Objective To analyze risk factors for occupational blood-borne exposures among healthcare workers, providing a basis for timely and targeted interventions to prevent occupational blood-borne exposure.
Methods The study included 103 healthcare workers who reported occupational blood-borne exposures from January 2016 to June 2023 at a tertiary hospital in Hulunbuir as the exposure group and 625 healthcare workers from the same shifts who did not experience occupational exposures as the control group. Relevant information was collected for all subjects. Random Forest algorithms and multivariate logistic regression analysis were used to construct predictive models for occupational blood-borne exposure among healthcare workers.
Results Among the 103 blood-borne exposure cases, the main exposure site was the hand in 89 cases (86.41%); the main mode of exposure was sharp force injury in 83 cases (80.58%); and the predominant exposure source was hepatitis B virus (HBV) in 65 cases (63.11%). Sixty-five cases (63.11%) were reported immediately after exposure, while 24 cases (23.30%) were reported within 12 hours. Logistic regression analysis revealed that compared to healthcare workers aged 30 years or more, likelihood of occupational blood-borne exposure increased to 4.142 times for those under 30 years (P < 0.05); compared with 5 or more years, the likelihood of occupational blood-borne exposure increased to 1.696 times for healthcare workers with less than 5 years of service (P < 0.05); the likelihood of occupational blood-borne exposure increased to 5.989 times for junior-title healthcare workers compared to intermediate and higher-level titles (P < 0.05); healthcare workers who attended occupational protection training only once per year were up to 1.864 times more likely to have occupational blood-borne exposure compared to those who participated multiple times annually (P < 0.05); the likelihood of occupational blood-borne exposure increased to 2.205 times for healthcare workers in the departmental categories of emergency, critical care, and operating room compared to those in other departments (P < 0.05). The Random Forest algorithm identified the top 6 influential factors in order of importance as professional title, age, years of work experience, annual training frequency of occupational protection, occupation type, and education level. The Random Forest predictive model demonstrated higher accuracy, precision, recall, and F1 score (the reconciled mean of precision and recall) compared to the logistic regression model. The area under the receiver operating characteristic curve (AUC) for the Random Forest model was 0.829 (P < 0.001), which was also higher than that of the logistic regression model of 0.818 (P < 0.001).
Conclusions The Random Forest model showed superior predictive performance for occupational blood-borne exposures, while the logistic regression model provided more intuitive analytical results. The combined use of both models can further enhance prediction accuracy. Enhanced training should be provided for high-risk groups, including younger healthcare workers, those with less work experience, and those with lower professional titles. Standardized measures should be implemented to prevent blood-borne exposure among healthcare workers.
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