Early-life air pollution and green space exposures as determinants of stunting among children under age five in Sub-Saharan Africa

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Childhood malnutrition is a major public health issue in Sub-Saharan Africa (SSA) and 61.4 million children under the age of five years in the region are stunted. Although insight from existing studies suggests plausible pathways between ambient air pollution exposure and stunting, there are limited studies on the effect of different ambient air pollutants on stunting among children.

Explore the effect of early-life environmental exposures on stunting among children under the age of five years.

In this study, we used pooled health and population data from 33 countries in SSA between 2006 and 2019 and environmental data from the Atmospheric Composition Analysis Group and NASA’s GIOVANNI platform. We estimated the association between early-life environmental exposures and stunting in three exposure periods – in-utero (during pregnancy), post-utero (after pregnancy to current age) and cumulative (from pregnancy to current age), using Bayesian hierarchical modelling. We also visualise the likelihood of stunting among children based on their region of residence using Bayesian hierarchical modelling.

The findings show that 33.6% of sampled children were stunted. In-utero PM2.5 was associated with a higher likelihood of stunting (OR = 1.038, CrI = 1.002–1.075). Early-life exposures to nitrogen dioxide and sulphate were robustly associated with stunting among children. The findings also show spatial variation in a high and low likelihood of stunting based on a region of residence.
TidsskriftJournal of Exposure Science and Environmental Epidemiology
StatusAccepteret/In press - 2024

Bibliografisk note

Funding Information:
Prince M. Amegbor and Clive E. Sabel were supported by Big Data Centre for Environment and Health (BERTHA) funded by the Novo Nordisk Foundation Challenge Programme (grant NNF17OC0027864). Mark W. Rosenberg is the Tier I Canada Research Chair in Aging, Health and Development and supported by funding from the Canada Research Chairs program. We are also grateful to the NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC), the USAID DHS programme, and Atmospheric Composition Analysis Group at Dalhousie University and Washington University for the data used in this study.

Funding Information:
There is no funding for this research; however, the authors wish to acknowledge support from the Big Data Centre for Environment and Health (BERTHA) funded by the Novo Nordisk Foundation Challenge Programme (grant NNF17OC0027864).

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.

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