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Association Between Anaemia in Children 6 to 23 Months Old and Child, Mother, Household and Feeding Indicators

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Association Between Anaemia in Children 6 to 23 Months Old and Child, Mother, Household and Feeding Indicators

Alberto Prieto-Patron et al. Nutrients.

Abstract

In Low and Lower-Middle-Income countries, the prevalence of anaemia in infancy remains high. In early childhood anaemia cause irreversible cognitive deficits and represents a higher risk of child mortality. The consequences of anaemia in infancy are a major barrier to overcome poverty traps. The aim of this study was to analyse, based on a multi-level approach, different factors associated with anaemia in children 6⁻23 months old based on recent available Standard Demographic Health Surveys (S-DHS). We identified 52 S-DHS that had complete information in all covariates of interest in our analysis between 2005 and 2015. We performed traditional logistic regressions and multilevel logistic regression analyses to study the association between haemoglobin concentrations and household, child, maternal, socio-demographic variables. In our sample, 70% of the 6⁻23 months-old children were anaemic. Child anaemia was strongly associated with maternal anaemia, household wealth, maternal education and low birth weight. Children fed with fortified foods, potatoes and other tubers had significantly lower rates of anaemia. Improving overall household living conditions, increasing maternal education, delaying childbearing and introducing iron rich foods at six months of age may reduce the likelihood of anaemia in toddlerhood.

Keywords: Anaemia; Demographic Health Surveys; infancy and toddlerhood; infant feeding; low and middle-income countries; multilevel regression.

Conflict of interest statement

Prieto Patron, Detzel, Hutton are employed by Nestec SA, Nestlé Research Center. Klazine Van Der Host previously has worked for Nestlé Research.

Figures

Figure 1
Figure 1
The hierarchy of the determinants of infant anaemia—conceptual framework for the multilevel analysis.
Figure 2
Figure 2
Inter class correlation by models. Model 0: Null model only variance level cluster; Model 1: adds national level variables; Model 2: adds state (subnational) level variables; Model 3: adds household socioeconomic variables; Model 4: adds child and mother characteristics; Model 5: adds feeding variables; Model 6: adds child health variables.
Figure 3
Figure 3
Contribution of individual level variables group to the incremental explanatory power of the regression (Pseudo R2).

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