Kapellusch Jay M, Bao Stephen S, Silverstein Barbara A, Merryweather Andrew S, Thiese Mathew S, Hegmann Kurt T, Garg Arun
a Department of Occupational Science & Technology , University of Wisconsin - Milwaukee , Milwaukee , Wisconsin.
b SHARP Program , Washington State Department of Labor and Industries , Olympia , Washington.
J Occup Environ Hyg. 2017 Dec;14(12):1011-1019. doi: 10.1080/15459624.2017.1366037.
The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Value for Hand Activity Level (TLV for HAL) use different constituent variables to quantify task physical exposures. Similarly, time-weighted-average (TWA), Peak, and Typical exposure techniques to quantify physical exposure from multi-task jobs make different assumptions about each task's contribution to the whole job exposure. Thus, task and job physical exposure classifications differ depending upon which model and technique are used for quantification. This study examines exposure classification agreement, disagreement, correlation, and magnitude of classification differences between these models and techniques.
Data from 710 multi-task job workers performing 3,647 tasks were analyzed using the SI and TLV for HAL models, as well as with the TWA, Typical and Peak job exposure techniques. Physical exposures were classified as low, medium, and high using each model's recommended, or a priori limits. Exposure classification agreement and disagreement between models (SI, TLV for HAL) and between job exposure techniques (TWA, Typical, Peak) were described and analyzed.
Regardless of technique, the SI classified more tasks as high exposure than the TLV for HAL, and the TLV for HAL classified more tasks as low exposure. The models agreed on 48.5% of task classifications (kappa = 0.28) with 15.5% of disagreement between low and high exposure categories. Between-technique (i.e., TWA, Typical, Peak) agreement ranged from 61-93% (kappa: 0.16-0.92) depending on whether the SI or TLV for HAL was used.
There was disagreement between the SI and TLV for HAL and between the TWA, Typical and Peak techniques. Disagreement creates uncertainty for job design, job analysis, risk assessments, and developing interventions. Task exposure classifications from the SI and TLV for HAL might complement each other. However, TWA, Typical, and Peak job exposure techniques all have limitations. Part II of this article examines whether the observed differences between these models and techniques produce different exposure-response relationships for predicting prevalence of carpal tunnel syndrome.
应变指数（SI）和美国政府工业卫生学家会议（ACGIH）手部活动水平阈限值（HAL的TLV）使用不同的构成变量来量化任务的身体暴露。同样，用于量化多任务工作中身体暴露的时间加权平均（TWA）、峰值和典型暴露技术，对每个任务对整个工作暴露的贡献做出了不同假设。因此，任务和工作的身体暴露分类取决于用于量化的模型和技术。本研究考察了这些模型和技术之间的暴露分类一致性、不一致性、相关性以及分类差异的大小。
使用SI和HAL的TLV模型以及TWA、典型和峰值工作暴露技术，对710名从事3647项任务的多任务工作者的数据进行了分析。根据每个模型推荐的或先验的限值，将身体暴露分为低、中、高三类。描述并分析了模型（SI、HAL的TLV）之间以及工作暴露技术（TWA、典型、峰值）之间的暴露分类一致性和不一致性。
无论采用何种技术，SI将更多任务分类为高暴露，而HAL的TLV将更多任务分类为低暴露。模型在48.5%的任务分类上达成一致（kappa = 0.28），在低暴露和高暴露类别之间有15.5%的不一致。技术之间（即TWA、典型、峰值）的一致性范围为61%-93%（kappa：0.16-0.92），具体取决于使用的是SI还是HAL的TLV。
SI与HAL的TLV之间以及TWA、典型和峰值技术之间存在不一致。这种不一致给工作设计、工作分析、风险评估和制定干预措施带来了不确定性。SI和HAL的TLV得出的任务暴露分类可能会相互补充。然而，TWA、典型和峰值工作暴露技术都有局限性。本文的第二部分考察了这些模型和技术之间观察到的差异是否会产生不同的暴露-反应关系，以预测腕管综合征的患病率。
