Control and management of airborne chemical exposures in the workplace
Occupational hygiene’s main goal is to identify hazards, and evaluate, control and manage risks in the workplace. A significant part of these activities relies on acquiring knowledge about exposure levels experienced by workers through breathing air contaminated with chemicals. Such exposure assessment can be required for several purposes. Chemical risk assessment in the workplace often relies on comparing workers’ exposures to occupational exposure limits (OELs) or guidelines set by various organizations or governing bodies. Exposure assessment can also be performed in order to understand factors that determine exposure intensity in order to target intervention. For example it is often interesting to compare exposures before and after the implementation of a control measure, to assess differences between different situations (e.g. day vs night shift, or task A vs task B), and evaluate changes over time to monitor gradual improvement or worsening of exposure conditions.
While some exposure assessment needs can be met through indirect methods such as control banding or the use of mathematical models, in most situations direct measurement of exposure through sampling and analysis of the air breathed by workers is necessary.
Implications of environmental variability on exposure assessment: the exposure distribution
When measuring exposures in the workplace, the aim is generally not to acquire knowledge only about the particular period of time sampled, but to infer from this period what is usually happening under the same circumstances. Hence the objective is to obtain a representative picture of exposures corresponding to a set of conditions. For example, when evaluating a worker’s exposure level for a full workshift, one would want to use that exposure information to gain knowledge about all other unmeasured days. Indeed, it’s the ensemble of exposure-days experienced by the workers, the so-called exposure distribution, which reflects risk.
It was recognized early on that exposure levels in the workplace vary considerably across both location and time. Even measurements integrated over a full work shift and taken repeatedly for a similar work situation can often show 10-fold variations from one day to another [1–4]. Therefore exposure corresponding to a particular situation (e.g. painting metal parts in a body shop) cannot be described by a typical single concentration value. It is rather characterized by an ensemble of different exposure levels due to minute variations in many determining factors (e.g. surface being painted, open/closed doors, air movement, worker’s experience, etc.). Designing a sampling strategy that accounts for this type of variability and allows drawing of an accurate portrait of the exposure distribution for any situation is consequently very challenging.
Statistics in occupational hygiene: the lognormal distribution
Statistical methods developed to address the challenge posed by environmental variability started to appear in the mid-1990s in guidelines published by prominent organizations. Namely, the American Occupational hygiene Association (AIHA) , the National Institute of Occupational Safety and Health (NIOSH) , the British Occupational Health Society (BOHS)  and the Institut National de Recherche et de Sécurité (INRS) in France  published guidelines for the comparison of exposure levels to exposure limits.
These methods are based on the assumption that environmental variability is adequately modelled by the lognormal distribution model [1–4]. Under this model, it is assumed, for a given exposure group (e.g. stainless steel welders in a parts manufacturing facility), that the ensemble of exposure levels experienced by workers in this group over a period of relatively stable work conditions (e.g. a year), referred to as the exposure distribution, follows a lognormal model. Then, when a set of measurements are taken, they are assumed to form a random sample from this exposure distribution. It is therefore possible to use this sample to draw conclusions on the exposure distribution itself, including measured and unmeasured days. There is now a large body of evidence suggesting the lognormal model is a reasonable default assumption for most exposure situations involving vapors and aerosols .
Several parameters of the lognormal distribution are recommended by current guidelines as risk indices and should be estimated from a set of exposure measurements. These parameters, presented in more detail near the end of this document in appendix A, are briefly summarized here. Exceedance is the proportion of exposures in the exposure distribution that are higher than the OEL. There is a general consensus that exceedance lower than 5% corresponds to acceptable exposure conditions (i.e. less than ~10 days in a year for fullshift exposures) . Alternatively, one could estimate the 95th percentile of the exposure distribution, i.e. the value below which lies 95% of the exposure distribution. Hence, if the 95th percentile is lower than the OEL, one could infer that exceedance is lower than 5%. For some agents that have chronic effects, the arithmetic mean of the exposure distribution over a long period of time provides a useful index of risk, and can be used for epidemiologic purposes or when a long term cumulative exposure threshold has been established . Risk, as measured by exceedance or the arithmetic mean, is often assessed at the group level (i.e. within so-called similar exposure groups), and the group risk is applied to all workers within that group. In some cases workers might experience different risk within an exposure group. In these cases, in addition to group risk (e.g. as measured by group exceedance) it is advisable to estimate the probability that a single worker would have an unacceptable exposure distribution (e.g. the probability that a single worker might have a personal level exceedance greater than 5%). Individual risk is not calculated for each individual, it is a global measure of the likelihood that a random individual might experience much higher risk than the rest of the group.
Numerical and statistical analysis needs for the interpretation of occupational exposure data
Statistical procedures for the above lognormal parameters and their uncertainty are not described in standard statistical textbooks, which are mostly centered on the normal distribution. Instead, they have been gradually developed since the 1990s onward and are continuing to evolve . While these developments trickled down from research papers into guidelines from occupational hygiene associations over time , their implementation can be complicated, making it difficult for practitioners who may lack the statistical knowledge and tools to perform such calculations. In Quebec, the Sampling guide for air contaminants in the workplace  is referred to by the regulation as a reference on the level of accuracy required for how to assess exposure regulatory compliance to OELs. The guide provides detailed instructions on how to compare one measurement to the OEL to determine whether exposure on the measured day was compliant, which is essential for regulatory compliance officers. However it does not include comprehensive documentation about the lognormal distribution and the associated risk metrics.
We identified only 4 freely available practical evaluation tools that permit estimation of at least one of the statistical parameters useful for risk assessment of airborne chemicals in occupational hygiene including: IHSTAT (Excel worksheet), AltrexChimie (standalone downloadable software), IHDA Lite (freeware version of standalone downloadable software), BW_Stat(Excel worksheet). IHSTAT is an excel worksheet developed by AIHA and recently improved with the collaboration of researchers from IRSST and University of Montréal. AltrexChimie was developed as a collaborative effort between industry and INRS in France. IHDA Lite is the freeware version of the IHDA software, developed by Exposure Assessment Solutions Inc, and based on a framework described recently in the literature. BW_Stat, like IHSTAT, is an excel worksheet. It was developed collaboratively by Theo Scheffers, from the Dutch society of occupational hygiene, and Tom Geens, from the Belgium society of occupational hygiene.
Bayesian methods to interpret occupational exposure data
The lognormal parameters useful for risk assessment (e.g. exceedance) have traditionally been estimated using so-called ‘frequentist’ methods. More recently, Bayesian statistics have been proposed as a worthy alternative estimation approach. In Bayesian inference, one establishes prior beliefs about a set of unknown parameters in the form of probability distributions. Bayes theorem is then used to update these beliefs with empirical observations, resulting in ‘posterior’ probability distributions for the parameters of interest. While the theory was established during the 18th century, Bayesian methods have only gained popularity relatively recently with the advent of high computing power. Bayesian statistics have been recently proposed for use in occupational hygiene because they permit the integration of expert judgement (in the form of prior beliefs) into measurement data [13–16].
There are other significant advantages to using Bayesian statistics to interpret occupational hygiene data. Bayesian inference is probabilistic in nature, therefore instead of a hypothesis test or a confidence interval, with interpretations that are often difficult to convey to the layman, Bayesian analysis provides answers to questions in the direct form of “what is the probability that” (e.g. "what is the probability that this group is overexposed more than 5% of days"; or, "what is the probability that this intervention reduced exposure levels by at least 50%"). This greatly facilitates risk communication. Furthermore, two technical challenges currently not appropriately tackled by traditional approaches, namely the handling of non-detects and incorporating measurement error into an assessment, are easily integrated into a Bayesian approach [17–22].
The Bayesian framework therefore appears to be a very promising avenue to improve data analysis and interpretation in occupational hygiene. Unfortunately, its implementation is currently out of reach of practitioners, as running Bayesian computations requires advanced software and technical knowledge, usually limited to academic specialists.
Challenges with data interpretation and risk communication using modern approaches
As mentioned above, the statistical skills required to understand and interpret occupational hygiene data as outlined in most recent guidelines are very specific. They are not part of the basic statistics taught in many traditional educational programs in basic sciences. More than 10 years of teaching these concepts to occupational hygiene practitioners by the principal investigator of this project (JL) showed that they are not well mastered. Recent studies on expert judgement also showed that hygienists performed better when taught specific courses about lognormal statistics [23,24]. In Québec, modern approaches to data interpretation were reviewed and summarized in a recent IRSST report . The authors specifically pointed out that these approaches require statistical notions and calculation tools not widespread in the field.
In addition to the need to facilitate the use of adequate statistics, risk communication is also a challenge that would benefit from any improvement as these concepts often appear obscure to decision makers and workers. For instance, it is possible to have an exposure situation where a set of measurements are all under the OEL, but the estimated proportion of exposures expected to be over the OEL during unmeasured days would be much greater than the generally accepted 5%. This particular assessment would probably seem counter-intuitive to an uninformed audience. The difficulty and lack of tools to efficiently communicate statistical results in a convincing way to non-specialists may also explain the slow appropriation of modern guidelines by practitioners in the field.
Summary of knowledge gaps and needs
Considerable spatial and temporal variability observed in levels of exposure has historically represented an important challenge to their interpretation. There now exists a consensus framework for their analysis based on the lognormal distribution. These developments, although permitting a better assessment of risk compared to historical approaches, have not been widely adopted by occupational hygiene practitioners. Indeed, they involve statistical notions not usually taught in traditional training programs and require calculations not usually feasible with common tools such as calculators or spreadsheet programs. The few specific tools currently available are an important step forward but do not yet represent a comprehensive answer to practitioners’ needs. Moreover, available tools are standalone, and are not easily amenable to integration within an existing data management structure. Finally, Bayesian methods represent a very promising approach to data interpretation in occupational hygiene, but are currently not accessible to practitioners. In conclusion, in order to support the adoption in the field of modern guidelines for occupational hygiene data interpretation and improve chemical risk assessment practice there is a significant need for better knowledge translation, and for accessible and comprehensive tools.
The gap between the refinement of statistical methods available to interpret exposure data in the literature and guidelines, and actual practice are the reason for this endeavour. This Website aims at supporting practitioners in using state of the art approaches. The first tools implemented make use of the Shiny application from the RStudio developers, which permits to interface a heavy duty statistical package (R) with a webpage. Therefore visitors can enter their data on our Webpage and have our server perform calculations otherwise not readily accessible. In particular, this permits the use of very the powerful Bayesian technology directly from your armchair. The expostats website is ever-evolving as we develop new tools, improve and validate existing ones, and research ways to better communicate outputs from statistical analyses.
In parallel to expostats.ca, our team recently got funded by IRSST (2015-2018) to create an integrated IH data interpretation platform that would use the same calculation engine (Bayesian) to treat a wide ensemble of data interpretation questions while addressing technical challenges (e.g. non detects). This project, provisionally called Webexpo, will primarily yield an open access library of algorithms available in several languages which anyone can use to create practical tools, online or standalone. Of course we will also create a Website based on these algorithms, with a heavy focus on user-friendliness and risk communication.
The recommended approaches to comparing measured exposure levels to an exposure limit have significantly evolved during the last 30 years. The initial guideline utilizing a statistical framework for interpretation, proposed by the NIOSH in 1977, recommended that exposures should be controlled so that less than 5% of exposure levels experienced by a worker exceed the OEL  ( i.e. ‘exceedance’ should be <5%; a concept also found in the more recent European standard ). At that time, NIOSH proposed to verify this by comparing a single exposure value to an action limit set at half the OEL. Although this proposition was based on statistical grounds and provided a practical way to perform risk assessment, it was later recognised that this action level was too close to the OEL to ensure adequate protection of the workers. In other words, comparing one measurement to half the OEL did not permit to ensure that 95% of unmeasured exposures would be under the OEL [27–30]. Further methodological developments in the following decades identified three risk metrics based on the lognormal distribution (see below) which were embraced in a recent workshop about the upcoming new set of guidelines from NIOSH . In all cases, the exposure distribution underlying the samples taken is the ensemble of exposure values experienced by a group of workers sharing similar exposure conditions (i.e. a homogenous, or similar, exposure group).
a. Proportion of exposures exceeding the OEL (exceedance)
This metric is directly related to NIOSH’s proposal that less than 5% of exposures should exceed the OEL. Applied to shift-long exposures, the exposure distribution of interest would comprise of all time-weighted-averaged (TWA) exposures occurring during a period of stable conditions, typically a year. One would then collect a random sample from this exposure distribution and estimate the proportion of days expected to be associated with exposure over the OEL. Because the estimate is made from a sample of the exposure distribution, uncertainty has to be taken into account through the calculation of confidence limits around the estimate. This approach, recommended by INRS in France, forms the basis of the current French regulation [8,32,33], which equates compliance to an OEL to the demonstration that, based on 9 measurements, the 70% upper confidence limit of the exceedance is smaller than 5%. Put in simpler terms, one has to demonstrate with at least 70% certainty that less than 5% of exposures are over the OEL. Comparing the exceedance fraction to 5% is numerically equivalent to comparing the estimated 95th percentile of the underlying distribution to the OEL. The latter calculation is recommended in the current guidelines from AIHA .
b. Long term arithmetic mean of the exposure distribution
Toxicokinetic models have shown that the arithmetic mean of the long term distribution of exposure levels is one of the most adequate risk metric for evaluating cumulative damage from exposure to most chronic toxicants, rather than exceedance . Within this framework, one would make a number of measurements, estimate the arithmetic mean of the underlying exposure distribution as well as confidence limits around the point estimate, and compare them with the OEL. The current guidelines from the AIHA recommend this approach in cases where the exposure limit has explicitly been defined as a long term cumulative dose index (‘LTA-OEL, Long term average OEL’) .
c. Individual risk: probability that a random worker within the group would have unacceptable risk despite an acceptable exposure distribution for the group.
Following seminal work by Kromhout, Rappaport and Symanski [35,36], it was recognized in the late 1990s that the traditional practice of grouping workers performing similar tasks in the same environment into so-called homogenous exposure groups could result in underestimation of risk for some members of the group. Thus, despite an acceptable group exposure distribution, high variability of exposure between workers could result in a distinct possibility that some workers would have an unacceptable individual exposure distribution. This was notably reflected in the AIHA guidelines, where ‘homogenous exposure group’ was replaced with ‘similar exposure group’ in most recent editions. The AIHA also recommends using analysis of variance methods when enough data is available to assess empirically whether the group is indeed ‘homogeneous’ [5,10,12]. This concept is an integral part of the most recent guidelines by the BOHS “Testing Compliance with Occupational Exposure Limits” . The guideline is a 2-step process. The exposure group distribution is first evaluated to assess whether less than 5% of exposures are above the OEL (similar to the French recommendation described above). If group risk is acceptable, then the guideline requires testing to determine whether there is significant exposure variability between workers within the group to estimate the probability that a random worker’s exposure distribution would correspond to more than 5% of overexposure. If this probability is estimated greater than 20%, the guideline’s diagnosis is “failure to comply”.
The previous metrics can also be used for analyses other than comparison with OELs, including evaluation of the effect of exposures determinants (e.g. effect of an intervention).
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