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

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 (CO2) pollution on malnutrition has not been adequately considered. While the number of industries emitting CO2 keeps increasing in Nigeria, the fight against malnutrition has not been successful. The CO2 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 CO2 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 CO2 were at higher risk of malnutrition, compared to the regions with a lower concentration. However, the Northern region with a lower concentration of CO2 was consistently at higher risk of malnutrition than other regions.