Estimating the burden of iron deficiency among African children

Abstract

Background: Iron deficiency (ID) is a major public health burden in African children and accurate prevalence estimates are important for effective nutritional interventions. However, ID may be incorrectly estimated in Africa because most measures of iron status are altered by inflammation and infections such as malaria. Through the current study, we have assessed different approaches to the prediction of iron status and estimated the burden of ID in African children.

Methods: We assayed iron and inflammatory biomarkers in 4853 children aged 0-8 years from Kenya, Uganda, Burkina Faso, South Africa, and The Gambia. We described iron status and its relationship with age, sex, inflammation, and malaria parasitemia. We defined ID using the WHO guideline (ferritin < 12 μg/L or < 30 μg/L in the presence of inflammation in children < 5 years old or < 15 μg/L in children ≥ 5 years old). We compared this with a recently proposed gold standard, which uses regression-correction for ferritin levels based on the relationship between ferritin levels, inflammatory markers, and malaria. We further investigated the utility of other iron biomarkers in predicting ID using the inflammation and malaria regression-corrected estimate as a gold standard.

Results: The prevalence of ID was highest at 1 year of age and in male infants. Inflammation and malaria parasitemia were associated with all iron biomarkers, although transferrin saturation was least affected. Overall prevalence of WHO-defined ID was 34% compared to 52% using the inflammation and malaria regression-corrected estimate. This unidentified burden of ID increased with age and was highest in countries with high prevalence of inflammation and malaria, where up to a quarter of iron-deficient children were misclassified as iron replete. Transferrin saturation < 11% most closely predicted the prevalence of ID according to the regression-correction gold standard.

Conclusions: The prevalence of ID is underestimated in African children when defined using the WHO guidelines, especially in malaria-endemic populations, and the use of transferrin saturation may provide a more accurate approach. Further research is needed to identify the most accurate measures for determining the prevalence of ID in sub-Saharan Africa.

Keywords: African children; Ferritin; Inflammation; Iron deficiency; Malaria; Transferrin saturation.

Fig. 1

Geometric means for different iron biomarkers by age in years and sex. Orange indicates females and blue males. Error bars indicate 95% confidence intervals. Star indicates Student’s t test p value < 0.05 for mean differences between sex. BIS, body iron stores; sTfR, soluble transferrin receptor; TSAT, transferrin saturation

Fig. 2

Predictors of individual iron biomarkers. Effect size represents coefficient from multivariable linear regression model with the iron biomarker as the outcome variable. Models were adjusted for age, sex, study site, inflammation, and malaria. Iron biomarkers were ln-transformed except hemoglobin, transferrin, and BIS. Error bars indicate 95% confidence intervals and values indicate effect size (95% CI). Inflammation was defined as C-reactive protein > 5 mg/L or α1-antichymotrypsin > 0.6 g/dL (in The Gambia). Malaria was defined as P. falciparum parasitemia. BIS, body iron stores; sTfR, soluble transferrin receptor; TSAT, transferrin saturation

Fig. 3

Prevalence of estimated iron deficiency across the study sites. The map shows the predicted posterior predictions of age-standardized P. falciparum prevalence (PfPR_2–10) as previously published by Snow et al. [30]. Map was reproduced with permission. Graph letter “a” indicates prevalence of iron deficiency using the WHO definition, “b” excluding children with inflammation, “c” adjusting for malaria only, “d” adjusting for inflammation only, “e” adjusting for both malaria and inflammation, and “f” using transferrin saturation cut-off of < 11%. Values indicate prevalence. Malaria only indicates percentage of children with malaria parasitemia without inflammation, inflammation only as percentage with inflammation and no parasitemia, and malaria and inflammation as percentage with both parasitemia and inflammation. Absolute increase in iron deficiency was calculated as the difference between regression-corrected prevalence (corrected for both malaria and inflammation) and WHO-defined prevalence. Error bars indicate 95% confidence intervals

Fig. 4

The burden of iron deficiency varies by age, sex, inflammation, and malaria parasitemia. Error bars indicate 95% confidence intervals for prevalence of iron deficiency regression-corrected for inflammation and malaria. Darker colors indicate the WHO definition of iron deficiency while lighter colors show the gap in the prevalence of iron deficiency between the two definitions (referred to as “hidden iron deficiency”). The values in the bars indicate the percentage of children with iron deficiency unaccounted for by the WHO definition. Line plots indicate how the prevalence of inflammation (black) and malaria (red) changed with age

Fig. 5

Relationship between the estimated prevalence of iron deficiency and inflammation. a How the prevalence of estimates of iron deficiency including WHO-defined ID, regression-corrected ID (corrected for inflammation and malaria), and TSAT < 11%, varied by deciles of C-reactive protein (CRP) and b ferritin levels were higher in children with malaria parasitemia compared to those without parasitemia at every CRP decile. Error bars indicate 95% confidence intervals. TSAT, transferrin saturation

Fig. 6

Receiver operating characteristic curves of the utility of iron markers in predicting regression-corrected iron deficiency. The “gold standard” was defined using the WHO definition adjusted for malaria and inflammation using regression correction. Green points indicate Youden’s optimal cut-offs for each marker. Sensitivity and specificity are for the optimal cut-off. TSAT, transferrin saturation; sTfR, soluble transferrin receptor; AUC, area under curve