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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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Multiplicative contribution of spatially unstructured and structured random effects to malaria relative risk. Spatial distribution of multiplicative contribution of posterior mean spatially (a) unstructured and (b) structured random effects.
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Figure 4: Multiplicative contribution of spatially unstructured and structured random effects to malaria relative risk. Spatial distribution of multiplicative contribution of posterior mean spatially (a) unstructured and (b) structured random effects.

Mentions: Figure4 shows the multiplicative effect of the two components of the convolution prior to the model. The key feature of the convolution prior is that it allows the assessment of relative contributions of unstructured heterogeneity and spatial clustering to the overall variation of the area effects[70]. From Figure4 it is evident that spatial heterogeneity is the dominant cause of overdispersion in Malawi. The spatially unstructured random effect ϕs accounts for residual overdispersion in districts that is not attributable to spatial correlation between districts. Here, the spatial correlation component has a minimal yet significant contribution to the convolution prior. Although other geographic and socio-economic covariates such as altitude, longitude, latitude, ecological zone, region, and proportion of the population in each district residing in traditional housing were significant in the fixed effects model, they became non-significant in the mixed effects model. This demonstrates the importance of accounting for spatial heterogeneity and correlation, when analysing geographical data in order to avoid under-estimation of the credible intervals of model covariates. The structure of the random spatial component of the model provides a combined measure of the various potential risk factors that might contribute to the underlying spatial variation in malaria risk. The advantage is that only two hyperpriors are estimated for the precisions of spatial random effects, rather than numerous parameters for each different fixed effect. This results in a more parsimonious model, containing few strong predictors that are more easily interpretable. Figure5 shows the contribution of the auto-correlated annual cycle and random yearly effects to the malaria relative risk, stratified by age group (under five years and five years and over) over the period July 2004 to June 2011. These effects help account for the annual cycle in malaria, that could be attributed to climate and/or seasonal population movements, and the overall upward trend that could be the result of improved reporting over the years as the health facilities became accustomed to the newly established HMIS.


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

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

Multiplicative contribution of spatially unstructured and structured random effects to malaria relative risk. Spatial distribution of multiplicative contribution of posterior mean spatially (a) unstructured and (b) structured random effects.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Multiplicative contribution of spatially unstructured and structured random effects to malaria relative risk. Spatial distribution of multiplicative contribution of posterior mean spatially (a) unstructured and (b) structured random effects.
Mentions: Figure4 shows the multiplicative effect of the two components of the convolution prior to the model. The key feature of the convolution prior is that it allows the assessment of relative contributions of unstructured heterogeneity and spatial clustering to the overall variation of the area effects[70]. From Figure4 it is evident that spatial heterogeneity is the dominant cause of overdispersion in Malawi. The spatially unstructured random effect ϕs accounts for residual overdispersion in districts that is not attributable to spatial correlation between districts. Here, the spatial correlation component has a minimal yet significant contribution to the convolution prior. Although other geographic and socio-economic covariates such as altitude, longitude, latitude, ecological zone, region, and proportion of the population in each district residing in traditional housing were significant in the fixed effects model, they became non-significant in the mixed effects model. This demonstrates the importance of accounting for spatial heterogeneity and correlation, when analysing geographical data in order to avoid under-estimation of the credible intervals of model covariates. The structure of the random spatial component of the model provides a combined measure of the various potential risk factors that might contribute to the underlying spatial variation in malaria risk. The advantage is that only two hyperpriors are estimated for the precisions of spatial random effects, rather than numerous parameters for each different fixed effect. This results in a more parsimonious model, containing few strong predictors that are more easily interpretable. Figure5 shows the contribution of the auto-correlated annual cycle and random yearly effects to the malaria relative risk, stratified by age group (under five years and five years and over) over the period July 2004 to June 2011. These effects help account for the annual cycle in malaria, that could be attributed to climate and/or seasonal population movements, and the overall upward trend that could be the result of improved reporting over the years as the health facilities became accustomed to the newly established HMIS.

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