Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana
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- 作者：Lutz Ehlkes (1)
Anne Caroline Krefis (1)
Benno Kreuels (1) (2) (3)
Ralf Krumkamp (1) (3)
Ohene Adjei (4) (5)
Matilda Ayim-Akonor (6)
Robin Kobbe (7)
Andreas Hahn (1) (3)
Christof Vinnemeier (2)
Wibke Loag (1)
Udo Schickhoff (8)
J眉rgen May (1) (3)
1. Bernhard-Nocht-Institute for Tropical Medicine ; Research Group Infectious Disease Epidemiology ; Hamburg ; Germany
2. First Department for Internal Medicine ; Division for Tropical Medicine ; University Medical Centre Hamburg-Eppendorf ; Hamburg ; Germany
3. German Center for Infection Research (DZIF) ; Hamburg-Borstel-L眉beck ; Kragujevac ; Germany
4. Kumasi Centre for Collaborative Research in Tropical Medicine ; Kumasi ; Ghana
5. Komfo Anokye Teaching Hospital ; Kumasi ; Ghana
6. Animal Health and Food Safety Division ; Animal Research Institute ; Achimota-Accra ; Ghana
7. Department of Paediatrics ; University Medical Centre Hamburg-Eppendorf ; Hamburg ; Germany
8. Department of Geography ; University of Hamburg ; Hamburg ; Germany - 关键词：Spatial epidemiology ; Land use ; Land cover ; Malaria risk ; Geographically weighted regression
- 刊名：International Journal of Health Geographics
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：13
- 期：1
- 全文大小：1,170 KB
- 参考文献：World Malaria Report: 2013.
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Google Earth. - 刊物主题：Public Health; Geographical Information Systems/Cartography; Human Geography; Epidemiology;
- 出版者：BioMed Central
- ISSN：1476-072X
文摘
Background 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. Methods 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. Results 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. Conclusions 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.