Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe

Musawenkoi Lh Mabaso,Stanely Midzi,Joaquim Da Silva,Thomas Smith,Penelope Vounatsou

doi:10.1186/1476-072x-5-20

Abstract

BackgroundOn 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.ResultsConsiderable 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.ConclusionOur 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.

Highlights

On the fringes of endemic zones climate is a major determinant of inter-annual variation in malaria incidence
As a result there is a need for the development of climate-based malaria early warning systems (MEWS) capable of predicting seasonal to inter-annual variations with a lead time that allows health authorities to respond in a timely manner with preparatory/preventative measures [5,6]
The highest malaria incidence recorded during the 12 year period was in 1988 with 15.5 cases per 1000 person years and the lowest was in 1992 with 5.2 cases per 1000 person years (Figure 1A)

Summary

Introduction

On the fringes of endemic zones climate is a major determinant of inter-annual variation in malaria incidence. In Southern Africa annual variation in climatic conditions and associated changes in malaria infection affect the timing and intensity of malaria incidence. This has an impact on the effectiveness of interventions [4]. As a result there is a need for the development of climate-based malaria early warning systems (MEWS) capable of predicting seasonal to inter-annual variations with a lead time that allows health authorities to respond in a timely manner with preparatory/preventative measures [5,6]. The disagreements seem to stem from differences in perspective and methods used [8]

Methods

Results

Conclusion