title: Spatial quantile regression using INLA with applications to childhood overweight in Malawi.
abstract: Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions.
authors.affiliation: Mathematics and Statistics, Polytechnic of Namibia, Windhoek 264, Namibia. Electronic address: owen.mtambo@yahoo.com.
authors.forename: Owen P L
authors.initials: OP
authors.lastname: Mtambo
authors.affiliation: Mathematics and Statistics, Polytechnic of Malawi, Blantyre 265, Malawi.
authors.forename: Salule J
authors.initials: SJ
authors.lastname: Masangwi
authors.affiliation: Mathematics and Statistics, University of Namibia, Windhoek 264, Namibia.
authors.forename: Lawrence N M
authors.initials: LN
authors.lastname: Kazembe
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002648:Child; D005260:Female; D006306:Health Surveys / Q000379:methods; D006801:Humans; D008295:Malawi / Q000453:epidemiology; D008297:Male; D063766:Pediatric Obesity / Q000453:epidemiology*; D012959:Socioeconomic Factors; D000068916:Spatial Regression
keywords: Childhood overweight; Fully Bayesian; INLA; Intrinsic CAR; Quantile regression; Structured spatial effects