Predicting needlestick and sharps injuries and determining preventive strategies using a Bayesian network approach in Tehran, Iran
0
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
OBJECTIVES: Recent studies have shown that the rate of needlestick and sharps injuries (NSIs) is unacceptably high in Iranian hospitals. The aim of the present study was to use a systematic approach to predict and reduce these injuries. METHODS: This cross-sectional study was conducted in 5 hospitals in Tehran, Iran. Eleven variables thought to affect NSIs were categorized based on the Human Factors Analysis and Classification System (HFACS) framework and modeled using a Bayesian network. A self-administered validated questionnaire was used to collect the required data. In total, 343 cases were used to train the model and 50 cases were used to test the model. Model performance was assessed using various indices. Finally, using predictive reasoning, several intervention strategies for reducing NSIs were recommended. RESULTS: The Bayesian network HFACS model was able to predict 86% of new cases correctly. The analyses showed that safety motivation and fatigue were the most important contributors to NSIs. Supervisors’ attitude toward safety and working hours per week were the most important factors in the unsafe supervision category. Management commitment and staffing were the most important organizational-level factors affecting NSIs. Finally, promising intervention strategies for reducing NSIs were identified and discussed. CONCLUSIONS: To reduce NSIs, both management commitment and sufficient staffing are necessary. Supervisors should encourage nurses to engage in safe behavior. Excessive working hours result in fatigue and increase the risk of NSIs.
Abstract
OBJECTIVES: Recent studies have shown that the rate of needlestick and sharps injuries (NSIs) is unacceptably high in Iranian hospitals. The aim of the present study was to use a systematic approach to predict and reduce these injuries. METHODS: This cross-sectional study was conducted in 5 hospitals in Tehran, Iran. Eleven variables thought to affect NSIs were categorized based on the Human Factors Analysis and Classification System (HFACS) framework and modeled using a Bayesian network. A self-administered validated questionnaire was used to collect the required data. In total, 343 cases were used to train the model and 50 cases were used to test the model. Model performance was assessed using various indices. Finally, using predictive reasoning, several intervention strategies for reducing NSIs were recommended. RESULTS: The Bayesian network HFACS model was able to predict 86% of new cases correctly. The analyses showed that safety motivation and fatigue were the most important contributors to NSIs. Supervisors’ attitude toward safety and working hours per week were the most important factors in the unsafe supervision category. Management commitment and staffing were the most important organizational-level factors affecting NSIs. Finally, promising intervention strategies for reducing NSIs were identified and discussed. CONCLUSIONS: To reduce NSIs, both management commitment and sufficient staffing are necessary. Supervisors should encourage nurses to engage in safe behavior. Excessive working hours result in fatigue and increase the risk of NSIs.
참고문헌 (38)
- Tarigan LH, "Prevention of needle-stick injuries in healthcare facilities: a meta-analysis", Infect Control Hosp Epidemiol, 36, pp.823~829 (2015)
- Green B, "Psychiatric consequences of needlestick injury", Occup Med (Lond), 63, pp.183~188 (2013)
- Prüss-Ustün A, "Estimation of the global burden of disease attributable to contaminated sharps injuries among health-care workers", Am J Ind Med, 48, pp.482~490 (2005)
- Dilie A, "Occupational exposure to needle stick and sharp injuries and associated factors among health care workers in Awi Zone, Amhara Regional State, Northwest Ethiopia, 2016", J Environ Public Health, 2017, pp.2438713~ (2016)
- Nunes C, "Comparison of different methods for work accidents investigation in hospitals: a Portuguese case study", Work, 51, pp.601~609 (2015)
- Keller SM, "Effects of extended work shifts and shift work on patient safety, productivity, and employee health", AAOHN J, 57, pp.497~502 (2009)
- Clarke SP, "Effects of hospital staffing and organizational climate on needlestick injuries to nurses", Am J Public Health, 92, pp.1115~1119 (2002)
- Kasatpibal N, "Prevalence and risk factors of needlestick injuries, sharps injuries, and blood and body fluid exposures among operating room nurses in Thailand", Am J Infect Control, 44, pp.85~90 (2016)
- Smith DR, "Epidemiology of needlestick and sharps injuries among nurses in a Japanese teaching hospital", J Hosp Infect, 64, pp.44~49 (2006)
- Smith DR, "Organizational climate and its relationship with needlestick and sharps injuries among Japanese nurses", Am J Infect Control, 37, pp.545~550 (2009)
- Yao WX, "Occupational safety training and education for needlestick injuries among nursing students in China: intervention study", Nurse Educ Today, 33, pp.834~837 (2013)
- Mehdi Jahangiri, "Needle Stick Injuries and their Related Safety Measures among Nurses in a University Hospital, Shiraz, Iran", Safety and Health at Work, 7(1), pp.72~77 (2016), 한국산업안전보건공단 산업안전보건연구원[DOI]
- Ghasemi F, "Selecting strategies to reduce high-risk unsafe work behaviors using the safety behavior sampling technique and bayesian network analysis", J Res Health Sci, 17, pp.e00372~ (2017)
- Mirzaei Aliabadi M, "Analysis of human and organizational factors that influence mining accidents based on Bayesian network", Int J Occup Saf Ergon, pp.1~8 (2018)
- Martin JE, "A Bayesian network analysis of workplace accidents caused by falls from a height", Saf Sci, 47, pp.206~214 (2009)
- Zheng Z, "Toward a causal interpretation from observational data: a new bayesian networks method for structural models with latent variables", Inf Syst Res, 21, pp.365~391 (2010)
- Lee B, "Discovery and representation of causal relationships in MIS research: a methodological framework", MIS Q, 21, pp.109~136 (1997)
- Correa M, "Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process", Expert Syst Appl, 36, pp.7270~7279 (2009)
- Abolbashari MH, "Smart buyer: a Bayesian network modelling approach for measuring and improving procurement performance in organisations", Knowledge-Based Syst, 142, pp.127~148 (2018)
- Jensen FV, "Bayesian networks and decision graphs", pp.33~45 (2007), Springer
- Zhou Q, "A method to identify strategies for the improvement of human safety behavior by considering safety climate and personal experience", Saf Sci, 46, pp.1406~1419 (2008)
- Lawshe CH, "A quantitative approach to content validity 1", Pers Psychol, 28, pp.563~575 (1975)
- Waltz CF, "Nursing research: design statistics and computer analysis", pp.66~75 (1981), Davis FA
- Jitwasinkul B, "A Bayesian belief network model of organizational factors for improving safe work behaviors in Thai construction industry", Saf Sci, 82, pp.264~273 (2016)
- Ghasemi F, "A path analysis model for explaining unsafe behavior in workplaces: the effect of perceived work pressure", Int J Occup Saf Ergon, 24, pp.303~310 (2018)
- Pearl J, "Probabilistic reasoning in intelligent systems: networks of plausible inference", pp.10~25 (1988), Morgan Kaufmann
- "The human factors analysis and classification systeme-HFACS" (2000)[URL]
- Dempster AP, "Maximum likelihood from incomplete data via the EM algorithm", J R Stat Soc Series B (Methodol), 39, pp.1~38 (1977)
- Mohammadfam I, "Constructing a Bayesian network model for improving safety behavior of employees at workplaces", Appl Ergon, 58, pp.35~47 (2017)
- Marcot BG, "Metrics for evaluating performance and uncertainty of Bayesian network models", Ecol Modell, 230, pp.50~62 (2012)
- Dlamini WM, "A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland", Environ Model Softw, 25, pp.199~208 (2010)
- "Bayesian artificial intelligence" (2004)[URL]
- Seo D, "An explicative model of unsafe work behavior", Saf Sci, 43, pp.187~211 (2005)
- Cho E, "Factors associated with needlestick and sharp injuries among hospital nurses: a cross-sectional questionnaire survey", Int J Nurs Stud, 50, pp.1025~1032 (2013)
- Clarke SP, "Organizational climate, staffing, and safety equipment as predictors of needlestick injuries and near-misses in hospital nurses", Am J Infect Control, 30, pp.207~216 (2002)
- Smith DR, "Hospital safety climate, psychosocial risk factors and needlestick injuries in Japan", Ind Health, 48, pp.85~95 (2010)
- Fakhradin Ghasemi, "Surprising Incentive: An Instrument for Promoting Safety Performance of Construction Employees", Safety and Health at Work, 6(3), pp.227~232 (2015), 한국산업안전보건공단 산업안전보건연구원[DOI]
- Lo WY, "Long work hours and chronic insomnia are associated with needlestick and sharps injuries among hospital nurses in Taiwan: a national survey", Int J Nurs Stud, 64, pp.130~136 (2016)