Abstract
Haemoglobin C (HbC) is one of the commonest structural haemoglobin variants in human populations. Although HbC causes mild clinical complications, its diagnosis and genetic counselling are important to prevent inheritance with other haemoglobinopathies. Little is known about its contemporary distribution and the number of newborns affected. We assembled a global database of population surveys. We then used a Bayesian geostatistical model to create maps of HbC frequency across Africa and paired our predictions with high-resolution demographics to calculate heterozygous (AC) and homozygous (CC) newborn estimates and their associated uncertainty. Data were too sparse outside Africa for this methodology to be applied. The highest frequencies were found in West Africa but HbC was commonly found in other parts of the continent. The expected annual numbers of AC and CC newborns in Africa were 672,117 (interquartile range (IQR): 642,116-705,163) and 28,703 (IQR: 26,027-31,958), respectively. These numbers are about two times previous estimates.
Highlights
Haemoglobin C (HbC) is one of the commonest structural haemoglobin variants in human populations
HbC is mainly of clinical significance when inherited in combination with HbS, causing chronic haemolytic anaemia and intermittent sickle cell crises, slightly less severe or frequent than in homozygous HbS patients (SS), and when co-inherited with b-thalassaemia, causing moderate haemolytic anaemia with splenomegaly[3]
Following careful inclusion criteria and georeferencing of these data, this database formed the evidence-base for a Bayesian model-based geostatistical (MBG) framework[24,25] which we developed to predict a continuous map of the distribution of HbC across Africa
Summary
Haemoglobin C (HbC) is one of the commonest structural haemoglobin variants in human populations. Following careful inclusion criteria and georeferencing of these data, this database formed the evidence-base for a Bayesian model-based geostatistical (MBG) framework[24,25] which we developed to predict a continuous map of the distribution of HbC across Africa. Pairing these predictions with high resolution www.nature.com/scientificreports population data and national crude birth rates allowed the expected numbers of newborns affected annually by HbC trait (AC) and disease (CC) to be estimated
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