Quantifying the Error Associated with Alternative GIS-based Techniques to Measure Access to Health Care Services

• Main Authors: Amy Mizen (Author), Richard Fry (Author), Daniel Grinnell (Author), Sarah E. Rodgers (Author)
• Format: Book
• Published: AIMS Press, 2015-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2327-8994; 10.3934/publichealth.2015.4.746
• Keywords: accessibility; GIS; public health; aggregation error; network distance; Euclidean distance; health inequalities; Public aspects of medicine; RA1-1270
• Source: AIMS Public Health, Vol 2, Iss 4, Pp 746-761 (2015)

The aim of this study was to quantify the error associated with different accessibility methods commonly used by public health researchers. Network distances were calculated from each household to the nearest GP our study area in the UK. Household level network distances were assigned as the gold standard and compared to alternate widely used accessibility methods. Four spatial aggregation units, two centroid types and two distance calculation methods represent commonly used accessibility calculation methods. Spearman's rank coefficients were calculated to show the extent which distance measurements were correlated with the gold standard. We assessed the proportion of households that were incorrectly assigned to GP for each method. The distance method, level of spatial aggregation and centroid type were compared between urban and rural regions. Urban distances were less varied from the gold standard, with smaller errors, compared to rural regions. For urban regions, Euclidean distances are significantly related to network distances. Network distances assigned a larger proportion of households to the correct GP compared to Euclidean distances, for both urban and rural morphologies. Our results, stratified by urban and rural populations, explain why contradicting results have been reported in the literature. The results we present are intended to be used aide-memoire by public health researchers using geographical aggregated data in accessibility research.