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ECOLOGIC COVARIATES OF HOOKWORM INFECTION AND REINFECTION IN RURAL KWAZULU-NATAL/SOUTH AFRICA: A GEOGRAPHIC INFORMATION SYSTEM–BASED STUDY

ELMAR SAATHOFFHarvard School of Public Health, Boston, Massachusetts; Danish Bilharziasis Laboratory, Charlottenlund, Denmark; Malaria Lead Programme of the Medical Research Council, Durban, South Africa; Child, Youth and Family Development, Human Sciences Research Council, Dalbridge, South Africa; School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

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ANNETTE OLSENHarvard School of Public Health, Boston, Massachusetts; Danish Bilharziasis Laboratory, Charlottenlund, Denmark; Malaria Lead Programme of the Medical Research Council, Durban, South Africa; Child, Youth and Family Development, Human Sciences Research Council, Dalbridge, South Africa; School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

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BRIAN SHARPHarvard School of Public Health, Boston, Massachusetts; Danish Bilharziasis Laboratory, Charlottenlund, Denmark; Malaria Lead Programme of the Medical Research Council, Durban, South Africa; Child, Youth and Family Development, Human Sciences Research Council, Dalbridge, South Africa; School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

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JANE D. KVALSVIGHarvard School of Public Health, Boston, Massachusetts; Danish Bilharziasis Laboratory, Charlottenlund, Denmark; Malaria Lead Programme of the Medical Research Council, Durban, South Africa; Child, Youth and Family Development, Human Sciences Research Council, Dalbridge, South Africa; School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

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CHRIS C. APPLETONHarvard School of Public Health, Boston, Massachusetts; Danish Bilharziasis Laboratory, Charlottenlund, Denmark; Malaria Lead Programme of the Medical Research Council, Durban, South Africa; Child, Youth and Family Development, Human Sciences Research Council, Dalbridge, South Africa; School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

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IMMO KLEINSCHMIDTHarvard School of Public Health, Boston, Massachusetts; Danish Bilharziasis Laboratory, Charlottenlund, Denmark; Malaria Lead Programme of the Medical Research Council, Durban, South Africa; Child, Youth and Family Development, Human Sciences Research Council, Dalbridge, South Africa; School of Life and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

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Information on hookworm infection and re-infection in a cohort of primary school children and interview data on their socioeconomic background and behavior were combined with environmental data using a geographic information system (GIS). Multivariate models served to explore the covariation of environmental and infection patterns adjusted for possible confounders. Our aim was to identify environmental factors that might serve to predict infection and thus guide control efforts when epidemiologic information is insufficient. Furthermore, we wanted to establish whether soil type has a genuine influence on hookworm infection. Prevalence maps and spatial statistics showed considerable spatial clustering of infection in the small (∼28 × 16 km) study area. The multivariate logistic regression models showed strong positive associations of infection at baseline (baseline prevalence = 83.2%) with settlement density (odds ratio [OR] = 1.24, 95% confidence interval [CI] = 1.10–1.38) and vegetation density (OR = 1.66, 95% CI = 1.25–2.22) and a strong negative association with the clay content of the soil (OR = 0.67, 95% CI = 0.62–0.73). Similar but weaker correlations were found after re-infection. Socioeconomic status and behavior did not seem to confound these associations. Spatial analysis of the model residuals suggested that because the models accounted for most of the spatial pattern, the model standard errors were not affected by spatial clustering. Our study shows that the pattern of hookworm infection is strongly influenced by several environmental factors. The GIS-aided prediction of areas in need of treatment is therefore a promising tool to guide control efforts when epidemiologic information is insufficient.

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