Arul Earnest1, Say Beng Tan, Nur Shahidah, Marcus Eng Hock Ong. 1. Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-National University Singapore Graduate Medical School, Singapore. arul.earnest@duke-nus.edu.sg
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.
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.
Authors: Christoph Hanefeld; Alexander Haschemi; Thomas Lampert; Hans J Trampisch; Andreas Mügge; Janine Miebach; Cordula Kloppe; Renate Klaaßen-Mielke Journal: Dtsch Arztebl Int Date: 2018-01-26 Impact factor: 5.594