UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525 (EPSP team- Environment and Health Prediction in Populations), Grenoble, France. ; Occupational and Environmental Diseases Centre, Grenoble Teaching Hospital (CHU Grenoble), Grenoble, France. ; The French National Occupational Diseases Surveillance and Prevention Network (RNV3P), France.
Saf Health Work. 2012 Jun;3(2):92-100. doi: 10.5491/SHAW.2012.3.2.92. Epub 2012 Jun 8.
The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease × exposure associations in RNV3P database (by analogy with the detection of potentially new health event × drug associations in the spontaneous reporting databases from pharmacovigilance).
2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease × exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease × exposure associations, are compared.
RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested.
This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.
法国国家职业疾病监测和预防网络（RNV3P）是一个由职业疾病专家组成的法国网络，它以标准化编码报告的形式收集所有病例，这些病例是由任何专科医生将患者转介到大学职业病中心，以确定所观察到的疾病与职业暴露之间的关系，而无需考虑与赔偿有关的法定考虑因素。目的是比较比例失调措施的相关性，这些措施在药物警戒中广泛用于检测 RNV3P 数据库中潜在的新疾病与暴露的关联（类似于从药物警戒中的自发报告数据库中检测潜在的新健康事件与药物的关联）。
使用了 RNV3P 2001-2009 年的数据（81132 例观察结果导致 11627 例疾病与暴露的关联）。比较了 RNV3P 数据库与药物警戒数据库的结构。测试了七种比例失调指标，并比较了它们的结果，特别是在对疾病与暴露的关联进行排名方面。
RNV3P 和药物警戒数据库具有相似的结构。频率论方法（比例报告比[PRR]，报告比值比[ROR]）和一种贝叶斯方法（称为“贝叶斯置信传播神经网络”[BCPNN]）在我们的数据上表现出相当相似的行为，而其他方法（如泊松分布）则不同。最后选择了 PRR 方法，因为更复杂的方法在 RNV3P 数据上并没有显示出更大的价值。因此，建议采用 PRR 方法检测信号的程序，自动进行排除已知关联的筛选，然后对这些信号进行调查。
在开展流行病学和/或实验研究之前，该程序可以作为生成假设的第一步。
