The role of exposure reconstruction in occupational human health risk assessment: current methods and a recommended framework

Accuracy of professional judgments for dermal exposure assessment using deterministic models

The accuracy of exposure judgments, particularly for scenarios where only qualitative information is available or a systematic approach is not used, has been evaluated and shown to have a relatively low level of accuracy. This is particularly true for dermal exposures, where less information is generally available compared to inhalation exposures. Relatively few quantitative validation efforts have been performed for scenarios where dermal exposures are of interest. In this study, a series of dermal exposure judgments were collected from 90 volunteer U.S. occupational health practitioners in a workshop format to assess the accuracy of their judgments for three specific scenarios. Accuracy was defined as the ability of the participants to identify the correct reference exposure category, as defined by the quantitative exposure banding categories utilized by the American Industrial Hygiene Association (AIHA^®). The participants received progressively additional information and training regarding dermal exposure assessments and scenario-specific information during the workshop, and the relative accuracy of their category judgments over time was compared. The results of the study indicated that despite substantial education and training in exposure assessment generally, the practitioners had very little experience in performing dermal exposure assessments and a low level of comfort in performing these assessments. Further, contrary to studies of practitioners performing inhalation exposure assessments demonstrating a trend toward underestimating exposures, participants in this study consistently overestimated the potential for dermal exposure without quantitative data specific to the scenario of interest. Finally, it was found that participants were able to identify the reference or "true" category of dermal exposure acceptability when provided with relevant, scenario-specific dermal and/or surface-loading data for use in the assessment process. These results support the need for additional training and education of practitioners in performing dermal exposure assessments. A closer analysis of default loading values used in dermal exposure assessments to evaluate their accuracy relative to real-world or measured dermal loading values, along with consistent improvements in current dermal models, is also needed.

Asbestos Exposure in Patients with Malignant Pleural Mesothelioma included in the PRIMATE Study, Lombardy, Italy

The PRIMATE study is an Italian translational research project, which aims to identify personalized biomarkers associated with clinical characteristics of malignant pleural mesothelioma (MPM). For this purpose, characteristics of MPM patients with different degrees of asbestos exposure will be compared to identify somatic mutations, germline polymorphism, and blood inflammatory biomarkers. In this framework, we assessed exposure to asbestos for 562 cases of MPM extracted from the Lombardy region Mesothelioma Registry (RML), for which a complete interview based on a standardized national questionnaire and histopathological specimens were available. Exposure assessment was performed: (1) through experts' evaluation (considered as the gold standard for the purpose of this study), according to the guidelines of the Italian National Mesothelioma Registry (ReNaM) and (2) using a job-exposure matrix (SYN-JEM) to obtain qualitative (ever/never) and quantitative estimates of occupational asbestos exposure (cumulative exposure expressed in fibers per mL (f/mL)). The performance of SYN-JEM was evaluated against the experts' evaluation. According to experts' evaluation, occupational asbestos exposure was recognized in 73.6% of men and 23.6% of women; furthermore, 29 men (7.8%) and 70 women (36.9%) had non-occupational exposure to asbestos. When applying SYN-JEM, 225 men (60.5%) and 25 women (13.2%) were classified as occupationally exposed, with a median cumulative exposure higher for men (1.7 f/mL-years) than for women (1.2 f/mL-years). The concordance between the two methods (Cohen’s kappa) for occupational exposure assessment was 0.46 overall (0.41 in men, and 0.07 in women). Sensitivity was higher in men (0.73) than in women (0.18), while specificity was higher in women (0.88) than in men (0.74). Overall, both methods can be used to reconstruct past occupational exposure to asbestos, each with its own advantages and limitations.

Risk management in occupational safety: A systematic mapping

BACKGROUND

Occupational safety risk management is a systemic process capable of promoting technical engineering solutions, considering a wide range of predictable, unexpected and subjective factors related to accident occurrences. In Brazil, the behavior of managers in relation to risk management tends to be reactive, and facilitates access to information for crucial practical and academic purposes when it comes to changing the attitude of managers, so that their actions become increasingly more proactive.

OBJECTIVE

To identify, classify, analyze, and discuss the existing literature related to the topic, produced from 2008 to 2020, besides contributing to a broader understanding of risk management in occupational safety.

METHODS

We did a systematic literature mapping. The research process was documented starting by the planning stage. Afterwards, the focus was on research conduction and information synthesis.

RESULTS

Knowledge systematization and stratification about OHS risk management through various perspectives to identify, analyze and manage risks in the workplace. Were identified 37 tools for identifying and analyzing risks, management-related practices and future research trends.

CONCLUSIONS

The set of tools and management practices identified can be used as a support for decision making in the selection process of tools and practices to reduce risks and improve occupational safety. Also, the results can help target future research.

Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH Tool

STOFFENMANAGER^® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER^® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER^® and ART are based on a modified multiplicative model where an exposure base level (mg m⁻³) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m⁻³) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER^® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER^® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER^® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER^® and ART. A literature review showed that the models have been validated only at the ‘operational analysis’ level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER^® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson’s correlations (r is <0.6) for both STOFFENMANAGER^® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER^® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

Retrospective Exposure Assessment Methods Used in Occupational Human Health Risk Assessment: A Systematic Review

As part of the assessment and management of chemical risk and occupational hygiene, retrospective exposure assessment (REA) to chemical agents can be defined as the estimate of exposure associated with a person's work history. The fundamental problem underlying the reconstruction of the exposure is that of transforming this type of information in quantitative terms to obtain an accurate estimate. REA can follow various approaches, some of which are technically complicated and both time and resource consuming. The aim of this systematic review is to present the techniques mainly used for occupational REA. In order to carry out this evaluation, a systematic review of the scientific literature was conducted. Forty-four studies were identified (published from 2010 to date) and analyzed. In exposure reconstruction studies, quantitative approaches should be preferable, especially when estimates will be used in the context of health impact assessment or epidemiology, although it is important to stress how, ideally, the experimental data available for the considered scenario should be used whenever possible as the main starting information base for further processing. To date, there is no single approach capable of providing an accurate estimate of exposure for each reasonably foreseeable condition and situation and the best approach generally depends on the level of information available for the specific case. The use of a combination of different reconstruction techniques can, therefore, represent a powerful tool for weighting and integrating data obtained through qualitative and quantitative approaches, in order to obtain the best possible estimate.

Potential Airborne Asbestos Exposure and Risk Associated with the Historical Use of Cosmetic Talcum Powder Products

Over time, concerns have been raised regarding the potential for human exposure and risk from asbestos in cosmetic-talc-containing consumer products. In 1985, the U.S. Food and Drug Administration (FDA) conducted a risk assessment evaluating the potential inhalation asbestos exposure associated with the cosmetic talc consumer use scenario of powdering an infant during diapering, and found that risks were below levels associated with background asbestos exposures and risk. However, given the scope and age of the FDA's assessment, it was unknown whether the agency's conclusions remained relevant to current risk assessment practices, talc application scenarios, and exposure data. This analysis updates the previous FDA assessment by incorporating the current published exposure literature associated with consumer use of talcum powder and using the current U.S. Environmental Protection Agency's (EPA) nonoccupational asbestos risk assessment approach to estimate potential cumulative asbestos exposure and risk for four use scenarios: (1) infant exposure during diapering; (2) adult exposure from infant diapering; (3) adult exposure from face powdering; and (4) adult exposure from body powdering. The estimated range of cumulative asbestos exposure potential for all scenarios (assuming an asbestos content of 0.1%) ranged from 0.0000021 to 0.0096 f/cc-yr and resulted in risk estimates that were within or below EPA's acceptable target risk levels. Consistent with the original FDA findings, exposure and corresponding health risk in this range were orders of magnitude below upper-bound estimates of cumulative asbestos exposure and risk at ambient levels, which have not been associated with increased incidence of asbestos-related disease.

Reconstructing Historical VOC Concentrations in Drinking Water for Epidemiological Studies at a U.S. Military Base: Summary of Results

A U.S. government health agency conducted epidemiological studies to evaluate whether exposures to drinking water contaminated with volatile organic compounds (VOC) at U.S. Marine Corps Base Camp Lejeune, North Carolina, were associated with increased health risks to children and adults. These health studies required knowledge of contaminant concentrations in drinking water—at monthly intervals—delivered to family housing, barracks, and other facilities within the study area. Because concentration data were limited or unavailable during much of the period of contamination (1950s–1985), the historical reconstruction process was used to quantify estimates of monthly mean contaminant-specific concentrations. This paper integrates many efforts, reports, and papers into a synthesis of the overall approach to, and results from, a drinking-water historical reconstruction study. Results show that at the Tarawa Terrace water treatment plant (WTP) reconstructed (simulated) tetrachloroethylene (PCE) concentrations reached a maximum monthly average value of 183 micrograms per liter (μg/L) compared to a one-time maximum measured value of 215 μg/L and exceeded the U.S. Environmental Protection Agency’s current maximum contaminant level (MCL) of 5 μg/L during the period November 1957–February 1987. At the Hadnot Point WTP, reconstructed trichloroethylene (TCE) concentrations reached a maximum monthly average value of 783 μg/L compared to a one-time maximum measured value of 1400 μg/L during the period August 1953–December 1984. The Hadnot Point WTP also provided contaminated drinking water to the Holcomb Boulevard housing area continuously prior to June 1972, when the Holcomb Boulevard WTP came on line (maximum reconstructed TCE concentration of 32 μg/L) and intermittently during the period June 1972–February 1985 (maximum reconstructed TCE concentration of 66 μg/L). Applying the historical reconstruction process to quantify contaminant-specific monthly drinking-water concentrations is advantageous for epidemiological studies when compared to using the classical exposed versus unexposed approach.

The application of global sensitivity analysis in the development of a physiologically based pharmacokinetic model for m-xylene and ethanol co-exposure in humans

Global sensitivity analysis (SA) was used during the development phase of a binary chemical physiologically based pharmacokinetic (PBPK) model used for the analysis of m-xylene and ethanol co-exposure in humans. SA was used to identify those parameters which had the most significant impact on variability of venous blood and exhaled m-xylene and urinary excretion of the major metabolite of m-xylene metabolism, 3-methyl hippuric acid. This analysis informed the selection of parameters for estimation/calibration by fitting to measured biological monitoring (BM) data in a Bayesian framework using Markov chain Monte Carlo (MCMC) simulation. Data generated in controlled human studies were shown to be useful for investigating the structure and quantitative outputs of PBPK models as well as the biological plausibility and variability of parameters for which measured values were not available. This approach ensured that a priori knowledge in the form of prior distributions was ascribed only to those parameters that were identified as having the greatest impact on variability. This is an efficient approach which helps reduce computational cost.

Historical intake and elimination of polychlorinated biphenyls and organochlorine pesticides by the Australian population reconstructed from biomonitoring data

Quantifying the competing rates of intake and elimination of persistent organic pollutants (POPs) in the human body is necessary to understand the levels and trends of POPs at a population level. In this paper we reconstruct the historical intake and elimination of ten polychlorinated biphenyls (PCBs) and five organochlorine pesticides (OCPs) from Australian biomonitoring data by fitting a population-level pharmacokinetic (PK) model. Our analysis exploits two sets of cross-sectional biomonitoring data for PCBs and OCPs in pooled blood serum samples from the Australian population that were collected in 2003 and 2009. The modeled adult reference intakes in 1975 for PCB congeners ranged from 0.89 to 24.5ng/kgbw/day, lower than the daily intakes of OCPs ranging from 73 to 970ng/kgbw/day. Modeled intake rates are declining with half-times from 1.1 to 1.3years for PCB congeners and 0.83 to 0.97years for OCPs. The shortest modeled intrinsic human elimination half-life among the compounds studied here is 6.4years for hexachlorobenzene, and the longest is 30years for PCB-74. Our results indicate that it is feasible to reconstruct intakes and to estimate intrinsic human elimination half-lives using the population-level PK model and biomonitoring data only. Our modeled intrinsic human elimination half-lives are in good agreement with values from a similar study carried out for the population of the United Kingdom, and are generally longer than reported values from other industrialized countries in the Northern Hemisphere.

Retrospective Exposure Assessment for Occupational Disease of an Individual Worker Using an Exposure Database and Trend Analysis

This article outlines a hierarchy of data required for retrospective exposure assessment for occupational disease of an individual worker. It then outlines in a step-wise manner how trend analysis using a relatively large exposure database can be used to estimate such exposure. The process of how a large database containing exposure measurements can be prepared for estimating historic occupational exposures of individual workers in relation to their illnesses is described. The asbestos subset from a large government collected air monitoring database called Medical Surveillance (MESU) was selected to illustrate the cleaning and analysis processes. After unidentifiable values were removed, the cleaned dataset was examined for possible sources of variability such as changes to sampling protocol. Limit of detection (LOD) values were substituted for all non-detectable values prior to the calculation of descriptive statistic using left censored analysis methods (i.e., maximum likelihood estimation (MLE), Kaplan Meier (KM), and simple substitution). The JoinPoint Regression Program was used to perform trend analysis and calculate an annual percentage change (APC) value for the available sampling period. An asbestos case study is presented to illustrate how the APC can then be combined with more recent job and/or process specific exposure data to estimate historic levels. The MESU asbestos dataset contained 1,610 samples from 1984-1995. An average of 17% of this data was left censored. The asbestos air sampling methods in Ontario changed around 1990. LOD values of 0.06 f/cc and 0.02 f/cc were substituted for LOD values pre- and post-1990, respectively. The annual mean fiber levels for the MLE method were an average of 44% lower than KM and substitution methods. The corresponding APC for MLE method was -6.5% and -7.7% for KM and simple substitution. The findings of this paper illustrate how the temporal trend of an exposure databases can be used to efficiently estimate historic contaminant levels in the presence of limited historical information.

Production and immunogenicity of VP2 protein of porcine parvovirus expressed in Pichia pastoris

Viral protein 2 (VP2) of porcine parvovirus (PPV) is the major viral structural protein and is responsible for eliciting neutralizing antibodies in immunized animals. In this study, we constructed and characterized a recombinant yeast vector encoding the VP2 protein, designated as pGAPZαA-VP2. The construct was confirmed by restriction enzyme digestion, PCR, and sequencing and then introduced into P. pastoris strain SMD1168 by electroporation. The expressed VP2 protein was analyzed by SDS-PAGE and western blot. Immunization of mice with the VP2 protein elicited a PPV-specific humoral immune response. Notably, a preparation of VP2 protein containing adjuvant induced a much better antibody response than VP2 alone. Clearly, the adjuvant strongly enhanced the immunogenicity of VP2. This study provides a foundation for the application of the VP2 protein in the clinical diagnosis of PPV and in vaccination against PPV in the future.

Retrospective assessment of exposure to static magnetic fields during production and development of magnetic resonance imaging systems

At present, the relationship between chronic exposure to static magnetic fields (SMF) and health effects is unclear. We developed a task-based deterministic model for estimating historical electromagnetic field exposure from the static B-field (B0) of magnetic resonance imaging (MRI) systems, for a cohort of employees working at an MRI systems development and production facility. Technical maps describing the spatial distribution of fringe fields of B0 surrounding different types of MRI systems of various core strengths were exploited to derive estimates of static B0 exposure as a function of distance from the bore of the MRI system. Detailed information on tasks performed per exposed job and other model determinants were acquired through face-to-face interviews and used to derive base estimates of most recent exposure (2009) for each job title. The model was partially validated with actual exposure measurements. The exposure estimates from the deterministic model were used to construct a job-exposure matrix that will enable estimation of cumulative exposures for each cohort member. The generic approach described for estimating chronic MRI-related SMF exposure makes it universally applicable in other studies investigating health effects of MRI-related SMF exposure.

An analysis of workplace exposures to benzene over four decades at a petrochemical processing and manufacturing facility (1962-1999)

Benzene, a known carcinogen, can be generated as a by-product during the use of petroleum-based raw materials in chemical manufacturing. The aim of this study was to analyze a large data set of benzene air concentration measurements collected over nearly 40 years during routine employee exposure monitoring at a petrochemical manufacturing facility. The facility used ethane, propane, and natural gas as raw materials in the production of common commercial materials such as polyethylene, polypropylene, waxes, adhesives, alcohols, and aldehydes. In total, 3607 benzene air samples were collected at the facility from 1962 to 1999. Of these, in total 2359 long-term (>1 h) personal exposure samples for benzene were collected during routine operations at the facility between 1974 and 1999. These samples were analyzed by division, department, and job title to establish employee benzene exposures in different areas of the facility over time. Sampling data were also analyzed by key events over time, including changes in the occupational exposure limits (OELs) for benzene and key equipment process changes at the facility. Although mean benzene concentrations varied according to operation, in nearly all cases measured benzene quantities were below the OEL in place at the time for benzene (10 ppm for 1974-1986 and 1 ppm for 1987-1999). Decreases in mean benzene air concentrations were also found when data were evaluated according to 7- to 10-yr periods following key equipment process changes. Further, an evaluation of mortality rates for a retrospective employee cohort (n = 3938) demonstrated that the average personal benzene exposures at this facility (0.89 ppm for the period 1974-1986 and 0.125 ppm for the period 1987-1999) did not result in increased standardized mortality ratio (SMRs) for diseases or malignancies of the lymphatic system. The robust nature of this data set provides comprehensive exposure information that may be useful for assessing human benzene exposures at similar facilities. The data also provide a basis for comparable measured exposure levels and the potential for adverse health effects. These data may also prove beneficial for comparing relative exposure potential for production versus nonproduction operations and the relationship between area and personal breathing zone samples.

Interpreting PCB levels in breast milk using a physiologically based pharmacokinetic model to reconstruct the dynamic exposure of Italian women

Polychlorinated biphenyls (PCBs) are persistent contaminants suspected to cause adverse health effects in humans. As PCBs levels in food have not been monitored frequently in the past, modeling approaches based on environmental data have been proposed to predict the human dietary intake. In this work, we propose to improve these approaches by taking into account internal levels of PCBs in humans. This methodology is based on the analysis of biomonitoring data using exposure and physiologically based pharmacokinetic (PBPK) modeling to determine the most probable scenario of exposure. Breast milk concentrations were measured in Italian women for PCB-138, PCB-153 and PCB-180. For each congener, three exposure scenarios were derived and a PBPK model was used to relate the lifetime exposure to the breast milk levels. For the three PCBs, we determined the most probable scenario of exposure. Our results support the adequacy of the exposure and the PBPK models for PCB-180 and PCB-153, whereas we observed discrepancies between the models and the biomonitoring data for PCB-138. Our intake estimates are in good agreement with previous exposure assessments based solely on food contamination demonstrating the relevance of our approach to reconstruct accurately the exposure and to fill in data gaps on exposure.

A Bayesian mixture modeling approach for assessing the effects of correlated exposures in case-control studies

Predisposition to a disease is usually caused by cumulative effects of a multitude of exposures and lifestyle factors in combination with individual susceptibility. Failure to include all relevant variables may result in biased risk estimates and decreased power, whereas inclusion of all variables may lead to computational difficulties, especially when variables are correlated. We describe a Bayesian Mixture Model (BMM) incorporating a variable-selection prior and compared its performance with logistic multiple regression model (LM) in simulated case-control data with up to twenty exposures with varying prevalences and correlations. In addition, as a practical example we re analyzed data on male infertility and occupational exposures (Chaps-UK). BMM mean-squared errors (MSE) were smaller than of the LM, and were independent of the number of model parameters. BMM type I errors were minimal (≤1), whereas for the LM this increased with the number of parameters and correlation between exposures. The numbers of type II errors were comparable. Re analysis of Chaps-UK data demonstrated more convincingly than by using a LM that occupational exposure to glycol ethers and VOCs are likely risk factors for male infertility. This BMM proves an appealing alternative to standard logistic regression when dealing with the analysis of (correlated) exposures in case-control studies.

Integration of gene chip and topological network techniques to screen a candidate biomarker gene (CBG) for predication of the source water carcinogenesis risks on mouse Mus musculus

Screening of a candidate biomarker gene (CBG) to predicate the carcinogenesis risks in the Yangtze River source of drinking water in Nanjing area (YZR-SDW-NJ) on mouse (Mus musculus) was conducted in this research. The effects of YZR-SDW-NJ on the genomic transcriptional expression levels were measured by the GeneChip(®) Mouse Genome and data treated by the GO database analysis. The 298 genes discovered as the differently expressed genes (DEGs) were down-regulated and their values were ≤-1.5-fold. Of the 298 DEGs, 25 were cancer-related genes selected as the seed genes to build a topological network map with Genes2Networks software, only 7 of them occurred at the constructed map. Smad2 gene was at the constructed map center and could be identified as a candidate biomarker gene (CBG) primarily which involves the genesis and development of colorectal, leukemia, lung and prostate cancers directly. Analysis of the gene signal pathway further approved that smad2 gene had the relationships closely to other 16 cancer-related genes and could be used as a CBG to indicate the carcinogenic risks in YZR-SDW-NJ. The data suggest that integration of gene chip and network techniques may be a way effectively to screen a CBG. And the parameter values for further judgment of the CBG through signal pathway relationship analysis also will be discussed.

Observing the human exposome as reflected in breath biomarkers: heat map data interpretation for environmental and intelligence research

Over the past decade, the research of human system biology and the interactions with the external environment has permeated all phases of environmental, medical and public health research. Similar to the fields of genomics and proteomics research, the advent of new instrumentation for measuring breath biomarkers and their associated meta-data also provide very useful, albeit complex, data structures. The biomarker research community is beginning to invoke tools from system biology to assess the impact of environmental exposures, as well as from internal health states, on the expression of suites of chemicals in exhaled breath. This new approach introduces the concept of the exposome as a complement to the genome in exploring the environment-gene interaction. In addition to answering questions regarding health status for the medical community, breath biomarker patterns are useful for assessing public health risks from environmental exposures. Furthermore, breath biomarker patterns can inform security risks from suspects via covert interrogation of blood borne chemical levels that reflect previous activities. This paper discusses how different classes of exhaled breath biomarker measurements can be used to rapidly assess patterns in complex data. We present exhaled breath data sets to demonstrate the value of the graphical 'heat map' approach for hypothesis development and subsequent guidance for stochastic and mixed effect data interpretation. We also show how to graphically interpret exhaled breath measurements of exogenous jet fuel components, as well as exhaled breath condensate measurements of endogenous chemicals.

A novel method for reducing the number of agents to be studied in an occupational epidemiologic study

A novel screening tool method to select chemicals for exposure reconstruction was developed and validated using data generated for a hypothetical work force consisting of 10 job classes (ranging from 10,000 to 55,000 person-years). To achieve the required efficiency in the reconstruction of exposures, this method treats each product (defined as a part or process) as an "exposure." Exposure to 10 products was assigned to each job class at random using a computer program. The expected rate of a given disease was assumed to be constant throughout the job classes (tested at five levels), and the observed numbers of cases in the job classes were generated based on neutral deviations from background with error rates of ± 1% to 16%. One job class was assigned to be the "excess-class" and the number of cases in that class was increased by a factor of Q, which was set at levels that ranged from 1.25 to 5. All of the experimental conditions were replicated 10,000 times in a Monte Carlo scheme for scenarios in which each job class had been designated as the excess-class. Following each run, significant excesses (if any) were determined using a modified version of Daniel's method, and the percentages of false positive and false negative identifications were tabulated. We found that the sensitivity of the method is largely dependent on the relative risk (Q) associated with the exposure. Specifically, the results indicate that as the relative risk increases, the percentage of false negative identifications of the excesses is reduced to nearly 0% and the percentage of false positive identifications is approximately 13%. When applied to real data, should an association be detected between any product and a health outcome, this preliminary analysis will yield a reduced "product" set that can then be investigated in detail and the agents involved considered further for quantitative reconstruction. The proposed method is highly efficient and has the potential to benefit future complex exposure reconstruction studies, particularly when there is no predetermined exposure associated with an observed increase in a cause-specific health end point.