Noise exposure reconstruction and evaluation of exposure trends in two large automotive plants.

This study used a task-based approach to reconstruct employee noise exposures at two large automotive manufacturing plants for the period 1970-1989, utilizing historic noise measurement data, work history records, documented changes in plant operations, focus group discussions, structured interviews with long-tenure employees, and task-based job profiles. Task-based job noise exposure profiles were developed in the 1990s when the plants conducted task-based noise monitoring. Under the assumption that tasks and time-at-task profile within jobs did not change over time, these profiles were applied to historic jobs. By linking historic noise exposure measurements to job tasks, this approach allowed task-based reconstructed noise exposure profiles to capture variability of daily noise exposures. Reconstructed noise exposures, along with task-based noise exposure measurements collected at each plant during the 1990s, were analyzed to examine time trends in workplace noise levels and worker noise exposure. Our analysis of noise exposure trends revealed that noise levels for many jobs declined by ≥3 dBA from 1970 to 1998 as operational and equipment changes occurred in the plants and some noise control measures were implemented, but for some jobs, noise levels increased in the mid- to late 1990s, most likely because of an increase in production at that time. Overall, the percentage of workers exposed to noise levels >90 dBA decreased from 95% in 1970 to 54% in 1998 at one of the plants and decreased from 36% in 1970 to ~5% in 1999 at the other plant. These reductions indicate a degree of success for the hearing conservation program. However, the actual number of employees with noise exposure >90 dBA increased because of a substantial increase in the number of production employees, particularly in jobs with high noise levels, which shows a hearing conservation program challenge that companies face during periods of increased production. Future analysis of hearing levels in these plant populations will help determine whether noise level reduction translates into decreased hearing loss at these plants.

Use of historical data and a novel metric in the evaluation of the effectiveness of hearing conservation program components.

OBJECTIVES: To evaluate the effectiveness of hearing conservation programs (HCP) and their specific components in reducing noise-induced hearing loss (NIHL). METHODS: This retrospective cohort study was conducted at one food-processing plant and two automotive plants. Audiometric and work-history databases were combined with historical noise monitoring data to develop a time-dependent exposure matrix for each plant. Historical changes in production and HCP implementation were collected from company records, employee interviews and focus groups. These data were used to develop time-dependent quality assessments for various HCP components. 5478 male (30,427 observations) and 1005 female (5816 observations) subjects were included in the analysis. RESULTS: Analyses were conducted separately for males and females. Females tended to have less NIHL at given exposure levels than males. Duration of noise exposure stratified by intensity (dBA) was a better predictor of NIHL than the standard equivalent continuous noise level (L(eq)) based upon a 3-dBA exchange. Within this cohort, efficient dBA strata for males were <95 versus ≥ 95, and for females <90 versus ≥ 90. The reported enforced use of hearing protection devices (HPDs) significantly reduced NIHL. The data did not have sufficient within-plant variation to determine the effectiveness of noise monitoring or worker training. An association between increased audiometric testing and NIHL was believed to be an artifact of increased participation in screening. CONCLUSIONS: Historical audiometric data combined with noise monitoring data can be used to better understand the effectiveness of HCPs. Regular collection and maintenance of quality data should be encouraged and used to monitor the effectiveness of these interventions.

Agreement between task-based estimates of the full-shift noise exposure and the full-shift noise dosimetry.

Noise assessments have been conducted using full-shift dosimetry and short-term task-based measurements. Advantages of the task-based method include the opportunity to directly identify high-noise exposure tasks and to target control measures, as well as obtain estimates of task-based full-shift exposures; however, there is little empirical evidence comparing the two methods. National Institute for Occupational Safety and Health assessed noise exposures at three industrial facilities using dosimetry and task-based methods with the objective of comparing the two strategies and assessing the degree of agreement and causes of disagreement. Eight indices of task-based full-shift exposures were created from task-based sampling using three methods to assess time-at-task (direct observation by industrial hygienist, end-of-shift worker estimates and supervisor estimates) and three methods to assign noise levels to tasks [direct measurement, arithmetic mean (AM) and geometric mean (GM)]. We assessed aspects of agreement (precision, bias and absolute agreement) using Bland-Altman plots and concordance correlation coefficient (CCC). Overall, the task-based methods worked fairly well, with mean biases less than +/-2.8 dBA and precision ranges of 3.3-4.4 dBA. By all measures, task-based full-shift estimates based on supervisor assessment of time-at-task agreed most poorly with the dosimetry data. The task-based full-shift estimates based on worker estimates of time-at-task generally agreed as well as those based on direct observation. For task noise level, task-based full-shift estimates based on directly measured task agreed the best with dosimetry data, while agreement for task-based indices based on task AM or GM was variable. Overall, the task-based full-shift estimates based on direct observation task and direct measured task noise level achieved the best agreement with the dosimetry data (CCC 0.84) with 95% of their differences being within 7.4 dBA and 56% of the differences <3 dBA. For this index, a high degree of accuracy was observed (accuracy coefficient = 0.96) with major cause of disagreement arising from a lack of precision (precision coefficient = 0.88). When the measurements were classified by job characteristics, significant improvements in the degree of agreement were observed in the low job mobility, low job complexity and low job variability categories. Our data suggest that a high degree of absolute agreement can be achieved between the task-based and dosimetry-based estimates of full-shift exposures. The task-based approach that uses worker reports combined with task AM or GM levels is similar to the more time-intensive direct observation method to estimate full-shift exposures.

Interactive RadioEpidemiological Program (IREP): a web-based tool for estimating probability of causation/assigned share of radiogenic cancers.

The Interactive RadioEpidemiological Program (IREP) is a Web-based, interactive computer code that is used to estimate the probability that a given cancer in an individual was induced by given exposures to ionizing radiation. IREP was developed by a Working Group of the National Cancer Institute and Centers for Disease Control and Prevention, and was adopted and modified by the National Institute for Occupational Safety and Health (NIOSH) for use in adjudicating claims for compensation for cancer under the Energy Employees Occupational Illness Compensation Program Act of 2000. In this paper, the quantity calculated in IREP is referred to as "probability of causation/assigned share" (PC/AS). PC/AS for a given cancer in an individual is calculated on the basis of an estimate of the excess relative risk (ERR) associated with given radiation exposures and the relationship PC/AS = ERR/ERR+1. IREP accounts for uncertainties in calculating probability distributions of ERR and PC/AS. An accounting of uncertainty is necessary when decisions about granting claims for compensation for cancer are made on the basis of an estimate of the upper 99% credibility limit of PC/AS to give claimants the "benefit of the doubt." This paper discusses models and methods incorporated in IREP to estimate ERR and PC/AS. Approaches to accounting for uncertainty are emphasized, and limitations of IREP are discussed. Although IREP is intended to provide unbiased estimates of ERR and PC/AS and their uncertainties to represent the current state of knowledge, there are situations described in this paper in which NIOSH, as a matter of policy, makes assumptions that give a higher estimate of the upper 99% credibility limit of PC/AS than other plausible alternatives and, thus, are more favorable to claimants.