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A quantile regression analysis of ambulance response time
Young Kyung Do1, Kelvin Foo, Yih Yng Ng
1Program in Health Services and Systems Research, Duke-NUS Graduate Medical School Singapore, Singapore. young.do@duke-nus.edu.sg
Prehospital Emergency Care
|December 11, 2012
Summary
Quantile regression analysis reveals that high emergency call volumes significantly increase ambulance response times (ART) in Singapore, especially during peak periods. Patient factors had minimal impact on ART.
Area of Science:
- Emergency medical services research
- Health services research
- Statistical modeling in healthcare
Background:
- Shorter ambulance response times (ART) are crucial for improving patient outcomes.
- Analyzing factors influencing ART is essential for optimizing emergency medical services.
- Existing methods for ART analysis have limitations in capturing complex relationships.
Purpose of the Study:
- To compare quantile regression with ordinary least squares (OLS) for identifying ART determinants in Singapore.
- To assess the relative importance of patient-level versus system-level factors on ART.
- To understand how emergency call volume impacts ART across its distribution.
Main Methods:
- Retrospective analysis of 30,687 emergency calls from January to May 2006.
- Modeling ART using quantile regression and OLS.
- Investigating patient (gender, ethnicity) and system (call volume) factors.
Main Results:
- Quantile regression showed call volume significantly increases ART, particularly at higher quantiles (93s at 90th percentile).
- OLS underestimated the impact of call volume (58s).
- Patient-level factors demonstrated minimal influence on ART.
Conclusions:
- Quantile regression offers superior insights into ART determinants compared to OLS.
- Emergency call volume is a key heterogeneous driver of ART in Singapore.
- Findings highlight the need to manage call volume for efficient ambulance dispatch.
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