Spatio–Temporal Clustering of Malaria Morbidity in Nigeria (2004-2008)

Abstract

Malaria is one of the primary causes of morbidity and mortality in the world. Many studies exist on the geographical patterns of malaria infection in different parts of the world. However, little is known on the spatial heterogeneity of malaria in Nigeria, where it is one of the principal sources of illness and death.  Understanding its spatio-temporal dynamics would be helpful in the design of effective location-specific malaria prevention and control programmes. The aim of the study was to analyse the spatio-temporal patterns of malaria morbidity in Nigeria, and identify climatic, environmental and socioeconomic risk factors underlying these patterns. Data used in this study included malaria cases from year 2004 to 2008, ground elevation, forest cover, wetlands, urbanisation, poverty, temperature, rainfall, minimum temperature, maximum temperature and humidity. Spatial analytical techniques such as Global Moran’s I and Anselin’s Moran I were used to determine the degree of spatial clustering of malaria  and detect malaria hotspots respectively for each year. Spatial distribution of malaria were mapped at the state level. There was significant clustering of malaria in 2006 and 2008. The spatio-temporal cluster analysis generally suggested the existence of a fairly stable Kano-Katsina malaria cluster in northwestern Nigeria. Urbanisation (R²= 14.4%; p < 0.05), poverty (R²= 13.9%; p < 0.05) and forest cover (R²= 10.7%; p < 0.05) had significant contributions to malaria morbidity. In the final regression model to determine the combined effect of the four factors using the backward selection approach, urbanisation was the only dominant factor. No doubt, malaria morbidity in Nigeria varies unevenly over space and through time. Its geographical distribution is significantly influenced by the level of urbanisation, forest cover and poverty. These results suggest the urban malaria phenomenon is present in Nigeria, and has a social gradient in morbidity. The study concludes that intervention efforts should take into consideration the spatial heterogeneity of malaria transmission in order to obtain optimum outcome.