Predictive variables of serious victims, critical or deceased in traffic accidents in Extremadura, Spain

e201911069

Authors

  • José Antonio Morales-Gabardino Unidad Medicalizada de Emergencias de Cabeza del Buey. Servicio Extremeño de Salud. Badajoz. España.
  • Laura Redondo-Lobato Centro de Salud de Solana de los Barros. Servicio Extremeño de Salud. Badajoz. España.
  • Francisco Buitrago-Ramírez Centro de Salud Universitario La Paz. Servicio Extremeño de Salud. Facultad de Medicina. Universidad de Extremadura. Badajoz. España.

Keywords:

Traffic accidents, Casualties, Mortality, Emergency health services

Abstract

Background: Traffic accidents constitute a public health problem and are the leading cause of accidental death in the world. Analyze if the type of accident, the age of the victim or the attention provided by the emergency medicalized units (UME) are related to the morbidity and mortality due to traffic accidents in Extremadura (Spain) during the years 2012, 2013, 2014 and 2015.

Methods: Descriptive study of the information in the records of the emergency response coordination center 112. A multivariate analysis was carried out. The prognostic status was introduced as a dependent variable and the type of accident, the age of the accident victims and the UME as independent variables.

Results: The type of accident [odds ratio (OR)=1.745; 95% confidence interval (95% CI=1.488-2.045), the victim’s age (OR=1.016; 95% CI=1.013-1.020), UME 4-3 (OR=4.304; 95% CI=2.158-8.587), UME 4-1 (OR=2.463; 95% CI=1.414-4.291) and UME 1-4 (OR=1.990; 95% CI=1.052-3.762) are related to the prognostic status of the victims.

Conclusions: Inter-urban traffic accidents, the victim’s age and three UME influence the prognostic status of the victims.

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References

World Health Organization, Global Status Report on Road Safety 2015. (Consultado 30 marzo 2019). Disponible en http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/.

Instituto Nacional de Estadística. Informes sobre la mortalidad de los años 2012, 2013, 2014 y 2015. (Consultado 30 marzo 2019). Disponible http://www.ine.es.

Smith AP. A UK survey of driving behaviour, fatigue, risk taking and road traffic accidents. BMJ Open 2016; http://dx.doi.org/10.1136/bmjopen-2016-011461.

Protocolo de actuación y buenas prácticas en la atención sanitaria inicial al accidentado de tráfico. Grupo de Trabajo de la Sociedad Española de Urgencias y Emergencias (SEMES). Ministerio de Sanidad y Política Social. Gobierno de España. Año 2010 (Consultado 30 marzo 2019). Disponible en http://www.mscbs.gob.es/novedades/docs/bpAccidentadoTrafico.pdf.

Informe de carreteras por provincias, 2015. Ministerio de Fomento. (Consultado 30 marzo 2019). Disponible en https://www.fomento.gob.es/MFOM.CP.Web/handlers/pdfhandler.ashx?idpub=BTW031.

Instituto Nacional de Estadística. Censo nacional y Comunidad Autónoma de Extremadura. Año 2015. (Consultado 30 marzo 2019). Disponible en http://www.ine.es.

Morales-Gabardino JA, Redondo-Lobato L, Buitrago-Ramírez F. Análisis de tiempos de las unidades medicalizadas de emergencia en la atención a los accidentes de tráfico en Extremadura. Emergencias 2018; 30:265-7.

Fredriksson R, Bylund PO, Oman M. Fatal Vehicle-to-Bicyclist Crashes in Sweden - an In-Depth Study of injuries and vehicle sources. Ann Adv Automot Med 2012; 56:25-30.

Yang CS, Chen SC, Yang YC, Huang LC, Guo HR, Yang HY. Epidemiology and patterns of facial fractures due to road traffic accidents in Taiwan. A 15 years retrospective study. Traffic Inj Prev. 2017; 18:724-9.

Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med 2002; 9:288–95.

Jaja BN, Eghwrudjakpor PO. Effect of demographic and injury etiologic factors on intensive care unit mortality after severe head injury in a low middle income country. Ann Afr Med. 2014; 13:204-9.

Dong C, Clarke DB, Yan X, Khattak A, Huang B. Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections. Accid Anal Prev. 2014; 70:320-9.

Published

2019-11-15

How to Cite

1.
Morales-Gabardino JA, Redondo-Lobato L, Buitrago-Ramírez F. Predictive variables of serious victims, critical or deceased in traffic accidents in Extremadura, Spain: e201911069. Rev Esp Salud Pública [Internet]. 2019 Nov. 15 [cited 2026 Apr. 2];93:7 páginas. Available from: https://ojs.sanidad.gob.es/index.php/resp/article/view/1187

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