Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach
Published: 28 October 2021
Abstract Views: 1724
PDF: 648
Appendix: 186
HTML: 111
Appendix: 186
HTML: 111
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Pre-hospital care is provided by emergency medical services (EMS) staff, the initial health care providers at the scene of disaster. This study aimed to describe the characteristics of EMS callers and space-time distribution of emergency requests in a large urban area. Descriptive thematic maps of EMS requests were created using an empirical Bayesian smoothing approach. Spatial, temporal and spatio-temporal clustering techniques were applied to EMS data based on Kulldorff scan statistics technique. Almost 225,000 calls were registered in the EMS dispatch centre during the study period. Approximately two-thirds of these calls were associated with an altered level of patient consciousness, and the median response time for rural and urban EMS dispatches was 12.2 and 10.1 minutes, respectively. Spatio-temporal clusters of EMS requests were mostly located in central parts of the city, particularly near the downtown area. However, high-response time clustered areas had a low overlap with these general, spatial clusters. This low convergence shows that some unknown factors, other than EMS requests, influence the high-response times. The findings of this study can help policymakers to better allocate EMS resources and implement tailored interventions to enhance EMS system in urban areas.
Altmetrics
Downloads
Download data is not yet available.
Citations
Adin A, Lee D, Goicoa T , Ugarte M D, 2019. A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters. Stat Methods Med Res 28:2595-613. DOI: https://doi.org/10.1177/0962280218767975
Ahmadian L, Salehi F, Bahaadinbeigy K, 2020. Application of geographic information systems in maternal health: a scoping review. East Mediterr Health J 26:1403-14. DOI: https://doi.org/10.26719/emhj.20.095
Aringhieri R, Bruni ME, Khodaparasti S, Van Essen JT, 2017. Emergency medical services and beyond: Addressing new challenges through a wide literature review. Comput Oper Res 78:349-68. DOI: https://doi.org/10.1016/j.cor.2016.09.016
Arntz H-R, Müller-Nordhorn J, Willich SN, 2001. Cold Monday mornings prove dangerous: epidemiology of sudden cardiac death. Curr Opin Crit Care 7(3). DOI: https://doi.org/10.1097/00075198-200106000-00001
Azimi A, Bagheri N, Mostafavi SM, Furst MA, Hashtarkhani S, Amin FH, Eslami S, Kiani F, Vafaeinezhad R, Akbari T, Golabpour A , Kiani B, 2021. Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice. BMC Public Health 21:7. DOI: https://doi.org/10.1186/s12889-020-10064-1
Bahadori M, Nasiripur A, Tofighi S, Gohari M, 2010. Emergency medical services in Iran: An overview. Australas Med J 3:335-9. DOI: https://doi.org/10.4066/AMJ.2010.218
Bahrami MA, Maleki A, Ranjbar Ezzatabadi M, Askari R, Ahmadi Tehrani GH, 2011. Pre-hospital emergency medical services in developing countries: A case study about EMS response time in Yazd, Iran. Iran Red Crescent Med J 13:735-8.
Bassil KL, Cole DC, Moineddin R, Craig AM, Lou WY, Schwartz B, Rea E, 2009. Temporal and spatial variation of heat-related illness using 911 medical dispatch data. Environ Res 109:600-6. DOI: https://doi.org/10.1016/j.envres.2009.03.011
Berlin GN, Liebman JC, 1974. Mathematical analysis of emergency ambulance location. Socio-Econ Plan Sci 8:323-8. DOI: https://doi.org/10.1016/0038-0121(74)90036-6
Bidari A, Abbasi S, Farsi D, Saeidi H, Mofidi M, Radmehr M, Rezaei M , Ashayeri N, 2007. Quality assessment of prehospital care service in patients transported to Hazrat-E-Rasoul Akram Hospital. Med J Tabriz Univ Med Sci 29:43-6.
Bigdeli M, Khorasani-Zavareh D , Mohammadi R, 2010. Pre-hospital care time intervals among victims of road traffic injuries in Iran. A cross-sectional study. BMC Public Health 10:406. DOI: https://doi.org/10.1186/1471-2458-10-406
Blackwell TH, Kaufman JS, 2002. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med 9:288-95. DOI: https://doi.org/10.1197/aemj.9.4.288
De Carvalho AG, Guimaraes Luz JG, Leite Dias JV, Tiwari A, Steinmann P, Ignotti E, 2020. Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil. Geospat Health 15:892. DOI: https://doi.org/10.4081/gh.2020.892
Gruska M, Gaul GB, Winkler M, Levnaic S, Reiter C, Voracek M, Kaff A, 2005. Increased occurrence of out-of-hospital cardiac arrest on Mondays in a community-based study. Chronobiol Int 22:107-20. DOI: https://doi.org/10.1081/CBI-200041046
Haddadi M, Sarvar M, Soori H, Ainy E, 2017. The pattern of pre-hospital medical service delivery in Iran; a cross sectional study. Emerg Tehran 5:e57.
Hashtarkhani S, Kiani B, Bergquist R, Bagheri N, Vafaeinejad R, Tara M, 2020. An age-integrated approach to improve measurement of potential spatial accessibility to emergency medical services for urban areas. Int J Health Plann Manage 35:788-98. DOI: https://doi.org/10.1002/hpm.2960
Kafashpor A, Ghasempour Ganji SF, Sadeghian S, Johnson LW, 2018. Perception of tourism development and subjective happiness of residents in Mashhad, Iran. Asia Pac J Tour Res 23:521-31. DOI: https://doi.org/10.1080/10941665.2018.1476392
Kulldorff M, 1997. A spatial scan statistic. Commun Stat Theory Methods 26:1481-96. DOI: https://doi.org/10.1080/03610929708831995
Leonardsen ACL, Helgesen AK, Ulvøy L, Grøndahl VA, 2021. Prehospital assessment and management of postpartum haemorrhage-healthcare personnel’s experiences and perspectives. BMC Emerg Med 21:Article 98. DOI: https://doi.org/10.1186/s12873-021-00490-8
Mackenzie EJ, Rivara FP, Jurkovich GJ, Nathens AB, Frey KP, Egleston BL, Salkever DS, Scharfstein DO, 2006. A national evaluation of the effect of trauma-center care on mortality. N Engl J Med 354:366-78. DOI: https://doi.org/10.1056/NEJMsa052049
Manton KG, Woodbury MA, Stallard E, Riggan WB, Creason JP, Pellom AC, 1989. Empirical Bayes procedures for stabilizing maps of U.S. cancer mortality rates. J Am Stat Assoc 84:637-50. DOI: https://doi.org/10.1080/01621459.1989.10478816
Mawani M, Kadir M M, Azam I, Mehmood A, Mcnally B, Stevens K, Nuruddin R, Ishaq M , Razzak J A, 2016. Epidemiology and outcomes of out-of-hospital cardiac arrest in a developing country-a multicenter cohort study. BMC Emerg Med 16:Article 28. DOI: https://doi.org/10.1186/s12873-016-0093-2
Mena C, Sepulveda C, Fuentes E, Ormazabal Y , Palomo I, 2018. Spatial analysis for the epidemiological study of cardiovascular diseases: a systematic literature search. Geospat Health 13:587. DOI: https://doi.org/10.4081/gh.2018.587
Mohammadebrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, Pishgar E, Kiani B, 2021. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 21:1373. DOI: https://doi.org/10.1186/s12889-021-11326-2
Moller TP, Ersboll AK, Tolstrup JS, Ostergaard D, Viereck S, Overton J, Folke F, Lippert F, 2015. Why and when citizens call for emergency help: an observational study of 211,193 medical emergency calls. Scand J Trauma Resusc Emerg Med 23:Article 88. DOI: https://doi.org/10.1186/s13049-015-0169-0
Moraga P, Kulldorff M, 2016. Detection of spatial variations in temporal trends with a quadratic function. Stat Methods Med Res 25:1422-37. DOI: https://doi.org/10.1177/0962280213485312
Mueller LR, Donnelly JP, Jacobson KE, Carlson JN, Mann NC, Wang HE, 2016. National characteristics of emergency medical services in frontier and remote areas. Prehosp Emerg Care 20:191-9. DOI: https://doi.org/10.3109/10903127.2015.1086846
Naus JL, 1965. Clustering of random points in two dimensions. Biometrika 52:263-6. DOI: https://doi.org/10.1093/biomet/52.1-2.263
Nichol G, Thomas E, Callaway CW, Hedges J, Powell JL, Aufderheide TP, Rea T, Lowe R, Brown T, Dreyer J, Davis D, Idris A, Stiell I, 2008. Regional variation in out-of-hospital cardiac arrest incidence and outcome. J Am Med Assoc 300:1423-31. DOI: https://doi.org/10.1001/jama.300.12.1423
Nykiforuk CI, Flaman LM, 2011. Geographic information systems (GIS) for Health Promotion and Public Health: a review. Health Promot Pract 12:63-73. DOI: https://doi.org/10.1177/1524839909334624
Ong ME, Ng FS, Overton J, Yap S, Andresen D, Yong DK, Lim SH, Anantharaman V, 2009. Geographic-time distribution of ambulance calls in Singapore: utility of geographic information system in ambulance deployment (CARE 3). Ann Acad Med Singap 38:184-91.
Openshaw S, Taylor PJ, 1981. The modifiable areal unit problem. In: Wrigley N and Bennett R (Eds.), Quantitative geography: a British view. Routledge and Kegan Paul, London, UK, pp. 60-69.
Peters J, Hall GB, 1999. Assessment of ambulance response performance using a geographic information system. Social Sci Med 49:1551-66. DOI: https://doi.org/10.1016/S0277-9536(99)00248-8
Pringle DG, 1996. Mapping disease risk estimates based on small numbers: An assessment of empirical bayes techniques. Econ Social Rev 27:341-63.
Punyapornwithaya V, Sansamur C, Charoenpanyanet A, 2020. Epidemiological characteristics and determination of spatio-temporal clusters during the 2013 dengue outbreak in Chiang Mai, Thailand. Geospat Health 15(2). DOI: https://doi.org/10.4081/gh.2020.857
Raeissi P, 2012. The relationship between job characteristics of emergency medical technicians and scene time in traumatic injuries. IJMMS 4:186-91.
Robertson IG, 1999. Spatial and multivariate analysis, random sampling error, and analytical noise: empirical Bayesian methods at Teotihuacan, Mexico. Am Antiquity 137-52. DOI: https://doi.org/10.2307/2694350
Sariyer G, Ataman MG, Akay S, Sofuoglu T, Sofuoglu Z, 2017. An analysis of emergency medical services demand: time of day, day of the week, and location in the city. Turk J Emerg Med 17:42-7. DOI: https://doi.org/10.1016/j.tjem.2016.12.002
Schuurman N, Cinnamon J, Crooks VA, Hameed SM, 2009. Pedestrian injury and the built environment: an environmental scan of hotspots. BMC Public Health 9:233. DOI: https://doi.org/10.1186/1471-2458-9-233
Silva AEP, Chiaravalloti Neto F, Conceicao GMS, 2020. Leptospirosis and its spatial and temporal relations with natural disasters in six municipalities of Santa Catarina, Brazil, from 2000 to 2016. Geospat Health 15:903. DOI: https://doi.org/10.4081/gh.2020.903
Spaite DW, Maio R, Garrison HG, Desmond JS, Gregor MA, Stiell IG, Cayten CG, Chew JL Jr., Mackenzie EJ, Miller DR , O’malley PJ, 2001. Emergency medical services outcomes project (EMSOP) II: developing the foundation and conceptual models for out-of-hospital outcomes research. Ann Emerg Med 37:657-63. DOI: https://doi.org/10.1067/mem.2001.115215
Tabari P, Shabanikiya H, Bagheri N, Bergquist R, Hashtarkhani S, Kiani F, Mohammadi A, Kiani B, 2020. Paediatric, pedestrian road traffic injuries in the city of Mashhad in north-eastern Iran 2015-2019: a data note. BMC Res Notes 13:363. DOI: https://doi.org/10.1186/s13104-020-05203-1
Wang HE, Mann NC, Jacobson KE, Ms MD, Mears G, Smyrski K, Yealy DM, 2012. National characteristics of emergency medical services responses in the United States. Prehosp Emerg Care 17:8-14. DOI: https://doi.org/10.3109/10903127.2012.722178
Warden CR, Daya M, Legrady LA, 2007. Using geographic information systems to evaluate cardiac arrest survival. Prehosp Emerg Care 11:19-24. DOI: https://doi.org/10.1080/10903120601023461
Willich SN, Löwel H, Lewis M, Hörmann A, Arntz HR, Keil U, 1994. Weekly variation of acute myocardial infarction. Increased Monday risk in the working population. Circulation 90:87-93. DOI: https://doi.org/10.1161/01.CIR.90.1.87
Xia T, Song X, Zhang H, Song X, Kanasugi H, Shibasaki R, 2019. Measuring spatio-temporal accessibility to emergency medical services through big GPS data. Health Place 56:53-62. DOI: https://doi.org/10.1016/j.healthplace.2019.01.012
How to Cite
Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach. (2021). Geospatial Health, 16(2). https://doi.org/10.4081/gh.2021.1043
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Abdoreza Salahi-Moghaddam, Majid Habibi-Nokhandam, Márius V. Fuentes, Low-altitude outbreaks of human fascioliasis related with summer rainfall in Gilan province, Iran , Geospatial Health: Vol. 6 No. 1 (2011)
- Mathuros Tipayamongkholgul, Sunisa Lisakulruk, Socio-geographical factors in vulnerability to dengue in Thai villages: a spatial regression analysis , Geospatial Health: Vol. 5 No. 2 (2011)
- Paolo Mulatti, Nicola Ferrè, Tommaso Patregnani, Lebana Bonfanti, Stefano Marangon, Geographical information systems in the management of the 2009-2010 emergency oral anti-rabies vaccination of foxes in north-eastern Italy , Geospatial Health: Vol. 5 No. 2 (2011)
- Nicola Ferrè, Paolo Mulatti, Matteo Mazzucato, Monica Lorenzetto, Matteo Trolese, Dario Pandolfo, Piero Vio, Guido Sitta, Stefano Marangon, GeoCREV: veterinary geographical information system and the development of a practical sub-national spatial data infrastructure , Geospatial Health: Vol. 5 No. 2 (2011)
- Norisse Tellman, Eric R. Litt, Caprice Knapp, Aaron Eagan, Jing Cheng, Lewis J. Jr Radonovich, The effects of the Health Insurance Portability and Accountability Act privacy rule on influenza research using geographical information systems , Geospatial Health: Vol. 5 No. 1 (2010)
- Sravan Kakani, A. Desirée LaBeaud, Charles H. King, Planning for Rift Valley fever virus: use of geographical information systems to estimate the human health threat of white-tailed deer (Odocoileus virginianus)-related transmission , Geospatial Health: Vol. 5 No. 1 (2010)
- Abdoreza Salahi-Moghaddam, Mehdi Mohebali, Ali Moshfae, Majid Habibi, Zabiholah Zarei, Ecological study and risk mapping of visceral leishmaniasis in an endemic area of Iran based on a geographical information systems approach , Geospatial Health: Vol. 5 No. 1 (2010)
- Gouri S. Bhunia, Shreekant Kesari, Algarsamy Jeyaram, Vijay Kumar, Pradeep Das, Influence of topography on the endemicity of Kala-azar: a study based on remote sensing and geographical information system , Geospatial Health: Vol. 4 No. 2 (2010)
- Leonard E. G. Mboera, Kesheni P. Senkoro, Benjamin K. Mayala, Susan F. Rumisha, Rwehumbiza T. Rwegoshora, Malongo R. S. Mlozi, Elizabeth H. Shayo, Spatio-temporal variation in malaria transmission intensity in five agro-ecosystems in Mvomero district, Tanzania , Geospatial Health: Vol. 4 No. 2 (2010)
- Benjamin G. Jacob, Fiorella Krapp, Mario Ponce, Eduardo Gotuzzo, Daniel A. Griffith, Robert J. Novak, Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space , Geospatial Health: Vol. 4 No. 2 (2010)
You may also start an advanced similarity search for this article.
