Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1553-2712.2011.01280.x
DC FieldValue
dc.titleGeographical variation in ambulance calls is associated with socioeconomic status
dc.contributor.authorEarnest, A.
dc.contributor.authorTan, S.B.
dc.contributor.authorShahidah, N.
dc.contributor.authorOng, M.E.H.
dc.date.accessioned2014-11-26T05:03:33Z
dc.date.available2014-11-26T05:03:33Z
dc.date.issued2012-02
dc.identifier.citationEarnest, A., Tan, S.B., Shahidah, N., Ong, M.E.H. (2012-02). Geographical variation in ambulance calls is associated with socioeconomic status. Academic Emergency Medicine 19 (2) : 180-188. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1553-2712.2011.01280.x
dc.identifier.issn10696563
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/108949
dc.description.abstractObjectives: 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. © 2012 by the Society for Academic Emergency Medicine.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1111/j.1553-2712.2011.01280.x
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.1111/j.1553-2712.2011.01280.x
dc.description.sourcetitleAcademic Emergency Medicine
dc.description.volume19
dc.description.issue2
dc.description.page180-188
dc.description.codenAEMEF
dc.identifier.isiut000300044500009
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