Both malaria prevalence (44% in the pooled data) and anaemia prevalence (83% in the pooled data) were high among young children in Burkina Faso between 2010 and 2018. We estimated a malaria-attributable haemoglobin decrease of -7.7 g/L during acute infection and of -7.1g/L in the time post-infection. Older children had higher haemoglobin levels than younger children and female sex improved haemoglobin levels by 2 g/L.
The malaria-induced haemoglobin changes can have large clinical implications. For instance, it has been shown that an increase of 10 g/L haemoglobin is associated with a 0.78 relative risk of mental retardation in young children . This implies that a malaria-attributable haemoglobin reduction as shown in our data might pose substantial threat of cognitive development disorders in affected children. Especially the predictions of anaemia prevalence illustrate the severity of the burden of malarial anaemia in Burkina Faso. Our analyses indicate that most cases of severe anaemia and a sizeable portion of moderate anaemia could be avoided if malaria were successfully eradicated.
Several studies have previously reported on the malaria-associated decrease in haemoglobin concentrations in clinical and national settings using different analytic methods [35–37]. The age group and sex dependent variation in haemoglobin values, as observed in our study, have been described previously. The observed differences in magnitude of the effect by age and sex are typical for early childhood development and in line with current research.
Our study is unique in that we could estimate the close-to-causal association between malaria and anaemia at the population-level, using a household fixed-effect approach controlling for all confounding that is constant within a given household. Furthermore, it is representative not only in its sampling design, but also in its seasonal composition, given that surveys were conducted on- and off malaria season. Finally, the study is based on a very large and nationally representative sample of 17 599 children and thus offers enough power to inspire confidence in our results as they are consistent even in the reduced subset analyses.
Our study is influenced by several limitations. Firstly, a large number of children had a positive rapid test, but no corresponding positive microscopy test result. Thick smear microscopy is considered the gold standard but has varying sensitivity (from 55–98%) and specificity (from 81% to > 98%), depending on the experience of the diagnostician and the slide quality [39–41]. To rule out malaria it is required to repeat the microscopy test over the course of several days, which has not been done in the surveys and thus likely results in an underestimation of the malaria prevalence in our data . The other method, RDTs, produce a comparatively high rate of false positives where plasmodium antigens are present on their gametocytes, even when the disease itself is controlled by the immune system or medical treatment. This can cause microscopy-negative cases to show RDT positive results for up to thirty days even after parasite elimination and clinical remission [30, 33]. We leveraged this effect to create a quasi-longitudinal perspective, where positive RDTs with negative microscopy results represent children that are currently recovering from malaria. Biologically, this prolonged effect might be a mix of several contributing factors, such as persistent bone-marrow suppression, delayed haemolysis, delayed recovery and false-negative microscopy tests .
A second limitation is the way in which the pooled cross-sectional data reflects the patterns of malaria and changes between survey years in Burkina Faso. Since we pooled several years and seasons of surveys, our study population is not representative of any malaria point-prevalence in Burkina Faso and thus our analysis neither reflects malarias seasonal pattern, nor does it reflect progress made in the fight against malaria between 2010 and 2018. It is, however, still comparable to the extremes of poverty, anaemia burden and malaria transmission intensity found in West African countries [2, 43, 44].
Thirdly, the fixed-effect method itself also comes with a caveat: It controls for all confounders above the household level but lacks control for the within household confounders, particularly anaemia risk factors that vary between children in a household. These risk factors include nutritional (e.g., iron deficiency) and genetical traits (e.g., sickle-cell anaemia), other infectious diseases (e.g., helminths) and other, frequently interacted factors[45–47]. For our model we assumed that these unmeasured confounders are reasonably similar for all children within the household.