CHRYSOTILE AND LUNG CANCER: TIME-RELATED EFFECTS AND POOLED ANALYSIS

说明(申请人提供)：最近有人呼吁重新评估管理石棉的标准，因为有人建议温石棉是相对安全的。支持温石棉相对安全的论点指出，尽管南卡罗来纳州一家使用温石棉的石棉纺织厂的工人患上了大量的肺癌，但加拿大的温石棉矿工却没有。对这些不同发现的一个可能的解释是，温石棉暴露的致癌效应部分取决于纤维的大小和形态。北卡罗来纳州的石棉纺织研究用四家以前没有研究过的石棉纺织厂的一群工人的数据来解决这一假设。在NIOSH(R01OH007803)支持的一个项目中，我们重建了这些植物的历史暴露，应用新技术来表征纤维的形态和尺寸分布，并进行了流行病学分析，发现相对于美国人口，肺癌的发病率显著增加(SMR 1.95%CI 1.73-2.20)。这个应用程序是该项目的竞争性延续。与最初的研究强调评估北卡罗来纳州石棉纺织工人的接触情况不同，这项继续研究主要侧重于使用来自北卡罗来纳州队列和南卡罗来纳州石棉纺织工人队列的数据进行定量风险评估。我们提出了一系列分析，远远超出了原始项目的计划。这些分析的动机是先前的研究结果，这些发现强烈表明，在接触石棉后，肺癌风险达到峰值，然后下降。我们在这里提出的分析使用先进的建模方法来估计自暴露以来每单位暴露的肺癌比率随时间变化的估计。如果暴露效应随着时间的推移而变化，那么这种方法可以为风险评估提供信息，并获得研究人群之间一致的结果。具体地说，我们建议：1)应用灵活的统计模型来描述潜伏期效应，包括暴露时间窗、双线性、Sigmoid和三次B-Spline潜伏期模型；2)应用生物数学癌症模型作为暴露-时间-反应关联经验模型的补充；以及3)对北卡罗来纳州和南卡罗来纳州石棉纺织工人队列的暴露和流行病学数据进行汇集分析。这里概述的工作将大大改进从美国温石棉石棉纺织工人的历史队列研究中得出的定量风险估计。这项拟议的工作将大大改进从美国温石棉纺织工人的历史队列研究中得出的定量风险估计，并将提供关于纤维特征的迫切需要的信息，这些特征最能预测过度的肺癌风险。这一结果可能有助于解释在接触温石棉的人群中观察到的一些异质性。对北卡罗来纳州和南卡罗来纳州石棉纺织队列的暴露和流行病学数据进行汇总分析，将加强暴露评估和风险估计的精确度，并提高确定暴露-时间-反应关系的能力。

DESCRIPTION (provided by applicant): Recently there have been calls to reevaluate standards governing asbestos in light of suggestions that chrysotile asbestos is relatively safe. Arguments for the relative safety of chrysotile note that while workers in a South Carolina asbestos textile plant using chrysotile experienced substantial excesses of lung cancer, Canadian chrysotile miners have not. One possible explanation for these divergent findings is that the carcinogenic effect of chrysotile exposure is determined, in part, by fiber size and morphology. The North Carolina Asbestos Textile Study addresses this hypothesis with data on a cohort of workers at four previously unstudied asbestos textile plants. In a project supported by NIOSH (R01OH007803), we have reconstructed historical exposures in these plants, applied novel techniques to characterize the morphology and size distribution of fibers and carried out epidemiological analyses revealing a significant excess of lung cancer relative to the US population (SMR 1.95 95% CI 1.73-2.20). This application is a competing continuation of that project. In contrast to the original study's emphasis on assessing exposures among North Carolina asbestos textile workers, this continuation focuses primarily on quantitative risk assessments using data from the North Carolina cohort and from a cohort of South Carolina asbestos textile workers. We propose a series of analyses that go well beyond those planned for the original project. These analyses are motivated by prior findings that strongly suggest a peak, then decline, in lung cancer risk following asbestos exposure. The analyses we propose here employ advanced modeling approaches to produce estimates of the change in the rate of lung cancer per unit of exposure that vary with time since exposure. If an exposure effect varies over time, then such methods can inform risk assessments and obtain results that are consistent between study populations. Specifically, we propose to: 1) apply flexible statistical models to describe latency effects including exposure time-windows, bilinear, sigmoid, and cubic B-spline latency models; 2) apply biomathematical cancer models as a complement to the empirical models of exposure-time-response associations; and, 3) conduct pooled analyses of exposure and epidemiologic data from North Carolina and South Carolina asbestos textile worker cohorts. The work outlined here will substantially improve the quantitative risk estimates derived from historical cohort studies of US chrysotile asbestos textile workers. The proposed work will substantially improve quantitative risk estimates derived from historical cohort studies of US chrysotile asbestos textile workers and will provide much needed information concerning fiber characteristics most strongly predictive of excess lung cancer risk. The results may help to explain some of the heterogeneity that has been observed among cohorts exposed to chrysotile. Pooled analyses of exposure and epidemiologic data from North Carolina and South Carolina asbestos textile cohorts will strengthen the precision of exposure assessment and risk estimates and improve the ability to characterize exposure-time- response associations.