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

描述（由申请人提供）：根据温石棉相对安全的建议，最近有人呼吁重新评估石棉管理标准。温石棉相对安全的论点指出，虽然南卡罗来纳州一家使用温石棉的石棉纺织厂的工人患肺癌的几率很高，但加拿大的温石棉矿工却没有。对于这些不同的发现，一种可能的解释是温石棉暴露的致癌作用部分取决于纤维的大小和形态。北卡罗莱纳石棉纺织研究通过对四家以前未被研究的石棉纺织厂的一组工人的数据来解决这一假设。在NIOSH支持的一个项目（R01OH007803）中，我们重建了这些植物的历史暴露，应用新技术表征纤维的形态和大小分布，并进行了流行病学分析，揭示了相对于美国人口的肺癌显著过剩（SMR 1.95 95% CI 1.73-2.20）。这个应用程序是那个项目的竞争性延续。与最初的研究强调评估北卡罗来纳州石棉纺织工人的暴露相比，这一后续研究主要侧重于使用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。我们提出了一系列的分析，这些分析远远超出了原项目的计划。这些分析的动机是先前的研究结果强烈表明，接触石棉后肺癌风险会达到峰值，然后下降。我们在这里提出的分析采用了先进的建模方法来估计每单位暴露后随时间变化的肺癌发病率变化。如果暴露效应随时间变化，那么这些方法可以为风险评估提供信息，并获得在研究人群之间一致的结果。具体来说，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、s型和三次b样条潜伏期模型；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；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总分析。本文概述的工作将大大改善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。拟议的工作将大大改善美国温石棉纺织工人的历史队列研究得出的定量风险估计，并将提供有关纤维特征的急需信息，这些特征最能预测肺癌的过度风险。该结果可能有助于解释在温石棉暴露的队列中观察到的一些异质性。对北卡罗莱纳和南卡罗莱纳石棉纺织品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准确性，并提高表征暴露-时间-反应关联的能力。

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.