Measuring and optimizing accessibility to emergency medical services

Emergency medical services (EMSs) undertake the responsibility of providing rapid medical care to patients suffering from unexpected illnesses or injuries and transferring them to definitive care facilities. This research concerns several research gaps that are associated with different EMS trips, real-time traffic conditions, improving EMS efficiency and equalities. This research aims to develop GIS-based spatial optimization methods to improve service efficiency and equality in EMS systems. Specifically, the research intends to achieve the following goals: (1) to measure spatiotemporal accessibility to EMS; (2) to improve EMS efficiency and provision through spatial optimization approaches; (3) to reduce urban-rural inequalities in EMS accessibility and coverage using spatial optimization approaches. The proposed approaches are applied in three empirical studies in Wuhan, China. To achieve the first objective, the proximity and the enhanced two-step floating catchment method (E-2SFCA) are adopted to evaluate spatiotemporal accessibility. First, the EMS travel time is estimated for the two related trips as an overall EMS journey: one is from the nearest EMS station to the scene (Trip 1), and the other is from the scene to the nearest emergency hospital (Trip 2). Then, the E-2SFCA method is employed to calculate the accessibility score that integrates both geographic accessibility and availability of EMS. Travel time is estimated by using both static road network with standard speed limits and online map service considering real-time traffic. To achieve the second objective, two facility location models are proposed to improve EMS service coverages for two-related trips (Trips 1 and 2). The first model maximizes the amount of demand covered by both ambulance coverage (EMS station – demand) and hospital coverage (demand – hospital). The second model maximizes the amount of demand that can be served by both ambulance coverage and overall coverage (EMS station – demand – hospital). To achieve the third objective, two bi-objective optimization models are developed. The two models have the same primary objective to maximize the total covered demand by ambulance. The second objective is to minimize one of the two inequality measures: one focuses on accessibility of uncovered rural people, and the other concerns the urban-rural inequality in service coverage. For the first empirical study with respect to spatiotemporal access to EMS, different spatial patterns are found for the three trips (two partial trips and the overall trip). Good accessibility to one trip cannot guarantee good accessibility to another trip. In addition, urban-rural inequalities in EMS accessibility and coverage are observed. Finally, it is observed that real-time traffic conditions greatly affect EMS accessibility, particularly in urban districts. Specifically, the accessibility of EMS becomes poor during the morning (7-9 am) and evening peak periods (5-7 pm). For the second empirical study in relation to EMS optimization involving two related trips, the results find that the first proposed model can guarantee that more demand to be covered by both ambulance and hospital coverages than the Maximum Coverage Location Problem (MCLP). The second proposed model can ensure that as many people as possible to be served by both ambulance and overall coverage than the work by ReVelle et al. (1976). For the third empirical study attempting to reduce urban-rural inequality in EMS, the results show that the first bi-objective model can improve EMS accessibility of uncovered rural demand, and the second model can reduce EMS service coverages between urban and rural areas. However, the improvement EMS inequalities between urban and rural areas leads to a cost of a decrease in the total covered population, especially in urban areas. Regarding policy implications, this research suggests that different EMS trips and traffic conditions should be considered when measuring spatial accessibility to EMS. Spatial optimization research can help improving service efficiency and reduce regional equalities in EMS systems. The work presented in this thesis can aid the planning practice of public services like EMS and provide decision support for policymakers

Integrating spatial and non-spatial dimensions to evaluate access to rural primary healthcare service: a case study of Songzi, China

Access to rural primary healthcare services has been broadly studied in the past few decades. However, most earlier studies that focused on examining access to rural healthcare services have conventionally treated spatial and non-spatial access as separate factors. This research aims to measure access to primary healthcare services in rural areas with the consideration of both spatial and non-spatial dimensions. The methodology of study is threefold. First, the Gaussian two-step floating catchment area (G-2SFCA) method was adopted to measure spatial access to primary healthcare services. Then, a questionnaire survey was conducted to investigate non-spatial access factors, including demographic condition, patient’s household income, healthcare insurance, education level, and patient satisfaction level with the services. After that, a comprehensive evaluation index system was employed to integrate both spatial and non-spatial access. The empirical study showed a remarkable disparity in spatial access to primary healthcare services. In total, 78 villages with 185,137 local people had a “low” or “very low” level of spatial access to both clinics and hospitals. For the non-spatial dimension, the results depicted that Songzi had significant inequalities in socioeconomic status (e.g., income, education) and patient satisfaction level for medical service. When integrating both spatial and non-spatial factors, the disadvantaged areas were mainly located in the eastern and middle parts. In addition, this study found that comprehensively considering the spatial and non-spatial access had a significant impact on results in healthcare access. In conclusion, this study calls for policymakers to pay more attention to primary healthcare inequalities within rural areas. The spatial and non-spatial access should be considered comprehensively when the long-term rural medical support policy is designated

Spatiotemporal access to emergency medical services in Wuhan, China: accounting for scene and transport time intervals

Background: Access as a primary indicator of Emergency Medical Service (EMS) efficiency has been widely studied over the last few decades. Most previous studies considered one-way trips, either getting ambulances to patients or transporting patients to hospitals. This research assesses spatiotemporal access to EMS at the shequ (the smallest administrative unit) level in Wuhan, China, attempting to fill a gap in literature by considering and comparing both trips in the evaluation of EMS access. Methods: Two spatiotemporal access measures are adopted here: the proximity-based travel time obtained from online map services and the enhanced two-step floating catchment area (E-2SFCA) which is a gravity-based model. First, the travel time is calculated for the two trips involved in one EMS journey: one is from the nearest EMS station to the scene (i.e. scene time interval (STI)) and the other is from the scene to the nearest hospital (i.e. transport time interval (TTI)). Then, the predicted travel time is incorporated into the E-2SFCA model to calculate the access measure considering the availability of the service provider as well as the population in need. For both access measures, the calculation is implemented for peak hours and off-peak hours. Results: Both methods showed a marked decrease in EMS access during peak traffic hours, and differences in spatial patterns of ambulance and hospital access. About 73.9% of shequs can receive an ambulance or get to the nearest hospital within 10 min during off-peak periods, and this proportion decreases to about 45.5% for peak periods. Most shequs with good ambulance access but poor hospital access are in the south of the study area. In general, the central areas have better ambulance, hospital and overall access than peripheral areas, particularly during off-peak periods. Conclusions: In addition to the impact of peak traffic periods on EMS access, we found that good ambulance access does not necessarily guarantee good hospital access nor the overall access, and vice versa

Rice plants respond to ammonium‐stress by adopting a helical root growth pattern

High levels of ammonium nutrition reduce plant growth and different plant species have developed distinct strategies to maximize ammonium acquisition while alleviate ammonium toxicity through modulating root growth. Up to now, the mechanism underlying plant tolerance or sensitivity towards ammonium remain unclear. Rice uses ammonium as its main N source. Here we show that ammonium supply restricts rice root elongation and induces a helical growth pattern, which is attributed to root acidification resulting from ammonium uptake. Ammonium-induced low pH triggers asymmetric auxin distribution in rice root tips through changes in auxin signaling, thereby inducing a helical growth response. Blocking auxin signaling completely inhibited this root response. In contrast, this root response is not activated in ammonium-treated Arabidopsis. Acidification of Arabidopsis roots leads to the protonation of IAA, and dampening the intracellular auxin signaling levels that are required for maintaining root growth. Our study suggests a different mode of action by ammonium on the root pattern and auxin response machinery in rice versus Arabidopsis, and the rice-specific helical root response towards ammonium is an expression of the ability of rice in moderating auxin signaling and root growth to utilize ammonium while confronting acidic stress

Optimal Home Energy Management System WithDemand Charge Tariff and ApplianceOperational Dependencies

Two-way communication facilities and advanced metering infra-structure enable residential buildings to be capable of actively participating in demand side management schemes. This paper proposes a new home energy management system (HEMS), which optimally schedules the operation of home energy resources, with the aim to minimize the home’s one-day electricity cost charged by the real-time pricing while taking into account the monthly basis peak power consumption penalty, charged by the demand charge tariff. To better ensure the user’s lifestyle requirements, the HEMS also models lifestyle-related operational dependencies of household appliances. Numerical simulations and case studies are conducted to validate the reasonability of the proposed method.Australian Research Counci

Locating emergency medical services to reduce urban-rural inequalities

Emergency Medical Service (EMS) systems provide fundamental services in relation to public health and safety. The spatial configuration of EMS stations is crucial to the efficiency and equality of service provision. While urban-rural inequalities in EMS have been widely acknowledged, how to optimize EMS station locations to reduce such inequalities remains challenging. This research proposes a multi-objective optimization model to reduce urban-rural inequalities in EMS accessibility and coverage, in addition to maximizing the total covered population. The proposed model is applied in an empirical study in Wuhan, China, to seek locations for new EMS stations in order to improve local EMS capacity in the pandemic period. The results indicate that the total covered population, particularly in urban area, decreases when urban-rural equality in service accessibility increases, but it has a U-shaped relationship with urban-rural inequality in service coverage. Pareto-optimal solutions suggest that all new stations should be located in rural areas if lower urban-rural inequality in EMS is to be obtained, but one new station is needed in the urban area if higher coverage of total population is more desirable. The work presented in this paper can aid the planning practice of public services like EMS systems where reducing urban-rural inequalities is an essential concern

Post-vaccination adverse reactions, decision regret, and willingness to pay for the booster dose of COVID-19 vaccine among healthcare workers: A mediation analysis

This study aimed to explore the relationship between post-vaccination adverse reactions, decision regret, and willingness to pay (WTP) for the booster dose. An online survey was conducted in Taizhou, China. Questionnaires were completed by 1,085 healthcare workers (HCWs) and 1,054 (97.1%) have received two doses of the COVID-19 vaccine. Mediation analysis method was adopted. Our study presented that post-vaccination adverse reactions in HCWs could decrease their WTP for the booster dose. Of note, HCWs experienced adverse reactions after vaccination would more likely regret their previous vaccination decisions, which, in turn, further reduced their WTP for a booster shot. Decision regret mediated the relationship between adverse post-vaccination reactions and WTP for the booster dose. The findings implied inextricable relationships among post-vaccination adverse reactions, decision regret, and WTP of the booster dose. It suggested that these post-vaccination adverse reactions should be further incorporated into vaccine campaigns to improve vaccine intention and potentially increase willingness to pay for booster doses of COVID-19 vaccine

Location optimization of emergency medical services: Considering joint service coverage of ambulances and emergency centers

Emergency Medical Services (EMS) play an essential role in saving lives and improving health outcomes by offering immediate medical care to individuals who experience sudden illnesses or injuries. A complete EMS journey consists of two related trips: one from an EMS station to a scene (Trip 1), and the other from a scene to a definitive care location (Trip 2), where the service is coordinately provided by two types of facilities: EMS stations/ambulances and emergency centers (e.g., trauma centers or stroke centers) that are often affiliated with general hospitals. Current work on EMS location optimization considers only one trip (Trip 1 or Trip 2) which ignores the coordination between EMS stations and emergency centers, or the overall trip alone that overlooks the response time requirement. This paper proposed a spatial optimization model, the maximal coverage location problem based on joint coverage (MCLP-JC), for siting EMS stations and emergency centers simultaneously with a consideration of the two related trips. An empirical study of stroke center planning in Wuhan, China, is implemented to compare the proposed approach with the maximal coverage location problem based on overall coverage (MCLP-OC). The results demonstrate that the MCLP-JC can ensure more people being able to receive the first care from an ambulance within the response time requirement, which is critical to subsequent treatment at emergency centers and the odds of survival. The findings from the two scenarios regarding service relocation and expansion offer insights for future health facility planning

Coordinated residential energy resource scheduling with vehicle-to-home and high photovoltaic penetrations

Home Energy Management System (HEMS) provides an effective solution to assist residential users in dealing with the complexity of dynamic electricity prices. This paper proposes a new HEMS in contexts of real-time electricity tariff (RTP) and high residential photovoltaic penetrations. Firstly, the HEMS accepts user-specified Residential Energy Resource (RER) operation restrictions as inputs. Then, based on the forecasted solar power outputs and electricity prices, an optimal scheduling model is proposed to support the decision-making of the RES operations. For the scheduling of Heating, Ventilating, and Air Conditioning (HVAC) system, an advanced adaptive thermal comfort model is employed to estimate the user’s indoor thermal comfort degree. For the controllable appliances, the ‘User Disturbance Value (UDV)’ metric is proposed to estimate the psychological disturbances of an appliance schedule on the user’s preference. The proposed scheduling model aims to minimize the future 1-day energy costs and disturbances to the user. A new biological self-aggregation intelligence inspired metaheuristic algorithm recently proposed by the authors (a Natural Aggregation Algorithm, NAA), is applied to solve the model. Extensive simulations are conducted to validate the proposed method.Australian Research Counci