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Estimating proximity to care: are straight line and zipcode centroid distances acceptable proxy measures?
Robin L Bliss1, Jeffrey N Katz, Elizabeth A Wright
1Department of Orthopedic Surgery, Orthopedic and Arthritis Center for Outcomes Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. rbliss@partners.org
Medical Care
|December 15, 2011
Summary
Estimating healthcare access using driving distance and time is crucial. While zipcode centroid and precise location distances offer reasonable approximations, they tend to overestimate actual travel, especially in rural areas.
Area of Science:
- Healthcare accessibility research
- Geospatial analysis in public health
- Epidemiologic methods
Background:
- Spatial accessibility of healthcare is often measured by driving distance or time.
- Precise driving distances and times are rarely available for research.
- Straight-line distances (zipcode centroids, precise addresses) are used as proxies but their accuracy is understudied.
Purpose of the Study:
- To assess the accuracy of estimated distances (zipcode centroids, precise locations) in predicting actual driving distances and times to healthcare.
- To evaluate the impact of urban/suburban/rural residential status on prediction accuracy.
Main Methods:
- A cohort of Medicare beneficiaries was used.
- Actual driving distances and times were obtained using commercial software (MapQuest).
- Linear regression models predicted driving distances/times from estimated distances, adjusting for residential status (urban/suburban/rural).
Main Results:
- Predicted driving distances and times were, on average, larger than actual values.
- Zipcode centroid distances alone overestimated rural driving distance by 19% and time by 36%.
- Urban/suburban categorization improved prediction accuracy for suburban and urban areas but decreased it for rural areas.
Conclusions:
- Distances between zipcode centroids and precise locations offer reasonable estimates for epidemiologic research.
- Augmenting estimates with urban/suburban/rural categorization improves accuracy for non-rural residences.
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