Occupational Exposure to Asbestos: Effects of Unregulated Fibers

描述（由申请人提供）：石棉相关疾病仍然是美国和国外的一个主要公共卫生问题，这是由于建筑基础设施中的大量石棉储存以及该材料的持续全球贸易。在美国，石棉接触的职业标准是基于相差显微镜（PCM）对颗粒的视觉计数。只有足够长和粗的纤维才能使用该方法可靠地计数。然而，在大多数职业环境中，大多数石棉纤维太小，无法通过PCM计数。特别令人关注的是太细而不能使用PCM可见的长纤维，但据信它们是不可见的。 吸入后非常危险。如果基于PCM方法的石棉暴露估计与所有病因相关石棉纤维的总暴露严格成比例， 通过PCM测量的受管制的石棉暴露可能足以控制与病因相关的职业石棉暴露。然而，有强有力的证据表明，石棉纤维的比例，不计入PCM因工作活动，纤维质量和其他条件而异。因此，工人可能有相对较低的PCM为基础的指标，但高总石棉暴露，由于大部分的纤维太短或太薄，PCM计数。在这种情况下，工人面临的职业病的风险，由于石棉纤维的不可测量，不受管制的部分。工人及其拥护者一再提出这个问题，他们主张使用允许量化短（<5米）和细（<0.25米）纤维的方法和规则来管理石棉暴露，例如空气样品的透射电子显微镜（TEM）分析。然而，这方面的一个障碍是，大多数石棉工人的职业研究都是根据PCM对接触的估计对人进行分类的。监管机构指出，目前还不清楚什么水平的暴露，如TEM量化，将是可接受的，因为TEM估计的纤维总数可能与PCM估计的纤维计数没有很好的相关性。我们建议利用独特的现有数据，美国石棉工人的高质量的估计石棉暴露已来自PCM和纤维尺寸的具体估计暴露已来自TEM。我们建议：1）使用分层回归方法来估计纤维的未调节部分的影响; 2）使用G方法来估计将产生与根据当前OSHA标准获得的死亡率相当的死亡率的基于TEM的策略;以及3）使用G方法来估计将产生比根据当前OSHA标准获得的死亡率更少的死亡率的替代的基于TEM的策略。目标1将评估的影响，不受管制的部分纤维使用的方法显示，在相关的环境暴露的设置执行良好。目标2和3利用当代方法来估计公共卫生政策的影响，由于健康的工人幸存者效应容易受到偏见的设置。该项目涉及诺拉预防与工作有关的呼吸系统恶性肿瘤的跨部门优先事项，采用创新方法估计复杂暴露的影响。1

DESCRIPTION (provided by applicant): Asbestos-related diseases remain a major public health problem in the US and abroad due to the large reservoir of asbestos in built infrastructure and the continued global trade in the material. Occupational standards for asbestos exposure in the US are based on visual counting of particles by phase contrast microscopy (PCM). Only fibers long and thick enough to reliably count using that method are regulated. However, in most occupational settings the majority of asbestos fibers are too small to count by PCM. Of particular concern are long fibers that are too thin to be visible using PCM but are believed to be very hazardous when inhaled. If asbestos exposure estimates based upon PCM methods were strictly proportional to total exposure to all etiologically-relevant asbestos fibers then a policy that regulated asbestos exposure as measured by PCM might suffice to control etiologically-relevant occupational asbestos exposures. However, there is strong evidence that the proportion of asbestos fibers that are not counted by PCM varies by work activity, fiber quality, and other conditions. Therefore, workers may have relatively low PCM-based metrics but high total asbestos exposure due to a large fraction of fibers that are too short or thin to count by PCM. In such settings, workers face risk of occupational disease due to the unmeasured, unregulated fraction of asbestos fibers. This issue has been raised repeatedly by workers and their advocates, who have argued for regulating exposure to asbestos using methods and rules that permit quantification of short (<5 �m) and thin (<0.25 �m) fibers, such as transmission electron microscopy (TEM) analysis of air samples. However, one obstacle to this has been that most occupational studies of asbestos workers have classified people based on PCM estimates of exposure. Regulators noted that it was unclear what level of exposure, as quantified by TEM, would be acceptable, since TEM estimates of the total number of fibers may not be well correlated with PCM estimates of fiber counts. We propose to draw upon unique existing data for US asbestos workers for whom high-quality estimates of asbestos exposure have been derived by PCM and fiber-size specific estimates of exposure have been derived by TEM. We propose to: 1) Use hierarchical regression methods to estimate the effects of the unregulated fraction of fibers; 2) Use G-methods to estimate TEM-based policies that would yield a comparable mortality to that obtained under current OSHA standards; and, 3) Use G- methods to estimate alternative TEM-based policies that would yield less mortality than that obtained under current OSHA standards. Aim 1 will assess the effects of the unregulated fraction of fibers using methods show to perform well in settings of correlated environmental exposures. Aims 2 and 3 draw upon contemporary methods to estimate public health impacts of policies in settings susceptible to bias due to the healthy worker survivor effect. The project, which addresses the NORA cross-sector priority to prevent work- related respiratory malignancies, applies innovative methods for estimating the effects of complex exposures. 1