Risk Factors
Rwanda / epidemiology
Socioeconomic Factors