Effect of individual, household and regional socioeconomic factors and PM2.5 on anaemia: A cross-sectional study of sub-Saharan African countries

Prince M Amegbor; Sergio S Borges; Alex Pysklywec; Clive E Sabel

doi:10.1016/j.sste.2021.100472

Effect of individual, household and regional socioeconomic factors and PM_2.5 on anaemia: A cross-sectional study of sub-Saharan African countries

Spat Spatiotemporal Epidemiol. 2022 Feb:40:100472. doi: 10.1016/j.sste.2021.100472. Epub 2021 Dec 8.

Authors

Prince M Amegbor¹, Sergio S Borges², Alex Pysklywec³, Clive E Sabel⁴

Affiliations

¹ Big Data Centre for Environment and Health (BERTHA), Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark; Department of Environmental Science, Aarhus University,Frederiksborgvej 399, DK-4000 Roskilde, Denmark. Electronic address: pma@envs.au.dk.
² Department of Geography, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
³ LevelUp Planning Collaborative, Dawson City, YT, Canada.
⁴ Big Data Centre for Environment and Health (BERTHA), Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark; Department of Environmental Science, Aarhus University,Frederiksborgvej 399, DK-4000 Roskilde, Denmark.

PMID: 35120685
DOI: 10.1016/j.sste.2021.100472

Abstract

There is limited knowledge on the effect of contextual and environmental factors on the risk of anaemia, as well as the spatial distribution of anaemia in the Sub-Saharan Africa region. In this study, we used multi-country data from the Demographic & Health survey (DHS) with 270,011 observations and PM_2.5 data from NASA, applied to the spatial risk pattern of anaemia in the SSA region. The prevalence of anaemia amongst women (41%) was almost twice that of men (22%). A Bayesian hierarchical model showed that individual household, neighbourhood and regional socioeconomic factors were significantly associated with the likelihood of being anaemic. 1 μg/m³ increase in cumulative lifetime PM_2.5 exposure accounted for 1% (β = 0.011, CI = 0.008 - 0.015) increase in the likelihood of being anaemic. The results suggest the need for a multidimensional approach to tackle anaemia in the Sub-Saharan African region and identify high-risk areas for target intervention policies or programs.

Keywords: Anaemia; Bayesian spatial analysis; Demographic and health survey; Multilevel modelling; R-INLA; Sub-Saharan Africa.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Africa South of the Sahara / epidemiology
Anemia* / epidemiology
Anemia* / etiology
Bayes Theorem
Cross-Sectional Studies
Female
Humans
Male
Particulate Matter / adverse effects
Socioeconomic Factors

Substances

Particulate Matter