Global epidemiology of sickle haemoglobin in neonates: a contemporary geostatistical model-based map and population estimates

Abstract

Background: Reliable estimates of populations affected by diseases are necessary to guide efficient allocation of public health resources. Sickle haemoglobin (HbS) is the most common and clinically significant haemoglobin structural variant, but no contemporary estimates exist of the global populations affected. Moreover, the precision of available national estimates of heterozygous (AS) and homozygous (SS) neonates is unknown. We aimed to provide evidence-based estimates at various scales, with uncertainty measures.

Methods: Using a database of sickle haemoglobin surveys, we created a contemporary global map of HbS allele frequency distribution within a Bayesian geostatistical model. The pairing of this map with demographic data enabled calculation of global, regional, and national estimates of the annual number of AS and SS neonates. Subnational estimates were also calculated in data-rich areas.

Findings: Our map shows subnational spatial heterogeneities and high allele frequencies across most of sub-Saharan Africa, the Middle East, and India, as well as gene flow following migrations to western Europe and the eastern coast of the Americas. Accounting for local heterogeneities and demographic factors, we estimated that the global number of neonates affected by HbS in 2010 included 5,476,000 (IQR 5,291,000-5,679,000) AS neonates and 312,000 (294,000-330,000) SS neonates. These global estimates are higher than previous conservative estimates. Important differences predicted at the national level are discussed.

Interpretation: HbS will have an increasing effect on public health systems. Our estimates can help countries and the international community gauge the need for appropriate diagnoses and genetic counselling to reduce the number of neonates affected. Similar mapping and modelling methods could be used for other inherited disorders.

Funding: The Wellcome Trust.

Figure 1

Schematic overview of the methods and Bayesian model-based geostatistical analysis HbS=sickle haemoglobin. Positive=number of HbS alleles in the population sample. Negative=number of non-HbS alleles in the population sample. Mbg-infer=model-based geostatistical inference process based on a Markov chain Monte Carlo (MCMC) algorithm. GRUMP=Global Rural Urban Mapping Project. Mbg-map=model-based geostatistical mapping process. Mbg-areal-predict=model-based geostatistical areal prediction process. AS=HbS heterozygotes. SS=HbS homozygotes.

Figure 2

Datapoint distribution and maps of the mean and uncertainty in the predicted HbS allele frequency (A) Distribution of the datapoints. Red circles and blue triangles indicate surveys showing presence and absence of HbS, respectively. (B) Mean of the posterior predictive distribution. (C) Bayesian model-based geostatistics prediction uncertainty (posterior standard deviations) of the HbS allele frequency.

Figure 3

Comparison plots of predicted national SS neonate estimates with Modell and Darlison's estimates for the AFRO and AMRO regions Red dots show our estimates (termed MAP) with IQRs shown as red lines. Blue dots represent Modell and Darlison's estimates. (A) AFRO. (B) AMRO. AFRO=Regional Office for Africa. AMRO=Regional Office for the Americas.

Figure 4

Comparison plots of predicted national SS neonate estimates with Modell and Darlison's estimates for the EMRO and EURO regions Red dots show our estimates (termed MAP) with IQRs shown as red lines. Blue dots represent Modell and Darlison's estimates. (A) EMRO. (B) EURO. EMRO=Regional Office for the Eastern Mediterranean Countries. EURO=Regional Office for Europe.

Figure 5

Comparison plots of predicted national SS neonate estimates with Modell and Darlison's estimates for the SEARO and WPRO regions Red dots show our estimates (termed MAP) with IQRs shown as red lines. Blue dots represent Modell and Darlison's estimates. (A) SEARO. (B) WPRO. SEARO=Regional Office for South-East Asia. WPRO=Regional Office for the Western Pacific.