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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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Malaria SMR and average climate in Malawi for the period July 2004 - June 2011. (a) Malaria standardised morbidity ratios (SMR) for the under five (dashed curve) and five years and over (solid curve) age categories and (b) average precipitation (solid bars) and average temperature (dashed curve) in Malawi for the period July 2004 - June 2011.
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Figure 1: Malaria SMR and average climate in Malawi for the period July 2004 - June 2011. (a) Malaria standardised morbidity ratios (SMR) for the under five (dashed curve) and five years and over (solid curve) age categories and (b) average precipitation (solid bars) and average temperature (dashed curve) in Malawi for the period July 2004 - June 2011.

Mentions: A precursory view of the temporal and spatial variation of the malaria SMR in Malawi is given in Figure1 and2, along with potential driver variables. Figure1a shows the temporal series of malaria SMR from July 2004 - June 2011 for the under five year and five year and over age categories. A strong annual cycle is apparent, with the peak in the early months of the year. Figure1b shows the corresponding monthly average precipitation and temperature. The known lag between the malaria transmission season and the rains is clearly apparent. The inter-annual variability in the peak SMR is superimposed on an upward trend over the period. While changes in climate and environmental conditions cannot be ruled out, it is far more likely that this trend is a result of the improved levels of reporting that resulted as districts moved to and became familiar with the electronic based reported system that was introduced in 2004. Figure2a and2b show the overall malaria SMR (for under fives and five years and over respectively) in each district over the whole time period (84 months). Figure2c-f shows the ecological zones, mean altitude, population density, proportion of households with one room for sleeping, the mean ITN distribution rate over the seven year period and the number of health facilities per 1000 inhabitants, respectively.


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

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

Malaria SMR and average climate in Malawi for the period July 2004 - June 2011. (a) Malaria standardised morbidity ratios (SMR) for the under five (dashed curve) and five years and over (solid curve) age categories and (b) average precipitation (solid bars) and average temperature (dashed curve) in Malawi for the period July 2004 - June 2011.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Malaria SMR and average climate in Malawi for the period July 2004 - June 2011. (a) Malaria standardised morbidity ratios (SMR) for the under five (dashed curve) and five years and over (solid curve) age categories and (b) average precipitation (solid bars) and average temperature (dashed curve) in Malawi for the period July 2004 - June 2011.
Mentions: A precursory view of the temporal and spatial variation of the malaria SMR in Malawi is given in Figure1 and2, along with potential driver variables. Figure1a shows the temporal series of malaria SMR from July 2004 - June 2011 for the under five year and five year and over age categories. A strong annual cycle is apparent, with the peak in the early months of the year. Figure1b shows the corresponding monthly average precipitation and temperature. The known lag between the malaria transmission season and the rains is clearly apparent. The inter-annual variability in the peak SMR is superimposed on an upward trend over the period. While changes in climate and environmental conditions cannot be ruled out, it is far more likely that this trend is a result of the improved levels of reporting that resulted as districts moved to and became familiar with the electronic based reported system that was introduced in 2004. Figure2a and2b show the overall malaria SMR (for under fives and five years and over respectively) in each district over the whole time period (84 months). Figure2c-f shows the ecological zones, mean altitude, population density, proportion of households with one room for sleeping, the mean ITN distribution rate over the seven year period and the number of health facilities per 1000 inhabitants, respectively.

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