Hobson Angela, Seixas Noah, Sterling David, Racette Brad A
Department of Neurology, Washington University, St Louis, MO 63116, USA.
Ann Occup Hyg. 2011 Jan;55(1):113-25. doi: 10.1093/annhyg/meq069. Epub 2010 Sep 24.
Welders are frequently exposed to Manganese (Mn), which may increase the risk of neurological impairment. Historical exposure estimates for welding-exposed workers are needed for epidemiological studies evaluating the relationship between welding and neurological or other health outcomes. The objective of this study was to develop and validate a multivariate model to estimate quantitative levels of welding fume exposures based on welding particulate mass and Mn concentrations reported in the published literature.
Articles that described welding particulate and Mn exposures during field welding activities were identified through a comprehensive literature search. Summary measures of exposure and related determinants such as year of sampling, welding process performed, type of ventilation used, degree of enclosure, base metal, and location of sampling filter were extracted from each article. The natural log of the reported arithmetic mean exposure level was used as the dependent variable in model building, while the independent variables included the exposure determinants. Cross-validation was performed to aid in model selection and to evaluate the generalizability of the models.
A total of 33 particulate and 27 Mn means were included in the regression analysis. The final model explained 76% of the variability in the mean exposures and included welding process and degree of enclosure as predictors. There was very little change in the explained variability and root mean squared error between the final model and its cross-validation model indicating the final model is robust given the available data.
This model may be improved with more detailed exposure determinants; however, the relatively large amount of variance explained by the final model along with the positive generalizability results of the cross-validation increases the confidence that the estimates derived from this model can be used for estimating welder exposures in absence of individual measurement data.
焊工经常接触锰(Mn),这可能会增加神经功能受损的风险。在评估焊接与神经或其他健康结果之间的关系时,流行病学研究需要对焊接暴露工人的既往暴露情况进行估计。本研究的目的是开发并验证一个多变量模型,该模型基于已发表文献中报告的焊接颗粒物质量和锰浓度来估计焊接烟尘暴露的定量水平。
通过全面的文献检索,识别出描述现场焊接活动期间焊接颗粒物和锰暴露情况的文章。从每篇文章中提取暴露的汇总指标以及相关决定因素,如采样年份、进行的焊接工艺、使用的通风类型、封闭程度、母材和采样过滤器的位置。在模型构建中,将报告的算术平均暴露水平的自然对数用作因变量,而自变量包括暴露决定因素。进行交叉验证以辅助模型选择并评估模型的可推广性。
回归分析共纳入了33个颗粒物均值和27个锰均值。最终模型解释了平均暴露量中76%的变异性,并将焊接工艺和封闭程度作为预测因子。最终模型与其交叉验证模型之间在解释的变异性和均方根误差方面变化很小,这表明在现有数据的情况下,最终模型是稳健的。
该模型可通过更详细的暴露决定因素得到改进;然而,最终模型解释的相对大量的方差以及交叉验证的积极可推广性结果增加了人们的信心,即从该模型得出的估计值可用于在没有个体测量数据的情况下估计焊工的暴露情况。