Barrero Jesús P, García-Herrero Susana, Mariscal Miguel A
University of Burgos, Faculty of Economic Sciences and Business Studies, Pza. de la Infanta Dª. Elena, s/n, 09001 Burgos, Spain.
University of Burgos, Higher Polytechnic School, Avda. Cantabria s/n, 09006 Burgos, Spain.
J Safety Res. 2022 Feb;80:428-440. doi: 10.1016/j.jsr.2021.12.025. Epub 2022 Jan 7.
This research relates the most important work-related factors affecting the development of hearing loss to the main methods used as medical assessment criteria in the diagnosis of occupational deafness. These criteria are the Speech Average Loss Index (SAL), the Early Loss Index (ELI) and the Percentage of Hearing Loss, and are applied to data obtained from audiograms performed on workers in occupational medical examinations.
Depending on the assessment method selected, these often return different results in grading an individual's hearing status and predicting how it will evolve. To address this problem, medical examinations (including audiograms) were carried out on a heterogeneous sample of 1,418 workers in Spain, from which demographic or personal data (gender, age, etc.), occupational data (noise level to which each individual is exposed, etc.) and other non-work-related factors (exposure to noise outside work, family history, etc.) were also gathered. Using Bayesian Networks, the conditional probability of an individual developing hearing loss was obtained taking into account all these factors and, specifically, noise level and length of service in the workplace. Sensitivity analyses were also carried out using the three scales (SAL, ELI and Percentage Hearing Loss Index), proving their suitability as tools the diagnosis and prediction of deafness. These networks were validated under the Receiver Operating Characteristic curve (ROC) criterion and in particular by the Area Under the Curve (AUC).
The results show that all three methods are deficient in so far as detecting preventive hearing problems related to noise in most workplaces.
The most restrictive methods for detecting possible cases of deafness are the SAL index and the Percentage Loss Index. The ELI index is the least restrictive of the three methods, but it is not able to discriminate the causes of hearing problems in an individual caused by exposure to noise, either by its intensity level or by the time of exposure to noise. Practical Applications: The use of the three methods in the field of occupational risk prevention is extremely limited and it seems reasonable to think that there is a need for the construction of new scales to correct or improve the existing ones.
本研究将影响听力损失发展的最重要工作相关因素与职业性耳聋诊断中用作医学评估标准的主要方法联系起来。这些标准是言语平均损失指数(SAL)、早期损失指数(ELI)和听力损失百分比,应用于职业医学检查中对工人进行听力图检查所获得的数据。
根据所选评估方法,在对个体听力状况进行分级以及预测其发展趋势时,这些方法往往会得出不同结果。为解决这一问题,对西班牙1418名工人的异质样本进行了医学检查(包括听力图检查),还收集了人口统计学或个人数据(性别、年龄等)、职业数据(每个人接触的噪声水平等)以及其他与工作无关的因素(工作外接触噪声情况、家族病史等)。使用贝叶斯网络,在考虑所有这些因素,特别是噪声水平和在工作场所的服务年限的情况下,得出个体发生听力损失的条件概率。还使用三种量表(SAL、ELI和听力损失百分比指数)进行了敏感性分析,证明了它们作为耳聋诊断和预测工具的适用性。这些网络在接受者操作特征曲线(ROC)标准下,特别是通过曲线下面积(AUC)进行了验证。
结果表明,就检测大多数工作场所中与噪声相关的预防性听力问题而言,这三种方法都存在不足。
检测可能的耳聋病例最严格的方法是SAL指数和损失百分比指数。ELI指数是这三种方法中限制最少的,但它无法区分个体因接触噪声,无论是其强度水平还是接触噪声时间而导致的听力问题的原因。实际应用:这三种方法在职业风险预防领域的应用极为有限,似乎有理由认为需要构建新的量表来纠正或改进现有量表。