Characterizing the influence of transportation infrastructure on Emergency Medical Services (EMS) in urban area-A case study of Seoul, South Korea

Effect of topography and weather on delivery of automatic electrical defibrillator by drone for out-of-hospital cardiac arrest

Delivery of automatic electrical defibrillator (AED) by unmanned aerial vehicle (UAV) was suggested for out-of-hospital cardiac arrest (OHCA). The goal of this study is to assess the effect of topographic and weather conditions on call to AED attach time by UAV-AED. We included OHCA patients from 2013 to 2016 in Seoul, South Korea. We developed a UAV-AED flight simulator using topographic information of Seoul for Euclidean and topographic flight pathway including vertical flight to overcome high-rise structures. We used 4 kinds of UAV flight scenarios according to weather conditions or visibility. Primary outcome was emergency medical service (EMS) call to AED attach time. Secondary outcome was pre-arrival rate of UAV-AED before current EMS based AED delivery. Call to AED attach time in topographic pathway was 7.0 min in flight and control advanced UAV and 8.0 min in basic UAV model. Pre-arrival rate in Euclidean pathway was 38.0% and 16.3% for flight and control advanced UAV and basic UAV. Pre-arrival rate in the topographic pathway was 27.0% and 11.7%, respectively. UAV-AED topographic flight took longer call to AED attach time than Euclidean pathway. Pre-arrival rate of flight and control advanced UAV was decreased in topographic flight pathway compared to Euclidean pathway.

Statewide Ambulance Coverage of a Mixed Region of Urban, Rural and Frontier under Travel Time Catchment Areas

This study examines the statewide service coverage of emergency medical services (EMS) in view of public health planners, policy makers, and ambulance service managers. The study investigates the statewide service coverage in a mixed region of urban, rural, and frontier regions to address the importance of ambulance service coverage at a large scale. The study incorporated statewide road networks for ambulance travel time, census blocks for population, and backup service coverage using geographic information systems (GIS). The catchment areas were delineated by the travel time after subtracting chute time for each Census Block as an analysis zone. Using the catchment areas from the ambulance base to the centroid of Census Block, the population and land coverage were calculated. The service shortage and multiple coverage areas were identified by the catchment areas. The study found that both reducing chute time and increasing the speed of emergency vehicles at the same time was significantly more effective than improving only one of two factors. The study shows that the service is improved significantly in frontier and urban areas by increasing driving time and chute time. However, in rural areas, the improvement is marginal owing to wider distribution than urban areas and shorter threshold response time than frontier areas. The public health planners and EMS managers benefit from the study to identify underserved areas and redistribute limited public resources.

Socioeconomic disparities in Rapid ambulance response for out-of-hospital cardiac arrest in a public emergency medical service system: A nationwide observational study

OBJECTIVES

This study aimed to examine whether county socioeconomic status (SES) is associated with emergency medical service (EMS) response time and dual dispatch response of out-of-hospital cardiac arrest (OHCA) patients using county property tax per capita in Korea.

METHODS

All EMS-treated adults who suffered OHCAs were enrolled between 2015 and 2017, excluding cases witnessed by EMS providers. The main exposure was property tax per capita in the county where the OHCA occurred. The primary outcome was response time interval, with a secondary outcome of dual dispatch response. Negative binomial regression analysis to calculate incidence rate ratio (IRR) with a 95% confidence interval (CI) was conducted for EMS response time. A multivariable logistic regression analysis for response time interval (<8 min) and dual dispatch response was also conducted.

RESULTS

A total of 71,326 patients in 228 counties were enrolled. Compared to the lowest SES quartile, OHCA patients in the highest SES quartile had shorter median (interquartile range [IQR]) response time intervals (9.5 [5.9] minutes vs. 7.6 [4.2] minutes, IRR [95% CI] 0.95 [0.94-0.96], respectively). The AOR (95% CI) for response time within 8 min was 1.07 (1.01-1.13) for the highest SES quartile compared to the lowest SES quartile. Those in the highest SES quartile also had higher rates of dual dispatch response compared to those in the lowest quantile (50.9% vs 26.6%; AOR [95% CI]: 2.16 [2.03-2.30]).

CONCLUSION

In OHCA patients, those in a lower SES are associated with longer response times and lower dual dispatch response.

Defining geographic emergency medical services coverage in trauma systems

BACKGROUND

Geographic distribution of trauma system resources including trauma centers and helicopter bases correlate with outcomes. However, ground emergency medical services (EMS) coverage is dynamic and more difficult to quantify. Our objective was to evaluate measures that describe ground EMS coverage in trauma systems and correlate with outcome.

METHODS

Trauma system resources in Pennsylvania were mapped. Primary outcome was county age-adjusted transportation injury fatality rate. Measures of county EMS coverage included average distance to the nearest trauma center, number of basic life support and advanced life support units/100 square miles, distance differential between the nearest trauma center and nearest helicopter base, and nearest neighbor ratio (dispersed or clustered geographic pattern of agencies). Spatial-lag regression determined association between fatality rates and these measures, adjusted for prehospital time, Injury Severity Score, and socioeconomic factors. Relative importance of these measures was determined by assessing the loss in R value from the full model by removing each measure. A Geographic Emergency Medical Services Index (GEMSI) was created based on these measures for each county.

RESULTS

Median fatality rate was higher in counties with fewer trauma system resources. Decreasing distance to nearest trauma center, increasing advanced life support units/100 square miles, greater distance reduction due to helicopter bases, and dispersed geographic pattern of county EMS agencies were associated with lower fatality rates. The GEMSI ranged from -6.6 to 16.4 and accounted for 49% of variation in fatality rates. Adding an EMS agency to a single county that produced a dispersed pattern of EMS coverage reduced predicted fatality rate by 6%, while moving a helicopter base into the same county reduced predicted fatality rate by 22%.

CONCLUSION

The GEMSI uses several measures of ground EMS coverage and correlates with outcome. This tool may be used to describe and compare ground EMS coverage across trauma system geographies, as well as help optimize the geographic distribution of trauma system resources.

LEVEL OF EVIDENCE

Ecological study, level IV.

Logistics of air medical transport: When and where does helicopter transport reduce prehospital time for trauma?

BACKGROUND

Trauma is a time sensitive disease. Helicopter emergency medical services (HEMS) have shown benefit over ground emergency medical services (GEMS), which may be related to reduced prehospital time. The distance at which this time benefit emerges depends on many factors that can vary across regions. Our objective was to determine the threshold distance at which HEMS has shorter prehospital time than GEMS under different conditions.

METHODS

Patients in the Pennsylvania trauma registry 2000 to 2013 were included. Distance between zip centroid and trauma center was calculated using straight-line distance for HEMS and driving distance from geographic information systems network analysis for GEMS. Contrast margins from linear regression identified the threshold distance at which HEMS had a significantly lower prehospital time than GEMS, indicated by nonoverlapping 95% confidence intervals. The effect of peak traffic times and adverse weather on the threshold distance was evaluated. Geographic effects across EMS regions were also evaluated.

RESULTS

A total of 144,741 patients were included with 19% transported by HEMS. Overall, HEMS became faster than GEMS at 7.7 miles from the trauma center (p = 0.043). Helicopter emergency medical services became faster at 6.5 miles during peak traffic (p = 0.025) compared with 7.9 miles during off-peak traffic (p = 0.048). Adverse weather increased the distance at which HEMS was faster to 17.1 miles (p = 0.046) from 7.3 miles in clear weather (p = 0.036). Significant variation occurred across EMS regions, with threshold distances ranging from 5.4 to 35.3 miles. There was an inverse but non-significant relationship between urban population and threshold distance across EMS regions (ρ, -0.351, p = 0.28).

CONCLUSION

This is the first study to demonstrate that traffic, weather, and geographic region significantly impact the threshold distance at which HEMS are faster than GEMS. Helicopter emergency medical services was faster at shorter distances during peak traffic while adverse weather increased this distance. The threshold distance varied widely across geographic region. These factors must be considered to guide appropriate HEMS triage protocols.

LEVEL OF EVIDENCE

Therapeutic, level IV.