OBJECTIVES: To identify environmental factors that could serve to predict Ascaris lumbricoides infection patterns and thus guide control efforts in the absence of epidemiological information; to assess whether A. lumbricoides infection is positively associated with the soil clay content. METHODS: Information on A. lumbricoides infection and re-infection in a cohort of primary schoolchildren and interview data on their socioeconomic background and behaviour were combined with environmental data using a geographical information system (GIS). Multivariate models served to explore the covariation of environmental and infection patterns adjusted for possible confounders. RESULTS: Prevalence maps and spatial statistics revealed considerable spatial clustering of infection in the small study area. Logistic multivariate regression models showed strong positive associations of infection with vegetation density measured as the normalized difference vegetation index (NDVI) at baseline [odds ratio (OR) for a 10% increase: 1.82; 95% confidence interval (95% CI): 1.24-2.68; P=0.002] and after re-infection (OR: 2.22; 95% CI: 1.71-2.87; P<0.001). We also found a strong negative association of re-infection with the sun exposure of the soil surface as estimated from digital elevation models (OR: 0.78; 95% CI: 0.88; P<0.001). The soil clay content was only moderately positively associated with infection and re-infection. Socioeconomic and behavioural variables, although correlated with A. lumbricoides infection, did not appear to confound the above associations in the demographically homogeneous study area. Spatial analysis of the model residuals suggested that as the models accounted for most of the spatial pattern, the model standard errors should not be affected by spatial clustering. CONCLUSION: NDVI seems to have a high potential for the prediction of A. lumbricoides infection as it was strongly associated with infection patterns in the study area. Further advantages are that NDVI information is easy to use, affordable and available with global coverage.
OBJECTIVES: To identify environmental factors that could serve to predict Ascaris lumbricoides infection patterns and thus guide control efforts in the absence of epidemiological information; to assess whether A. lumbricoidesinfection is positively associated with the soil clay content. METHODS: Information on A. lumbricoidesinfection and re-infection in a cohort of primary schoolchildren and interview data on their socioeconomic background and behaviour were combined with environmental data using a geographical information system (GIS). Multivariate models served to explore the covariation of environmental and infection patterns adjusted for possible confounders. RESULTS: Prevalence maps and spatial statistics revealed considerable spatial clustering of infection in the small study area. Logistic multivariate regression models showed strong positive associations of infection with vegetation density measured as the normalized difference vegetation index (NDVI) at baseline [odds ratio (OR) for a 10% increase: 1.82; 95% confidence interval (95% CI): 1.24-2.68; P=0.002] and after re-infection (OR: 2.22; 95% CI: 1.71-2.87; P<0.001). We also found a strong negative association of re-infection with the sun exposure of the soil surface as estimated from digital elevation models (OR: 0.78; 95% CI: 0.88; P<0.001). The soil clay content was only moderately positively associated with infection and re-infection. Socioeconomic and behavioural variables, although correlated with A. lumbricoidesinfection, did not appear to confound the above associations in the demographically homogeneous study area. Spatial analysis of the model residuals suggested that as the models accounted for most of the spatial pattern, the model standard errors should not be affected by spatial clustering. CONCLUSION: NDVI seems to have a high potential for the prediction of A. lumbricoidesinfection as it was strongly associated with infection patterns in the study area. Further advantages are that NDVI information is easy to use, affordable and available with global coverage.
Authors: Kirsi M Manz; Petra Clowes; Inge Kroidl; Dickens O Kowuor; Christof Geldmacher; Nyanda E Ntinginya; Leonard Maboko; Michael Hoelscher; Elmar Saathoff Journal: PLoS One Date: 2017-04-06 Impact factor: 3.240
Authors: Steffen Andreas Schüle; Petra Clowes; Inge Kroidl; Dickens O Kowuor; Anthony Nsojo; Chacha Mangu; Helene Riess; Christof Geldmacher; Rüdiger Paul Laubender; Seif Mhina; Leonard Maboko; Thomas Löscher; Michael Hoelscher; Elmar Saathoff Journal: PLoS One Date: 2014-03-18 Impact factor: 3.240