Spatially Clustered Associations in Health Related Geospatial Data

Abstract

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.

Local and global spatio-temporal entropy indices based on distance-ratios and co-occurrences distributions

“…Overall, the framework identified gives a high degree of flexibility to the whole approach and a potential ability of adaptation to a wide range of applications in the environmental and urban domains. In previous work the measure of spatial entropy has been already applied to the classification of agricultural and land-use data in China (Li and Claramunt 2006), in epidemiology for outbreak detection of spatial association of risk factors (Leibovici et al 2011a), in ecology for plant communities characterizations (Leibovici et al 2011b) and in census data population dynamics (Leibovici and Birkin 2013) where areal data was considered. Nonetheless most of these examples were mainly focusing on global assessments and were dealing with time in a non-integrated way.…”

“…The spatial entropy indices developed in the previous section are global statistics that should be completed by local indices that can identify where (or when or both) the spatial or time structure is the most determinant. Particularly low and high values along with their autocorrelation and potential grouping may be then looked for and a post-hoc testing analysis may be then applied to estimate their significance (Leibovici et al 2011a). Complementing global statistics by their local equivalent has been studied by spatial correlation measures in geostatistics and spatial analysis with applications in geographical information science (GIS).…”

“…When considering categorical data, scan statistics have been also applied using for example SaTScan and GAM methods (Openshaw et al 1987, Kulldorff andNagarwalla 1995), and where a window around a set of observations is applied using a maximum likelihood ratio statistic that compares inner and outer windows: the set of observations form a cluster if the statistic is significant enough. As described in Leibovici et al (2011a), a SaTScan method for (5), (7), (6) and (4), respectively. multinomial data has been developed by Jung et al (2010) with as alternative hypothesis that the proportions for at least one of the categories show different values between the inner and outer windows of the scan, so not necessarily pointing out a particular distributional structure of the categories in the inner window.…”

“…The same vicinity rule can be also applied for local evaluations but the sample sizes have their importance. This approach has been used (Leibovici et al 2011a) for a spatial scan statistic where the number of points considered for each local evaluation is given by the number of nearest neighbors within a 'hot-spot' mapping approach. It can be applied for both spatial entropy approaches taken here.…”

Geospatial information infrastructures to address spatial needs in health: Collaboration, challenges and opportunities

“…Some data (e.g., number of diseases, population at risk) are summarized by regions or administrative units, i.e., in spatial clusters. In this context, new methods to compute clusters in spatially aggregated health data and identify multiple variable associations have been recently proposed [54,55]. For TB data analysis, this kind of spatial clustering may ease the identification of "hot spots" (e.g.…”