Spatial Weighted Analysis of Malnutrition Among Children in Nigeria: A Bayesian Approach

Osafu Augustine Egbon (), Omodolapo Somo-Aina and Ezra Gayawan
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Osafu Augustine Egbon: University of São Paulo
Omodolapo Somo-Aina: University of North Carolina Greensboro
Ezra Gayawan: The Federal University of Technology

Statistics in Biosciences, 2021, vol. 13, issue 3, No 7, 495-523

Abstract: Abstract Research on malnutrition in children has undergone in-depth study across different disciplines ranging from health demography to statistics. However, the effect of carbon(IV) oxide ( $$\text{CO}_2$$ CO 2 ) pollution on malnutrition has not been adequately considered. While the number of industries emitting $$\text{CO}_2$$ CO 2 keeps increasing in Nigeria, the fight against malnutrition has not been successful. The $$\text{CO}_2$$ CO 2 generated by industrial activities is known to contribute to the decline in the nutrition content of crops and consequently increases the risk of malnutrition in the population. In order to account for the spatial impact, and identify susceptible areas in Nigeria, this study weighted the spatial variation using the average volume of $$\text{CO}_2$$ CO 2 emitted from 2001 to 2018. The Conditional Auto-Regressive (CAR) spatial model was adopted to model the spatial component in a Bayesian Hierarchical statistical model. Data were acquired from the Nigeria Demographic and Health Survey, and Mongabay databases. Adjusting for demographic and socioeconomic variables, the result shows that regions with a higher concentration of $$\text{CO}_2$$ CO 2 were at higher risk of malnutrition, compared to the regions with a lower concentration. However, the Northern region with a lower concentration of $$\text{CO}_2$$ CO 2 was consistently at higher risk of malnutrition than other regions.

Keywords: Carbon(IV) oxide; Malnutrition; Bayesian spatial model; Conditional auto-regressive prior; Hierarchical model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12561-021-09303-9

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