Public Health, GIS, and Spatial Analytic Tools

Abstract

Monte Carlo simulations Abstract We review literature that uses spatial analytic tools in contexts where Geographic Information Systems (GIS) is the organizing system for health data or where the methods discussed will likely be incorporated in GIS-based analyses in the future. We conclude the review with the point of view that this literature is moving toward the development and use of systems of analysis that integrate the information geo-coding and data base functions of GISystems with the geo-information processing functions of GIScience. The rapidity of this projected development will depend on the perceived needs of the public health community for spatial analysis methods to provide decision support. Recent advances in the analysis of disease maps have been influenced by and benefited from the adoption of new practices for georeferencing health data and new ways of linking such data geographically to potential sources of environmental exposures, the locations of health resources and the geodemographic characteristics of populations. This review focuses on these advances. INTRODUCTIONThe analytic capabilities of geographic information systems, available in a few cases as fully integrated systems, but more commonly as loosely coupled software systems, have developed rapidly in recent years. Public health is now presented with the opportunity to examine key relationships between the health characteristics of populations and both human and physical environmental characteristics. Some authors suggest that a new discipline of spatial epidemiology now exists, recently described (22, p. v) as being "concerned with describing, quantifying, and explaining geographical variations in disease, especially with respect to variations in environmental exposures at the small-area scale." Although few implemented examples of such systems currently exist outside of the area of national disease and mortality mapping systems, this is clearly the direction in which spatial analytic tools will be used in the future. Some descriptions of such systems in various stages of implementation can be found in the public health literature (3,62,74). This period, too, has seen the beginning of a more critical literature that points out 0163 MAPPING DISEASE RATESWith the increased availability of spatial coverages of individual disease incidences in GIS, statements that were commonly made a decade ago to the effect that disease data are available for areas but rarely as point patterns, now appear quite dated. Several authors have pointed out that choropleth maps (where areas are shaded according to a defined category in a map legend) can be seen as smoothed maps of disease data with the areas chosen acting as filters-albeit of different spatial size. Viewed in this light, such maps are seen by many as inferior representations of the basic data. The traditional public health databases in which observations of diseases are columns and areas are rows reflect the practical requirements for databases prior to GIS. GIS bring the possibility not ...

Applying GIS Methods to Public Health Research at Harvard University

“…During the past two decades, technological advances have made it possible to examine spatial and temporal trends in largescale epidemiological data (Rushton 2003;Paolino 2005;Jerrett 2010). The wide variety of GIS applications toward public health research have been documented in several publications including GIS and Public Health by Ellen K. Cromley and Sara L. McLafferty (2002), Public health, GIS, and spatial analytic tools by G. Rushton (2003), and GIS-a proven tool for public health analysis" by R. H. Jenks and J. M. Malecki (2004).…”

“…Understanding spatial and temporal distribution of disease is integral to public health research (Rushton 2003;Ricketts 2003). Quantitative and statistical analysis methods within a GIS facilitate exploration of a broad range of determinants, including demographic, socioeconomic, geographic, and environmental factors that influence disease transmission.…”

“…If specific features form data clusters, descriptive statistics can also be used to examine non-spatial aspects of data (e.g., comparison of data distributions to normal distributions) (Rushton 2003;Chung 2004). Analytical statistical methods are used to test hypotheses and develop conclusions regarding the distribution or characterization of the analyzed data.…”

The future of epidemiology

“…A few stra te gies at avoid ing this are to pro vide ag gre gate data in grids of a large enough scale to pro tect con fi den tial i ty, or to map rates of dis ease in as fine a ge o graph ic unit as possi ble. In ru ral ar eas with low pop u la tion den si ty and un sta ble rates, in cor po ra tion of spa tial or sta tis ti cal smooth ing is recom mend ed [14].…”

Using Geographic Information Systems for Health Research

“…The medical research applications of GIS are numerous and include finding disease clusters and their possible causes (Murray et al, 2009;Srivastava et al, 2009), improving deployment for emergency services (Ong et al, 2009;Peleg & Pliskin, 2004) and determining if an area is being served adequately by health services (Cinnamon et al, 2009;Schuurman et al, 2008). There have been several reviews and textbooks published in the past decade that focus on the application of GIS to different areas of health research (Cromley & McLafferty, 2002;Kurland & Gorr, 2009;McLafferty, 2003;Parchman et al, 2002;Rushton, 2003).…”