Spatial and environmental impacts on adverse birth outcomes in Ontario

This study assesses the overall spatial variations and neighbourhood‐level “hot spots” of low birth weight and preterm birth incidence within three public health units in Ontario, Canada. The analysis uses a stepwise approach of intra‐class correlation analysis, a spatial scan statistic, and multilevel spatial modeling. Results show that neighbourhood level variation accounts for only 2–3 percent of the total variation of adverse birth outcomes in the study area. However, strong spatial autocorrelation is observed at the neighbourhood level, and spatial clusters of relatively high adverse birth outcome rates exist in areas that are associated with environmental risks, including pollution sources and proximity to highways. Thus, although estimated neighbourhood impacts on adverse birth outcomes are small compared with those of individual‐level risks, local high potential environmental risk areas are identifiable. Environmental surveillance and spatial statistical analysis should be conducted regularly by local health authorities to identify and monitor the impact of environmental changes on health in general and on birth outcomes in particular. Specific community‐oriented health interventions may be required to reduce observed local health impacts.     

A Geospatial Analysis of China's Exports, 1991-2008

Three Taiwan-based economists employ a range of exploratory spatial data analysis tools (e.g., Moran's I and LISA statistics) to investigate trends in the growth of China's exports over the period 1991-2008. A particular focus is on the detection of spatial correlations between China and 40 export destination countries in major world regions. Emphasis in the paper on the key years of 1991, 2001, 2006, and 2008 has enabled the authors to analyze the impacts on China's trade of such major events as the country's accession to the World Trade Organization and the global economic crisis of 2008-2009. The results of the spatial analysis reveal the continuing importance of the U.S. and Asian countries in China's export trade (despite changes in the character of trade relations) and identify the spatial outliers (e.g., in Latin America) that may serve as the basis for new export markets for China in the future.     

Conceptual and practical issues in the detection of local disease clusters: a study of mortality in Hamilton, Ontario

Recent advances in local spatial statistics and operational computing capacity have led to growing interest in the detection of disease clusters for public health surveillance and for improving understanding of disease pathogenesis. Although conceptual reviews and applied examples have appeared in the literature, few studies have addressed the connection between conceptual and practical issues that confront researchers interested in using local statistics to detect disease clusters. Here we review recent literature on the use of local statistics for cluster assessment and focus on the practical issue of assigning correct geographic coordinates. The process of assigning geographic coordinates to an address or postal code, known as ‘geocoding’, is a necessary step in conducting smallarea health analyses. With a study of mortality data from Hamilton, Ontario, we illustrate inaccuracies that may be encountered when using Statistics Canada postal code conversion files. Using the Moran's I and Getis‐Ord Gi and Gi* local spatial statistics to identify significant mortality clusters or ‘hot spots’, we demonstrate that small geocoding errors, even those that affect less than one percent of a total dataset, can have a discernible impact on analytic results. To assist other researchers, we supply guidelines to minimize error introduced by geocoding. These results emphasize the importance of accurate geocoding in local health analyses. Les avancées récentes en statistiques spatiales localisées et en capacité informatique opérationnelle ont conduit à un intérêt croissant dans la détection de foyers de maladies pour fins de surveillance de santé publique, et dans l'approfondissement de la compréhension de leur pathogénèse. Bien que des revues conceptuelles et des exemples concrets aient été publiés dans la littérature, peu d'études ont adressé le lien entre les problèmes conceptuels et pratiques auxquels sont confrontés les chercheurs intéressés à utiliser les statistiques locales pour détecter les foyers de maladies. Nous revoyons ici la littérature récente sur l'utilisation de statistiques locales dans l'évaluation de foyers et focalisons sur le problème pratique d'assigner des coordonnées géographiques correctes. Le procédé d'assigner des coordonnées géographiques à une adresse ou à un code postal, nommé‘géocodage’, est une étape nécessaire dans la conduite d'analyses de santéà petite échelle. À l'aide d'une étude sur des données de mortalitéà Hamilton, en Ontario, nous illustrons que des inexactitudes peuvent être rencontrées lorsque les fichiers de codes postaux et de conversion de Statistique Canada sont utilisés. En utilisant les statistiques spatiales localisées I de Moran, Gi and Gi* de Getis et Ord pour identifier des foyers de mortalité significatifs ou des ‘points chauds’, nous démontrons que de petites erreurs de géocodage, même celles n'affectant moins qu'un pour cent de la base de données, peuvent avoir un impact discernable sur les résultats analytiques. Afin d'aider d'autres chercheurs, nous fournissons des recommandations pour minimiser les erreurs introduites par le géocodage. Ces résultats soulignent l'importance d'un géocodage exact dans les analyses de santé locale.