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[ Recorded presentation ] Recorded presentation

Mapping inequalities in access to health and health outcomes in Ontario and the Commonwealth of Virginia

Sorina O. Vlaicu, MD, MPH, PhD, Department of Epidemiology and Biostatistics, University of Western Ontario, Kresge Building, London, ON N6A5C2, Canada, 519-661-2111, ext.81171, svlaicu@uwo.ca and Connie L. McNeely, PhD, School of Public Policy, George Mason University, 4400 University Drive, MS-3C6, Fairfax, VA 22030-4444.

This paper promotes a new perspective on inequalities in access to health and health outcomes, while looking at the role governmental policies play in widening (or narrowing) the gap between ‘have’ and ‘have-nots.’ It provides evidence to inform the program design, policies and practices. Similar regions from Canada (Ontario) and the United States (Commonwealth of Virginia) are analyzed, to isolate the effect of governmental policies and national culture on health disparities. Health varies with socio-economic status. Geography matters as well. Where we live not only defines our health, but also the support and resources we get from the community, and the quantity and quality of health services we receive. Traditionally focused on differences in income, research on health disparities has lately embraced a more comprehensive approach, defining health as the resultant of biological, physical, socio-economic and cultural characteristics. The study is constructed within a GIS (geographical information system) which allows the linkage of outcome variables with data on socio-economic, and community data, census and health surveys with administrative information. Several statistical methods are used for analysis, such as mapping the spatial distribution of variables, map smoothing techniques, tests for spatial randomness, and spatial regression. Spatial regression provides a valuable tool for statistical inference; unlike linear regression, it allows for pure spatial correlation to be built into the model. Most analyses on health inequalities have tried to force the same variables—some of which could be spatially, but not linearly correlated—into a linear regression model, leading to potentially biased results.

Learning Objectives:

Keywords: Access to Health Care, Geographic Information Systems

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

[ Recorded presentation ] Recorded presentation

GIS Facilitating Health Planning and Evaluation

The 132nd Annual Meeting (November 6-10, 2004) of APHA