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American Journal of Epidemiology Vol. 153, No. 12 : 1213-1221
Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health

ORIGINAL CONTRIBUTIONS

Use of Generalized Linear Mixed Models in the Spatial Analysis of Small-Area Malaria Incidence Rates in KwaZulu Natal, South Africa

I. Kleinschmidt1, B. L. Sharp1, G. P. Y. Clarke2, B. Curtis1 and C. Fraser1

1 Medical Research Council (South Africa), Durban, South Africa.
2 Department of Statistics and Biometry, University of Natal, Pietermaritzburg, South Africa.

Spatial statistical analysis of 1994–1995 small-area malaria incidence rates in the population of the northernmost districts of KwaZulu Natal, South Africa, was undertaken to identify factors that might explain very strong heterogeneity in the rates. In this paper, the authors describe a method of adjusting the regression analysis results for strong spatial correlation in the rates by using generalized linear mixed models and variograms. The results of the spatially adjusted, multiple regression analysis showed that malaria incidence was significantly positively associated with higher winter rainfall and a higher average maximum temperature and was significantly negatively associated with increasing distance from water bodies. The statistical model was used to produce a map of predicted malaria incidence in the area, taking into account local variation from the model prediction if this variation was supported by the data. The predictor variables showed that even small differences in climate can have very marked effects on the intensity of malaria transmission, even in areas subject to malaria control for many years. The results of this study have important implications for malaria control programs in the area.

malaria; models; statistical; spatial analysis; variogram

Abbreviations: GIS, geographic information system; GLMM, generalized linear mixed model; SD, standard deviation


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