Ecological covariates of Ascaris lumbricoides infection in schoolchildren from rural KwaZulu-Natal, South Africa.
OBJECTIVE
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.


CONCLUSIONS
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.
Saathoff
Elmar
E
Harvard School of Public Health, Department of Nutrition, Boston, MA 02115, USA. elmarsaathoff@compuserve.de
Olsen
Annette
A
Kvalsvig
Jane D
JD
Appleton
Chris C
CC
Sharp
Brian
B
Kleinschmidt
Immo
I
D000538:Aluminum Silicates; D000818:Animals; D001196:Ascariasis / Q000453:epidemiology* / Q000517:prevention & control; D017164:Ascaris lumbricoides / Q000502:physiology*; D002648:Child; D000077215:Clay; D016000:Cluster Analysis; D017753:Ecosystem; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010944:Plants; D015995:Prevalence; D012423:Rural Health; D012959:Socioeconomic Factors; D012987:Soil / Q000469:parasitology; D013019:South Africa / Q000453:epidemiology; D013472:Sunlight
D000538:Aluminum Silicates; D012987:Soil; D000077215:Clay