Spatiotemporal clusters of malaria cases at village level, northwest Ethiopia

Abstract

BackgroundMalaria attacks are not evenly distributed in space and time. In highland areas with low endemicity, malaria transmission is highly variable and malaria acquisition risk for individuals is unevenly distributed even within a neighbourhood. Characterizing the spatiotemporal distribution of malaria cases in high-altitude villages is necessary to prioritize the risk areas and facilitate interventions.MethodsSpatial scan statistics using the Bernoulli method were employed to identify spatial and temporal clusters of malaria in high-altitude villages. Daily malaria data were collected, using a passive surveillance system, from patients visiting local health facilities. Georeference data were collected at villages using hand-held global positioning system devices and linked to patient data. Bernoulli model using Bayesian approaches and Marcov Chain Monte Carlo (MCMC) methods were used to identify the effects of factors on spatial clusters of malaria cases. The deviance information criterion (DIC) was used to assess the goodness-of-fit of the different models. The smaller the DIC, the better the model fit.ResultsMalaria cases were clustered in both space and time in high-altitude villages. Spatial scan statistics identified a total of 56 spatial clusters of malaria in high-altitude villages. Of these, 39 were the most likely clusters (LLR = 15.62, p < 0.00001) and 17 were secondary clusters (LLR = 7.05, p < 0.03). The significant most likely temporal malaria clusters were detected between August and December (LLR = 17.87, p < 0.001). Travel away home, males and age above 15 years had statistically significant effect on malaria clusters at high-altitude villages.ConclusionThe study identified spatial clusters of malaria cases occurring at high elevation villages within the district. A patient who travelled away from home to a malaria-endemic area might be the most probable source of malaria infection in a high-altitude village. Malaria interventions in high altitude villages should address factors associated with malaria clustering.

Impact of recent and future climate change on vector‐borne diseases

“…Current malaria interventions have the ultimate goal of malaria elimination, but are unlikely to be capable of eradicating the disease completely in all regions of Africa, even in current climate conditions. 42 The number of reported human infections with P. falciparum is increasing in tropical highland regions across the globe, for example, in Eastern Africa, 24,25,[43][44][45][46] Nepal, [47][48][49][50] and Colombia. 43 Competent malaria vectors have also recently been found at higher altitudes.…”

Hotspots of unimproved sources of drinking water in Ethiopia: Mapping and Spatial analysis of Ethiopia Demographic and Health Survey Data 2016

“…For each potential cluster, a likelihood ratio test statistic was used to determine if the number of observed cases within the cluster were significantly higher than expected or not. The primary and secondary clusters were identified and assigned p-values and ranked based on their likelihood ratio test, on the basis of 999 Monte Carlo replications [15,20,21]. The spatial pattern of unimproved sources of drinking water was non-random all over the study area.…”

Spatial clustering and risk factors of malaria infections in Bata district, Equatorial Guinea

“…Cluster analysis has been used in other studies to detect malaria clusters in Africa, allowing detection of risk areas in different parts of the continent [2, 20, 36, 37]. …”