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Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana.

ABSTRACT: 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.

SUBMITTER: Krefis AC

PROVIDER: S-EPMC3063166 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana.

Krefis Anne Caroline AC Schwarz Norbert Georg NG Nkrumah Bernard B Acquah Samuel S Loag Wibke W Oldeland Jens J Sarpong Nimako N Adu-Sarkodie Yaw Y Ranft Ulrich U May Jürgen J

PloS one 20110323 3

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 assess ...[more]

PMID: 21448277

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Project description:Malaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. As the vectors predominantly bite between dusk and dawn, risk of infection is determined by the abundance of P. falciparum infected mosquitoes in the surroundings of the households. Remote sensing is commonly employed to detect associations between land use/land cover (LULC) and mosquito-borne diseases. Due to challenges in LULC identification and the fact that LULC merely functions as a proxy for mosquito abundance, assuming spatially homogenous relationships may lead to overgeneralized conclusions.Data on incidence of P. falciparum parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of LULC and P. falciparum parasitaemia rate were described in a semi-parametric geographically weighted Poisson regression model.Complete data were available for 878 individuals, with an annual P. falciparum rate of 3.2 infections per person-year at risk. The influences of built-up areas (median incidence rate ratio (IRR): 0.94, IQR: 0.46), forest (median IRR: 0.9, IQR: 0.51), swampy areas (median IRR: 1.15, IQR: 0.88), as well as banana (median IRR: 1.02, IQR: 0.25), cacao (median IRR: 1.33, IQR: 0.97) and orange plantations (median IRR: 1.11, IQR: 0.68) on P. falciparum rate show strong spatial variations within the study area. Incorporating spatial variability of LULC variables increased model performance compared to the spatially homogenous model.The observed spatial variability of LULC influence in parasitaemia would have been masked by traditional Poisson regression analysis assuming a spatially constant influence of all variables. We conclude that the spatially varying effects of LULC on P. falciparum parasitaemia may in fact be associated with co-factors not captured by remote sensing, and suggest that future studies assess small-scale spatial variation of vegetation to circumvent generalised assumptions on ecological associations that may in fact be artificial.

Project description:Research on spatial non-stationarity of land-cover classification accuracy has been ongoing for over two decades with most of the work focusing on single date maps. We extend the understanding of thematic map accuracy spatial patterns by: (1) quantifying spatial patterns of map-reference agreement for class-specific land-cover change rather than class-specific land cover for both omission and commission expressions of map error; (2) reporting goodness-of-fit estimates for the empirical models, which have been lacking in previous assessments, and; (3) using the empirical model results to map the locations of the relative likelihoods of map-reference agreement for specific land-cover change classes. We evaluated 10 map-based explanatory variables in single and multivariable logistic regression models to predict the likelihood of agreement between map and reference land-cover change (2001-2011) labels using the National Land Cover Database (NLCD) 2011 land cover and accuracy data. Logistic models for omission error had better goodness-of-fit estimates than models for commission error. For the omission error models, the explanatory variable, density of the mapped class-specific change in the immediate neighbourhood surrounding the sample pixel, produced the best model fit results (Tjur coefficient of discrimination, D, ranged from 0.59 to 0.98) compared to multivariable models and all other single explanatory variable models. Maps of the predicted likelihood of map-reference agreement produced from the best fitting omission error models provide a spatially explicit description of spatial variation of classification uncertainty at both local and regional scales. Application of the models indicated higher likelihoods of agreement (>50%) comprised a greater proportion of the land-cover change class area than the proportion of the land-cover change class with lower likelihoods of agreement. NLCD users can apply reported equations to map land-cover change uncertainty.

Project description:Background: We aimed at investigating the impact of malaria on the haematological parameters of residents from different demographic settlements in the Ashanti Region of Ghana. Malaria parasites trigger changes in certain haematological parameters, which may result in a number of clinical manifestations. Differences in demographic settlements, such as rural, peri-urban and urban settlements may also influence these changes, but this has not been extensively studied in Ghana. Methods: We conducted a hospital-based, cross-sectional study from January to December 2018 in three different settlements. A total of 598 participants were recruited. Blood smears were examined to detect and quantify malaria parasitaemia, while haematological parameters were measured using a haematology analyser. Results: Participants from the rural settlement had the highest malaria prevalence (21.3%) compared to urban (11.8%) and peri-urban areas (13.3%); however, the peri-urban area had the highest median parasite density (568; IQR=190.0-1312.0). Age was significantly associated with the odds of malaria positivity (OR: 0.97; CI:0.96 - 0.99; p=4.96*10 -4). When haematological parameters of the malaria-infected study participants were compared to the parameters of uninfected participants, red blood cell count (p=0.017), haemoglobin (p=0.0165), haematocrit (p=0.0015), mean corpuscular volume (p=0.0014), plateletcrit (p<0.0001) and platelet count (p<0.0001) were all significantly lower in the malaria infected group. In addition to age, haemoglobin and plateletcrit levels were also inversely correlated with the odds of testing positive for malaria, suggesting that children who were anaemic and/or thrombocytopaenic were likely to be infected. After fitting the data to a logistic regression model comprising the three variables, the model correctly categorised 78% of uninfected study participants, but only 50% of the malaria-positive participants. Conclusions: Study participants who were positive for malaria were younger and had low haemoglobin and plateletcrit levels compared to uninfected individuals. Further studies are needed to more precisely elucidate the relationship between malaria infection,demographic and haematological parameters.

Dataset Information

Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana.

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Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana.

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