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. 2018:40:e2018042.
doi: 10.4178/epih.e2018042. Epub 2018 Aug 20.

Predicting needlestick and sharps injuries and determining preventive strategies using a Bayesian network approach in Tehran, Iran

Hamed Akbari  1 , Fakhradin Ghasemi  1 , Hesam Akbari  1 , Amir Adibzadeh  1
Affiliations

Predicting needlestick and sharps injuries and determining preventive strategies using a Bayesian network approach in Tehran, Iran

Hamed Akbari et al. Epidemiol Health. 2018.

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.

Keywords: Accident prevention; Bayes theorem; Iran; Needlestick injuries.

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Conflict of interest statement

The authors have no conflicts of interest to declare for this study.

Figures

Figure 1.
Figure 1.
The Human Factors Analysis and Classification System framework.
Figure 2.
Figure 2.
The Bayesian network model for analysis and predicting needlestick and sharps injuries (NSIs) after training the model (the values in the last row of each node represent mean±standard deviation).
Figure 3.
Figure 3.
The joint strategy for reducing needlestick and sharps injuries (NSIs) in hospitals (the values in the last row of each node represent mean±standard deviation).
See this image and copyright information in PMC

References

    1. Tarigan LH, Cifuentes M, Quinn M, Kriebel D. Prevention of needle-stick injuries in healthcare facilities: a meta-analysis. Infect Control Hosp Epidemiol. 2015;36:823–829. - PubMed
    1. Green B, Griffiths EC. Psychiatric consequences of needlestick injury. Occup Med (Lond) 2013;63:183–188. - PubMed
    1. Prüss-Ustün A, Rapiti E, Hutin Y. Estimation of the global burden of disease attributable to contaminated sharps injuries among health-care workers. Am J Ind Med. 2005;48:482–490. - PubMed
    1. Dilie A, Amare D, Gualu T. 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;2017:2438713. - PMC - PubMed
    1. Nunes C, Santos J, da Silva MV, Lourenço I, Carvalhais C. Comparison of different methods for work accidents investigation in hospitals: a Portuguese case study. Work. 2015;51:601–609. - PubMed

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