Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach

Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data

This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence.

Predictors of in-hospital mortality among patients with symptoms of stroke, Mashhad, Iran: an application of auto-logistic regression model

BACKGROUND

Stroke is the second leading cause of death in adults worldwide. There are remarkable geographical variations in the accessibility to emergency medical services (EMS). Moreover, transport delays have been documented to affect stroke outcomes. This study aimed to examine the spatial variations in in-hospital mortality among patients with symptoms of stroke transferred by EMS, and determine its related factors using the auto-logistic regression model.

METHODS

In this historical cohort study, we included patients with symptoms of stroke transferred to Ghaem Hospital of Mashhad, as the referral center for stroke patients, from April 2018 to March 2019. The auto-logistic regression model was applied to examine the possible geographical variations of in-hospital mortality and its related factors. All analysis was performed using the Statistical Package for the Social Sciences (SPSS, v. 16) and R 4.0.0 software at the significance level of 0.05.

RESULTS

In this study, a total of 1,170 patients with stroke symptoms were included. The overall mortality rate in the hospital was 14.2% and there was an uneven geographical distribution. The results of auto-logistic regression model showed that in-hospital stroke mortality was associated with age (OR = 1.03, 95% CI: 1.01-1.04), accessibility rate of ambulance vehicle (OR = 0.97, 95% CI: 0.94-0.99), final stroke diagnosis (OR = 1.60, 95% CI: 1.07-2.39), triage level (OR = 2.11, 95% CI: 1.31-3.54), and length of stay (LOS) in hospital (OR = 1.02, 95% CI: 1.01-1.04).

CONCLUSION

Our results showed considerable geographical variations in the odds of in-hospital stroke mortality in Mashhad neighborhoods. Also, the age- and sex-adjusted results highlighted the direct association between such variables as accessibility rate of an ambulance, screening time, and LOS in hospital with in-hospital stroke mortality. Thus, the prognosis of in-hospital stroke mortality could be improved by reducing delay time and increasing the EMS access rate.

Prehospital Time Interval for Urban and Rural Emergency Medical Services: A Systematic Literature Review

The aim of this study was to discuss the differences in pre-hospital time intervals between rural and urban communities regarding emergency medical services (EMS). A systematic search was conducted through various relevant databases, together with a manual search to find relevant articles that compared rural and urban communities in terms of response time, on-scene time, and transport time. A total of 37 articles were ultimately included in this review. The sample sizes of the included studies was also remarkably variable, ranging between 137 and 239,464,121. Twenty-nine (78.4%) reported a difference in response time between rural and urban areas. Among these studies, the reported response times for patients were remarkably variable. However, most of them (number (n) = 27, 93.1%) indicate that response times are significantly longer in rural areas than in urban areas. Regarding transport time, 14 studies (37.8%) compared this outcome between rural and urban populations. All of these studies indicate the superiority of EMS in urban over rural communities. In another context, 10 studies (27%) reported on-scene time. Most of these studies (n = 8, 80%) reported that the mean on-scene time for their populations is significantly longer in rural areas than in urban areas. On the other hand, two studies (5.4%) reported that on-scene time is similar in urban and rural communities. Finally, only eight studies (21.6%) reported pre-hospital times for rural and urban populations. All studies reported a significantly shorter pre-hospital time in urban communities compared to rural communities. Conclusions: Even with the recently added data, short pre-hospital time intervals are still superior in urban over rural communities.

One-year spatiotemporal database of Emergency Medical Service (EMS) calls in Mashhad, Iran: data on 224,355 EMS calls

Objectives

Emergency Medical Services (EMS) is the first point of service for the people who are in critical condition and in need of urgent health care. In Iran, as in other countries, people in need of emergency services often die or are left with a permanent injury due to the poor EMS-related infrastructure. It has been shown that a detailed examination of the response times and the spatiotemporal pattern of EMS calls for service can lead to improvements in time-sensitive patient outcomes. We performed a spatiotemporal study in city of Mashhad, the second-most populous city of Iran, to investigate the pattern of the EMS calls and now wish to release a comprehensive dataset resulting from this study.

Data description

The data include three data files plus a data dictionary file. Data file 1 contains the characteristics of EMS requests including sex, age group, date of call, different time periods of each EMS missions, the census tracts’ ID of callers, the chief complaint, and the EMS mission result. Two spatial data files include the boundaries of the census tracts in Mashhad and the point location of all EMS stations, respectively. A data dictionary file defines all fields and values across the data files.