Spatial Patterns of Fetal Loss and Infant Death in an Arsenic-affected Area in Bangladesh

International Journal of Health Geographics, 2010, 9:53, Published 26 October 2010

Nazmul Sohel, Marie Vahter, Mohammad Ali, Mahfuzar Rahman, Anisur Rahman, Peter Kim-Streatfield, Pavlos S Kanaroglou, and Lars Ake Persson

“Background: Arsenic exposure in pregnancy is associated with adverse pregnancy outcome and infant mortality. Knowledge of the spatial characteristics of the outcomes and their possible link to arsenic exposure are important for planning effective mitigation activities. The aim of this study was to identify spatial and spatiotemporal clustering of fetal loss and infant death, and spatial relationships between high and low clusters of fetal loss and infant death rates and high and low clusters of arsenic concentrations in tube-well water used for drinking.

“Method: Pregnant women from Matlab, Bangladesh, who used tube-well water for drinking while pregnant between 1991 and 2000, were included in this study. In total 29,134 pregnancies were identified. A spatial scan test was used to identify unique non-random spatial and spatiotemporal clusters of fetal loss and infant death using a retrospective spatial and spatiotemporal permutation and Poisson probability models. Result: Two significant clusters of fetal loss and infant death were identified and these clusters remained stable after adjustment for covariates. One cluster of higher rates of fetal loss and infant death was in the vicinity of the Meghna River, and the other cluster of lower rates was in the center of Matlab. The average concentration of arsenic in the water differed between these clusters (319 ug/L for the high cluster and 174 ug/L for the low cluster). The spatial patterns of arsenic concentrations in tube-well water were found to be linked with the adverse pregnancy outcome clusters. In the spatiotemporal analysis, only one high fetal loss and infant death cluster was identified in the same high cluster area obtained from purely spatial analysis. However, the cluster was no longer significant after adjustment for the covariates.

“Conclusion: The finding of this study suggests that given the geographical variation in tube-well water contamination, higher fetal loss and infant deaths were observed in the areas of higher arsenic concentrations in groundwater. This illustrates a possible link between arsenic contamination in tube-well water and adverse pregnancy outcome. Thus, these areas should be considered a priority in arsenic mitigation programs.”

Nazmul Sohel email, Marie Vahter email, Mohammad Ali email, Mahfuzar Rahman email, Anisur Rahman email, Peter Kim-Streatfield email, Pavlos S Kanaroglou email and Lars Ake Persson

Visualization of Attributed Hierarchical Structures in a Spatiotemporal Context

International Journal of Geographical Information Science, Volume 24 Issue 10 2010, Pages 1497 – 1513: Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization

S. Hadlak; C. Tominski; H. -J. Schulz; H. Schumann

“When visualizing data, spatial and temporal references of these data often have to be considered in addition to the actual data attributes. Nowadays, structural information is becoming more and more important. Hierarchies, for instance, are frequently applied to make large and complex data manageable. Hence, a visual depiction of hierarchical structures in space and time is required.

“Although there are several techniques addressing specific aspects of spatio-temporal visualization, approaches that cope with space, time, data, and structure are rare. With this article we take a step to fill this gap. By combining various well-established concepts, we achieve a reasonably complete visualization of all of the aforementioned aspects, where our focus is on the hierarchical structures. We embed hierarchies directly into the regions of a map display using variants of the point-based layout. Layering and animation are applied to visualize temporal aspects. Based on the analysis goals, users can switch between representations that emphasize data attributes or hierarchical structures. Interaction techniques support users in navigating the data and their visualization. We demonstrate the usefulness of our approach by adapting it to implement a visualization for spatiotemporal human health data.”

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