The First Model-Based Geostatistical Map of Anaemia

Anaemia is the most common blood disorder globally, is a major underlying cause of death among children and pregnant women in the developing world, and because it is most prevalent in the poorest communities, represents a useful marker of social and economic marginalization [1–3]. The aetiology of anaemia is often multi-factorial including nutritional deficiencies, parasitic and inflammatory diseases, haemorrhage, and genetic defects in the molecular structure of the haemoglobin (Hb) [3]. Many of these risk factors for anaemia co-exist in communities and affect individuals in composite ways not adequately understood. In Africa three dominant contributors to anaemia in young children are malaria, helminth infections, and iron-deficient diets. To date, only national-level estimates of anaemia burden developed by the World Health Organization are available to guide control [3]. There are limited data on within-country variations and no detailed information on the uncertainty around these estimates or on the combination of causal factors important to a country. The article published in PLoS Medicine this week by Magalhães and Clements [4] explores the use of model-based geostatistics to investigate the risks of anaemia and mean Hb concentrations among preschool-age children (aged 1–4 years) attributable to malnutrition, malaria, and helminth infections in three West African countries (Burkina Faso, Ghana, and Mali). The authors develop high resolution maps of the geographic distribution of anaemia accounting for these factors and compute the estimated number of anaemic children. The advantage of using modelbased geostatistics to model anaemia is that it allows the use of the survey data from a sample of locations to predict continuous surfaces of risk, informed by environmental and demographic covariates, but in a way that takes into account the sample size and spatial characteristic of the data to robustly assess uncertainty in the modelled outputs. The study identified malnutrition as the main driver of anaemia, followed by malaria and helminth infections, accounting for a population attributable fraction (PAF) of almost 60% of all anaemia. The mean Hb concentration increased with age and varied by country. The continuous surfaces of anaemia prevalence and Hb concentration show interesting variations within the national borders that allow for the computation of the location and radius of foci of high risks. The estimated case burden for 2011 was similar to that provided by the World Health Organization in its 1993–2005 worldwide anaemia prevalence report [3].