Suppr超能文献

Mat-O-Covid:利用全国职业 COVID-19 赔偿系统的数据验证 SARS-CoV-2 工作暴露矩阵(JEM)。

Mat-O-Covid: Validation of a SARS-CoV-2 Job Exposure Matrix (JEM) Using Data from a National Compensation System for Occupational COVID-19.

机构信息

Univ. Angers (University of Angers), CHU Angers, Univ. Rennes, Inserm, EHESP, IRSET (Institut de Recherche en Santé, Environnement et Travail)-UMR_S 1085, IRSET-ESTER, SFR ICAT, CAPTV CDC, F-49000 Angers, France.

Department of Occupational Medicine, Epidemiology and Prevention, Donald and Barbara Zucker School of Medicine, Hosftra University Northwell Health, New York, NY 11021, USA.

出版信息

Int J Environ Res Public Health. 2022 May 8;19(9):5733. doi: 10.3390/ijerph19095733.

Abstract

Background. We aimed to assess the validity of the Mat-O-Covid Job Exposure Matrix (JEM) on SARS-CoV-2 using compensation data from the French National Health Insurance compensation system for occupational-related COVID-19. Methods. Deidentified compensation data for occupational COVID-19 in France were obtained between August 2020 and August 2021. The case acceptance was considered as the reference. Mat-O-Covid is an expert-based French JEM on workplace exposure to SARS-CoV-2. Bi- and multivariable models were used to study the association between the exposure assessed by Mat-O-Covid and the reference, as well as the area under the curve (AUC), sensitivity, specificity, predictive values, and likelihood ratios. Results. In the 1140 cases included, there was a close association between the Mat-O-Covid index and the reference (p < 0.0001). The overall predictivity was good, with an AUC of 0.78 and an optimal threshold at 13 per thousand. Using Youden’s J statistic resulted in 0.67 sensitivity and 0.87 specificity. Both positive and negative likelihood ratios were significant: 4.9 [2.4−6.4] and 0.4 [0.3−0.4], respectively. Discussion. It was possible to assess Mat-O-Covid’s validity using data from the national compensation system for occupational COVID-19. Though further studies are needed, Mat-O-Covid exposure assessment appears to be accurate enough to be used in research.

摘要

背景

我们旨在使用法国国家健康保险因职业相关 COVID-19 而支付的补偿系统的数据,评估 Mat-O-Covid 新冠病毒职业暴露矩阵(JEM)对 SARS-CoV-2 的有效性。

方法

我们获得了 2020 年 8 月至 2021 年 8 月期间法国职业 COVID-19 的匿名补偿数据。病例的接受被视为参考标准。Mat-O-Covid 是一种基于专家的法国 SARS-CoV-2 工作场所暴露 JEM。我们使用双变量和多变量模型来研究 Mat-O-Covid 评估的暴露与参考标准之间的关联,以及曲线下面积(AUC)、敏感性、特异性、预测值和似然比。

结果

在纳入的 1140 例病例中,Mat-O-Covid 指数与参考标准之间存在密切关联(p < 0.0001)。总体预测性能良好,AUC 为 0.78,最佳阈值为 13/千。使用 Youden 的 J 统计量得出 0.67 的敏感性和 0.87 的特异性。阳性和阴性似然比均具有统计学意义:分别为 4.9 [2.4−6.4] 和 0.4 [0.3−0.4]。

讨论

使用国家职业 COVID-19 补偿系统的数据,评估 Mat-O-Covid 的有效性是可行的。尽管还需要进一步的研究,但 Mat-O-Covid 的暴露评估似乎足够准确,可以用于研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0975/9105377/d933d248607f/ijerph-19-05733-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验