Gerard Rushton
Department of Geography and College of Public Health
The University of Iowa

Improvements in Health with GIS: Inequality Issues

Research Interests

I�m interested in modeling the effects of personal behavioral factors and geographic/contextual factors on inequalities in the burden of disease among different populations. Spatial dimensions include:

• spatial variations in age-adjusted rates of disease incidence and mortality,
• the reliability of small-area rates based on small populations and rare events,
• spatial patterns of preventive health services,
• spatial patterns of key measures of disease severity�e.g. stage of cancer at first diagnosis.

Spatial Data Models and Techniques

New opportunities for researching this question with GIS have recently emerged. Regional, population-based, disease registers are rapidly being developed in most U.S. states and in many countries. Records in these registers are being geocoded. Large, national, behavioral-health surveys are increasingly being geocoded and, at a minimum, census IDs are being attached to each record. Many service outlets are becoming geocoded (e.g. Yellow Pages) and record linkages between population-based, disease surveillance registries and health systems utilization records (Medicare, Medicaid, Insurers, etc.) are revealing both the context of available health-related services as well as their actual use, and, ultimately, their effect on health. A large and interesting literature is emerging.

The techniques I have used so far are based on kernel density estimation techniques, (Bithell, 1990; Lolonis and Rushton, 1996) and, more recently, on general linear models to set up as priors, spatial rates of cancer incidences adjusted for age and some other demographic factors. These "residual" maps provide a better basis for examining spatial patterns of incidences not explained by known factors. I am now considering moving toward the use of geographic feature extraction algorithms, possibly based on the headbanging algorithms that have recently appeared in the public health literature.

Best Practice Examples of Spatially-Oriented Research

I regard Bithell (1990) and Gatrell et al. (1996) as excellent examples of methodological research on disease data geocoded to the individual level. Gelman and Price (1999) examine and shed considerable light on the problems of many currently used methods that attempt to adjust small-area disease rates for the small-number problem. Gelman et al. (2000) and Jacquez et al. (2000) introduce the application of geographic feature extraction methods to the field of disease pattern analysis. Yen and Kaplan (1999) model the effects of both individual and geographic contextual factors on risk of death. Finally, I like the spatially-oriented research of Wallace and Wallace (1998) in their study of the spread of two infectious diseases in New York.

Learning Resources that CSISS might Provide or Workshops that it Might Offer

• Subjects include: Methods for disease mapping at the small-area geographic scale.
• Multi-level modeling at the individual level of health outcomes.
• Appending geocodes to survey data and using them to add geographic contextual variables for subsequent analysis.

References

Bithell, J.F. 1990. "An application of density estimation to geographical epidemiology." Statistics in Medicine 9:691-701.

Gatrell, A.C., T.C.Bailey, P.J.Diggle, and B.S. Rowlingson. 1996. "Spatial point pattern analysis and its application in geographical epidemiology." Transactions, Institute of British Geographers NS 21:256-274.

Gelman, A. and P.N. Price. 1999. "All maps of parameter estimates are misleading." Statistics in Medicine 18:3221-3234.

Gelman, A., P.N.Price and C-Yu Lin. 2000. "A method for quantifying artifacts in mapping methods illustrated by application to headbanging." Statistics in Medicine 19:2309-2320.

Jacquez, G.M., S. Maruca and M.-J. Fortin. 2000. "From fields to objects: a review of geographic boundary analysis." Journal of Geographical Systems 2:221-241.

Rushton, G., and P. Lolonis. 1996. "Exploratory spatial analysis of birth defect rates in an urban population." Statistics in Medicine 15:717-726.

Talbot, T.O., M. Kulldorff, S.P. Forand, and V.B.Haley. 2000. "Evaluation of spatial filters to create smoothed maps of health data." Statistics in Medicine 19:2399-2408.

Wallace, D., and R. Wallace. 1998. A Plague on Your Houses: How New York Was Burned Down and National Public Health Crumbled. New York; Verso.

Yen, I.H., and G. A. Kaplan. 1999. "Neighborhood social environment and risk of death: multilevel evidence from the Alameda County study." American Journal of Epidemiology 149:898-907.