Applying two general population job exposure matrices to predict incident carpal tunnel syndrome: A cross-national approach to improve estimation of workplace physical exposures.
Applying two general population job exposure matrices to predict incident carpal tunnel syndrome: A cross-national approach to improve estimation of workplace physical exposures.
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应用两个一般人群工作暴露矩阵来预测腕管综合症事件：一种改进工作场所物理暴露估计的跨国方法。
DOI：
10.5271/sjweh.3855
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发表时间：
2020-05-01
期刊：
影响因子：
--
通讯作者：
Dale AM
中科院分区：
文献类型：
--
作者：
Yung M;Evanoff BA;Buckner-Petty S;Roquelaure Y;Descatha A;Dale AM
工作接触矩阵是一种估计工人接触职业性身体危险因素的工具。我们在一项美国前瞻性队列研究中评估了两种普通人群JEM（CONSTANCES和O*NET）检测已知疾病-疾病关系的性能。我们比较了来自三个数据源的暴露估计值，并探讨了将这两种JEM的暴露结合起来，或将每种JEM的暴露与个人水平的测量结合起来，是否可以提高腕管综合征（CTS）的预测。使用考克斯比例风险模型，我们评估了2393名工人使用JEM分配的和个人水平的测量暴露信息的体力工作暴露和事件CTS之间的关系。我们使用斯皮尔曼的等级相关和科恩的kappa比较暴露估计。我们使用二项逻辑回归比较了组合暴露模型与单源暴露模型，并根据模型拟合和性能检查了差异。O*NET JEM [风险比（HR）范围1.3-2.01]与个体水平测量（HR范围1.00-1.42）显示了大致相似的风险-疾病相关性;我们发现与CONSTANCES JEM的相关性较低（HR范围1.08-2.05）。这三个来源之间的比较显示，在工作与工人层面上的相关性和一致性更强。与单源模型相比，组合模型提高了拟合优度，并具有较低的赤池信息标准（AIC）值。JEM可以在全国范围内应用，并且有可能将联合收割机互补暴露方法结合起来，以改善CTS预测中工作场所物理暴露的估计。需要进行更多的调查，以探索其他样本中的疾病关联以及不同方法的暴露数据组合。
A job exposure matrix (JEM) is a tool to estimate workers’ exposure to occupational physical risk factors. We evaluated the performance of two general population JEM (CONSTANCES and O*NET) to detect known exposure–disease relationships in an American prospective cohort study. We compared exposure estimates from three data sources and explored whether combining exposures from these two JEM, or combining exposure from each JEM with individual-level measures, improved prediction of carpal tunnel syndrome (CTS). Using Cox proportional hazard models, we evaluated relationships between physical work exposure and incident CTS of 2393 workers using JEM-assigned and individual-level measure exposure information. We compared exposure estimates using Spearman’s rank correlation and Cohen’s kappa. We compared combined exposure models to single source exposure models by using binomial logistic regression and examined differences based on model fit and performance. The O*NET JEM [hazard ratio (HR) range 1.3–2.01] demonstrated generally similar exposure–disease associations as individual-level measures (HR range 1.00–1.42); we found fewer associations with the CONSTANCES JEM (HR range 1.08–2.05). Comparisons between the three sources showed stronger correlations and agreement at the job versus worker level. Combined models improved goodness-of-fit and had lower Akaike information criterion (AIC) values compared to single-source models. JEM can be applied cross nationally and there is potential to combine complementary exposure methods to improve estimation of workplace physical exposures in the prediction of CTS. More investigations are needed to explore exposure-disease associations in other samples and combinations of exposure data from different methods.
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影响因子：
6.3
作者：
Hanvold, Therese Nordberg;Sterud, Tom;Mehlum, Ingrid Sivesind
通讯作者：
Mehlum, Ingrid Sivesind
影响因子：
6
作者：
Sauve, Jean-Francois;Lavoue, Jerome;Parent, Marie-Elise
通讯作者：
Parent, Marie-Elise
影响因子：
--
作者：
Rijs, Kelly J.;van der Pas, Suzan;Deeg, Dorly J. H.
通讯作者：
Deeg, Dorly J. H.
DOI：
10.1080/15428119791012793
发表时间：
1997-04-01
期刊：
AMERICAN INDUSTRIAL HYGIENE ASSOCIATION JOURNAL
影响因子：
--
作者：
Latko, WA;Armstrong, TJ;Ulin, SS
通讯作者：
Ulin, SS
影响因子：
4.9
作者：
Dale, Ann Marie;Ekenga, Christine C.;Evanoff, Bradley A.
通讯作者：
Evanoff, Bradley A.