Assessment of Occupational Exposure Patterns by Frequency-Domain Analysis of Time Series Data

Abstract

Laboratory evidence increasingly points to exposure pattern characteristics, including the duration, frequency, and timing of the exposure during the day, as important factors influencing the biological response to extremely low-frequency magnetic fields. An exploratory analysis of exposure patterns was conducted in 113 electric utility workers employed as electricians, cable splicers, line workers, and power plant operators. The purpose of the study was to describe extremely low-frequency magnetic field exposure pattern characteristics of electric utility workers and evaluate grouping strategies for classifying occupational exposures based on their exposure pattern characteristics. Exposure patterns describe the cyclic fluctuation in exposures over time, and were evaluated by partitioning the variation of the time series into frequency components using frequency-domain analysis of the transformed and processed time series. The study samples were classified using traditional grouping strategies based on occupation and time-weighted average (TWA), and non-traditional grouping strategies based on cluster analysis of the standardized, low-frequency exposure pattern components. Rules for classifying samples into each group were developed using linear discriminant analysis, with the performance of each grouping strategy evaluated using a crossvalidation study design to estimate the rate of misclassification. Exposure patterns appeared unrelated to grouping strategies based on quartiles of the workday TWA, but were related to pattern clusters and occupation. The linear discriminant function produced very low misclassification error rates for the cluster grouping strategy (10%) compared to occupation (50%) and TWA quartile (69%) grouping strategies. Significant differences in the exposure patterns occurring between clusters and between occupational groups were observed, indicating that at least one of the spectral estimates in two of the groups were significantly different. However, patterns clusters produced the greatest contrast in exposure patterns of all grouping strategies, explaining 99 percent of the total variation compared to 58 percent of the total variation by occupation.

Peak Inhalation Exposure Metrics Used in Occupational Epidemiologic and Exposure Studies

“…A study of healthcare workers utilized a Bayesian approach to model VOC exposure time-series to estimate 5-and 15-min, task-specific exposure summaries including (posterior distributions of) geometric means (GM), geometric standard deviations (GSD), and quantiles such as 95th percentile (P95) (147). In another study of electric utility workers exposed to extremely low-frequency magnetic fields, exposure patterns were described by conducting a frequencydomain time-series analysis and grouping workers based on the frequency patterns of exposure time series (203). Studies of VOC exposures in spray painting operators (13) and dust exposures in flour processing workers (14), created a range of relative or absolute peaks using 5-s, 1-or 15-min moving averages, including: number of peaks per hour, duration of peaks, average, and maximum concentrations of the peaks and their ratios, and average duration between peaks.…”

“…The often-used time-integrated sampling methods, while specific, are not time-resolved and do not capture peaks, requiring use of peak surrogates. There is renewed and growing interest in the development of direct reading instruments that have improved sensitivity, detection limit, specificity, multiplexing capability, and other enhanced performance characteristics (206), which hold tremendous promise of novel peak exposure metrics for use in epidemiologic studies of acute effects (13,14,203,207). Real-time exposure monitoring provides flexibility in post-hoc definitions of peaks, and in examining the correlations among the various peak metrics and their utility in predicting risk of health effects.…”

“…As shown in this review, while real-time data are often used in exposure and epidemiologic studies, they are not appropriately summarized or fully utilized, often resorting to calculating averages, selecting highest exposures, or displaying plots of typical profiles. Some studies have provided guidance on extracting various metrics of peak exposure in the time or frequency domains of realtime data based on intensity, duration, time interval between peaks, frequency, aggregation of peaks, and patterns based on the frequencies at which high amplitude (peaks) occur (13,14,203). Principal components analysis of these peak metrics revealed three independent factors related to intensity, variability, and duration that sufficiently characterized peak exposures (13), which is consistent with their expression in terms of realtime exposure distribution parameters and autocorrelation, i.e., the GM, GSD, and autocorrelation coefficient (207).…”

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring

“…With the recent advances in sensor technology, especially nanotechnology-enabled sensors, there is renewed and growing interest in the development of direct reading instruments that have improved sensitivity, detection limit, specificity, multiplexing capability, and other performance characteristics (Sadik et al ., 2009; van Zee et al ., 2009). Direct reading instruments hold tremendous promise of novel exposure metrics for use in epidemiologic studies of acute effects or sensitization where peak exposures and patterns of exposure are important (Mihlan et al ., 2000; Preller et al ., 2004), as well as of identifying important time-varying factors such as tasks that affect exposure levels, thus offering opportunities for targeted control measures. To emphasize the importance of developing and using direct reading real-time instrumentation in exposure assessment, the National Institute for Occupational Safety and Health (NIOSH) launched the direct reading exposure assessment methods (DREAM) initiative in 2008 with the goal of enhancing the use of these technologies to improve worker health and safety (NIOSH, 2014).…”