Faculty of Health, University of Newcastle, NSW, Australia.
Hear Res. 2012 Mar;285(1-2):65-76. doi: 10.1016/j.heares.2012.01.007. Epub 2012 Feb 3.
This paper introduces a new method to calculate relative risks of elevated hearing thresholds, at various ages and frequencies, between a study population and ISO1999:2003: Annex A Screened, Annex B Unscreened and ISO1999 Section 5.3 adjustment for noise exposure using Annex A Screened data. We demonstrate this method on a study population of male Royal Australian Air Force personnel.
Using a retrospective cohort design, hearing thresholds were assessed in 583 F-111 aircraft maintenance personnel, 377 technical-trade comparisons and 492 non-technical comparisons using pure-tone audiometry. A quantile regression model was used determine whether an association exists between median hearing thresholds and F-111 maintenance, adjusting for possible confounders. The new method involves using quantile regression models with bootstrapped standard errors to estimate percentiles for the study population and thus determine the probability of a greater than 25 dB hearing threshold. This was done for the three ISO datasets as follows; for the ISO1999 Annex A screened population data the formula provided allows the calculation of these probabilities. ISO1999 Annex B unscreened population data only provides the values for the 10th, 50th and 90th percentiles at ages 30, 40, 50 and 60 only, therefore it was necessary to fit a curve to these values in order to estimate the probabilities. For ISO1999 Section 5.3 adjustment for noise exposure population we used the Annex A screened population data plus the formula. The probabilities were then divided to give the relative risks of a greater than 25 dB hearing threshold, at various ages and frequencies.
While no difference was observed between the three groups, the model identified a number of significant confounders, namely tinnitus, smoking, diabetes and the use of anti-depressant medications. Relative risks were high at frequencies 2 kHz and less for the study population of all ages compared to ISO A screened data. The increased relative risks at 4 and 6 kHz give the appearance of a "noise notch" for ages 30 and 40 years. The comparison with the ISO B unscreened data are significantly less than one for frequencies above 2 kHz, particularly for young men and greater than one less than 2 kHz. The relative risks for the comparison to the ISO A screened data with ISO 5.3 adjustments, are highest for young men decreasing with age, with the highest relative risk are at frequencies less than 2 kHz.
This paper demonstrates a new method for quantifying the probability of a clinically relevant hearing loss and the relative risk of the loss due to a risk factor. Prior to this, researchers were reduced to simplistic methods such as visual comparison of deciles which did not enable the estimation of risk. The new method can use all observed hearing thresholds per study participant, adjust for known confounding factors such age and gender, and calculate the relative risk of a clinically relevant increase in hearing threshold due to a risk factor of interest.
本文介绍了一种新方法,用于计算研究人群与 ISO1999:2003:附件 A 筛选、附件 B 未筛选和 ISO1999 第 5.3 节调整噪声暴露的 Annex A 筛选数据之间,在不同年龄和频率下升高的听力阈值的相对风险。我们以男性澳大利亚皇家空军人员的研究人群为例演示了这种方法。
使用回顾性队列设计,使用纯音听力计对 583 名 F-111 飞机维修人员、377 名技术贸易比较和 492 名非技术比较进行听力阈值评估。使用分位数回归模型确定中位数听力阈值与 F-111 维修之间是否存在关联,同时调整可能的混杂因素。新方法涉及使用带有自举标准误差的分位数回归模型来估计研究人群的百分位数,从而确定大于 25 dB 听力阈值的概率。这是按照以下三种 ISO 数据集进行的;对于 ISO1999 附件 A 筛选的人群数据,提供的公式允许计算这些概率。ISO1999 附件 B 未筛选的人群数据仅在 30、40、50 和 60 岁时提供第 10、50 和 90 百分位的值,因此需要拟合曲线这些值,以便估计概率。对于 ISO1999 第 5.3 节调整噪声暴露人群,我们使用 Annex A 筛选的人群数据加公式。然后将概率进行划分,以给出不同年龄和频率下大于 25 dB 听力阈值的相对风险。
虽然三个组之间没有观察到差异,但该模型确定了一些显著的混杂因素,即耳鸣、吸烟、糖尿病和使用抗抑郁药物。与 ISO A 筛选数据相比,所有年龄组的研究人群在 2 kHz 及以下频率的相对风险较高。30 岁和 40 岁时,4 kHz 和 6 kHz 的相对风险增加出现了“噪声缺口”。与 ISO B 未筛选数据的比较明显低于 2 kHz 以上频率,尤其是年轻男性和低于 2 kHz 以上频率。与 ISO A 筛选数据与 ISO 5.3 调整的比较,年轻男性的相对风险最高,随年龄增长而降低,最高相对风险出现在 2 kHz 以下频率。
本文展示了一种用于量化临床相关听力损失概率和由于风险因素导致损失的相对风险的新方法。在此之前,研究人员只能使用简单的方法,例如十分位的视觉比较,无法估计风险。新方法可以使用每个研究参与者的所有观察到的听力阈值,调整年龄和性别等已知混杂因素,并计算由于感兴趣的风险因素导致临床相关听力阈值升高的相对风险。