Evaluation of the role of location and distance in recruitment in respondent-driven sampling

Background

The purpose of the project was to delineate a series of contiguous neighbourhood-based "Data Zones" within the Region of Peel (Ontario) for the purpose of health data analysis and dissemination. Zones were to be built on Census Tracts (N = 205) and obey a series of requirements defined by the Region of Peel. This paper explores a method that combines statistical analysis with ground-truthing, consultation, and the use of a decision tree.

Data

Census Tract data for Peel were derived from the 2006 Canadian Census Master file.

Methods

Following correlation analysis to reduce the data set, Principal Component Analysis was applied to the data set to reduce the complexity and derive an index. The Getis-Ord Gi*statistic was then applied to look for statistically significant clusters of like Census Tracts. A detailed decision tree for the amalgamation of remaining zones and ground-truthing with Peel staff verified the resulting zones.

Results

A total of 15 Data Zones that are similar with respect to socioeconomic and sociodemographic attributes and that met criteria defined by Peel were derived for the region.

Conclusion

The approach used in this analysis, which was bolstered by a series of checks and balances throughout the process, gives statistical validity to the defined zones and resulted in a robust series of Data Zones for use by Peel Public Health. We conclude by offering insight into alternative uses of the methodology, and limitations.