H.E.L.P: A GIS-based Health Exploratory AnaLysis Tool for Practitioners
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The last two decades have been characterized by a growing number of Geographical Information System (GIS) applications to the field of health science. From a decision-making and policy perspective, undeniable benefits of GIS include the assessment of health needs and delivery of services, and also the appropriate allocation of workforce and prevention resources. Despite these attractive attributes, the literature suggests that there has been limited GIS uptake among health care decision makers. This paper presents a GIS-based Health Exploratory and anaLysis tool for Practitioners (H.E.L.P.) for the analysis and visualization of space-time point events, applied to hospital patients. H.E.L.P. is viewed as a spatial decision support system which provides a set of powerful analytical tools integrating the computational capabilities of Matlab with the visualization and database functionalities of GIS. The system outputs improve the understanding of disease dynamics and provide resources for decision-makers in allocating appropriate staffing. As an example, H.E.L.P. is applied to a dataset of hospital patients in Cali, Colombia.
KeywordsClustering Decision Support System (DSS) Geographical Information System (GIS) Health Care Policy Matlab-GIS Integration
The authors would like to thank Alejandro Varela Secretary of Health of the Municipality of Cali - Colombia and his staff of doctors, engineers, administrators, and other key personnel without which this project would have not been possible, also Bradley Biggers from the Gaston County Health Department (North Carolina, USA) for his comments on an earlier version of the paper. The authors thank the reviewers and the editor of ASAP for their insightful and detailed comments which helped improve the paper.
- Albert, D. P., Gesler, W. M., & Levergood, B. (Eds.) (2000). Spatial analysis, GIS and remote sensing applications in the health sciences. CRC Press, 212p.Google Scholar
- Bailey, T. C., & Gatrell, A. C. (1995). Interactive spatial data analysis (2nd ed.). Harlow: Longman.Google Scholar
- Bonner, M., Han, D., Nie, J., Rogerson, P., Vena, J., & Freudenheim, J. (2003). Positional accuracy of geocoded addresses in epidemiologic research. Epidemiology, 14, 408–412.Google Scholar
- Boots, B., & Getis, A. (1988). Point pattern analysis. Sage Scientific Geography Series. London: Sage.Google Scholar
- Cromley, E., & McLafferty, S. (2002). GIS and public health. New York: Guilford. 340p.Google Scholar
- Delmelle, E. (2010). H.E.L.P. Matlab GIS Interface. Available at http://webpages.uncc.edu/∼edelmell/HELP, last accessed February 21 2010.
- Gatrell, A., & Loytonen, M. (Eds.) (1998). GIS and health. Houghton Mifflin Harcourt, 212p.Google Scholar
- Han, D., & Rogerson, P. (2002). Application of a GIS-based statistical method to assess spatio-temporal changes in breast cancer clustering in the Northeastern United States. In O. Khan & R. Skinner (Eds.), Geographic information systems and health applications. Hershey: Idea Group.Google Scholar
- Jacquez, G. M., Greiling, D. A., Durbeck, H., Estberg, L., Do, E., Long, A., et al. (2002). ClusterSeer User Guide 2: software for identifying disease clusters. Ann Arbor: TerraSeer.Google Scholar
- Kulldorff, M. (2006). SaTScan User Guide for Version 7.0: www.satscan.org (last accessed: January 15 2010).
- Kwan, M.-P., Casas, I., & Ben, C. (2004). Protection of geo-privacy and accuracy of spatial information: how effective are geographical masks? Cartographica, 39, 15–38.Google Scholar
- Levine, N. (2005). Space-time analysis. In N. Levine (Ed.), CrimeStat v.3.1: a spatial statistics program for the analysis of crime incident locations, chapter 9. Houston: Ned Levine & Associates/The National Institute of Justice, http://www.icpsr.umich.edu/crimestat (last accessed: January 15 2010).
- Malczewski, J. (1999). GIS and multicriteria decision analysis. New York: Wiley. 392p.Google Scholar
- Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209–220.Google Scholar
- Parchman, M. L., Ferrer, R. L., & Blanchard, K. S. (2002). Geography and geographic information systems in family medicine research. Family Medicine, 34, 132–137.Google Scholar
- Yamada, I., Rogerson, P., & Lee, G. (2009). GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters. Journal of Geographical Systems. In press.Google Scholar