Improving the risk assessment capability of the revised NIOSH lifting equation by incorporating personal characteristics. (January 2019) - Barim, Menekse Salar; Sesek, Richard F.; Capanoglu, M. Fehmi; Drinkaus, Phil; Schall, Mark C.; Gallagher, Sean; Davis, Gerard A.
Abstract: 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. Highlights: To explore the utility of adding personal characteristics to the RNLE with the goal to improve risk assessment. To validate the RNLE by demonstrating a strong association between the CLI and the occurrence of negative health outcomes. To provide a simple method for adjusting the CLI for individuals with differing characteristics.