BackgroundDespite recent advances in malaria diagnosis and treatment, many isolated communities in rural settings continue to lack access to these life-saving tools. Community-case management of malaria (CCMm), consisting of lay health workers (LHWs) using malaria rapid diagnostic tests (RDTs) and artemisinin-based combination therapy (ACT) in their villages, can address this disparity.MethodsThis study examined routine reporting data from a CCMm programme between 2008 and 2011 in Saraya, a rural district in Senegal, and assessed its impact on timely access to rapid diagnostic tests and ACT.ResultsThere was a seven-fold increase in the number of LHWs providing care and in the number of patients seen. LHW engagement in the CCM programme varied seasonally, 24,3% of all patients prescribed an ACT had a negative RDT or were never administered an RDT, and less than half of patients with absolute indications for referral (severe symptoms, age under two months and pregnancy) were referred. There were few stock-outs.DiscussionThis CCMm programme successfully increased the number of patients with access to RDT and ACT, but further investigation is required to identify the cause for over-prescription, and low rates of referrals for patients with absolute indications. In contrast, previous widespread stock-outs in Saraya’s CCMm programme have now been resolved.ConclusionThis study demonstrates the potential for CCMm programmes to substantially increase access to life-saving malarial diagnostics and treatment, but also highlights important challenges in ensuring quality.

BackgroundMalaria is major public health problem in Senegal. In some parts of the country, it occurs almost permanently with a seasonal increase during the rainy season. There is evidence to suggest that the prevalence of malaria in Senegal has decreased considerably during the past few years. Recent data from the Senegalese National Malaria Control Programme (NMCP) indicates that the number of malaria cases decrease from 1,500,000 in 2006 to 174,339 in 2010. With the decline of malaria morbidity in Senegal, the characterization of the new epidemiological profile of this disease is crucial for public health decision makers.MethodsSaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using confirmed malaria cases in 74 villages. ArcMAp was used to map malaria hotspots. Logistic regression was used to investigate risk factors for malaria hotspots in Keur Soce health and demographic surveillance site.ResultsA total of 1,614 individuals in 440 randomly selected households were enrolled. The overall malaria prevalence was 12%. The malaria prevalence during the study period varied from less than 2% to more than 25% from one village to another. The results showed also that rooms located between 50 m to 100 m away from livestock holding place [adjusted O.R = 0.7, P = 0.044, 95% C.I (1.02 - 7.42)], bed net use [adjusted O.R = 1.2, P = 0.024, 95% C.I (1.02 –1.48)], are good predictors for malaria hotspots in the Keur Soce health and demographic surveillance site. The socio economic status of the household also predicted on hotspots patterns. The less poor household are 30% less likely to be classified as malaria hotspots area compared to the poorest household [adjusted O.R = 0.7, P = 0.014, 95% C.I (0.47 – 0.91)].ConclusionThe study investigated risk factors for malaria hotspots in small communities in the Keur Soce site. The result showed considerable variation of malaria prevalence between villages which cannot be detected in aggregated data. The data presented in this paper are the first step to understanding malaria in the Keur Soce site from a micro-geographic perspective.

BackgroundIn Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance.MethodsThis study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis.ResultsFrom Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of −0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R2 = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast of the district where hotspots are more accurate while low values are mainly found in the centre and in the north.ConclusionGeographically-weighted regression and OLS showed important risks factors of malaria hotspots in Keur Soce. The outputs of such models can be a useful tool to understand occurrence of malaria hotspots in Senegal. An understanding of geographical variation and determination of the core areas of the disease may provide an explanation regarding possible proximal and distal contributors to malaria elimination in Senegal.

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