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，原因是已建成的基础设施中储存了大量的石棉，以及这种材料的持续全球贸易。美国石棉暴露的职业标准是基于相差显微镜(PCM)对颗粒的目测计数。只有足够长和足够粗的纤维可以使用这种方法进行可靠的计数，才会受到监管。然而，在大多数职业环境中，大多数石棉纤维太小，无法用相变显微镜进行计数。特别值得关注的是长纤维，这些纤维太细，使用PCM看不见，但据信 吸入时非常危险。如果基于PCM方法的石棉暴露量估计与所有与病因相关的石棉纤维的总暴露量严格成比例，那么一项政策 PCM测量的受管制的石棉暴露量可能足以控制与病因学相关的职业石棉暴露量。然而，有强有力的证据表明，PCM没有计算的石棉纤维比例因工作活动、纤维质量和其他条件而异。因此，工人可能有相对较低的基于PCM的指标，但由于很大一部分纤维太短或太薄，无法通过PCM计数，因此总石棉暴露较高。在这种情况下，工人面临着职业病的风险，因为石棉纤维的比例未经测量，不受监管。工人和他们的倡导者一再提出这个问题，他们主张使用允许对短纤维(&lt；5�m)和细纤维(&lt；0.25�m)进行量化的方法和规则来管理石棉暴露，例如对空气样本进行透射电子显微镜分析。然而，这方面的一个障碍是，大多数关于石棉工人的职业研究都是根据PCM对暴露的估计对人进行分类。监管机构指出，目前尚不清楚由透射电子显微镜量化的暴露水平是可以接受的，因为透射电子显微镜对纤维总数的估计可能与PCM对纤维数量的估计没有很好的相关性。我们建议利用美国石棉工人独特的现有数据，通过PCM得出石棉暴露的高质量估计，并通过透射电子显微镜得出纤维尺寸特定的暴露估计。我们建议：1)使用分层回归方法来估计纤维的无规则分数的影响；2)使用G方法来估计基于TEM的策略，其产生的死亡率将与当前OSHA标准下获得的死亡率相当；以及3)使用G方法来估计替代的基于TEM的策略，其产生的死亡率将低于在当前OSHA标准下获得的死亡率。目标1将使用在相关环境暴露环境中表现良好的方法来评估纤维的不受监管部分的影响。目标2和目标3借鉴当代方法，在容易因工人健康幸存者效应而产生偏见的环境中评估政策对公共健康的影响。该项目针对NORA预防与工作相关的呼吸道恶性肿瘤的跨部门优先事项，采用创新的方法来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