2013 Conference of the International Medical Geology Association (25–29 August 2013)

Paper No. 9
Presentation Time: 12:00 PM-11:55 PM


MOLLA, Yordanos B.1, LE BLOND, Jennifer2, WARDROP, Nicola3, ATKINSON, Peter3, NEWPORT, Melanie1 and DAVEY, Gail1, (1)Medical Research, BSMS, Falmer, East Sussex, Brighton, BN1 9PS, United Kingdom, (2)Department of Earth Sciences, Natural History Museum, Cromwell Road, London, SW7 5BD, United Kingdom, (3)Geography and Environment, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, United Kingdom, jordi_belayneh@yahoo.com

Podoconiosis is a non-filarial form of elephantiasis resulting in lymphedema of the lower legs. Previous studies have suggested that podoconiosis arises from the interplay of individual and environmental factors. In this study we measured a range of individual and environmental covariates, and modelled their relationship to podoconiosis prevalence.

The study was conducted over a 30 x 30 km area in northern Ethiopia. Data was collected on: disease magnitude (prevalence), soil composition and characterisation, and meteorology. House-to-house visits were made to find case counts and population at risk. Surface soils were collected from designated sampling sites and fully characterised (including chemical oxides and trace elements, mineral phase identification and particle size). The meteorology data (i.e. rainfall and temperature) were extracted from WorldClim.org and triangulated with data from the Ethiopian Meteorology Agency. We analysed data using R and geo-statistical packages.

The distribution of each predictor covariate and disease distribution was plotted in a histogram and summarized using mean, standard deviation, skewness and kurtosis. The presence of outliers was tested using box plots and Cleveland dot plots. Since our outcome variable is count of cases and we needed a probability distribution that allows greater variation for mean values, we opted for Poisson distribution. Collinearity between covariates was evaluated using scatter plots and correlation coefficients, and variables were discounted based on their correlation coefficient and theoretical understanding of the covariates. Next, we explored the relationship between the disease distribution and the remaining predictor covariates using univariate analysis. Variables that showed statistically significant relationship with the disease and returned the lowest AIC (Akaki Information Criterion) were selected for further investigation. The remaining variables, which included soil oxides, trace elements and clay minerals were assessed for interaction, spatial dependence and homogeneity of variance.

The final outputs will be a model for the environmental correlates of podoconiosis using stepwise model selection techniques, and one that accounts for the individual correlates.

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