Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe.
BACKGROUND
On the fringes of endemic zones climate is a major determinant of inter-annual variation in malaria incidence. Quantitative description of the space-time effect of this association has practical implications for the development of operational malaria early warning system (MEWS) and malaria control. We used Bayesian negative binomial models for spatio-temporal analysis of the relationship between annual malaria incidence and selected climatic covariates at a district level in Zimbabwe from 1988-1999.


RESULTS
Considerable inter-annual variations were observed in the timing and intensity of malaria incidence. Annual mean values of average temperature, rainfall and vapour pressure were strong positive predictors of increased annual incidence whereas maximum and minimum temperature had the opposite effects. Our modelling approach adjusted for unmeasured space-time varying risk factors and showed that while year to year variation in malaria incidence is driven mainly by climate, the resultant spatial risk pattern may to large extent be influenced by other risk factors except during high and low risk years following the occurrence of extremely wet and dry conditions, respectively.


CONCLUSIONS
Our model revealed a spatially varying risk pattern that is not attributable only to climate. We postulate that only years characterized by extreme climatic conditions may be important for developing climate based MEWS and for delineating areas prone to climate driven epidemics. However, the predictive value of climatic risk factors identified in this study still needs to be evaluated.
Mabaso
Musawenkoi L H
ML
Malaria Research Lead Programme, Medical Research Council, P.O. Box 70380, Overport 4067, Durban, South Africa. mabasom@mrc.ac.za
Vounatsou
Penelope
P
Midzi
Stanely
S
Da Silva
Joaquim
J
Smith
Thomas
T
D001499:Bayes Theorem; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology*; D008834:Microclimate; D009010:Monte Carlo Method; D012044:Regression Analysis; D012621:Seasons; D013029:Space-Time Clustering; D014887:Weather; D015030:Zimbabwe / Q000453:epidemiology