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result sql = Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana.
Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana.
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char = t;code = 116
char = o;code = 111
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char = n;code = 110
char = g;code = 103
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char = m;code = 109
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char = a;code = 97
char = r;code = 114
char = i;code = 105
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char = o;code = 111
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char = a;code = 97
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char = u;code = 117
char = r;code = 114
char = e;code = 101
char = s;code = 115
char = ;code = 32
char = s;code = 115
char = h;code = 104
char = o;code = 111
char = u;code = 117
char = l;code = 108
char = d;code = 100
char = ;code = 32
char = b;code = 98
char = e;code = 101
char = ;code = 32
char = a;code = 97
char = s;code = 115
char = s;code = 115
char = i;code = 105
char = s;code = 115
char = t;code = 116
char = e;code = 101
char = d;code = 100
char = ;code = 32
char = b;code = 98
char = y;code = 121
char = ;code = 32
char = m;code = 109
char = o;code = 111
char = d;code = 100
char = e;code = 101
char = l;code = 108
char = s;code = 115
char = ;code = 32
char = b;code = 98
char = a;code = 97
char = s;code = 115
char = e;code = 101
char = d;code = 100
char = ;code = 32
char = o;code = 111
char = n;code = 110
char = ;code = 32
char = g;code = 103
char = e;code = 101
char = o;code = 111
char = g;code = 103
char = r;code = 114
char = a;code = 97
char = p;code = 112
char = h;code = 104
char = i;code = 105
char = c;code = 99
char = ;code = 32
char = i;code = 105
char = n;code = 110
char = f;code = 102
char = o;code = 111
char = r;code = 114
char = m;code = 109
char = a;code = 97
char = t;code = 116
char = i;code = 105
char = o;code = 111
char = n;code = 110
char = ;code = 32
char = s;code = 115
char = y;code = 121
char = s;code = 115
char = t;code = 116
char = e;code = 101
char = m;code = 109
char = s;code = 115
char = .;code = 46
char = <;code = 60
char = b;code = 98
char = r;code = 114
char = /;code = 47
char = >;code = 62
result sql = Malaria belongs to the infectious diseases with the highest morbidity and mortality worldwide. As a vector-borne disease malaria distribution is strongly influenced by environmental factors. The aim of this study was to investigate the association between malaria risk and different land cover classes by using high-resolution multispectral Ikonos images and Poisson regression analyses. The association of malaria incidence with land cover around 12 villages in the Ashanti Region, Ghana, was assessed in 1,988 children <15 years of age. The median malaria incidence was 85.7 per 1,000 inhabitants and year (range 28.4-272.7). Swampy areas and banana/plantain production in the proximity of villages were strong predictors of a high malaria incidence. An increase of 10% of swampy area coverage in the 2 km radius around a village led to a 43% higher incidence (relative risk [RR] = 1.43, p<0.001). Each 10% increase of area with banana/plantain production around a village tripled the risk for malaria (RR = 3.25, p<0.001). An increase in forested area of 10% was associated with a 47% decrease of malaria incidence (RR = 0.53, p = 0.029). Distinct cultivation in the proximity of homesteads was associated with childhood malaria in a rural area in Ghana. The analyses demonstrate the usefulness of satellite images for the prediction of malaria endemicity. Thus, planning and monitoring of malaria control measures should be assisted by models based on geographic information systems.

Publication: PLoS ONE 2011 ;6(3)::e17905. :doi: 10.1371/journal.pone.0017905.
Publication Date: 2011
Study Author(s): Krefis, Anne Caroline;Schwarz, Norbert Georg;Nkrumah, Bernard;Acquah, Samuel;Loag, Wibke;Oldeland, Jens;Sarpong, Nimako;Adu-Sarkodie, Yaw;Ranft, Ulrich;May, Jürgen;
Institution: Infectious Disease Epidemiology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany. [email protected]
Shortcut link to this study:
Malaria belongs to the infectious diseases with the highest morbidity and mortality worldwide. As a vector-borne disease malaria distribution is strongly influenced by environmental factors. The aim of this study was to investigate the association between malaria risk and different land cover classes by using high-resolution multispectral Ikonos images and Poisson regression analyses. The association of malaria incidence with land cover around 12 villages in the Ashanti Region, Ghana, was assessed in 1,988 children <15 years of age. The median malaria incidence was 85.7 per 1,000 inhabitants and year (range 28.4-272.7). Swampy areas and banana/plantain production in the proximity of villages were strong predictors of a high malaria incidence. An increase of 10% of swampy area coverage in the 2 km radius around a village led to a 43% higher incidence (relative risk [RR] = 1.43, p<0.001). Each 10% increase of area with banana/plantain production around a village tripled the risk for malaria (RR = 3.25, p<0.001). An increase in forested area of 10% was associated with a 47% decrease of malaria incidence (RR = 0.53, p = 0.029). Distinct cultivation in the proximity of homesteads was associated with childhood malaria in a rural area in Ghana. The analyses demonstrate the usefulness of satellite images for the prediction of malaria endemicity. Thus, planning and monitoring of malaria control measures should be assisted by models based on geographic information systems.
PMID: 21448277

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