Please use this identifier to cite or link to this item: https://doi.org/10.3109/10903127.2011.615974
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dc.titleSpatial analysis of ambulance response times related to prehospital cardiac arrests in the city-state of Singapore
dc.contributor.authorEarnest, A.
dc.contributor.authorHock Ong, M.E.
dc.contributor.authorShahidah, N.
dc.contributor.authorMin Ng, W.
dc.contributor.authorFoo, C.
dc.contributor.authorNott, D.J.
dc.date.accessioned2014-11-26T08:30:39Z
dc.date.available2014-11-26T08:30:39Z
dc.date.issued2012-04
dc.identifier.citationEarnest, A., Hock Ong, M.E., Shahidah, N., Min Ng, W., Foo, C., Nott, D.J. (2012-04). Spatial analysis of ambulance response times related to prehospital cardiac arrests in the city-state of Singapore. Prehospital Emergency Care 16 (2) : 256-265. ScholarBank@NUS Repository. https://doi.org/10.3109/10903127.2011.615974
dc.identifier.issn10903127
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/110285
dc.description.abstractObjectives. The main objective of this study was to establish the spatial variation in ambulance response times for out-of-hospital cardiac arrests (OHCAs) in the city-state of Singapore. The secondary objective involved studying the relationships between various covariates, such as traffic condition and time and day of collapse, and ambulance response times. Methods. The study design was observational and ecological in nature. Data on OHCAs were collected from a nationally representative database for the period October 2001 to October 2004. We used the conditional autoregressive (CAR) model to analyze the data. Within the Bayesian framework of analysis, we used a Weibull regression model that took into account spatial random effects. The regression model was used to study the independent effects of each covariate. Results. Our results showed that there was spatial heterogeneity in the ambulance response times in Singapore. Generally, areas in the far outskirts (suburbs), such as Boon Lay (in the west) and Sembawang (in the north), fared badly in terms of ambulance response times. This improved when adjusted for key covariates, including distance from the nearest fire station. Ambulance response time was also associated with better traffic conditions, weekend OHCAs, distance from the nearest fire station, and OHCAs occurring during nonpeak driving hours. For instance, the hazard ratio for good ambulance response time was 2.35 (95% credible interval [CI] 1.97-2.81) when traffic conditions were light and 1.72 (95% CI 1.51-1.97) when traffic conditions were moderate, as compared with heavy traffic. Conclusions. We found a clear spatial gradient for ambulance response times, with far-outlying areas' exhibiting poorer response times. Our study highlights the utility of this novel approach, which may be helpful for planning emergency medical services and public emergency responses. © 2012 National Association of EMS Physicians.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.3109/10903127.2011.615974
dc.sourceScopus
dc.subjectAmbulance
dc.subjectBayesian analysis
dc.subjectCardiac arrest
dc.subjectResponse times
dc.subjectSingapore
dc.subjectSpatial variation
dc.subjectTraffic conditions
dc.typeArticle
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.3109/10903127.2011.615974
dc.description.sourcetitlePrehospital Emergency Care
dc.description.volume16
dc.description.issue2
dc.description.page256-265
dc.description.codenPEMCF
dc.identifier.isiut000300788700011
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