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Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia.

Midekisa A, Beyene B, Mihretie A, Bayabil E, Wimberly MC - Parasit Vectors (2015)

Bottom Line: These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission.Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts.

View Article: PubMed Central - PubMed

Affiliation: Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD, USA.

ABSTRACT

Background: The impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.

Methods: This study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January-April), early transition (May-June), and wet (July-September) seasons with malaria incidence in the early peak (May-July) and late peak (September-December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).

Results: We found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.

Conclusions: These findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.

No MeSH data available.


Related in: MedlinePlus

Comparisons of root mean square error (RMSE) for models using climate (grey) and climate plus early peak malaria incidence (black) to predict late peak malaria incidence in (a) Westen districts and (b) Eastern districts in the Amhara Region of Ethiopia. RMSE is in units of the natural logarithm of malaria incidence (per 1000)
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Fig5: Comparisons of root mean square error (RMSE) for models using climate (grey) and climate plus early peak malaria incidence (black) to predict late peak malaria incidence in (a) Westen districts and (b) Eastern districts in the Amhara Region of Ethiopia. RMSE is in units of the natural logarithm of malaria incidence (per 1000)

Mentions: For late peak season malaria, we compared two GLM models including model 1 (without early peak malaria) and model 2 (with early peak malaria) based on the best model for each season in order to evaluate the influence of early peak malaria incidence on subsequent late peak season epidemics (Table 4). In the western districts, model 2 was the best model as compared to model 1 across all the seasons including the dry (Akaike weight = 1.0), early transition (Akaike weight = 0.99) and wet (Akaike weight = 1.0) seasons. Similarly, in the eastern districts, model 2 was the best model as compared to model 1 across the dry (Akaike weight = 1.0), early transition (Akaike weight = 1.0) and wet (Akaike weight = 1.0) seasons. Additionally, model comparison based on root mean square error (RMSE) showed that model 2 was the best model in all the three seasons across the two regions (Fig. 5). Overall, our results showed that the addition of early peak malaria incidence as an independent variable in model 2 substantially improved model fit in the dry, early transition and wet season models for both regions.Table 4


Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia.

Midekisa A, Beyene B, Mihretie A, Bayabil E, Wimberly MC - Parasit Vectors (2015)

Comparisons of root mean square error (RMSE) for models using climate (grey) and climate plus early peak malaria incidence (black) to predict late peak malaria incidence in (a) Westen districts and (b) Eastern districts in the Amhara Region of Ethiopia. RMSE is in units of the natural logarithm of malaria incidence (per 1000)
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4488986&req=5

Fig5: Comparisons of root mean square error (RMSE) for models using climate (grey) and climate plus early peak malaria incidence (black) to predict late peak malaria incidence in (a) Westen districts and (b) Eastern districts in the Amhara Region of Ethiopia. RMSE is in units of the natural logarithm of malaria incidence (per 1000)
Mentions: For late peak season malaria, we compared two GLM models including model 1 (without early peak malaria) and model 2 (with early peak malaria) based on the best model for each season in order to evaluate the influence of early peak malaria incidence on subsequent late peak season epidemics (Table 4). In the western districts, model 2 was the best model as compared to model 1 across all the seasons including the dry (Akaike weight = 1.0), early transition (Akaike weight = 0.99) and wet (Akaike weight = 1.0) seasons. Similarly, in the eastern districts, model 2 was the best model as compared to model 1 across the dry (Akaike weight = 1.0), early transition (Akaike weight = 1.0) and wet (Akaike weight = 1.0) seasons. Additionally, model comparison based on root mean square error (RMSE) showed that model 2 was the best model in all the three seasons across the two regions (Fig. 5). Overall, our results showed that the addition of early peak malaria incidence as an independent variable in model 2 substantially improved model fit in the dry, early transition and wet season models for both regions.Table 4

Bottom Line: These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission.Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts.

View Article: PubMed Central - PubMed

Affiliation: Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD, USA.

ABSTRACT

Background: The impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.

Methods: This study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January-April), early transition (May-June), and wet (July-September) seasons with malaria incidence in the early peak (May-July) and late peak (September-December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).

Results: We found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.

Conclusions: These findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.

No MeSH data available.


Related in: MedlinePlus