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The experience paradox: a 12-year analysis reveals bimodal occupational exposure risk in a high-consequence
1Department of Hospital Infection Management, Qishan Hospital of Yantai, Yantai City, Shandong Province, People's Republic of China.
Background:
Conventional occupational exposure models neglect experience-dependent risk nuances in infectious disease hospitals, leading to persistent prevention gaps. This 12-year study redefines occupational exposure epidemiology in high-consequence settings.
Methods:
We conducted a retrospective analysis of all 124 documented occupational exposure incidents from 2013 to 2024. To standardize risk assessment across a fluctuating workforce, we introduced 'exposure density' (events per 1000 healthcare workers) as a novel key metric. Statistical comparisons utilized χ2 tests (P<0.05).
Findings:
Our analysis revealed a striking 'experience paradox': occupational exposure risk follows a distinct bimodal curve. The highest exposure density occurred in early-career staff with less than 2 years of experience (81.90‰), driven by procedural errors. Paradoxically, risk resurged significantly in senior staff with over 10 years of experience (17.89‰), linked to complacency and burnout. This pattern was amplified by temporal peaks coinciding with the COVID-19 pandemic onset (2020) and post-policy transition (2023). Sharps injuries from hollow-bore needles were the primary mechanism (89.52%), with 66.67% attributable to operator error and 59.68% occurring without gloves. Critical system failures were evident, including a 54.54% underreporting rate among re-exposed staff and low post-exposure prophylaxis (PEP) uptake for unknown-source exposures (52.38%), largely due to financial barriers.
Conclusion:
Occupational safety in high-consequence settings is not a linear function of experience. Both novice enthusiasm and veteran fatigue represent critical vulnerability points. We call for a paradigm shift from one-size-fits-all protocols to an experience-stratified safety model. Key actions include: (1) national PEP subsidies; (2) institution-level AI-training for novices and burnout screening for veterans; (3) resource targeting to high-risk units (hepatology/medical intensive care).
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