Spatial modelling of malaria risk factors in Ruhuha sector in the east of Rwanda

Abstract

Malaria is a vector borne disease posing a severe health risk to the population of Sub Saharan Africa and particularly in the East African Rift-Valley Region. The fact that malaria is still killing hundreds of thousands of people annually is due to  insufficient researchers about its causing factors and the inefficiency of its control  measures. With three main objectives: (1) to identify and map potential anopheles  mosquito habitat, (2) to determine and map malaria prevalence, and, (3) to assess  the relationship between malaria prevalence and malaria risk factors; the aim of this study is to spatially model malaria risk factors in a primarily rural area situated in  South Eastern Rwanda. The data used in this research were obtained through a  combination of high-resolution ortho-photography obtained from Rwanda Natural Resources Authority (RNRA) and primary data collection. In addition to this,  detailed malaria occurrence and socio-economic data were obtained from Rwanda Biomedical Center (RBC). Spatial clusters of malaria occurrence were subsequently determined using Getis and Ord spatial statistics. This cluster analysis showed that malaria distribution is characterized by zones with high malaria risk, so called hot spots, zones with moderate malaria risk known as not significant spots and zones of low malaria risk known as cold spots. The current research demonstrates that malaria prevalence varies from one household to another and from one   administrative unit (village) to another. The relationship between malaria prevalence and malaria risk factors was assessed using a logistic regression model. Results clearly indicate that malaria infection increases with the proximity to irrigated  farmland. It also increases with household size. It was also proven that lower  housing quality (mud houses; unburnt brick walls, earth floor) is associated with higher risk of malaria infection. This research proves that the proximity to irrigated farmland, the household size and housing quality are the main malaria underlying factors in the study area. It therefore suggests that people should not only live far from irrigated farmlands which are considered as the main anopheles mosquito breeding sites but also the housing quality should be improved.

Key words: Spatial modelling, Malaria risk, malaria causing factors, Ruhuha, Rwanda