ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Application of the Bayesian Spline Model to Estimate Task-Specific Exposures for Volatile Organic Compounds

4 September 2018, 12:00 – 12:20


Submitted by
M. Abbas Virji
M. Abbas Virji (National Institute for Occupational Safety and Health), E. Andres Houseman (Consultant)
There is renewed and growing interest in the development and use of direct reading instruments that have improved sensitivity, detection limit, specificity, multiplexing capability, and other performance characteristics. Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making, information on the short-term exposure variability for identification of exposure excursions and development of control strategies, and metrics of peak exposure for epidemiologic studies. However, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time-series, and presence of left-censoring due to limit of detection. A Bayesian framework is proposed that addresses these issues in order to model workplace factors that affect exposure and to estimate summary statistics for tasks or other covariates of interest. Specifically, a spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time total volatile organic compounds measurements collected in hospital setting. The model provides estimates of task means, standard deviations, quantiles (e.g., 95th percentile), and parameter estimates for covariates that can be used to identify and prioritize control measures or as metrics of peak exposure in epidemiologic studies. Ongoing effort is focused on exploring the possibility of extending the method to analyze multivariate data such as multimodal particle size distribution or multiple specific real-time VOC exposures.
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