3:30 PM - 3:45 PM
[HTT09-07] The use of GIS and spatial statistics to study the spatial distribution of strokes in Rhone-Alpes (France) to target health care location priorities
Keywords:stroke, GIS, cluster, Moran's index, local indicator of spatial association (LISA), Getis-Ord Gi* statistic
In the presented study, GIS is a tool for the analysis of spatial distribution. Indeed, it has allowed studying the spatial distribution of stroke, particularly to identify the presence of clusters. Thus, after data processing, 900 patients were selected for analysis. The distribution of stroke has been studied from the stroke density per 1,000 inhabitants across the town (fig. 1).
To characterize this distribution, calculating Moran’s index was performed. This index indicates that the distribution of stroke is clustered (Moran’s I = 0.16 ; z-score = 27.5 and p-value < 0.001). In addition, the LISA (Local Indicator of Spatial Association - Anselin, 1995) method is applied to explore the spatial patterns of stroke in different clusters (Marijon et al., 2013 ; Sasson et al., 2012). LISA method allows in particular identifying the High High-type clusters (HH), bringing together towns with high stroke rates surrounded by towns with similar characteristics. It also identifies the High Low-type clusters (HL) that are towns having lots of strokes surrounded by cities with few of them, thus being isolated. In addition, the Hot Spot Analysis Getis-Ord Gi* statistic is applied to identify spatial clusters of statistically significant areas with high strokes rate (Ord and Getis, 1995). The Getis-Ord Gi* statistic indicates whether high or low values of strokes are likely to cluster, and confirms the results obtained with the LISA method (fig. 1). The clusters (HH) are mainly distributed along the eastern part of the Rhône-Alpes (fig. 1). These results are used by the health and social services to target health care location priorities and focus on these precise cities. The HH and HL clusters are mainly rural. This factor might start explaining this distribution. However, additional socioeconomic and environmental factors (Mechtouff et al., 2012) are also considered to understand this dissemination, using a PCA with varimax rotation whose results will be mapped by using a GIS.
Anselin L., 1995. Local indicators of spatial association — LISA. Geographical analysis, vol. 27, no 2, p. 93-115.
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Sasson, C., Cudnik, M.T., Nassel, A., Semple, H., Magid, D.J., Sayre, M., Keseg, D., Haukoos, J.S., Warden, C.R., (The Columbus Study Group), 2012. Identifying High-risk Geographic Areas for Cardiac Arrest Using Three Methods for Cluster Analysis. Academic Emergency Medicine 19, 139–146.