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Relative importance of climatic, geographic and socio-economic determinants of malaria in Malawi.

Lowe R, Chirombo J, Tompkins AM - Malar. J. (2013)

Bottom Line: Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, but also socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat.A hierarchical Bayesian framework using Markov chain Monte Carlo simulation was used for model fitting and prediction.Once intervention variations, unobserved confounding factors and spatial correlation were considered in a Bayesian framework, a final model emerged with statistically significant predictor variables limited to average precipitation (quadratic relation) and average temperature during the three months previous to the month of interest.

View Article: PubMed Central - HTML - PubMed

Affiliation: Abdus Salam International Centre for Theoretical Physics, Trieste, Italy. rachel.lowe@ic3.cat.

ABSTRACT

Background: Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, but also socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for the modelling of malaria risk in space and time.

Methods: A statistical mixed model framework is proposed to model malaria risk at the district level in Malawi, using an age-stratified spatio-temporal dataset of malaria cases from July 2004 to June 2011. Several climatic, geographic and socio-economic factors thought to influence malaria incidence were tested in an exploratory model. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a generalized linear mixed model was adopted, which included structured and unstructured spatial and temporal random effects. A hierarchical Bayesian framework using Markov chain Monte Carlo simulation was used for model fitting and prediction.

Results: Using a stepwise model selection procedure, several explanatory variables were identified to have significant associations with malaria including climatic, cartographic and socio-economic data. Once intervention variations, unobserved confounding factors and spatial correlation were considered in a Bayesian framework, a final model emerged with statistically significant predictor variables limited to average precipitation (quadratic relation) and average temperature during the three months previous to the month of interest.

Conclusions: When modelling malaria risk in Malawi it is important to account for spatial and temporal heterogeneity and correlation between districts. Once observed and unobserved confounding factors are allowed for, precipitation and temperature in the months prior to the malaria season of interest are found to significantly determine spatial and temporal variations of malaria incidence. Climate information was found to improve the estimation of malaria relative risk in 41% of the districts in Malawi, particularly at higher altitudes where transmission is irregular. This highlights the potential value of climate-driven seasonal malaria forecasts.

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Kernel density estimates for significant explanatory variables. Kernel density estimates for the marginal posterior distributions for the parameters associated with (a) average precipitation, (b) precipitation squared, (c) average temperature (d) temperature squared, (e) health facilities per inhabitant and (f) ITN distribution rate.
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Figure 3: Kernel density estimates for significant explanatory variables. Kernel density estimates for the marginal posterior distributions for the parameters associated with (a) average precipitation, (b) precipitation squared, (c) average temperature (d) temperature squared, (e) health facilities per inhabitant and (f) ITN distribution rate.

Mentions: Figure3 shows the kernel density estimates for the marginal posterior distributions for the statistically significant parameters associated with the variables rainfall and rainfall squared, temperature and temperature squared, the proportion of health facilities per inhabitant and the ITN distribution rate. As in the fixed effects model, a statistically significant quadratic relation between average rainfall during the proceeding three months and malaria risk was found (see Figures3a and3b). Although temperature was statistically significant, once confounding factors were accounted for, a quadratic relationship between temperature and malaria risk in Malawi was not found to be statistically significant (see Figure3d). This is likely due to the monthly average temperature range (15.8 - 28.9°C) in Malawi over the time period not exceeding values at which mosquito activity is suppressed[13].


Relative importance of climatic, geographic and socio-economic determinants of malaria in Malawi.

Lowe R, Chirombo J, Tompkins AM - Malar. J. (2013)

Kernel density estimates for significant explanatory variables. Kernel density estimates for the marginal posterior distributions for the parameters associated with (a) average precipitation, (b) precipitation squared, (c) average temperature (d) temperature squared, (e) health facilities per inhabitant and (f) ITN distribution rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4225758&req=5

Figure 3: Kernel density estimates for significant explanatory variables. Kernel density estimates for the marginal posterior distributions for the parameters associated with (a) average precipitation, (b) precipitation squared, (c) average temperature (d) temperature squared, (e) health facilities per inhabitant and (f) ITN distribution rate.
Mentions: Figure3 shows the kernel density estimates for the marginal posterior distributions for the statistically significant parameters associated with the variables rainfall and rainfall squared, temperature and temperature squared, the proportion of health facilities per inhabitant and the ITN distribution rate. As in the fixed effects model, a statistically significant quadratic relation between average rainfall during the proceeding three months and malaria risk was found (see Figures3a and3b). Although temperature was statistically significant, once confounding factors were accounted for, a quadratic relationship between temperature and malaria risk in Malawi was not found to be statistically significant (see Figure3d). This is likely due to the monthly average temperature range (15.8 - 28.9°C) in Malawi over the time period not exceeding values at which mosquito activity is suppressed[13].

Bottom Line: Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, but also socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat.A hierarchical Bayesian framework using Markov chain Monte Carlo simulation was used for model fitting and prediction.Once intervention variations, unobserved confounding factors and spatial correlation were considered in a Bayesian framework, a final model emerged with statistically significant predictor variables limited to average precipitation (quadratic relation) and average temperature during the three months previous to the month of interest.

View Article: PubMed Central - HTML - PubMed

Affiliation: Abdus Salam International Centre for Theoretical Physics, Trieste, Italy. rachel.lowe@ic3.cat.

ABSTRACT

Background: Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, but also socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for the modelling of malaria risk in space and time.

Methods: A statistical mixed model framework is proposed to model malaria risk at the district level in Malawi, using an age-stratified spatio-temporal dataset of malaria cases from July 2004 to June 2011. Several climatic, geographic and socio-economic factors thought to influence malaria incidence were tested in an exploratory model. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a generalized linear mixed model was adopted, which included structured and unstructured spatial and temporal random effects. A hierarchical Bayesian framework using Markov chain Monte Carlo simulation was used for model fitting and prediction.

Results: Using a stepwise model selection procedure, several explanatory variables were identified to have significant associations with malaria including climatic, cartographic and socio-economic data. Once intervention variations, unobserved confounding factors and spatial correlation were considered in a Bayesian framework, a final model emerged with statistically significant predictor variables limited to average precipitation (quadratic relation) and average temperature during the three months previous to the month of interest.

Conclusions: When modelling malaria risk in Malawi it is important to account for spatial and temporal heterogeneity and correlation between districts. Once observed and unobserved confounding factors are allowed for, precipitation and temperature in the months prior to the malaria season of interest are found to significantly determine spatial and temporal variations of malaria incidence. Climate information was found to improve the estimation of malaria relative risk in 41% of the districts in Malawi, particularly at higher altitudes where transmission is irregular. This highlights the potential value of climate-driven seasonal malaria forecasts.

Show MeSH
Related in: MedlinePlus