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Improving the risk assessment capability of the revised NIOSH lifting equation by incorporating personal characteristics

机译:通过纳入个人特征,提高修订的Niosh提升方程的风险评估能力

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摘要

The impact of manual material handling such as lifting, lowering, pushing, pulling and awkward postures have been studied, and models using these external demands to assess risk of injury have been developed and employed by safety and health professionals. However, ergonomic models incorporating personal characteristics into a comprehensive model are lacking. This study explores the utility of adding personal characteristics such as the estimated L5/S1 Intervertebral Disc (IVD) cross-sectional area, age, gender and Body Mass Index to the Revised NIOSH Lifting Equation (RNLE) with the goal to improve risk assessment. A dataset with known RNLE Cumulative Lifting Indices (CLIs) and related health outcomes was used to evaluate the impact of personal characteristics on RNLE performance. The dataset included 29 cases and 101 controls selected from a cohort of 1022 subjects performing 667 jobs. RNLE risk assessment was improved by incorporation of personal characteristics. Adding gender and intervertebral disc size multipliers to the RNLE raised the odds ratio for a CLI of 3.0 from 6.71 (CI: 2.2-20.9) to 24.75 (CI: 2.8-215.4). Similarly, performance was either unchanged or improved when some existing multipliers were removed. The most promising RNLE change involved incorporation of a multiplier based on the estimated IVD cross-sectional area (CSA). Results are promising, but confidence intervals are broad and additional, prospective research is warranted to validate findings.
机译:已经研究了手动材料处理的影响,如提升,降低,推动,拉动和尴尬姿势,并且使用这些外部要求评估伤害风险的模型已经开发并受雇于安全和卫生专业人员。然而,缺乏将个人特征的人体工学模型纳入全面模型。本研究探讨了添加个人特征的效用,例如估计的L5 / S1椎间盘(IVD)横截面积,年龄,性别和体重指数,以改善风险评估的目标。具有已知RNLE累积提升指数(CLIS)和相关健康结果的数据集用于评估个人特征对RNLE性能的影响。数据集包括从执行667个工作的1022个科目的队列中选择的29个案例和101个控件。通过纳入个人特征,改善了Rnle风险评估。向RNLE添加性别和椎间盘尺寸倍增器提高了3.0的CLI的差距,从6.71(CI:2.2-20.9)到24.75(CI:2.8-215.4)。同样,当一些现有乘法器被移除时,性能不变或改善。最有前途的RNLE改变涉及基于估计的IVD横截面积(CSA)的乘法器掺入。结果很有希望,但置信区间是广泛的,额外的,前瞻性研究是有权验证的调查结果。

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