• Alfred Ngwira.
• Lilongwe University of Agriculture and Natural Resources, Basic Sciences, Lilongwe, Malawi. Electronic address: alfngwira@yahoo.com.
• Nutrition. 2020 Jan 1; 70S: 100010.

ObjectiveThe aim of this study was to investigate the effects of climate and location while using the multivariate model of malnutrition.MethodsThe joint semiparametric model of stunting, wasting, and overweight was fitted to 2015 child Malawi demographic health survey data with 5149 records. The MDHS was a cross-sectional study. The smooth functions for the non-parametric terms were the regression splines and the effect of location was smoothed by the Markov random field (MRF).ResultsRainfall had a positive effect on stunting (β = 0.076, P = 0.044) and overweight (β = 0.854, P = 0.039). Mean temperature (β = 1.220, P = 0.031) and distance to water body (β = 0.009, P = 0.049) also had a positive effect on wasting. Increased length of rainy season was associated with reduced overweight (β = -0.163, P = 0.042). Location was not a significant predictor of all malnutrition indicators, although there was observable spatial variation regarding overweight and wasting. There was significant positive correlation between stunting and overweight (ρ = 0.234; 95% confidence interval, 0.135-0.324). The findings on socioeconomic determinants are consistent with the literature.ConclusionNutrition interventions may target hot spot areas that have shown increased risk for overweight and wasting. The strategies to minimize malnutrition should focus on consequences of climate change like high rainfall, length of season, and temperature.Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

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