Armstrong T W, Pearlman E D, Schnatter A R, Bowes S M, Murray N, Nicolich M J
Occupational Health Division, Exxon Biomedical Sciences, Inc., East Millstone, NJ 08875-2350, USA.
Am Ind Hyg Assoc J. 1996 Apr;57(4):333-43. doi: 10.1080/15428119691014864.
A quantitative exposure-estimating algorithm for benzene and total hydrocarbons was developed for a case control study of petroleum marketing and distribution workers. The algorithm used a multiplicative model to adjust recently measured quantitative exposure data to past scenarios for which representative exposure measurement data did not exist. This was accomplished through the development of exposure modifiers to account for differences in the workplace, the materials handled, the environmental conditions, and the tasks performed. Values for exposure modifiers were obtained empirically and through physical/chemical relationships. Dates for changes that altered exposure potential were obtained from archive records, retired employee interviews, and from current operations personnel. Exposure modifiers were used multiplicatively, adjusting available measured data to represent the relevant exposure scenario and time period. Changes in exposure modifiers translated to step changes in exposure estimates. Though limited by availability of data, a validation exercise suggested that the algorithm provided accurate exposure estimates for benzene (compared with measured data in industrial hygiene survey reports); the estimates generally differed by an average of less than 20% from the measured values. This approach is proposed to quantify exposures retrospectively where there are sufficient data to develop reliable current era estimates and where a historical accounting of key exposure modifiers can be developed, but where there are insufficient historic exposure measurements to directly assess historic exposures.
针对石油营销与配送工人的病例对照研究,开发了一种用于估算苯和总烃暴露量的定量算法。该算法采用乘法模型,将近期测量的定量暴露数据调整到过去不存在代表性暴露测量数据的情景中。这是通过开发暴露修正因子来实现的,以考虑工作场所、处理的材料、环境条件和执行的任务等方面的差异。暴露修正因子的值通过经验以及物理/化学关系获得。改变暴露可能性的变化日期从存档记录、退休员工访谈以及当前运营人员处获取。暴露修正因子以乘法方式使用,调整可用的测量数据以代表相关的暴露情景和时间段。暴露修正因子的变化转化为暴露估计值的阶跃变化。尽管受到数据可用性的限制,但一项验证工作表明,该算法为苯提供了准确的暴露估计值(与工业卫生调查报告中的测量数据相比);估计值与测量值的差异通常平均小于20%。建议采用这种方法在有足够数据来制定可靠的当前时代估计值且能够对关键暴露修正因子进行历史核算,但历史暴露测量不足以直接评估历史暴露的情况下,对暴露进行回顾性量化。