Use of HRP-2-based rapid diagnostic test for Plasmodium falciparum malaria: assessing accuracy and cost-effectiveness in the villages of Dielmo and Ndiop, Senegal

Background: In 2006, the Senegalese National Malaria Control Programme (NMCP) has recommended artemisinin-based combination therapy (ACT) as the first-line treatment for uncomplicated malaria and, in 2007, mandated testing for all suspected cases of malaria with a Plasmodium falciparum HRP-2-based rapid diagnostic test for malaria (RDT(Paracheck (R)). Given the higher cost of ACT compared to earlier anti-malarials, the objectives of the present study were i) to study the accuracy of Paracheck (R) compared to the thick blood smear (TBS) in two areas with different levels of malaria endemicity and ii) analyse the cost-effectiveness of the strategy of the parasitological confirmation of clinically suspected malaria cases management recommended by the NMCP. Methods: A cross-sectional study was undertaken in the villages of Dielmo and Ndiop (Senegal) nested in a cohort study of about 800 inhabitants. For all the individuals consulting between October 2008 and January 2009 with a clinical diagnosis of malaria, a questionnaire was filled and finger-prick blood samples were taken both for microscopic examination and RDT. The estimated costs and cost-effectiveness analysis were made considering five scenarios, the recommendations of the NMCP being the reference scenario. In addition, a sensitivity analysis was performed assuming that all the RDT-positive patients and 50% of RDT-negative patients were treated with ACT. Results: A total of 189 consultations for clinically suspected malaria occurred during the study period. The sensitivity, specificity, positive and negative predictive values were respectively 100%, 98.3%, 80.0% and 100%. The estimated cost of the reference scenario was close to 700(sic) per 1000 episodes of illness, approximately twice as expensive as most of the other scenarios. Nevertheless, it appeared to us cost-effective while ensuring the diagnosis and the treatment of 100% of malaria attacks and an adequate management of 98.4% of episodes of illness. The present study also demonstrated that full compliance of health care providers with RDT results was required in order to avoid severe incremental costs. Conclusions: A rational use of ACT requires laboratory testing of all patients presenting with presumed malaria. Use of RDTs inevitably has incremental costs, but the strategy associating RDT use for all clinically suspected malaria and prescribing ACT only to patients tested positive is cost-effective in areas where microscopy is unavailable

An Exhaustive, Non-Euclidean, Non-Parametric Data Mining Tool for Unraveling the Complexity of Biological Systems – Novel Insights into Malaria

Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies. However, current methodologies lack exhaustivity and general applicability. Here we use a novel non-parametric, non-euclidean data mining tool, HyperCube®, to explore exhaustively a complex epidemiological malaria data set by searching for over density of events in m-dimensional space. Hotspots of over density correspond to strings of variables, rules, that determine, in this case, the occurrence of Plasmodium falciparum clinical malaria episodes. The data set contained 46,837 outcome events from 1,653 individuals and 34 explanatory variables. The best predictive rule contained 1,689 events from 148 individuals and was defined as: individuals present during 1992–2003, aged 1–5 years old, having hemoglobin AA, and having had previous Plasmodium malariae malaria parasite infection ≤10 times. These individuals had 3.71 times more P. falciparum clinical malaria episodes than the general population. We validated the rule in two different cohorts. We compared and contrasted the HyperCube® rule with the rules using variables identified by both traditional statistical methods and non-parametric regression tree methods. In addition, we tried all possible sub-stratified quantitative variables. No other model with equal or greater representativity gave a higher Relative Risk. Although three of the four variables in the rule were intuitive, the effect of number of P. malariae episodes was not. HyperCube® efficiently sub-stratified quantitative variables to optimize the rule and was able to identify interactions among the variables, tasks not easy to perform using standard data mining methods. Search of local over density in m-dimensional space, explained by easily interpretable rules, is thus seemingly ideal for generating hypotheses for large datasets to unravel the complexity inherent in biological systems

The Geographical Study of Anopheline Densities on a Small Space, using Satellite Imagery and Geographical Information Systems

International audienceTo predict the spatial distribution of anopheles in the Dielmo village (located in the southeastern part of Senegal), we used residual fauna collected from 104 different rooms during four separate trips conducted in 1994 and 1995. Thanks Generalized Estimating Equations, we were able to identify factors influencing the distribution of Anopheles in the village. Several variables, such as the number of persons sleeping in the room, population density around the hut, construction materials, presence of mosquito nets, were found to be significant, while many spatial variables relevant to the scale of a region (vegetation index, distance to larval sites...) were not found to be significant on the village level. As a result, it became clear that it is difficult to correctly predict the anopheline density for each house even with precise spatial data created with Satellite imagery and Geographical Information Systems (GIS). This work highlights the complexity of the geographical study of anopheline density and its limits on a small space

The Geographical Study of Anopheline Densities on a Small Space, using Satellite Imagery and Geographical Information Systems

International audienceTo predict the spatial distribution of anopheles in the Dielmo village (located in the southeastern part of Senegal), we used residual fauna collected from 104 different rooms during four separate trips conducted in 1994 and 1995. Thanks Generalized Estimating Equations, we were able to identify factors influencing the distribution of Anopheles in the village. Several variables, such as the number of persons sleeping in the room, population density around the hut, construction materials, presence of mosquito nets, were found to be significant, while many spatial variables relevant to the scale of a region (vegetation index, distance to larval sites...) were not found to be significant on the village level. As a result, it became clear that it is difficult to correctly predict the anopheline density for each house even with precise spatial data created with Satellite imagery and Geographical Information Systems (GIS). This work highlights the complexity of the geographical study of anopheline density and its limits on a small space

A consolidated and geolocated list of health facilities in Senegal: consolidated list

Consolidated facility list, or CFL, which reflects 4,685 hospitals, health centers, health posts, and health huts as de-duplicated and matched across 16 data sources. All matched facility groups, at the facility level, are found in the "full list"

A consolidated and geolocated list of health facilities in Senegal: full list

Full facility list, which reflects a pooled list of 12,965 facility observations across 16 health facility data sources with group identifiers and unit level characteristics (as processed and originally observed in source data)

Correlation of individual estimates of (i) the intra-individual and (ii) additive genetic effects underlying the variation in the number of <i>P. falciparum</i> clinical episodes according to drug period.

<p>*P<0.05,</p><p>**P<0.01,</p><p>***P<0.001.</p