No abstract

Exposure judgments made without personal exposure data and based instead on subjective inputs tend to underestimate exposure, with exposure judgment accuracy not significantly more accurate than random chance. Therefore, objective inputs that contribute to more accurate decision making are needed. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. This may be attributable to a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research addresses the need to systematically evaluate two widely applicable models, the Well-Mixed Room (WMR) and Near-Field-Far-Field (NF-FF) models. The evaluation, conducted under highly controlled conditions in an exposure chamber, allowed for model inputs to be accurately measured and controlled, generating over 800 pairs of high quality measured and modeled exposure estimates. By varying conditions in the chamber one at a time, model performance across a range of conditions was evaluated using two sets of criteria: the ASTM Standard 5157 and the AIHA Exposure Assessment categorical criteria. Model performance for the WMR model was excellent, with ASTM performance criteria met for 88-97% of the pairs across the three chemicals used in the study, and 96% categorical agreement observed. Model performance for the NF-FF model, impacted somewhat by the size of the chamber was nevertheless good to excellent. NF modeled estimates met modified ASTM criteria for 67-84% of the pairs while 69-91% of FF modeled estimates met these criteria. Categorical agreement was observed for 72% and 96% of NF and FF pairs, respectively. These results support the use of the WMR and NF-FF models in guiding decision making towards improving exposure judgment accuracy.

The health risks of exposure to antineoplastic drugs (ADs) are well established, and healthcare professionals can be exposed while caring for cancer patients receiving AD therapy. Studies conducted worldwide over the past two decades indicate continuing widespread surface contamination by ADs. No occupational exposure limits have been established for ADs, but concerns over exposures have led to the development of guidelines, such as United States Pharmacopeia (USP) General Chapter <800> Hazardous Drugs—Handling in Healthcare. While recommending regular surveillance for surface contamination by ADs these guidelines do not provide guidance on sampling strategies. Better characterization of spatial and temporal variability of multidrug contamination would help to inform such strategies. We conducted surface-wipe monitoring of nine cancer care centers in Alberta, Canada and Minnesota, USA, with each center sampled eight times over a 12-month period. Twenty surfaces from within pharmacy and drug administration areas were sampled, and 11 drugs were analyzed from each wipe sample. Exposure data were highly left-censored which restricted data analysis; we examined prevalence of samples above limit of detection (LOD), and used the 90th percentile of the exposure distribution as a measure of level of contamination. We collected 1984 wipe samples over a total of 75 sampling days resulting in 21 824 observations. Forty-five percent of wipe samples detected at least one drug above the LOD, but only three of the drugs had more than 10% of observations above the LOD: gemcitabine (GEM) (24%), cyclophosphamide (CP) (16%), and paclitaxel (13%). Of 741 wipe samples with at least one drug above LOD, 60% had a single drug above LOD, 19% had two drugs, and 21% had three drugs or more; the maximum number of drugs found above LOD on one wipe was 8. Surfaces in the compounding area of the pharmacy and in the patient area showed the highest prevalence of samples above the LOD, including the compounding work surface, drug fridge handle, clean room cart, passthrough tray, and hazardous drug room temperature storage, the IV pump keypad, patient washroom toilet handle, patient washroom door handle, nurses’ storage shelf/tray, and patient side table. Over the course of the study, both 90th percentiles and prevalence above LOD varied without clear temporal patterns, although some centers appeared to show decreasing levels with time. Within centers, the degree of variability was high, with some centers showing changes of two to three orders of magnitude in the 90th percentile of drug concentrations month to month. A clear difference was observed between the six centers located in Alberta and the three in Minnesota, with Minnesota centers having substantially higher percentages of samples above the LOD for CP and GEM. Other factors that were associated with significant variability in exposures were drug compounding volume, size of center, number of patients seen, and age of the center. We hope that demonstrating variability associated with drug, surface, clinic-factors, and time will aid in a better understanding of the nature of AD contamination, and inform improved sampling strategies.

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m3 room, no local ventilation, and NF ventilation of 1.6 m3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the exposure level was 12.5% of the OELV.