Cheng et al. BMC Public Health 2011, 11:380 http://www.biomedcentral.com/1471-2458/11/380 or proximal geographic areas, where neighboring loca- tions may share similar prescribing patterns due to, for example, inhabitants’ lifestyle behaviors or environmen- tal exposures. Although the trend in utilization of cardi- ovascular drugs in Taiwan was investigated, little was known regarding its geographical pattern across town- ships. The spatial analysis presented here may add essential information to pharmacoepidemiological stu- dies and policy-making. On the one hand, spatial analy- sis allows us to examine spatial processes underlying the prescribing behavior of physicians and provides the first inquiry into whether spatial components should be accounted for in modeling to avoid erroneous inference [49]. On the other hand, given the limited resources, the government can focus their policy efforts to target areas to improve drug utilization. For the reasons described above, our approach should be applicable worldwide. Developing countries, for which medical resources are increasingly disproportionately distributed, may see more distinct prescribing patterns than those for Taiwan or other developed countries. Although spatial analysis with Moran’s I is useful for this study, it does have limitations of which all should be aware. First, Moran’s I (and other global indicators) is not powerful enough to differentiate well between a random pattern and a pattern without substantial spatial variations. Second, Moran’s I is conditional, tied to how we define localities through a spatial weights matrix wij. Different definitions of the matrix can lead to different values of spatial dependence. Finally, the measures of spatial dependence are subject to the modifiable areal unit problem. The problem arises due to the arbitrary choice and the modifiable nature of areal units in spatial analysis, in the sense that they can be aggregated to form units of different sizes of spatial arrangements. The degree of spatial association, as measured by Mor- an’s I, changes with different levels of aggregation in areal units. While we are aware of the limitations of the spatial autocorrelation index, our results in this paper can be interpreted as a broad indication of the presence and magnitude of spatial dependence. Other potential limitations of our study also should be noted. First, the utilization pattern of cardiovascular medications could be different as urbanization changes. Second, we only analyzed the classes of cardiovascular drugs based on the first three digits of ATC which have DDD assigned. Although we were able to depict the spatial patterns of drug use, whether the knowledge of new drugs was attributable to the geographic clustering requires a detailed classification of drug classes based on ATC. Third, we might underestimate the utilization of group A, B, D because complex medication (i.e., a compound made of two or more ingredients) without DDD assigned were not estimated in this study. The Page 7 of 9 most complex medication are combinations with group D (i.e., group A plus group D or group B plus group D) to improve patient’s compliance. Fourth, this study only analyzed one year of data, but the prescribing pattern could extend across years. Fifth, the cardiovascular med- ication pattern did not correspond to disease distribu- tion. Most patients need two more classes of medication to control their disease, except simple essen- tial hypertension[47]. Finally, the fact that we have observed significant spatial autocorrelation in our data does not determine whether these are true spatial effects or are spurious in the sense that they can be attributed entirely to patterns in other variables, such as income or individual characteristics (such as age, gen- der, etc). If we control for all of these other factors and the spatial variable remains significant, then we have evidence that the patterns are consistent with a neigh- borhood effect and are not solely attributable to socioe- conomic characteristics of these areas. Exploring theses sources of spatial patterns and the mechanisms that may be driving them is an obvious extension of the ana- lysis presented here, and more detailed studies are necessary to better understand the factors that influence prescribing patterns. Conclusions The cardiovascular medication prescribing patterns were heterogeneous across Taiwan. In particular, a clear pat- tern of north-south disparity exists. The spatial variabil- ity of prescribing patterns documented in this study shows that priority-setting in research may heavily depend on the neighborhood association. List of abbreviations AMI: Acute myocardial infarction; ATC: Anatomical Therapeutic Chemical; CVD: Cardiovascular Disease; GIS: Geographic Information Systems; LISA: Local Indicators of Spatial Association; NHIRD: National Health Insurance Research Database; WHO: World Health Organization Acknowledgements This research was funded by the Taiwan National Science Council (NSC 96- 2320-B-006-028-MY3) and Multidisciplinary Center of Excellence for Clinical Trial and Research (DOH100-TD-B-111-002), Department of Health, Executive Yuan, Taiwan. The funding source had no role in the design, analysis, interpretation, or reporting of results or in the decision to submit the manuscript for publication. Author details 1Institute of Biopharmaceutical Science, College of Medicine, National Cheng Kung University, Tainan, Taiwan. 2Department of Economics, National Cheng Kung University, Tainan, Taiwan. 3Graduate Institute of Sustainable Tourism and Recreation Management, National Taichung University, Taichung, Taiwan. 4Institute of Clinical Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan. Authors’ contributions CLC, YCC, TML, and YHKY designed the study. CLC and TML analyzed the data. CLC and YCC drafted the initial article. All authors were involved in critical evaluation and editing of the manuscript and read and approved the final version.