Estimating the number of paediatric fevers associated with malaria infection presenting to Africa's public health sector in 2007

Abstract

Background: As international efforts to increase the coverage of artemisinin-based combination therapy in public health sectors gather pace, concerns have been raised regarding their continued indiscriminate presumptive use for treating all childhood fevers. The availability of rapid-diagnostic tests to support practical and reliable parasitological diagnosis provides an opportunity to improve the rational treatment of febrile children across Africa. However, the cost effectiveness of diagnosis-based treatment polices will depend on the presumed numbers of fevers harbouring infection. Here we compute the number of fevers likely to present to public health facilities in Africa and the estimated number of these fevers likely to be infected with Plasmodium falciparum malaria parasites.

Methods and findings: We assembled first administrative-unit level data on paediatric fever prevalence, treatment-seeking rates, and child populations. These data were combined in a geographical information system model that also incorporated an adjustment procedure for urban versus rural areas to produce spatially distributed estimates of fever burden amongst African children and the subset likely to present to public sector clinics. A second data assembly was used to estimate plausible ranges for the proportion of paediatric fevers seen at clinics positive for P. falciparum in different endemicity settings. We estimated that, of the 656 million fevers in African 0-4 y olds in 2007, 182 million (28%) were likely to have sought treatment in a public sector clinic of which 78 million (43%) were likely to have been infected with P. falciparum (range 60-103 million).

Conclusions: Spatial estimates of childhood fevers and care-seeking rates can be combined with a relational risk model of infection prevalence in the community to estimate the degree of parasitemia in those fevers reaching public health facilities. This quantification provides an important baseline comparison of malarial and nonmalarial fevers in different endemicity settings that can contribute to ongoing scientific and policy debates about optimum clinical and financial strategies for the introduction of new diagnostics. These models are made publicly available with the publication of this paper.

Figure 1. Schematic overview of mapping procedures and methods.

Blue rods describe input data; yellow boxes denote operations in a geographical information system; orange rods denote adjusted data; green rods indicate output data, with dashed lines denoting intermediate output and solid lines final outputs. U5, children aged under 5 y old.

Figure 2. Transmission intensity, fevers, and care seeking for fever across Africa.

(A) Predicted transmission intensity across Africa. Transmission is classified into areas of risk free, unstable, and stable transmission on the basis of country-reported case data and the limiting effects on transmission of aridity and low temperatures . The latter class is further divided into low, medium, and high transmission settings from a model-based geostatistical prediction of P. falciparum prevalence in the epidemiologically informative 2-y up to 10-y age range, PfPR_2–10 . (B) 14-d period prevalence of reported fevers among children aged 0–4 y derived from national sample survey data (yellow, no risk; grey, no data). (C) Proportion of paediatric fevers using a public health facility at some stage of the illness to treat the fever (yellow, no risk; grey, no data). Footnote: The reference ADMIN1 digital boundaries for Africa were obtained through a combination of data from the United Nations Geographic Information Working Group, Second Administrative Level Boundary project (UNGIWG-SALB [56]) and the Food & Agriculture Organization - Global Administrative Units Layers (FAO-GAUL [57]). These boundary units matched reported information on fever prevalence for 31 of 42 national survey reports assembled. For Angola, Burundi, Central African Republic, Chad, Congo, Gabon, Guinea Bissau, Mauritania, and Nigeria nonstandard ADMIN1 units were reported by the national sample surveys and these were digitized using ArcGIS 9.3 (ESRI, Inc.) to replace existing ADMIN1 boundaries and thus create a single fever spatial reporting surface, similar to recent approaches to assemble mosquito net use from national survey data .

Figure 3. Risks of febrile children being infected when presenting to clinics within three epidemiological strata of unstable/≤5% PfPR_2–10, >5% to <40% PfPR_2–10, and ≥40% PfPR_2–10.

The box indicates the IQR (25% and 75%); the thick line within the box represents the median; the whiskers represent the 2.5% and 97.5% centiles; and outliers are plotted as circles outside this range.