Effects of hourly precipitation and temperature on ambulance response time.

BACKGROUND: Longer ambulance response time (ART) delaying treatment would worsen conditions of seriously ill or injured patients, but limited evidence is available on the effects of weather factors on ART. This study aims to assess precipitation- and temperature-ART associations and their potential lagged effects using a novel modeling strategy. METHODS: Based on 779,156 emergency records during 2010-2016 from the whole population in Shenzhen, China, we creatively combined quantile regression with distributed-lag nonlinear models to examine the non-linear and lagged effects of hourly precipitation and temperature on ART at the 50th and 90th percentiles. RESULTS: A linear precipitation-ART association with a delay of 9.01 (95%CI, 7.82-10.20) seconds at median ART for a 1 mm increase in hourly precipitation, and the effects lasted for 5 h with the greatest effect at the current hour. A two linear thresholds temperature-ART association revealed 1 °C decrease below 19 °C caused 1.68 (95%CI, 0.92-2.44) seconds delay in total ART over lag 0-7 h, and 1 °C increase above 24 °C caused 2.44 (95%CI, 1.55-3.33) seconds delay. The hourly call volumes exceeding 54 calls caused 8.79 (95%CI, 8.71-8.86) seconds delay in total ART for 1 more call, but not affected the effects of weather factors. The internal ART suffered more from the hourly call volumes, while the external ART suffered more from precipitation and temperature. The effects were apparently greater on ART at the 90th percentile than median. CONCLUSIONS: Precipitation and temperature are independent risk factors for ambulance services performance, and their lagged effects are notable. The external ART and patients with long ART are vulnerable. More attention should be paid to weather and ART, and these findings may have implications for effective policies to reduce ART to protect public health.

Effect of Speed Humps on Ambulance Delay.

INTRODUCTION: Speed humps allow vehicles to slow down their speeds, but they also cause emergency vehicles to waste time on their way to their destinations. The study aims to determine the delay times alone and queue delay time of ambulances passing through speed humps. METHODS: Three types of ambulances (Van, Multiple Victim Assistance, Bariatric) and vehicles (Truck, Lorry, Van) are passed in a controlled manner through speed humps at different speeds in three streets of Adiyaman province of Turkey. Ambulances and vehicles are slowed down to 15 km/h while passing the speed hump for safe passage. Passing and lost times were calculated with the help of a stopwatch (Catiga CG-503; Catiga Electronics Company, Hong Kong) and a global positioning system (GPS) speedometer (Vjoycar smart speedometer; Vjoy Car Electronics Limited, China). Differences in passing times in the absence and the presence of speed humps, determined with the speed equation formula (t=x / V), were lost timings or delay timings for ambulances and vehicles. RESULTS: In the first region, the lost time for the van ambulance with a speed of 70 km/h was 8.41 seconds, 10.14 seconds for the multiple victim assistance ambulances, and 9.56 seconds for the bariatric ambulance. While there was a truck in front of the van ambulance with a speed of 50 km/h, the lost time was also the queue delay time for the ambulance and was 54.96 seconds, with a lorry 42.81 seconds, and 7.02 seconds with a van. In the second region with a double-speed hump, the lost time for the van ambulance with a speed of 60 km/h was 9.94 seconds, 16.32 seconds for the multiple victim assistance ambulances, and 14.49 seconds for the bariatric ambulance. Ambulances did not waste time in the third region, as ambulances and other vehicles do not have to slow down. CONCLUSION: Ambulances waste time by themselves or due to the vehicles in front of them passing speed hump. As the speed of ambulances increased, the lost time also increased. So, more time is lost when the ambulance needs to go faster.

Use of discrete event simulation and genetic algorithms to estimate the necessary resources to respond in a timely manner in the Medical Emergency System in Bogotá. / Uso de simulación de eventos discretos y algoritmos genéticos para estimar los recursos necesarios para responder oportunamente en el sistema de emergencias médicas en Bogotá.

Introduction: Bogotá has a Medical Emergency System of public and private ambulances that respond to health incidents. However, its sufficiency in quantity, type and location of the resources demanded is not known. Objective: Based on the data from the Medical Emergency System of Bogotá, Colombia, we first sought to characterize the prehospital re- sponse in cardiac arrest and determine with the model which is the least number of resources necessary to respond within eight minutes, taking into account their location, number, and type. Methods: A database of incidents reported in administrative records of the district health authority of Bogotá (2014 to 2017) was obtained. Based on this information, a hybrid model based on discrete event simulation and genetic algorithms was designed to establish the amount, type and geographic location of resources according to the frequencies and typology of the events. Results: From the database, Bogotá presented 938 671 ambulances dispatches in the period. 47.4% high priority, 18.9% medium and 33.74% low. 92% of these corresponded to 15 of 43 medical emergency codes. The response times recorded were longer than expected, especially in out-of-hospital cardiac arrest (median 19 minutes). In the proposed model, the best scenario required at least 281 ambulances, medicalized and basic in a 3:1 ratio, respectively, to respond in adequate time. Conclusions: Results suggest the need for an increase in the resources that respond to these incidents to bring these response times to the needs of our population.

Introducción: Bogotá cuenta con un sistema de emergencias médicas de ambulancias públicas y privadas que responden a incidentes de salud. No se conoce, sin embargo, su suficiencia en cantidad, tipo y ubicación de recursos demandados. Objetivos: A partir de los datos del sistema de emergencias médicas de Bogotá, Colombia, se buscó primero caracterizar la respuesta pre hospitalaria en paro cardiaco. Luego, con el modelo se buscó determinar cuál sería el menor número de recursos necesarios para responder antes de ocho minutos, teniendo en cuenta su ubicación, número y tipo. Métodos: Se obtuvo una base de datos de incidentes reportados en registros administrativos de la autoridad sanitaria distrital de Bogotá (de 2014 a 2017). A partir de esa información, se diseñó un modelo híbrido basado en la simulación de eventos discretos y algoritmos genéticos para establecer la cantidad, tipo y ubicación geográfica de recursos, conforme a frecuencias y tipología de los eventos. Resultados: De la base de datos, Bogotá presentó 938 671 envíos de ambulancias en el período. El 47,4% de prioridad alta, 18,9% media y 33,74% baja. El 92% de estos correspondieron a 15 de 43 códigos de emergencias médicas. Los tiempos de respuesta registrados fueron mayores a lo esperado, especialmente en paro cardiaco extra hospitalario (mediana de 19 minutos). En el modelo planteado, el mejor escenario requirió al menos 281 ambulancias, medicalizadas y básicas en proporción de 3:1 respectivamente para responder en tiempos adecuados. Conclusiones: Los resultados sugieren la necesidad de incrementar los recursos que responden a estos incidentes para acercar estos tiempos de respuesta a las necesidades de nuestra población.

Emergency Medical Services (EMS) Calls During COVID-19: Early Lessons Learned for Systems Planning (A Narrative Review).

BACKGROUND: Over the course of the COVID-19 progress, reports from many locations around the world indicated major increases in EMS call volume, which imposed great pressure on EMS dispatch centers (EMSDC) globally. No studies yet have been done to examine this phenomenon. OBJECTIVE: This paper examines the interrelated effects of the unprecedented global increase of EMS call, the effect of the COVID-19 crisis on responding to non-COVID-19 emergencies, and the concurrent effects of having overwhelmed dispatch centers. It tries to explain the current evidence of the bottleneck of EMS calls during the early phase of the worldwide pandemic. ELIGIBILITY CRITERIA: We examine the numbers of EMS calls internationally between March and June 2020, derived from published literature and news media. Only articles in English were selected, with certain keywords related to EMS calls, ambulance delay, stroke and cardiac arrest. SOURCE OF EVIDENCE: Google Scholar was the main searching source. RESULTS: After applying the selection criteria, a total of 29 citations were chosen, and a pattern of knowledge resulted in the emergence of five themes: EMS calls during COVID-19, Reduced EMS operator response time, Ambulance response delays, Collateral mortality and morbidity among non-COVID-19 cases, and Total ambulance call time. CONCLUSION: Over the course of COVID-19 progress, there was a global phenomenon of exponential increases in EMS calls, which is expected to impose a great pressure on EMS dispatch centers. Several factors contributing to the bottleneck of EMS calls are identified and explained.

Strategies to improve survival outcomes of out-of-hospital cardiac arrest (OHCA) given a fixed budget: A simulation study.

BACKGROUND: Our study aimed to identify a strategy that maximizes survival upon hospital discharge or 30-days post out-of-hospital cardiac arrest (OHCA) in Singapore for fixed investments of S$1, S$5, or S$10 million. Four strategies were compared: (1) no additional investment; (2) reducing response time via leasing of more ambulances; (3) increasing number of people trained in cardiopulmonary resuscitation (CPR); and (4) automated external defibrillators (AED). METHODS: We estimated the effect of ambulance response time, bystander CPR and AED on survival based on Singapore's 2010-2015 OHCA registry data. We simulated the changes in ambulance response times and likelihood of (1) CPR and (2) AED usage as a function of their increased availability, which was then combined with the effect of each factor to determine the increase in survival for each strategy. RESULTS: Survival given no additional investment was 4.03% (95% CI: 3.96%, 4.10%). The investments in ambulances, CPR training and AEDs for a given budget of S$1M changed survival to 4.03% (95% CI: 3.96%, 4.10%), 4.04% (95% CI: 3.98%, 4.11%), and 4.44% (95% CI: 4.35%, 4.54%), respectively. This generated 0, 2 and 102 additional life years saved respectively. Given a budget of S$5M or S$10M, 509 or 886 additional life years could be saved, by investing in an additional 10,000 or 20,000 AEDs respectively. The strategies reached a saturation effect whereby improvement in survival was marginal when the budget was increased to ≥S$5M for investment in ambulances and CPR training. CONCLUSIONS: Investing in AEDs had the most gain in survival.

Uso de simulación de eventos discretos y algoritmos genéticos para estimar los recursos necesarios para responder oportunamente en el sistema de emergencias médicas en Bogotá / Use of discrete event simulation and genetic algorithms to estimate the necessary resources to respond in a timely manner in the Medical Emergency System in Bogotá

INTRODUCCIÓN: Bogotá cuenta con un sistema de emergencias médicas de ambulancias públicas y privadas que responden a incidentes de salud. No se conoce, sin embargo, su suficiencia en cantidad, tipo y ubicación de recursos demandados. OBJETIVOS: A partir de los datos del sistema de emergencias médicas de Bogotá, Colombia, se buscó primero caracterizar la respuesta pre hospitalaria en paro cardiaco. Luego, con el modelo se buscó determinar cuál sería el menor número de recursos necesarios para responder antes de ocho minutos, teniendo en cuenta su ubicación, número y tipo. MÉTODOS: Se obtuvo una base de datos de incidentes reportados en registros administrativos de la autoridad sanitaria distrital de Bogotá (de 2014 a 2017). A partir de esa información, se diseñó un modelo híbrido basado en la simulación de eventos discretos y algoritmos genéticos para establecer la cantidad, tipo y ubicación geográfica de recursos, conforme a frecuencias y tipología de los eventos. RESULTADOS: De la base de datos, Bogotá presentó 938 671 envíos de ambulancias en el período. El 47,4% de prioridad alta, 18,9% media y 33,74% baja. El 92% de estos correspondieron a 15 de 43 códigos de emergencias médicas. Los tiempos de respuesta registrados fueron mayores a lo esperado, especialmente en paro cardiaco extra hospitalario (mediana de 19 minutos). En el modelo planteado, el mejor escenario requirió al menos 281 ambulancias, medicalizadas y básicas en proporción de 3:1 respectivamente para responder en tiempos adecuados. CONCLUSIONES: Los resultados sugieren la necesidad de incrementar los recursos que responden a estos incidentes para acercar estos tiempos de respuesta a las necesidades de nuestra población.

INTRODUCTION: Bogotá has a Medical Emergency System of public and private ambulances that respond to health incidents. However, its sufficiency in quantity, type and location of the resources demanded is not known. OBJECTIVE: Based on the data from the Medical Emergency System of Bogotá, Colombia, we first sought to characterize the prehospital response in cardiac arrest and determine with the model which is the least number of resources necessary to respond within eight minutes, taking into account their location, number, and type. METHODS: A database of incidents reported in administrative records of the district health authority of Bogotá (2014 to 2017) was obtained. Based on this information, a hybrid model based on discrete event simulation and genetic algorithms was designed to establish the amount, type and geographic location of resources according to the frequencies and typology of the events. RESULTS: From the database, Bogotá presented 938 671 ambulances dispatches in the period. 47.4% high priority, 18.9% medium and 33.74% low. 92% of these corresponded to 15 of 43 medical emergency codes. The response times recorded were longer than expected, especially in out-of-hospital cardiac arrest (median 19 minutes). In the proposed model, the best scenario required at least 281 ambulances, medicalized and basic in a 3:1 ratio, respectively, to respond in adequate time. CONCLUSIONS: Results suggest the need for an increase in the resources that respond to these incidents to bring these response times to the needs of our population.

Factors affecting the ambulance response times of trauma incidents in Singapore.

OBJECTIVES: Time to definitive care is important for trauma outcomes, thus many emergency medical services (EMS) systems in the world adopt response times of ambulances as a key performance indicator. The objective of this study is to examine the underlying risk factors that can affect ambulance response times (ART) for trauma incidents, so as to derive interventional measures that can improve the ART. MATERIAL AND METHODS: This was a retrospective study based on two years of trauma data obtained from the national EMS operations centre of Singapore. Trauma patients served by the national EMS provider over the period from 1 January 2011 till 31 December 2012 were included. ART was categorized into "Short" (<4min), "Intermediate" (4-8min) and "Long" (>8min) response times. A modelling framework which leveraged on both multinomial logistic (MNL) regression models and Bayesian networks was proposed for the identification of main and interaction effects. RESULTS: Amongst the process-related risk factors, weather, traffic and place of incident were found to be significant. The traffic conditions on the roads were found to have the largest effect-the odds ratio (OR) of "Long" ART in heavy traffic condition was 12.98 (95% CI: 10.66-15.79) times higher than that under light traffic conditions. In addition, the ORs of "Long ART" under "Heavy Rain" condition were significantly higher (OR 1.58, 95% CI: 1.26-1.97) than calls responded under "Fine" weather. After accounting for confounders, the ORs of "Long" ART for trauma incidents at "Home" or "Commercial" locations were also significantly higher than that for "Road" incidents. CONCLUSION: Traffic, weather and the place of incident were found to be significant in affecting the ART. The evaluation of factors affecting the ART enables the development of effective interventions for reducing the ART.