Skip to main content

Geographical Variation in Ambulance Calls Is Associated With Socioeconomic Status

Buy Article:

$48.00 plus tax (Refund Policy)

Abstract:



ACADEMIC EMERGENCY MEDICINE 2012; 19:180–188 © 2012 by the Society for Academic Emergency Medicine
Abstract

Objectives:  The main objective was to explore the relationship between socioeconomic status and the spatial distribution of ambulance calls, as modeled in the island nation of Singapore, at the Development Guide Plan (DGP) level (equivalent to census tracts in the United States).

Methods:  Ambulance call data came from a nationwide registry from January to May 2006. We used a conditional autoregressive (CAR) model to create smoothed maps of ambulance calls at the DGP level, as well as spatial regression models to evaluate the relationship between the risk of calls with regional measures of socioeconomic status, such as household type and both personal and household income.

Results:  There was geographical correlation in the ambulance calls, as well as a socioeconomic gradient in the relationship with ambulance calls of medical‐related (but not trauma‐related) reasons. For instance, the relative risk (RR) of medical ambulance calls decreased by a factor of 0.66 (95% credible interval [CrI] = 0.56 to 0.79) for every 10% increase in the proportion of those with monthly household income S$5000 and above. The top three DGPs with the highest risk of medical‐related ambulance calls were Changi (RR = 29, 95% CrI = 24 to 35), downtown core (RR = 8, 95% CrI = 6 to 9), and Orchard (RR = 5, 95% CrI = 4 to 6).

Conclusions:  This study demonstrates the utility of geospatial analysis to relate population socioeconomic factors with ambulance call volumes. This can serve as a model for analysis of other public health systems.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1553-2712.2011.01280.x

Affiliations: 1: From the Centre for Quantitative Medicine, Office of Clinical Sciences, Duke–National University Singapore Graduate Medical School (AE, SBT), Singapore; and the Department of Emergency Medicine, Singapore General Hospital (NS, MEHO), Singapore. 2: From the Centre for Quantitative Medicine, Office of Clinical Sciences, Duke–National University Singapore Graduate Medical School (AE, SBT), Singapore; and the Department of Emergency Medicine, Singapore General Hospital (NS, MEHO), Singapore.

Publication date: February 1, 2012

bpl/aem/2012/00000019/00000002/art00011
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more