chapter 10: health and gis an introduction to the geography of health data source: world resources...

Chapter 10: Health and GIS

An Introduction to the Geography of Health

Data Source: WorldResources Institute (2010)

Cartography by Peter Anthamatten

2 An Introduction to the Geography of HealthAnthamatten and Hazen Chapter 10

GIS Concepts and Terminology

A GIS is “a computer system that can manipulate, store and analyze data that is referenced by location” (USGS 2007).


All spatial information in a GIS must be georeferenced to

known features in some way.

In a geographic coordinate system, locations of features are described in terms of their angle from imaginary

planes on earth, most commonly using latitude and longitude coordinates.

Cartography by Peter Anthamatten


In projected coordinate systems, location is defined by its distance from an established x- or y-axis on

a planar surface.

UTM coordinates


Spatial data in a GIS are stored primarily using one of two spatial models.

Each of these models is designed to represent the complicated nature of reality in a form that can be stored, manipulated, and analyzed in a GIS.


A vector data model is built from a series of spatial coordinates that define the locations of features on the map.


In this vector data GIS, country boundaries are represented by a

polygon, which is comprised of points, whose location are specified in the

database by geographic coordinates.

An “attribute table” is connected to each feature and contains data related to that feature. This example contains attribute data on infant mortality rate

and percent overweight.


The map is comprised of a grid; each cell has a value (an

“attribute”) representing elevation in meters above sea level.

In raster data models cells are used to represent a particular area on earth, in this case one square kilometer; the space represented by each cell defines the resolution of the model. Raster models work

well with continuous data such as elevation and temperature.


Sometimes translating between data models can produce some

novel forms of visualization. In this example of density

analysis, for instance, the brown shading on the map (raster data) represents the density of grocery

stores (vector data).


One especially useful feature of GIS is its ability to combine multiple layers of data, enabling the discovery and exploration of spatial patterns in different phenomena.


If we are interested in exploring heart disease mortality patterns in Colorado, we could use a GIS to link a table containing the number

of age-adjusted deaths from heart disease in each county to a file of county boundaries in order to produce a map.

Data Source: Colorado Department of Public Health and the Environment (2010)


Other spatial data can then be overlaid to see if there is any relation between the patterns. The red areas on the

map are urban areas, as defined by the US Census.

Does heart disease appear to bear any relationship to

urban areas?

Data Source: Colorado Department of Public Health and the Environment (2010),Nationalatlas.gov (2010)


Additional layers of data can be added or removed as we consider different ideas. The hatched area on this map shows areas of

elevation greater than 2000 meters above sea level.

What other data might be worth

exploring?

Data Source: Colorado Department of Public Health and the Environment (2010),Nationalatlas.gov (2010), USGSEROS (2010)


A query is a way to examine a subset of features of a GIS.

In this example, a query has been performed to identify all districts that are within five kilometers of a major airfield in Kenya in order

to estimate the number of people that would be served by clinics

attached to the airfields.

Data Source: WRI (2010)


Remote sensing is the measurement of electromagnetic radiation reflected from the earth’s surface in order to record geographic

information such as vegetation cover, land use, rainfall, or atmospheric conditions.

Why is remote sensing particularly useful for studies

in low-income contexts?Source: NASA/GSFC, MODIS Rapid Response (2006)

This satellite image shows smoke and haze

in northern India.


GIS Applications in Health Studies

The ability of GIS to link information on the basis of location has enabled or enhanced many geographic studies of health.

GIS has been used in health studies in a variety of ways including:1) Exposure assessment2) Disease surveillance3) Cluster Analysis4) Analyzing accessibility and utilization of healthcare facilities


GIS can greatly facilitate exposure assessment: estimation of the amount of a toxin that people are

exposed to on the basis of available information such as data from pollution measuring stations.

Photo by Shane Houdek (2009)Pollution in Beijing

1) Exposure Assessment


In an example of a geographic exposure

assessment, Brown et al. (1984) conducted a case-

control study to determine whether proximity to

industrial facilities was associated with increased

risk for lung cancer.

Reprinted from Environmental Research, vol. 34, Brown, L, Pottern, L., and W. Blot, “Lung cancer in relation to environmental pollutants emitted from industrial sources”, page 251, © 1984, with permission from Elsevier.


The study area was divided into a grid of one-by-one kilometer

cells. Each cell was then assigned an exposure value based on

proximity to a polluting stack and results from soil samples.



Basic information on the direction and speed of wind

was also taken into consideration in building the

exposure assessments.The authors concluded that

there was a weak association between cancer risk and

proximity to industrial sources.



Reprinted from Environmental Research, vol. 103, Dubnov, J., Barchana, M., Rishpon, S., Leventhal, A., Segal, I, Carel R., and Portnov, B., “Estimating the effect of air pollution from a coal-

fired power station on the development of children’s pulmonary function, page 89, © 2007, with permission from Elsevier.

In another study, Dubnov et al. estimated children’s exposure to

nitrogen oxides and sulfur dioxide in Israel by interpolating measurements

from monitoring stations.Interpolation is a method for producing estimates for locations where no data were collected, based on information

collected at other locations


2) Disease Surveillance

A disease surveillance system uses systematic data collection to monitor for signals that could indicate new outbreaks of

disease or increasing incidence of a disease. GIS has enabled spatial data to be utilized in a more dynamic

and systematic way than is possible with paper maps.


Source: Srivastava et al. (2009)Courtesy of the International Journal of Health Geographics

In this study, researchers identified malaria “hotspots” in Madhya Pradesh,

India, suggesting where government anti-malaria efforts could be focused.


3) Cluster Analysis

The term disease cluster refers to a group of disease cases or high incidence rates beyond what we would normally expect

to observe in a particular time and place (CDC 1990). The term is generally reserved for unusual groupings of cases

of non-infectious diseases—particularly cancers.


While it is tempting to visually examine a map of

disease incidence in order to identify clusters, great care

must be taken to distinguish between disease clusters resulting from a specific

cause and those that have occurred randomly.

Source: Goovaerts (2006)Courtesy of the International Journal of Health Geographics


Source: Chi et al. (2009)

In spite of some reservations about cluster studies, careful work can reveal useful findings. This map is from a study of birth defects.

Although high rates of birth defects were found in several villages, only one legitimate disease cluster was identified (circled).


4) Analyzing Access and Utilization of Healthcare

GIS can inform discussions of healthcare in a variety of ways, including mapping healthcare services,

evaluating access to services, and analyzing utilization patterns (Cromley and McLafferty 2002).

A variety of techniques can be used to assess healthcare access and utilization, e.g.:

1) Density mapping2) Network analysis3) Least cost path analysis


1) Density mapping uses population and physician data that are collected for administrative units roughly equivalent to

the distance a person could reasonably be expected to travel to visit a certain type of healthcare service in order to produce maps of the number of people per general practice

physician for each administrative unit.


2) Network analysis analyzes distance between patients and health facilities, taking into account the transportation network.

Paez et al. analyzed accessibility to healthcare by comparing different populations in Montreal, Canada. The map on the left shows the average length of trips to healthcare

facilities for residents without a car and the map on the right shows the average trip length for car-owning residents. Darker shading represents greater trip length.

Source: Paez et al. (2010). Courtesy of the International Journal of Health Geographics


Reprinted from Health Informatics Journal, vol. 12, Brabyn, L. and P. Beere, “Population access to hospital emergency departments and the impacts of health reform in New Zealand”, page 234, © 2008 by SAGE. Reprinted by Permission of SAGE.

The shading on this map of New Zealand represents the

average total time for residents to travel to the nearest hospital emergency department in 1991

and 2001.

3) Least cost path analysis uses information about the transportation network to calculate the shortest distance and travel time required to

go from any point to the nearest healthcare facility.


Public Participant GIS (PPGIS)

The concept behind Public Participation GIS (PPGIS) is using GIS to enable communities that have traditionally assumed the role of subject in studies to become active participants in the

formulation, analysis, and dissemination of data.


Challenges in GIS and Health

Reprinted from Health Policy, vol. 74, Hassan, M., “Arsenic poisoning in Bangladesh: spatial mitigation planning with GIS and public participation”, page 225, © 2005, with permission from Elsevier.

In this example of PPGIS, citizens in Bangladesh were given maps of their local area and asked to discuss suitable locations for tube wells, to explain their reasoning for

their proposed placements, and to plot their proposals on maps.


While there is much power, utility, and potential of GIS in the study of the geography of health, it should not be viewed as a panacea for all spatial-analytical problems. A number of problems and challenges must be carefully

considered in GIS-based health research.



Access


Despite efforts in participatory GIS, the substantial investment and maintenance costs associated with GIS

technologies continue to put them out of reach of many individuals and organizations.


Access


Data Quality



The error inherent in original sources of data may be amplified when the data are translated into digital format, and further amplified when they are transferred between

different GISs, modeled into different formats, or converted from one data model to another.


Access


Data Quality

Privacy



The error inherent in original sources of data may be amplified when the data are translated into digital format, and further amplified when they are transferred between

different GISs, modeled into different formats, or converted from one data model to another.

While guidelines are in place to protect an individual’s confidentiality in data that are usually stored in a

spreadsheet or database, there are few equivalent rules to protect an individual’s “spatial confidentiality” (Leitner

and Curtis 2006).


Conclusion

GIS has had an important impact on the way that health geographers approach their work, making it possible to store,

display, and analyze huge quantities of spatial data.Health geographers must always bear in mind that GIS is only useful to the study of health as a tool, however. Without an understanding of the biomedical, ecological, demographic,

social, political, and cultural facets of health, GIS may provide answers but offers little insight.

1. We discuss some of the factors that GIS is able to incorporate into exposure assessment, such as wind and weather patterns, residence history, and daytime activity. Assuming that expense is no barrier, what other factors could be incorporated into a GIS-based exposure model to improve its accuracy? For example, what should we model in order to estimate exposure to diesel fumes?

2. Think of a health problem that you have studied. How could a GIS serve to shed light on the problem? What sorts of data would you combine in your analysis?

3. In your opinion, what are the most important GIS applications for health in a low-income context? What are the key applications in a high-income context? In what ways could a GIS contribute to the health problems that are particularly relevant to where you live?


Discussion Questions

4. In some places, spatial data are not available to the public. In other places data are freely available, while in yet other places governments charge people for spatial data. Do you think that spatial data collected by the government should be freely available for the public to use? Why or why not?

5. One criticism of GIS is that it enforces a specific, dominant worldview that is not compatible with some cultural and social contexts. Can you think of examples of health contexts in which GIS is unable to “model reality” in a useful way for a particular group of people?


Discussion Questions


References

Brabyn, L. and Beere, P. (2006) ‘Population access to hospital emergency departments and the impacts of health reform in New Zealand’, Health Informatics Journal, 12: 227–37.

Brown, L. M., Pottern, L. M. and Blot, W. J. (1984) ‘Lung cancer in relation to environmental-pollutants emitted from industrial sources’, Environmental Research, 34: 250–61.

[CDC] Centers for Disease Control and Prevention. (1990) Guidelines for Investigating Clusters of Health Events [Online]. Available: <http://www.cdc.gov/mmwr/preview/mmwrhtml/00001797.htm> (Accessed 20 May 2010).

Chi, W. X., Wang, J. F., Li, X. H., Zheng, X. Y. and Liao, Y. L. (2008) ‘Analysis of geographical clustering of birth defects in Heshun county, Shanxi province’, International Journal of Environmental Health Research, 18: 243-52.

Colorado Department of Public Health and Environment. (2010) Colorado Health Information Dataset, Death Statistics [Online]. Available: <http://www.cdphe.state.co.us/cohid/deathmenu.html> (Accessed 03 November 2010).

Cromley, E. K. and McLafferty, S. (2002) GIS and Public Health, New York: Guilford Press.

Dubnov, J., Barchana, M., Rishpon, S., Leventhal, A., Segal, I., Carel, R. and Portnov, B. A. (2007) ‘Estimating the effect of air pollution from a coal-fired power station on the development of children's pulmonary function’, Environmental Research, 103: 87–98.

Goovaerts, P. (2006). ‘Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation’, International Journal of Health Geographics, 5(7). [Online]. Available: <http://www.ij-healthgeographics.com/content/5/1/7> (Accessed 12 January 2011).


References

Leitner, M. and Curtis, A. (2006) ‘A first step towards a framework for presenting the location of confidential point data on maps - results of an empirical perceptual study’, International Journal of Geographical Information Science, 20: 813–22.

NASA/GSFC, MODIS Rapid Response. (2006). Haze across Northern India. [Online] Available: <http://rapidfire.sci.gsfc.nasa.gov/gallery/?2006036-0205/India.A2006036.0740.2km.jpg > (Accessed 03 January 2011).

National Atlas.gov (2010) Map Layers. [Online]. Available: <http://www.nationalatlas.gov/maplayers.html> (Accessed 21 Nov 2010).

Paez, A., Mercado, R., Farber, S., Morency, C. and M. Roorda. (2010). ‘Accessibility to health care facilities in Montreal Island: an application of relative accessibility indicators from the perspective of senior and non-senior residents. International Journal of Health Geographics, 9. [Online] Available: <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987784/figure/F4/> (Accessed 12 January 2011).

Rothman, K. J. (1990) ‘A sobering start for the cluster buster's conference’, American Journal of Epidemiology, 132: S6-S13.

[USGS] United States Geological Survey. (2007) Geographic Information Systems [Online]. Available: <http:// egsc.usgs.gov /isb/pubs/gis_poster/index.html> (Accessed 1 March 2010).

[USGSEROS] United States Geological Survey Earth Resources Observation and Science Center. (2010) Global 30 Arc-Second Elevation (GTOPO30) [Online]. Available: <http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/GTOPO30> (Accessed 03 November 2010).

[WRI] World Resources Institute. (2010) Download Kenya GIS Data [Online]. Available: <http://www.wri.org/publication/content/9291> (Accessed 7 November 2010).

chapter 10: health and gis an introduction to the geography of health data source: world resources...

Documents

geography of healthanthamatten

spatial data

vector data gis

attribute data

continuous data

map raster data

vector data model

raster data models cells