Skip to main content

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

Abstract

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.

Objective

Given the vital role of high-quality and efficient emergency care in saving patients’ lives from unpredicted life-threatening injuries or critical conditions, the Emergency Medical Service (EMS) goal is to concentrate on providing timely and well-monitored care [1, 2]. To achieve this goal, it is crucial to have a deeper understanding of EMS system including the demographic and geographical characteristics of the callers and distribution of EMS infrastructure resources [3]. In this regard, Geographical Information Systems (GISs) is a highly useful toolbox for decision making and it has capabilities to visualize space-time information [4] Since adjacent areas have similar characteristics in terms of spatial and temporal aspects, applying GIS to discover patterns of health events has received more attention recently [5, 6].

Mashhad is the capital city of Razavi Khorasan Province with an estimated population of 3,800,000 inhabitants [7]. Mashhad also is the most popular tourist destination in the country, with over 20+ million visitors per annum [8]. A central dispatch center in the city of Mashhad manages all EMS requests of Mashhad County. Spatio-temporal patterns of EMS requests were investigated using spatio-temporal scan statistics approach in Mashhad [9]. Here, a comprehensive dataset related to this study is offered for further investigation to identify clustering patterns and potential geographic characteristics of EMS requests in a densely populated city.

Data description

Table 1 shows the details of the three data files plus the data dictionary file. Data on 224,355 ambulance calls from 1st of Jun 2018, to 31st of May 2019 were obtained from the database of Mashhad EMS centre (Data file 1). This dataset contains 12 fields including: (a) date of call, (b) patient sex [male (M), female (F), or Not Available (N/A)], (c) patient age (categorized in 10-year intervals), (d) location (inside or outside the city), (e) census tract ID (1 to 1301 for calls in urban area), (f) primary or chief medical complaint (in 12 categories), (g) EMS mission result (in five categories), (h) call time (the time EMS receives the call), (i) start time of the mission, (j) time of arrival at the scene by EMS, (k) time of starting the transfer from the scene to a hospital, and (l) time of arrival at the hospital. Notably, the location of callers was recorded by latitude and longitude in the EMS system. Altered level of consciousness, trauma injuries and cardiovascular incidents are the main reasons of emergency requests in Mashhad [9]. In order to protect the privacy of callers, the point location was linked to a larger area, a census tract, using the spatial join tool of ArcGIS v. 10.6.

Table 1 Overview of the data files

Census tracts’ boundaries and their population were obtained from municipality of Mashhad (Data file 2). Data for the locations of 53 ambulance stations in urban area as well as 13 ambulance stations in the surrounding countryside were obtained from EMS dispatch centre of Mashhad (Data file 3). Finally, following another data note [11], a data dictionary document is included that provides definitions of all variables and values used in the three data files.

The datasets provided in this study can be used by researchers in different disciplines such as health geography, urban planning, public health and health disparities. Descriptive thematic maps can be produced to highlight the distribution of EMS requests during the different time intervals (by time of day, day, week or month).

Spatial clustering techniques like local Moran’s I [12] or Getis-Ord Gi* [13] can be applied to this data to identify high-demand or low-demand clusters of emergency services [14]. High-demand clusters (hot spots) identify the areas having significantly greater EMS requests than expected while low-demand clusters reveal the regions with a significantly lower EMS request rate (cold spots) [15]. Spatiotemporal clustering techniques like space–time scan statistics [16] can be applied to discover high-risk areas in different time intervals (hour, day, week, month). These analytical approaches can help policymakers effectively allocate resources across the city and based on different temporal intervals. Also, these techniques can be applied separately for specific primary or chief medical complaints such as trauma or cardiovascular diseases. In addition to spatiotemporal analyses, these data can be linked to contextual factors for different geographically defined areas—contextual factors such as socio-economic variables (e.g., socioeconomic status) or environmental variables (e.g., air pollution)—to further investigate the impact of contextual factors on EMS requests. The research outputs can inform health policymakers and urban planners to develop appropriate strategies to provide efficient and timely pre-hospital care services.

Limitations

Mashhad is a dynamic city with millions of visitors per annum. There are many religious festivals across the year but the spatiotemporal patterning of visitors across census tracts in the city during the study period is not known. We do not know if a patient receiving EMS care is a resident of Mashhad or a tourist. While we can use recent population totals for each census tract the true population at risk is not known. As with all EMS data there are issues of quality. While some data are automated (e.g., dispatch time, arrival time) EMS staff are working in emergency and high-stress situations and specific times of treatment at the service site may have varying levels of temporal precision. There is no valid data for visitors in Mashhad. However, most of the visitors come to the city in summers.

Availability of data and materials

The data described in this Data note can be freely and openly accessed on the Harvard Dataverse under (https://doi.org/10.7910/DVN/JZEVI8) [10]. Please see Table 1 and the reference list for details and links to the data.

Abbreviations

EMS:

Emergency Medical Services

GIS:

Geographical information system

References

  1. Aringhieri R, Bruni ME, Khodaparasti S, van Essen JT. Emergency medical services and beyond: addressing new challenges through a wide literature review. Comput Oper Res. 2017;78:349–68. https://doi.org/10.1016/j.cor.2016.09.016.

    Article  Google Scholar 

  2. Martinez R. New vision for the role of emergency medical services. Ann Emerg Med. 1998;32(5):594–9. https://doi.org/10.1016/s0196-0644(98)70039-3.

    Article  CAS  PubMed  Google Scholar 

  3. Wang HE, Mann NC, Jacobson KE, Mears G, Smyrski K, Yealy DM. National characteristics of emergency medical services responses in the United States. Prehosp Emerg Care. 2013;17(1):8–14. https://doi.org/10.1016/j.cor.2016.09.016.

    Article  PubMed  Google Scholar 

  4. Wang F. Why public health needs GIS: a methodological overview. Annals GIS. 2020;26(1):1–12. https://doi.org/10.1080/19475683.2019.1702099.

    Article  Google Scholar 

  5. MohammadEbrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, et al. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health. 2021;21(1):1373. https://doi.org/10.1186/s12889-021-11326-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Dadashi A, Mohammadi A, MohammadEbrahimi S, Bergquist R, Shamsoddini A, Hesami A, et al. Spatial analysis of the 10 most prevalent cancers in north-eastern Iran, 2017–2018. J Spat Sci. 2021. https://doi.org/10.1080/14498596.2021.1975583.

    Article  Google Scholar 

  7. Iran SCo. Census 2016 results 2016. https://www.amar.org.ir/english/Population-and-Housing-Censuses. Accessed 12 July 2021.

  8. Kafashpor A, Ghasempour Ganji SF, Sadeghian S, Johnson LW. Perception of tourism development and subjective happiness of residents in Mashhad, Iran. Asia Pacific J Tourism Res. 2018;23(6):521–31. https://doi.org/10.1080/10941665.2018.1476392.

    Article  Google Scholar 

  9. Hashtarkhani S, Kiani B, Mohammadi A, MohammadEbrahimi S, Dehghan-Tezerjani M, Samimi T, et al. Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach. Geospat Health. 2021. https://doi.org/10.4081/gh.2021.1043.

    Article  PubMed  Google Scholar 

  10. Kiani B. Emergency Medical Services (EMS) calls geodatabase in the city of Mashhad Iran. Harvard Dataverse. 2021. https://doi.org/10.7910/DVN/JZEVI8.

    Article  Google Scholar 

  11. Mohammad Ebrahimi S, Mohammadi A, Bergquist R, Akbarian M, Arian M, Pishgar E, et al. A spatial-epidemiological dataset of subjects infected by SARS-CoV-2 during the first wave of the pandemic in Mashhad, second-most populous city in Iran. BMC Res Notes. 2021;14(1):292. https://doi.org/10.1186/s13104-021-05710-9.

    Article  CAS  Google Scholar 

  12. Anselin L. Local indicators of spatial association—LISA. Geogr Anal. 1995;27(2):93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x.

    Article  Google Scholar 

  13. Songchitruksa P, Zeng X. Getis-Ord spatial statistics to identify hot spots by using incident management data. Transp Res Rec. 2010;2165(1):42–51. https://doi.org/10.3141/2165-05.

    Article  Google Scholar 

  14. Azimi A, Bagheri N, Mostafavi SM, Furst MA, Hashtarkhani S, Amin FH, et al. Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice. BMC Public Health. 2021;21(1):7. https://doi.org/10.1186/s12889-020-10064-1.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hasan MM, Oh J-S. GIS-based multivariate spatial clustering for traffic pattern recognition using continuous counting data. Transp Res Rec. 2020;2674(10):583–98. https://doi.org/10.1177/0361198120937019.

    Article  Google Scholar 

  16. Kulldorff M, Heffernan R, Hartman J, Assunçao R, Mostashari F. A space–time permutation scan statistic for disease outbreak detection. Plos Med. 2005;2(3): e59. https://doi.org/10.1371/journal.pmed.0020059.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Special thanks are extended to the staff of EMS center of Mashhad university of Medical science who provided the information needed for this study.

Funding

This project is funded by Mashhad University of Medical Sciences (Grant Number: 970861).

Author information

Authors and Affiliations

Authors

Contributions

SH and SM drafted the manuscript. BK, MT, and SAM revised the text. SH, AM and SE contributed to data cleaning and preparing for analyses. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Behzad Kiani or Mahmood Tara.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethical committee of Mashhad University of Medical Sciences (Number: IR.MUMS.REC. 970861). Informed consent was not required to be obtained due to the nature of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hashtarkhani, S., Kiani, B., Mohammadi, A. et al. One-year spatiotemporal database of Emergency Medical Service (EMS) calls in Mashhad, Iran: data on 224,355 EMS calls. BMC Res Notes 15, 22 (2022). https://doi.org/10.1186/s13104-022-05905-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13104-022-05905-8

Keywords