Evaluating the Latent Determinants of Childhood Malnutrition: A Hierarchical Analysis of Nigeria and Kenya

Abstract

Background: Malnutrition is responsible for 45% of all deaths among children worldwide. Despite a remarkable reduction in global poverty and famines, substantial childhood malnutrition continues to persist. In 2017, acute malnutrition (wasting) menaced over 50 million young children while over 150 million young children suffered from chronic malnutrition (stunting). Intervention organizations increasingly demand data-based decision making with a particular propensity for spatial and hierarchical heterogeneity. Methods: We review 57 disaggregate empirical studies of the determinants of childhood malnutrition in Africa published since 1990. We survey this corpus to identify deficiencies which may impede progress in reducing malnutrition. We develop hierarchical econometric models (four-level random intercept hierarchical generalized logit model), to perform a reanalysis of malnutrition in Kenya and Nigeria employing repeated cross-sectional data from the Demographic and Health Surveys and spatially explicit climatic inputs. We evaluate the responsiveness of malnutrition indicators to latent determinants, judged on the basis of their epidemiological and actionable significance. Findings: Of the 57 studies, we find a heterogenous patchwork of research which neglects countries where malnutrition is most severe. Only three studies interpret and discuss how much factors determine malnutrition with rigorous methodologies, and only 11 studies acknowledge the heterogeneity of social experience and consider how the effects change across and between different hierarchies. In our example reanalysis, we find that 28 to 36 percent of variation in malnutrition outcomes is explained through the hierarchical specification. And the most impactful latent determinants of malnutrition include mother’s education, household wealth, healthcare practices, and climatic stress, each with the capacity to reduce malnutrition prevalence rates by as much as 50%. Interpretation: Geospatial and disaggregated data helps one to understand better who is malnourished as well as where and how to target mitigation efforts at subnational levels. With explicit measurements of how much factors determine malnutrition, intervention organizations and governments may begin to make substantial progress to reduce childhood malnutrition. Funding Statement: The work was supported by the UK Aid from the UK Department for International Development (DFID) (grant numbers. PO 7442). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Not required.