Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia

<p>Abstract</p> <p>Background</p> <p>Malaria is the leading cause of morbidity and mortality in Ethiopia, accounting for over five million cases and thousands of deaths annually. The risks of morbidity and mortality associated with malaria are characterized by spatial and temporal variation across the country. This study examines the spatial and temporal patterns of malaria transmission at the local level and implements a risk mapping tool to aid in monitoring and disease control activities.</p> <p>Methods</p> <p>In this study, we examine the global and local patterns of malaria distribution in 543 villages in East Shoa, central Ethiopia using individual-level morbidity data collected from six laboratory and treatment centers between September 2002 and August 2006.</p> <p>Results</p> <p>Statistical analysis of malaria incidence by sex, age, and village through time reveal the presence of significant spatio-temporal variations. Poisson regression analysis shows a decrease in malaria incidence with increasing age. A significant difference in the malaria incidence density ratio (IDRs) is detected in males but not in females. A significant decrease in the malaria IDRs with increasing age is captured by a quadratic model. Local spatial statistics reveals clustering or hot spots within a 5 and 10 km distance of most villages in the study area. In addition, there are temporal variations in malaria incidence.</p> <p>Conclusion</p> <p>Malaria incidence varies according to gender and age, with males age 5 and above showing a statistically higher incidence. Significant local clustering of malaria incidence occurs between pairs of villages within 1–10 km distance lags. Malaria incidence was higher in 2002–2003 than in other periods of observation. Malaria hot spots are displayed as risk maps that are useful for monitoring and spatial targeting of prevention and control measures against the disease.</p

Detection of foci of residual malaria transmission through reactive case detection in Ethiopia

Abstract Background Sub-microscopic and asymptomatic infections could be bottlenecks to malaria elimination efforts in Ethiopia. This study determined the prevalence of malaria, and individual and household-level factors associated with Plasmodium infections obtained following detection of index cases in health facilities in Jimma Zone. Methods Index malaria cases were passively detected and tracked in health facilities from June to November 2016. Moreover, family members of the index houses and neighbours located within approximately 200 m from the index houses were also screened for malaria. Results A total of 39 index cases initiated the reactive case detection of 726 individuals in 116 households. Overall, the prevalence of malaria using microscopy and PCR was 4.0% and 8.96%, respectively. Seventeen (43.6%) of the index cases were from Doyo Yaya kebele, where parasite prevalence was higher. The majority of the malaria cases (90.74%) were asymptomatic. Fever (AOR = 12.68, 95% CI 3.34–48.18) and history of malaria in the preceding 1 year (AOR = 3.62, 95% CI 1.77–7.38) were significant individual-level factors associated with detection of Plasmodium infection. Moreover, living in index house (AOR = 2.22, 95% CI 1.16–4.27), house with eave (AOR = 2.28, 95% CI 1.14–4.55), area of residence (AOR = 6.81, 95% CI 2.49–18.63) and family size (AOR = 3.35, 95% CI 1.53–7.33) were main household-level predictors for residual malaria transmission. Conclusion The number of index cases per kebele may enhance RACD efforts to detect additional malaria cases in low transmission settings. Asymptomatic and sub-microscopic infections were high in the study area, which need new or improved surveillance tools for malaria elimination efforts

Temperature and population density determine reservoir regions of seasonal persistence in highland malaria.

A better understanding of malaria persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial reservoir of the pathogen in the low season. In these 'unstable' malaria regions, such reservoirs play a critical role by allowing persistence during the low transmission season and therefore, between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to where in space the disease is able to persist because of pronounced altitudinal gradients. Here, we explore other environmental and demographic factors that may contribute to malaria's highland reservoir. We use an extensive spatio-temporal dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. With a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. This population effect is not accounted for in typical models for the transmission dynamics of the disease, but is consistent in part with a more complex functional form of the force of infection proposed by theory for vector-borne infections, only during the low season as we discuss. As malaria risk usually decreases in more urban environments with increased human densities, the opposite counterintuitive finding identifies novel control targets during the low transmission season in African highlands

Count of neighboring kebeles

This data set contains the count of kebeles neighboring each kebele. This file should be used in combination with the Nieghborhood.csv. For example the first kebele (ID=1) has 4 neighbors. Thus, the first four numbers in neighborhood.csv are kebele ID's of those kebeles neighboring kebele 1. Similarly, the second kebele has 3 neighbors, and thus the next three number in neighborhood.csv are IDs of its three neighbors. The remaining neighbors are identified by matching them with corresponding kebele IDs in the file neighborhood.csv in this manner

Data from: Temperature and population density determine reservoir regions of spatial persistence in highland malaria

This data has all variables used in the statistical model as they entered the generalized linear model and the generalized linear mixed model. The variables included are (in the order they appear): year, kebeleID, JFMA total cases, log expected cases, scaled log ratio of SOND cases to the expected SOND cases, scaled DJF mean temperature in degree Celsius, scaled DJF total rainfall in mm, scaled population density from overlapping circles of 5km radius, scaled population density from overlapping circles of 10km radius, scaled weighted distance to roads, scaled inverse square distance to perennial water bodies, scaled average soil water holding capacity, scaled average slope, scaled average NDVI, scaled SST anomalies from the Nino 3.4 region, and IRS status (0/1)

neighborhood

This data set contains the IDs of kebeles neighboring each kebele in the file named "Count of neighboring kebeles". This file should be used in combination with the "Count of neighboring kebeles". For example the first kebele (ID=1) has 4 neighbors. The first four number in this data set are the kebele ID's of those kebeles neighboring kebele 1. Similarly, the second kebele has 3 neighbors, and the next three number in this data set are IDs of its three neighbors. The remaining neighbors are identified by matching them with corresponding kebele IDs in similar manner