首页> 外文期刊>La Medicina del lavoro >The OCRA method: updating of reference values and prediction models of occurrence of work-related musculo-skeletal diseases of the upper limbs (UL-WMSDs) in working populations exposed to repetitive movements and exertions of the upper limbs
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The OCRA method: updating of reference values and prediction models of occurrence of work-related musculo-skeletal diseases of the upper limbs (UL-WMSDs) in working populations exposed to repetitive movements and exertions of the upper limbs

机译:OCRA方法:更新暴露于上肢反复运动和劳累的工作人群中上肢与工作有关的肌肉骨骼疾病的参考值和预测模型

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BACKGROUND: The paper considers a database of old (already published) and new data concerning 23 groups of workers (Total number of subjects examined=5373) with different levels of exposure to repetitive movements of the upper limbs: for all these groups data were available regarding exposure indexes (OCRA index and Checklist "OCRA" score) and clinically determined UL-WMSD outcomes (PA=Prevalence of workers Affected by one or more UL- WMSDs; PC=Prevalence of single diagnosed Cases of an UL- WMSDs). OBJECTIVES: Using these data, the paper aimed at presenting and discussing the results obtained in order to estimate: new critical values of OCRA index for discriminating different exposure levels (green, yellow, red areas); new prediction models of expected PA and PC in exposed populations based on exposure indexes. METHODS: New critical values of the OCRA index (and, consequently, of the checklist score) were estimated by an original approach in which data of the effect variable PA in a reference population not exposed to the specific risks were combined with the regression function between OCRA and PA, as resulting from the 23 available groups. RESULTS: The resulting critical values and the consequent classification system of the OCRA index and of the checklist score are synthetically reported in the following table: [table: see text]. The best simple regression functions between exposure indexes (OCRA; checklist) and health outcome variables (PA; PC) were then sought, in order to obtain prediction models of effects starting from exposure. The following were the main prediction models derived from the available set of data (standard error of b in brackets): [formula: see text]. Finally, a multiple regression model was computed for estimating PA (Y) based on OCRA index and gender structure of the group (SEXRATIO=n. females x 100. total) with its 5 degrees and 95 degrees percentiles (in brackets); the resulting model was. Y = 2.02 (1.72-2.32) x OCRA + 0.075 (0.035-0.115) x SEXRATIO. This model showeda very high association between the two independent variables and the effect variable (PA) (R2=0.96). DISCUSSION: Discussion of the results obtained considers their intrinsic limits, as they are based on prevalence studies, and also suggests due recommendations and caution in the use of the proposed classification system and prediction models when the OCRA methods are applied for the evaluation of occupational risk associated with repetitive movements of the upper limbs.
机译:背景:本文考虑了涉及23组工人(检查对象总数= 5373)的上肢重复运动水平不同的旧数据(已发布)和新数据的数据库:对于所有这些数据组均可用关于暴露指数(OCRA指数和清单“ OCRA”分数)和临床确定的UL-WMSD结果(PA =受一个或多个UL-WMSD影响的工人患病率; PC =单个UL-WMSDs诊断病例的患病率)。目的:利用这些数据,本文旨在介绍和讨论所获得的结果,以便估计:OCRA指数的新临界值,用于区分不同的暴露水平(绿色,黄色,红色区域);基于暴露指数的暴露人群预期PA和PC的新预测模型。方法:通过原始方法估算OCRA指数的新临界值(以及因此得出的清单分数),其中将未暴露于特定风险的参考人群的影响变量PA数据与回归函数相结合。 OCRA和PA,来自23个可用组。结果:下表综合报告了OCRA指数和清单分数的最终临界值和随之而来的分类系统:[表:请参见文本]。然后寻求暴露指数(OCRA;清单)和健康结果变量(PA; PC)之间最好的简单回归函数,以获得从暴露开始的效应预测模型。以下是从可用数据集中得出的主要预测模型(括号中b的标准误差):[公式:请参见文本]。最后,根据OCRA指数和该性别组(SEXRATIO = n。女性x 100 / n。总数)分别为5度和95度百分位数(在方括号中),计算了多元回归模型以估算PA(Y);结果模型是。 Y = 2.02(1.72-2.32)x OCRA + 0.075(0.035-0.115)x SEXRATIO。该模型显示了两个独立变量与效果变量(PA)之间的高度关联(R2 = 0.96)。讨论:对所得结果的讨论考虑了其固有的局限性,因为它们基于患病率研究,并且还建议在将OCRA方法应用于职业风险评估时,在使用建议的分类系统和预测模型时应有适当的建议和谨慎与上肢的重复运动有关。

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