Measuring Geographical Accessibility of Urban Health Services
Author Information
Author(s): Philippe Apparicio, Abdelmajid Mohamed, Mylène Riva, Richard Shearmur
Primary Institution: Université du Québec
Hypothesis
How do different distance types and aggregation methods affect the measurement of geographical accessibility to health care services in urban areas?
Conclusion
Using the best estimation methods for evaluating geographical accessibility is crucial, especially in suburban areas where errors can significantly impact health studies.
Supporting Evidence
- Cartesian distances are strongly correlated with network distances across the metropolitan area.
- Important local variations in correlation were observed in suburban areas.
- Accessibility measures from census tract centroids yield significant measurement errors for 5% to 10% of census tracts.
Takeaway
This study looks at how we measure how easy it is for people to get to health services in cities, showing that the way we measure this can really change the results.
Methodology
The study compared different distance types (Euclidean, Manhattan, shortest network, and shortest network time) and aggregation methods to measure accessibility to health services in the Montréal census metropolitan area.
Potential Biases
Potential bias due to the choice of distance types and aggregation methods affecting the accuracy of accessibility measures.
Limitations
Errors in measurement can occur in about 10% of census tracts, particularly in suburban areas with lower population density.
Participant Demographics
The study focuses on the population living in the Montréal census metropolitan area, which has about 3.4 million inhabitants.
Statistical Information
P-Value
p<0.0001
Statistical Significance
p<0.0001
Digital Object Identifier (DOI)
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