A quantile regression analysis of ambulance response time

Young Kyung Do, Kelvin Foo, Yih Yng Ng, Marcus Eng Hock Ong

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Background. Shorter ambulance response time (ART) contributes to improved clinical outcomes. Various methods have been used to analyze ART. Objectives. We aimed to compare the use of quantile regression with the standard ordinary least squares (OLS) model for identifying factors associated with ART in Singapore. A secondary aim was to determine the relative importance of patient-level (e.g., gender and ethnicity) versus system-level (e.g., call volumes within the last one hour) factors contributing to longer ART. Methods. We conducted a retrospective review of data electronically captured from ambulance dispatch records and patient case notes of emergency calls to the national ambulance service from January to May 2006 (n = 30,687). The primary outcome was ART, defined as the time taken for an ambulance to arrive at the scene upon receiving an emergency call, and modeled as a function of patient- and system-level factors. We used a quantile regression model to account for potential heterogeneous effects of explanatory variables on ART across different quantiles of the ART distribution, and compared estimates derived with the corresponding OLS estimates. Results. Quantile regression estimates suggested that the call volume in the previous one hour predicted increased ART, with the effect being more pronounced in higher ART quantiles. At the 90th and 50th percentiles of ART, each additional call in the last one hour was predicted to increase ART to the next call from the same area by 93 and 57 seconds, respectively. The corresponding OLS estimate was 58 seconds. Patient factors had little effect on ART. Conclusion. The quantile regression model is more useful than the OLS model for estimating ART, revealing that in Singapore, ART is influenced heterogeneously by the volume of emergency calls in the past one hour.

Original languageEnglish
Pages (from-to)170-176
Number of pages7
JournalPrehospital Emergency Care
Volume17
Issue number2
DOIs
StatePublished - 1 Apr 2013

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Ambulances
Reaction Time
Regression Analysis
Least-Squares Analysis
Emergencies
Singapore

Keywords

  • Ambulances
  • Emergency medical services
  • Quantile regression analysis
  • Response times
  • Singapore

Cite this

Do, Young Kyung ; Foo, Kelvin ; Ng, Yih Yng ; Ong, Marcus Eng Hock. / A quantile regression analysis of ambulance response time. In: Prehospital Emergency Care. 2013 ; Vol. 17, No. 2. pp. 170-176.
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A quantile regression analysis of ambulance response time. / Do, Young Kyung; Foo, Kelvin; Ng, Yih Yng; Ong, Marcus Eng Hock.

In: Prehospital Emergency Care, Vol. 17, No. 2, 01.04.2013, p. 170-176.

Research output: Contribution to journalArticle

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N2 - Background. Shorter ambulance response time (ART) contributes to improved clinical outcomes. Various methods have been used to analyze ART. Objectives. We aimed to compare the use of quantile regression with the standard ordinary least squares (OLS) model for identifying factors associated with ART in Singapore. A secondary aim was to determine the relative importance of patient-level (e.g., gender and ethnicity) versus system-level (e.g., call volumes within the last one hour) factors contributing to longer ART. Methods. We conducted a retrospective review of data electronically captured from ambulance dispatch records and patient case notes of emergency calls to the national ambulance service from January to May 2006 (n = 30,687). The primary outcome was ART, defined as the time taken for an ambulance to arrive at the scene upon receiving an emergency call, and modeled as a function of patient- and system-level factors. We used a quantile regression model to account for potential heterogeneous effects of explanatory variables on ART across different quantiles of the ART distribution, and compared estimates derived with the corresponding OLS estimates. Results. Quantile regression estimates suggested that the call volume in the previous one hour predicted increased ART, with the effect being more pronounced in higher ART quantiles. At the 90th and 50th percentiles of ART, each additional call in the last one hour was predicted to increase ART to the next call from the same area by 93 and 57 seconds, respectively. The corresponding OLS estimate was 58 seconds. Patient factors had little effect on ART. Conclusion. The quantile regression model is more useful than the OLS model for estimating ART, revealing that in Singapore, ART is influenced heterogeneously by the volume of emergency calls in the past one hour.

AB - Background. Shorter ambulance response time (ART) contributes to improved clinical outcomes. Various methods have been used to analyze ART. Objectives. We aimed to compare the use of quantile regression with the standard ordinary least squares (OLS) model for identifying factors associated with ART in Singapore. A secondary aim was to determine the relative importance of patient-level (e.g., gender and ethnicity) versus system-level (e.g., call volumes within the last one hour) factors contributing to longer ART. Methods. We conducted a retrospective review of data electronically captured from ambulance dispatch records and patient case notes of emergency calls to the national ambulance service from January to May 2006 (n = 30,687). The primary outcome was ART, defined as the time taken for an ambulance to arrive at the scene upon receiving an emergency call, and modeled as a function of patient- and system-level factors. We used a quantile regression model to account for potential heterogeneous effects of explanatory variables on ART across different quantiles of the ART distribution, and compared estimates derived with the corresponding OLS estimates. Results. Quantile regression estimates suggested that the call volume in the previous one hour predicted increased ART, with the effect being more pronounced in higher ART quantiles. At the 90th and 50th percentiles of ART, each additional call in the last one hour was predicted to increase ART to the next call from the same area by 93 and 57 seconds, respectively. The corresponding OLS estimate was 58 seconds. Patient factors had little effect on ART. Conclusion. The quantile regression model is more useful than the OLS model for estimating ART, revealing that in Singapore, ART is influenced heterogeneously by the volume of emergency calls in the past one hour.

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