Analysis of the spatial distribution of road accidents attended by the Mobile Emergency Service (SAMU-192) in a municipality of northeastern Brazil

Cristine Viera do Bonfim; Aline Galdino Soares da Silva; Weinar Maria de Araújo; Carmela Alencar; Betise Mery Alencar Furtado

doi:10.18294/sc.2018.1211

Análisis de la distribución espacial de los accidentes de transporte terrestre atendidos por el Servicio Móvil de Urgencia (SAMU-192), en un municipio de la región nordeste de Brasil

Cristine Viera do Bonfim Doctora en Salud Pública. Investigadora titular, Fundação Joaquim Nabuco Profesora, Programa de Pós-Graduação en Saúde Coletiva, Universidade Federal de Pernambuco. Recife, Pernambuco, Brasil. , Aline Galdino Soares da Silva Diseñadora Gráfica. Núcleo en Geoprocesamiento, Secretaria de Saúde. Olinda, Pernambuco, Brasil. , Weinar Maria de Araújo Estudiante de Enfermería. Universidade de Pernambuco. Recife, Pernambuco, Brasil. , Carmela Alencar Estudiante de Maestría. Profesora, Fundação de Ensino Superior de Olinda. Recife, Pernambuco, Brasil. , Betise Mery Alencar Furtado Doctora en Ciencias. Profesora Adjunta, Universidade de Pernambuco. Recife, Pernambuco, Brasil

Recibido: 1 November 2016, Aceptado: 1 June 2017, Publicado: 11 April 2018

https://doi.org/10.18294/sc.2018.1211

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Resumen

Se describen las características epidemiológicas de las víctimas de accidentes de transporte terrestre atendidas por el Servicio Móvil de Urgencia (SAMU-192) y se localizan las áreas de mayor densidad de accidentes en el municipio de Olinda (Pernambuco, Brasil). Se empleó la estimación de densidad kernel para la detección de aglomerados espaciales de accidentes. En 2015 se registraron 724 accidentes. El 73,48% de las personas afectadas fueron del sexo masculino, y de entre 20 y 39 años de edad. Hubo un predominio de los accidentes con motocicletas (54,97%). Los aglomerados de accidentes se localizaron en las principales vías de tránsito y, los atropellamientos, cercanos a las terminales de ómnibus. El análisis espacial se mostró como un instrumento relevante para la identificación de los aglomerados de accidentes y una aplicación eficaz de las medidas de prevención y la mejora en la seguridad del tránsito vehicular.

Palabras clave: Accidentes de Tránsito, Servicios Médicos de Urgencia, Análisis Espacial, Brasil

Referencias bibliográficas

1. Khatib M, Gaidhane A, Quazi Z, Khatib N. Prevalence pattern of road traffic accidents in developing countries: a systematic review. International Journal of Medical Science and Public Health. 2015;4(10):1324-1333.

2. World Health Organization. Global status report on road safety 2015 ENT#091;InternetENT#093;. Italy; 2015 ENT#091;citado 20 oct 2016ENT#093;. Disponible en: https://tinyurl.com/oxk5ruv

3. Bougueroua M, Carnis L. Economic development, mobility and traffic accidents in Algeria. Accident Analysis and Prevention. 2016;92:168-174.

4. Chisholm D, Naci H, Hyder AA, Tran NT, Peden M. Cost effectiveness of strategies to combat road traffic injuries in sub-Saharan Africa and South East Asia: mathematical modelling study. British Medical Journal. 2012;344:e612.

5. Hadley KHW, Boikhutso N, Abdulgafoor MB, Hofman KJ, Hyder AA. The cost of injury and trauma care in lowand middle-income countries: a review of economic evidence. Health Policy and Planning. 2014;29(6):795-808.

6. Polinder S, Haagsma J, Bos N, Panneman M, Wolt KK, Brugmans M, Weijermars W, Beeck E. Burden of road traffic injuries: disability-adjusted life years in relation to hospitalization and the maximum abbreviated injury scale. Accident Analysis and Prevention. 2015;80:193-200.

7. Neto OLM et al. Mortality due to Road Traffic Accidents in Brazil in the last decade: trends and risk clusters. Ciência & Saúde Coletiva. 2012;17(9):2223-2236.

8. Mascarenhas MDM, Barros MBA. Characterization of hospitalizations due to external causes in the public health system, Brazil, 2011. Revista Brasileira de Epidemiologia. 2015;18(4):771-784.

9. Andrade L, Vissoci JRN, Rodrigues CG, Finato K, Carvalho E, et al. Brazilian Road Traffic Fatalities: a spatial and environmental analysis. PLoS One. 2014;9(1):e87244.

10. Cabral APS, Souza WV. Serviço de atendimento móvel de urgência (SAMU): análise da demanda e sua distribuição espacial em uma cidade do nordeste brasileiro. Revista Brasileira de Epidemiologia. 2008;11(4):530-540.

11. Harmsen AMK, Giannakopoulos GF, Moerbeek PR, Jansma EP, Bonjer HJ, Bloemers FW. The influence of prehospital time on trauma patients outcome: a systematic review. International Journal of the Care of the Injured. 2015;46(4):602-609.

12. Williamson K, Ramesh R, Grabinsky A. Advances in prehospital trauma care. International Journal of Critical Illness and Injury Science. 2011;1(1):44-50.

13. Paravar M, Hosseinpour M, Salehi S, Mohammadzadeh M, Shojaee A, Akbari H, et al. Pre-hospital trauma care in road traffic accidents in Kashan, Iran. Archives of Trauma Research. 2013;1(4):166-171.

14. McNicholl BP. The golden hour and prehospital trauma care. International Journal of Critical Illness and Injury Science. 1994;25(4):251-254.

15. McCoy CE, Menchine M, Sampson S, Anderson C, Kahn C. Emergency medical services out-of-hospital scene and transport times and their association with mortality in trauma patients presenting to an urban Level I trauma center. Annals of Emergency Medicine. 2013;61(2):167-174.

16. Meizoso JP, Valle EJ, Allen CJ, Ray JJ, Jouria JM, et al. Decreased mortality after prehospital interventions in severely injured trauma patients. The Journal of Trauma and Acute Care Surgery. 2015;79(2):227-231.

17. Richards TB, Croner CM, Rushton G, Brown CK, Fowler L. Geographic information systems and public health: mapping the future. Public Health Reports. 1999;114(4):359-373.

18. Krieger N. Place, space, and health: GIS and epidemiology. Epidemiology. 2003;14(4):384-385.

19. Ruankaew N. GIS and epidemiology. Journal of the Medical Association of Thailand. 2005;88(11):1735-1738.

20. Auchincloss AH, Gebreab SY, Mair C, Roux AVD. A review of spatial methods in epidemiology, 2000-2010. Annual Review of Public Health. 2012;33:107-122.

21. Shaw NT. Geographical information systems and health: current state and future directions. Healthcare Informatics Research. 2012;18(2):88-96.

22. Gómez-Barroso D, López-Cuadrado T, Llácer A, Suárez RP, Fernández-Cuenca R. Análisis espacial de los accidentes de tráfico con víctimas mortales en carretera en España, 2008-2011. Gaceta Sanitaria. 2015;29(1):24-29.

23. Anderson TK. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention. 2009;41(3):359-364.

24. Erdogan S. Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. Journal of Safety Research. 2009;40(5):341-351.

25. Soares RAS, Pereira APJT, Moraes RM, Vianna RPT. Caracterização das vítimas de acidentes de trânsito atendidas pelo Serviço de Atendimento Móvel de Urgência (SAMU) no Município de João Pessoa, Estado da Paraíba, Brasil, em 2010. Epidemiologia e Serviços de Saúde. 2012;21(4):589-600.

26. Blazquez CA, Celis MS. A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile. Accident Analysis & Prevention. 2013;50:304-311.

27. Leveau CM. Spatial variations in motorcycle registrations and the mortality of motorcycle users due to traffic injuries in Argentina. Salud Colectiva. 2013;9(3):353-362.

28. Lawrence BM, Stevenson MR, Oxley JA, Logan DB. Geospatial analysis of cyclist injury trends: an investigation in Melbourne, Australia. Traffic Injury Prevention. 2015;16(5):513-518.

29. Hashimoto S, Yoshiki S, Saeki R, Mimura Y, Ando R, Nanba S. Development and application of traffic accident density estimation models using kernel density estimation. Journal of Traffic and Transportation Engineering (English Edition). 2016;3(3):262-270.

30. Cabral APS, Souza WV. Serviço de atendimento móvel de urgência (SAMU): análise da demanda e sua distribuição espacial em uma cidade do nordeste brasileiro. Revista Brasileira de Epidemiologia. 2008;11(4):530-540.

31. Silverman BW. Density estimation for statistics and data analysis. In: Silverman BW. Monographs on statistics and applied probability. London: Chapman and Hall; 1986.

32. Bailey TC, Gatrell AC. Interactive spatial data analysis. England: Harlow Essex; 1995.

33. Shaw NT. Geographical information systems and health: current state and future directions. Healthcare Informatics Research. 2012;18(2):88-96.

34. Cai X, Wu Z, Cheng J. Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation. International Journal of Geographical Information Science. 2013;27(2):222-230.

35. Hsiao M, Malhotra A, Thakur JS, Sheth JK, Nathens AB, Dhingra N, Jha P. Road traffic injury mortality and its mechanisms in India: nationally representative mortality survey of 1.1 million homes. British Medical Journal Open. 2013;3(8):e002621.

36. Karkee R, Lee AH. Epidemiology of road traffic injuries in Nepal, 2001-2013: systematic review and secondary data analysis. British Medical Journal Open. 2016;6(4):e010757

37. Singh R, Singh HK, Gupta SC, Kumar Y. Pattern, severity and circumtances of injuries sustained in road traffic accidents: a tertiary care hospital-based study. Indian Journal of Community Medicine. 2014;39(1)30-34.

38. Pulido J, Barrio G, Hoyos J, Jiménez-Mejías E, Martín-Rodríguez Mdel M, Houwing S, Lardelli-Claret P. The role of exposure on differences in driver death rates by gender and age: results of a quasi-induced method on crash data in Spain. Accident Analysis & Prevention. 2016;94:162-167.

39. Russo F, Biancardo SA, Dell’Acqua G. Road safety from the perspective of driver gender and age as related to the injury crash frequency and road scenario. Traffic Injury Prevention. 2014;15(1):25-33.

40. Quitian-Reyes H, Gómez-Restrepo C, Gómez MJ, Naranjo S, Heredia P, Villegas JJ. Latin American clinical epidemiology network series - paper 5: years of life lost due to premature death in traffic accidents in Bogota, Colombia. Journal of Clinical Epidemiology. 2017;86:101-105.

41. Andrade SS, Mello-Jorge MH. Mortality and potential years of life lost by road traffic injuries in Brazil, 2013. Revista de Saúde Pública. 2016;50:59.

42. Almeida APB, Lima MLC, Oliveira Júnior FJM, Abath MB, Lima MLLT. Potential years of life lost because of road traffic accidents in Pernambuco state, Brazil, 2007. Epidemiologia e Serviços de Saúde. 2013;22(2):235-242.

43. Andrade LM, Lima MA, Silva CHC, Caetano JA. Acidentes de motocicleta: características das vítimas e dos acidentes em hospital de Fortaleza-Ce, Brasil. Revista da Rede de Enfermagem do Nordeste. 2009;10(4):52-59.

44. Leporati M, Salvo RA, Pirro V, Salomone A. Driving under the influence of alcohol. A 5-year overview in Piedmont, Italy. Journal of Forensic and Legal Medicine. 2015;34:104-108.

45. Brasil. Lei Nº 12.760, de 20 de dezembro de 2012 ENT#091;InternetENT#093;. Brasília; 2012 ENT#091;citado 21 mar 2017ENT#093;. Disponible en: https://tinyurl.com/y8z32alp

46. Malta DC, Berna RT, Silva MM, Claro RM, Silva Júnior JB, Reis AA. Consumption of alcoholic beverages, driving vehicles, a balance of dry law, Brazil 2007-2013. Revista de Saúde Pública. 2014;48(4):692-966.

47. Moafian G, Aghabeigi MR, Heydari ST, Hoseinzadeh A, Lankarani KB, Sarikhani Y. An epidemiologic survey of road traffic accidents in Iran: analysis of driver-related factors. Chinese Journal of Traumatology. 2013;16(3):140-144.

48. Estado de Pernambuco, Secretaria Estadual de Saúde Secretaria Executiva de Regulação em Saúde, Diretoria Geral de Fluxos Assistenciais. Central de Regulação de Leitos Manual Operacional ENT#091;InternetENT#093;. Recife; 2014 ENT#091;citado 21 mar 2017ENT#093;. Disponible en: https://tinyurl.com/y7f7y3ca

49. Auchincloss AH, Gebreab SY, Mair C, Roux AVD. A review of spatial methods in epidemiology, 2000-2010. Annual Review of Public Health. 2012;33:107-122.

50. Avci C, Durduran SS. Analysis of pedestrian accidents using a geographical information system (GIS) in Konya city, Turkey. WIT Transactions on The Built Environment. 2014;134:495-501.

51. Carvalho CHR. Mortes por acidentes de transporte terrestre no Brasil: análise dos sistemas de informação do ministério da saúde. Brasília: Instituto de Pesquisa Econômica Aplicada; 2016.

52. Marín-León L, Belon AP, Barros MBA, Almeida SDM, Restitutti MC. Trends in traffic accidents in Campinas, São Paulo State, Brazil: the increasing involvement of motorcyclists. Cadernos de Saúde Pública. 2012:28(1):39-51.

53. Diniz EPH, Pinheiro LC, Proietti FA. Quando e onde se acidentam e morrem os motociclistas em Belo Horizonte, Minas Gerais, Brasil. Cadernos de Saúde Pública. 2015;31(12):2621-2634.

54. Thakali L, Kwon TJ, Liping F. Identification of crash hotspots using kernel density estimationand kriging methods: a comparison. Journal of Modern Transportation. 2015;23(2):93-106.

55. Deshpande N, Chanda I, Arkatkar SS. Accident mapping and analysis using geographical information systems. International Journal of Earth Sciences and Engineering. 2011;4(6):342-345.