Cheng et al. BMC Public Health 2011, 11:380 http://www.biomedcentral.com/1471-2458/11/380 R E S E A R C H A R T I C L E Open Access Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug- prescribing pattern in Taiwan Ching-Lan Cheng1, Yi-Chi Chen2, Tzu-Ming Liu3 and Yea-Huei Kao Yang1,4* Abstract Background: Geographic Information Systems (GIS) combined with spatial analytical methods could be helpful in examining patterns of drug use. Little attention has been paid to geographic variation of cardiovascular prescription use in Taiwan. The main objective was to use local spatial association statistics to test whether or not the cardiovascular medication-prescribing pattern is homogenous across 352 townships in Taiwan. Methods: The statistical methods used were the global measures of Moran’s I and Local Indicators of Spatial Association (LISA). While Moran’s I provides information on the overall spatial distribution of the data, LISA provides information on types of spatial association at the local level. LISA statistics can also be used to identify influential locations in spatial association analysis. The major classes of prescription cardiovascular drugs were taken from Taiwan’s National Health Insurance Research Database (NHIRD), which has a coverage rate of over 97%. The dosage of each prescription was converted into defined daily doses to measure the consumption of each class of drugs. Data were analyzed with ArcGIS and GeoDa at the township level. Results: The LISA statistics showed an unusual use of cardiovascular medications in the southern townships with high local variation. Patterns of drug use also showed more low-low spatial clusters (cold spots) than high-high spatial clusters (hot spots), and those low-low associations were clustered in the rural areas. Conclusions: The cardiovascular drug prescribing patterns were heterogeneous across Taiwan. In particular, a clear pattern of north-south disparity exists. Such spatial clustering helps prioritize the target areas that require better education concerning drug use. Background Spatial analysis can identify inequities in disease distri- bution or in resources for healthcare utilization, result- ing in financial incentives in payment policies [1-3]. Spatial analysis has taken giant steps forward in the recent decade, and Geographic Information Systems (GIS) software makes it possible to undertake a sophisti- cated visual approach to data analysis in medical issues [4,5]. However, most studies using GIS have relied on their mapping capabilities rather than performance of statistical analyses [6-8]. Only when GIS are combined with spatial analytical methods can the result provide a helpful tool in the study of public health issues [9,10]. * Correspondence: yhkao@mail.ncku.edu.tw 1Institute of Biopharmaceutical Science, College of Medicine, National Cheng Kung University, Tainan, Taiwan Full list of author information is available at the end of the article Spatial analyses can be compared to societal norms or objectives. The traditional descriptive statistics fail to show the complexity of interactions between individual geographic units. In contrast, spatial analysis can reveal the co-variation of properties with a geo-space, since characteristics at proximal locations tend to be corre- lated, either positively or negatively, which leads to spa- tial autocorrelation [11]. For example, a study shows that people are indirectly protected when more people in their neighborhood are vaccinated with cholera vac- cine [12]. Another potential use of the spatial analysis is to examine patterns of drug use. Spatial analysis provides a method for delineating the complex nature of the asso- ciations between multiple levels of social structure and illicit drug use [13,14]. These applications have impor- tant implications in policy-making, as the associations © 2011 Cheng et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.