A Bayesian network model for analysis of causes and consequences of accidents

Document Type : Original Article

Author

Department of Mechanical and Industrial Engineering, College of Engineering and Computers in Al-Qunfudah, Umm Al-Qura University, Makkah 21955, Saudi Arabia

Abstract

Background: Accidents are chains of events that lead to identifiable injuries and illnesses. Among various traumas, traffic accidents have the highest mortality rate.
Objectives: The aim of this study was to examine history data for analysis of the causes and consequences of accidents in Kingdom of Saudi Arabia during 2016-2020.
Methods: To collect the necessary data, the researchers utilized the Saudi open data portal, named as the National e-Government Portal. The data on consequence, type, seasons, location, and gender were extracted from the database. To analyze the collected data, GeNIe academic software was employed to conduct Bayesian network analysis.
Results: In total, 106513 accidents occurred in the Kingdom of Saudi Arabia during 2016-2020. Among them, 40287 and 9492 cases had the consequences of injuries and deaths, respectively. Regarding a sensitivity analysis of accident types, the worst variations in accident consequences were related to accident types of collapse, drowning, and car accidents. Therefore, those could increase the probability of death consequence by 5%, 4%, and 3%, respectively. Regarding a sensitivity analysis of location, for west and east regions, the probability of death consequences decreased by 1%. Moreover, regarding a sensitivity analysis of the season, the probability of deaths decreased by 1% in the autumn season. Regarding a sensitivity analysis of gender, females could decrease the probability of deaths by 4%. Other factors could not make variation in the probability of deaths. 
Conclusions: These findings show most important accident types associated with death consequences are collapse, drowning, and car accidents. The locations of the west and east of the kingdom of Saudi Arabia, the season of autumn, and the gender of females could also decrease the death consequences.

Keywords

References

  1. Khatibi M, Bagheri H, Khakpash M, Movahed KZ. Prevalence and causes of hospitalization in victims admitted to emergency department of Imam Hossein hospital in Shahroud. Knowl Health 2006;2(3): 42-46.
  2. Beheshti MH, Amkani M, Zamani A, Tabrizi A, Jafari M. Investigating the Prevalence and Etiology of Accidents Recorded at Emergency Management Center of Gonabad City Using the Pareto Chart in 2018. Intern Med Today. 2020;27(1):48-61. doi:10.32598/hms.27.1.3348.1
  3. Nouhjah S, Ghanavatizadeh A, Eskandri N, Daghlavi M. Prevalence of non-fatal home injuries and its related factors among children attending health centers in Ahvaz: a pilot study. Hakim Res J. 2012;15(3):238-42.
  4. Snashall D. Occupational health in the construction industry. Scand J Work Environ Health. 2005:5-10.
  5. Hopkins A. Lessons from Longford: the Esso gas plant explosion: Cch Australia; 2000.
  6. Niyonkuru V. Computational Fluid Dynamics (CFD) for blood flow in cardiovascular medical devices and blood damage prediction. Novel Clin Med 2023;2(3):136-142. doi: 10.22034/ncm.2023.408048.1101
  7. Alizadeh F, Taghdisi M, Mirilavasani M. A study of the logical tree method of MORT and TRIPOD Beta in causal analysis of incident events by combining hierarchical model. J Health Safety Work. 2014;4(4):48-39.
  8. Pearson M, Hunt H, Garside R, Moxham T, Peters J, Anderson R. Preventing unintentional injuries to children under 15 years in the outdoors: a systematic review of the effectiveness of educational programs. Injury Prev. 2012;18(2):113-23. doi:10.1136/injuryprev-2011-040043 PMid:21890579 PMCid:PMC3311869
  9. Heidari SM, Naseri M, Akhavan H, Rafiee M, Rajaei M, Pouyanfar A. The Epidemiological Pattern of Childhood Injuries and Accidents among Iranian Children: A Systematic Review. Health Provid. 2022;1(3):119-29.
  10. Yadollahi M, Gholamzadeh S. Five-year forecasting deaths caused by traffic accidents in Fars Province of Iran. Bull Emerg Trauma. 2019; 7 (4):373. doi:10.29252/beat-070406 PMid:31858000 PMCid:PMC6911725
  11. World Health Organization Global status report on road safety 2018. World Health Organization: Geneva, Switzerland. 2018.
  12. Global action plan on physical activity 2018-2030: more active people for a healthier world: World Health Organization; 2019.
  13. Zamani M, Esmailian M, Mirazimi MS, Ebrahimian M, Golshani K. Cause and final outcome of trauma in patients referred to the emergency department: a cross sectional study. Iran J Emerg Med. 2014;1(1):22-7.
  14. Taylor JE, Alpass F, Stephens C, Towers A. Driving anxiety and fear in young older adults in New Zealand. Age Ageing. 2011;40(1):62-6. doi:10.1093/ageing/afq154 PMid:21087989
  15. Violence WHO, Prevention I, Organization WH. Global status report on road safety: time for action: World Health Organization; 2009.
  16. Victorino GP, Chong TJ, Pal JD. Trauma in the elderly patient. Arch Surg. 2003;138(10):1093-8. doi:10.1001/archsurg.138.10.1093 PMid:14557126
  17. Zhang J, Fraser S, Lindsay J, Clarke K, Mao Y. Age-specific patterns of factors related to fatal motor vehicle traffic crashes: focus on young and elderly drivers. Public Health. 1998; 112 (5):289-95. doi:10.1016/S0033-3506(98)00257-1 doi:10.1038/sj.ph.1900485 PMid:9807923
  18. Bucsuházy K, Matuchová E, Zůvala R, Moravcová P, Kostíková M, Mikulec R. Human factors contributing to the road traffic accident occurrence. Transport Res Procedia. 2020;45:555-61. doi:10.1016/j.trpro.2020.03.057
  19. Perdue PW, Watts DD, Kaufmann CR, Trask AL. Differences in mortality between elderly and younger adult trauma patients: geriatric status increases risk of delayed death. J Trauma Acute Care Surg. 1998; 45(4):805-10. doi:10.1097/00005373-199810000-00034 PMid:9783625
  20. Tornetta P, Mostafavi H, Riina J, Turen C, Reimer B, Levine R, et al. Morbidity and mortality in elderly trauma patients. J Trauma Acute Care Surg. 1999;46(4):702-6. doi:10.1097/00005373-199904000-00024 PMid:10217237
  21. Khoshakhlagh AH, Ghasemi M. Occupational Noise Exposure and Hearing Impairment among SpinningWorkers in Iran. Iranian Red Crescent Med J. 2017;19(5). doi:10.5812/ircmj.42712
  22. Szer I, Szer J, Kaszubska M, Miszczak J, Hoła B, Błazik-Borowa E, et al. Influence of the seasons on construction site accidents. Arch Civil Engin. 2021:489-504-489-504.
  23. AlRushaid MW, Saudagar AKJ. Measuring the data openness for the open data in Saudi Arabia e-government-a case study. Int J Adv Comput Sci Appl. 2016;7(12). doi:10.14569/IJACSA.2016.071215
  24. Elbadawi IA, editor The state of open government data in GCC countries. 12th European Conference on eGovernment (ECEG 2012); 2012: Barcelona, Spain.
  25. Sentz K, Ferson S. Combination of evidence in Dempster-Shafer theory: Citeseer; 2002. doi:10.2172/800792 PMid:11883569
  26. Mittal A, Kassim A. Bayesian network technologies: applications and graphical models: applications and graphical models: IGI global; 2007. doi:10.4018/978-1-59904-141-4
  27. Scanagatta M, Salmerón A, Stella F. A survey on Bayesian network structure learning from data. Progress Artif Intell. 2019;8(4):425-39. doi:10.1007/s13748-019-00194-y
  28. Liu X, Huang G, Huang H, Wang S, Xiao Y, Chen W. Safety climate, safety behavior, and worker injuries in the Chinese manufacturing industry. Saf Sci. 2015;78:173-8. doi:10.1016/j.ssci.2015.04.023
  29. Mohammadfam I, Ghasemi F, Kalatpour O, Moghimbeigi A. Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Appl Ergonom. 2017;58:35-47. doi:10.1016/j.apergo.2016.05.006 PMid:27633196
  30. Cao Y, Fang X, Ottosson J, Näslund E, Stenberg E. A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery. J Clin Med. 2019;8(5):668. doi:10.3390/jcm8050668 PMid:31083643 PMCid:PMC6571760
  31. Halabi Y, Xu H, Long D, Chen Y, Yu Z, Alhaek F, et al. Causal factors and risk assessment of fall accidents in the US construction industry: A comprehensive data analysis (2000-2020). Saf Sci. 2022;146:105537. doi:10.1016/j.ssci.2021.105537
  32. Zermane A, Tohir MZM, Zermane H, Baharudin MR, Yusoff HM. Predicting fatal fall from heights accidents using random forest classification machine learning model. Saf Sci. 2023;159: 106023. doi:10.1016/j.ssci.2022.106023
  33. Rafindadi ADu, Napiah M, Othman I, Mikić M, Haruna A, Alarifi H, et al. Analysis of the causes and preventive measures of fatal fall-related accidents in the construction industry. Ain Shams Engin J. 2022; 13(4):101712. doi:10.1016/j.asej.2022.101712
  34. Rostamzadeh S, Abouhossein A, Chalak MH, Vosoughi S, Norouzi R. An integrated DEMATEL-ANP approach for identification and prioritization of factors affecting fall from height accidents in the construction industry. Int J Occupat Saf Ergonom. 2023;29(2):474-83. doi:10.1080/10803548.2022.2052479 PMid:35272574
  35. Chau PH, Lau KK-L, Qian XX, Luo H, Woo J. Visits to the accident and emergency department in hot season of a city with subtropical climate: association with heat stress and related meteorological variables. Int J Biomet. 2022;66(10):1955-71. doi:10.1007/s00484-022-02332-z PMid:35900375 PMCid:PMC9330976
  36. Kazar G, Comu S. Exploring the relations between the physiological factors and the likelihood of accidents on construction sites. Engin Construct Arch Manag. 2022;29(1):456-75. doi:10.1108/ECAM-11-2020-0958
  37. Mohamed M, Bromfield NF. Attitudes, driving behavior, and accident involvement among young male drivers in Saudi Arabia. Transp Res Part F Traffic Psychol Behav 2017;47:59-71. doi:10.1016/j.trf.2017.04.009
  38. Cordellieri P, Baralla F, Ferlazzo F, Sgalla R, Piccardi L, Giannini AM. Gender effects in young road users on road safety attitudes, behaviors and risk perception. Front Psychol. 2016; 7:1412. doi:10.3389/fpsyg.2016.01412 PMid:27729877 PMCid:PMC5037216
  39. Laflamme L, Diderichsen F. Social differences in traffic injury risks in childhood and youth-a literature review and a research agenda. Injury Prev. 2000;6(4):293-8. doi:10.1136/ip.6.4.293 PMid:11144632 PMCid:PMC1730678
  40. Baker SP. The injury fact book: Oxford University Press, USA; 1992. doi:10.1093/oso/9780195061949.001.0001
  41. Roberts I, Norton R, Jackson R, Dunn R, Hassall I. Effect of environmental factors on risk of injury of child pedestrians by motor vehicles: a case-control study. BMJ. 1995;310(6972):91-4. doi:10.1136/bmj.310.6972.91 PMid:7833733 PMCid:PMC2548498
Archives of Trauma Research
Volume 13, Issue 1
March 2024
Pages 59-66

Files

History

  • Receive Date: 29 January 2024
  • Revise Date: 17 March 2024
  • Accept Date: 19 March 2024

Share

How to cite

Statistics