Cheng et al. BMC Public Health 2011, 11:380 http://www.biomedcentral.com/1471-2458/11/380 Page 3 of 9 Figure 1 Map of metropolitan areas and aboriginal townships in Taiwan. The map of the study region in Taiwan includes 7 metropolitan areas and the area of 55 aboriginal townships. 352 townships. We presented the descriptive statistics of drug consumption by metropolitan areas, including the mean (± standard deviation). Coefficient of variation (COV, standard deviation divided by mean) is used to describe the variations of consumption in various areas. Bivariate analysis using the Student’s t test was con- ducted to compare the south and north regions of Tai- wan; a two-sided p value < 0.05 was considered statistically significant. Data were extracted from NHIRD, and descriptive analysis were performed with SAS version 9.1 (SAS Institute Inc., Cary, North Carolina) Local Indicators of Spatial Association (LISA) [29] allows us to decompose the study area into small loca- tions, thus enabling the assessment of significant local spatial clustering around an individual location. In addi- tion to the degree of spatial clustering, the detailed var- iations of clustering in the locally defined geo-space are identified as well as the locations of the spatial clusters. The local version of Moran’s I at location i is given by: Ii = 1 n i (xi − x¯) (xi − x¯)2 j wij(xj − x¯) where n indicates the total number of locations (352 townships), xi denotes the value of the variable of inter- est, X, at location i, xj denotes the observation at neigh- boring locations j, and x¯ is the sample average of X. wij is the so-called spatial weights matrix (connectivity matrix), which defines spatial interaction across study regions. In general, wij = 1 if location i and location j are neighboring, (share a common boundary); otherwise, wij = 0. In the LISA analysis, if the test statistic is not significant at any sensible level, no spatial pattern is present in the areas; i.e., observations are spatially ran- dom. When it is significant, however, two possible pat- terns of local spatial clusters are likely to be exhibited: (1) When xi is higher than the average of the entire study area (x) and so are its neighbors, a high-high ¯ (HH) association, a so-called hot spot, is indicated. (2) When both xi and its neighbors are lower than the average, the spatial tendency is low-low (LL), or a cold spot. The major limitation of Moran’s I is that it only measures spatial clustering (spatial autocorrelation) at a global scale, but cannot detect important clusters at a local scale and determine the spatial patterns for the specific locations. To make spatial autocorrelation visually meaningful, the local Moran I is represented by cluster maps, in which the locations of significant spa- tial clusters are highlighted to identify the patterns of associations and p value < 0.01 was considered statisti- cally significant. Underlying spatial clusters can be geographic changes in risk factors, which introduce a degree of spatial auto- correlation in the outcome. In this study, spatial clusters of drug use reflect inefficiency in medical resources and utilization. Still, a variety of possible factors could lead to such inefficiency and they are location-specific. How- ever, if there exist common factors creating diffusion or local clusters of medical inefficiency, it would be helpful and necessary to identify the locations of risk clusters and the extent of their clustering. Based on the sizes of risk areas and the significance of clusters, important risk factors shared by neighboring areas could be uncovered. Thus, from the perspective of policy making and research, priority should be given to these clustered areas. We used the geographic information system software ArcGIS v9.2 to combine drug prescription data with Taiwan digital maps at the township level. The data was then processed by spatial analysis software GeoDa [30] to create the spatial weights matrix and to calculate the LISA value for each township.