Establishing the extent of malaria transmission and challenges facing pre-elimination in the Republic of Djibouti

Abstract

Background: Countries aiming for malaria elimination require a detailed understanding of the current intensity of malaria transmission within their national borders. National household sample surveys are now being used to define infection prevalence but these are less efficient in areas of exceptionally low endemicity. Here we present the results of a national malaria indicator survey in the Republic of Djibouti, the first in sub-Saharan Africa to combine parasitological and serological markers of malaria, to evaluate the extent of transmission in the country and explore the potential for elimination.

Methods: A national cross-sectional household survey was undertaken from December 2008 to January 2009. A finger prick blood sample was taken from randomly selected participants of all ages to examine for parasitaemia using rapid diagnostic tests (RDTs) and confirmed using Polymerase Chain Reaction (PCR). Blood spots were also collected on filter paper and subsequently used to evaluate the presence of serological markers (combined AMA-1 and MSP-119) of Plasmodium falciparum exposure. Multivariate regression analysis was used to determine the risk factors for P. falciparum infection and/or exposure. The Getis-Ord G-statistic was used to assess spatial heterogeneity of combined infections and serological markers.

Results: A total of 7151 individuals were tested using RDTs of which only 42 (0.5%) were positive for P. falciparum infections and confirmed by PCR. Filter paper blood spots were collected for 5605 individuals. Of these 4769 showed concordant optical density results and were retained in subsequent analysis. Overall P. falciparum sero-prevalence was 9.9% (517/4769) for all ages; 6.9% (46/649) in children under the age of five years; and 14.2% (76/510) in the oldest age group (≥50 years). The combined infection and/or antibody prevalence was 10.5% (550/4769) and varied from 8.1% to 14.1% but overall regional differences were not statistically significant (χ2=33.98, p=0.3144). Increasing age (p<0.001) and decreasing household wealth status (p<0.001) were significantly associated with increasing combined P. falciparum infection and/or antibody prevalence. Significant P. falciparum hot spots were observed in Dikhil region.

Conclusion: Malaria transmission in the Republic of Djibouti is very low across all regions with evidence of micro-epidemiological heterogeneity and limited recent transmission. It would seem that the Republic of Djibouti has a biologically feasible set of pre-conditions for elimination, however, the operational feasibility and the potential risks to elimination posed by P. vivax and human population movement across the sub-region remain to be properly established.

Figure 1

Map of the Republic of Djibouti showing inland water, roads, railway, administrative regions and major settlements geo-coded in Google Earth and obtained from DEPHA [64], showing location of clusters with persons indentified as sero-positive for P. falciparum antibodies and/or RDT-PCR confirmed parasite positive (red) and clusters where no person identified as sero-positive or parasite positive (green) overlaid on 1 × 1 kilometre resolution surface of aridity as defined by GlobCover [33].

Figure 2

Age specific sero-prevalence plot of P. falciparum antibodies using maximum-likelihood fits from reversible catalytic equilibrium model [37]. The vertical axis shows the proportion of sero-positive individuals in each age group, the horizontal axis shows the midpoint age. Red triangles represent observed prevalence and blue fitted line is predicted age prevalence curve. λ = 0.0134 (95% CI, 0.010 - 0.018) and ρ = 0.0799 (95% CI, 0.0532 - 0.1200).

Figure 3

The location of clusters that are P. falciparum antibody/infection prevalence hot spots (red dots, n = 5), cold spots (blue dots, n = 34) and those that don't show significant clustering (green dots, n = 90) relative to neighbouring clusters. Significance of clustering was analysed using the Getis-Ord G-statistic [38]. Positive Z-scores with significant G-statistic indicated hot spots while negative Z-scores indicated cold spots. The G-statistic was computed using ArcGIS 9.3 Spatial Statistics toolbox (ESRI Inc. USA). 33/34 of the cold spots were in Djibouti Ville while all hot spots were in Dikhil region.