Birth registration and child undernutrition in sub-saharan Africa Articles uri icon

publication date

  • July 2016

start page

  • 1757

end page

  • 1767

issue

  • 10

volume

  • 19

abstract

  • Objective In many countries of the world millions of people are not registered at birth. However, in order to assess children's nutritional status it is necessary to have an exact knowledge of their age. In the present paper we discuss the effects of insufficient or imprecise age data on estimates of undernutrition prevalence.Design Birth registration rates and levels of stunting, underweight and wasting were retrieved from Multiple Indicator Cluster Surveys and Demographic and Health Surveys of thirty-seven sub-Saharan African countries, considering the subdivision in wealth quintiles. The composition of the cross-sectional sample used for nutritional evaluation was analysed using a permutation test. Logistic regression was applied to analyse the relationship between birth registration and undernutrition. The 95 % probability intervals and Student's t test were used to evaluate the effect of age bias and error.Results Heterogeneous sampling designs were detected among countries, with different percentages of children selected for anthropometry. Further, registered children were slightly more represented within samples used for nutritional analysis than in the total sample. A negative relationship between birth registration and undernutrition was recognized, with registered children showing a better nutritional status than unregistered ones, even within each wealth quintile. The over- or underestimation of undernutrition in the case of systematic over- or underestimation of age, respectively, the latter being more probable, was quantified up to 28 %. Age imprecision was shown to slightly overestimate undernutrition.Conclusions Selection bias towards registered children and underestimation of children's age can lead to an underestimation of the prevalence of undernutrition.

keywords

  • stunting; wasting; underweight; demographic and health surveys; multiple indicator cluster surveys