Ecologic covariates of hookworm infection and reinfection in rural Kwazulu-natal/south Africa: a geographic information system-based study


Saathoff, E; Olsen, A; Sharp, B; Kvalsvig, JD; Appleton, CC; Kleinschmidt, I; (2005) Ecologic covariates of hookworm infection and reinfection in rural Kwazulu-natal/south Africa: a geographic information system-based study. The American journal of tropical medicine and hygiene, 72 (4). pp. 384-91. ISSN 0002-9637

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Abstract

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 ( approximately 28 x 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.

Item Type: Article
Keywords: Child, Confounding Factors (Epidemiology), Female, Geographic Information Systems, Hookworm Infections/*epidemiology, Humans, Male, Recurrence, Social Class, South Africa/epidemiology, Child, Confounding Factors (Epidemiology), Female, Geographic Information Systems, Hookworm Infections, epidemiology, Humans, Male, Recurrence, Research Support, Non-U.S. Gov't, Social Class, South Africa, epidemiology
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
PubMed ID: 15827274
Web of Science ID: 228303400006
URI: http://researchonline.lshtm.ac.uk/id/eprint/9979

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