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O38-3 Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the interocc study
Abstract
Introduction
To estimate occupational exposures to electromagnetic fields (EMF) for the INTEROCC study, we constructed a database of source-based measurements from published and unpublished literature. The aim of the current work was to summarise these measurements into a source-exposure matrix (SEM), accounting for their quality and relevance.
Methods
We developed methods for combining available measurements, weighting the pooled estimates by our confidence in these data. Arithmetic and geometric means, as well as estimates of variability and maximum exposure were calculated by EMF source, frequency band and dosimetry type.
Results
The SEM contains confidence-weighted exposure estimates for the electric and magnetic fields for 312 EMF exposure sources (from 0 Hz to 300 GHz). Operator position geometric mean electric field levels for RF sources ranged between 0.8 V/m (plasma etcher) and 320 V/m (RF sealer), while magnetic fields ranged from 0.02 A/m (speed radar) to 0.6 A/m (microwave heating). For ELF sources, electric fields ranged between 0.2 V/m (electric forklift) and 11,700 V/m (HVTL-hotsticks), while magnetic fields ranged between 0.14 µT (visual display terminals) and 17 µT (TIG welding).
Conclusion
The methodology developed allowed the construction of an EMF-SEM and may be used to summarise similar exposure data for other physical or chemical agents. The SEM will be used together with detailed information on distance to the source, automation, and other determinants of exposure reported by the study subjects, to calculate indices of cumulative exposure to EMF for their use in the analysis of brain tumours risk associated with these exposures. The SEM will also be offered publicly for its use by other researchers.
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Introduction: To date, occupational exposure assessment of electromagnetic fields (EMF) has relied on occupation-based measurements and
exposure estimates. However, misclassification due to between-worker variability remains an unsolved challenge. A source-based
approach, supported by detailed subject data on determinants of exposure, may allow for a more individualized exposure assessment.
Detailed information on the use of occupational sources of exposure to EMF was collected as part of the INTERPHONE-INTEROCC
study. To support a source-based exposure assessment effort within this study, this work aimed to construct a measurement
database for the occupational sources of EMF exposure identified, assembling available measurements from the scientific literature.
Background
Retrospective exposure assessment of occupational lead exposure in population-based studies requires historical exposure information from many occupations and industries.Methods
We reviewed published US exposure monitoring studies to identify lead measurement data. We developed an occupational lead exposure database from the 175 identified papers containing 1,111 sets of lead concentration summary statistics (21% area air, 47% personal air, 32% blood). We also extracted ancillary exposure-related information, including job, industry, task/location, year collected, sampling strategy, control measures in place, and sampling and analytical methods.ResultsThe measurements were published between 1940 and 2010 and represented 27 2-digit standardized industry classification codes. The majority of the measurements were related to lead-based paint work, joining or cutting metal using heat, primary and secondary metal manufacturing, and lead acid battery manufacturing.Conclusions
This database can be used in future statistical analyses to characterize differences in lead exposure across time, jobs, and industries. Am. J. Ind. Med. 58:605–616, 2015. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
We present an update to an electric shock job exposure matrix (JEM) that assigned ordinal electric shocks exposure for 501 occupational titles based on electric shocks and electrocutions from two available data sources and expert judgment. Using formal expert elicitation and starting with data on electric injury, we arrive at a consensus-based JEM. In our new JEM, we quantify exposures by adding three new dimensions: (1) the elicited median proportion; (2) the elicited 25th percentile; and (3) and the elicited 75th percentile of those experiencing occupational electric shocks in a working lifetime. We construct the relative interquartile range (rIQR) based on uncertainty interval and the median. Finally, we describe overall results, highlight examples demonstrating the impact of cut point selection on exposure assignment, and evaluate potential impacts of such selection on epidemiologic studies of the electric work environment. In conclusion, novel methods allowed for consistent exposure estimates that move from qualitative to quantitative measures in this population-based JEM. Overlapping ranges of median exposure in various categories reflect our limited knowledge about this exposure.
Retrospective dose estimation, particularly dose reconstruction that supports epidemiological investigations of health risk, relies on various strategies that include models of physical processes and exposure conditions with detail ranging from simple to complex. Quantification of dose uncertainty is an essential component of assessments for health risk studies since, as is well understood, it is impossible to retrospectively determine the true dose for each person. To address uncertainty in dose estimation, numerical simulation tools have become commonplace and there is now an increased understanding about the needs and what is required for models used to estimate cohort doses (in the absence of direct measurement) to evaluate dose response. It now appears that for dose-response algorithms to derive the best, unbiased estimate of health risk, we need to understand the type, magnitude and interrelationships of the uncertainties of model assumptions, parameters and input data used in the associated dose estimation models. Heretofore, uncertainty analysis of dose estimates did not always properly distinguish between categories of errors, e.g., uncertainty that is specific to each subject (i.e., unshared error), and uncertainty of doses from a lack of understanding and knowledge about parameter values that are shared to varying degrees by numbers of subsets of the cohort. While mathematical propagation of errors by Monte Carlo simulation methods has been used for years to estimate the uncertainty of an individual subject's dose, it was almost always conducted without consideration of dependencies between subjects. In retrospect, these types of simple analyses are not suitable for studies with complex dose models, particularly when important input data are missing or otherwise not available. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies of analytical or simple Monte Carlo error propagation methods and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. Simply put, the 2DMC method simulates alternative, possibly true, sets (or vectors) of doses for an entire cohort rather than a single set that emerges when each individual's dose is estimated independently from other subjects. Moreover, estimated doses within each simulated vector maintain proper inter-relationships such that the estimated doses for members of a cohort subgroup that share common lifestyle attributes and sources of uncertainty are properly correlated. The 2DMC procedure simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the values of dosimetric parameters across multiple realizations of possibly true vectors of cohort doses. The primary characteristic of the 2DMC approach, as well as its strength, are defined by the proper separation between uncertainties shared by members of the entire cohort or members of defined cohort subsets, and uncertainties that are individual-specific and therefore unshared.
The epidemiologic evidence for the carcinogenicity of lead is inconsistent and requires improved exposure assessment to estimate risk. We evaluated historical occupational lead exposure for a population-based cohort of women (n=74,942) by calibrating a job-exposure matrix (JEM) with lead fume (n=20,084) and lead dust (n=5383) measurements collected over four decades in Shanghai, China. Using mixed-effect models, we calibrated intensity JEM ratings to the measurements using fixed-effects terms for year and JEM rating. We developed job/industry-specific estimates from the random-effects terms for job and industry. The model estimates were applied to subjects' jobs when the JEM probability rating was high for either job or industry; remaining jobs were considered unexposed. The models predicted that exposure increased monotonically with JEM intensity rating and decreased 20-50-fold over time. The cumulative calibrated JEM estimates and job/industry-specific estimates were highly correlated (Pearson correlation=0.79-0.84). Overall, 5% of the person-years and 8% of the women were exposed to lead fume; 2% of the person-years and 4% of the women were exposed to lead dust. The most common lead-exposed jobs were manufacturing electronic equipment. These historical lead estimates should enhance our ability to detect associations between lead exposure and cancer risk in the future epidemiologic analyses.Journal of Exposure Science and Environmental Epidemiology advance online publication, 22 August 2012; doi:10.1038/jes.2012.86.
A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.
Generic job-exposure matrices (JEMs) are often used in population-based epidemiologic studies to assess occupational risk factors when only the job and industry information of each subject is available. JEM ratings are often based on professional judgment, are usually ordinal or semi-quantitative, and often do not account for changes in exposure over time. We present an empirical Bayesian framework that combines ordinal subjective JEM ratings with benzene measurements. Our aim was to better discriminate between job, industry, and time differences in exposure levels compared to using a JEM alone.
We combined 63 221 short-term area air measurements of benzene exposure (1954-2000) collected during routine health and safety inspections in Shanghai, China, with independently developed JEM intensity ratings for each job and industry using a mixed-effects model. The fixed-effects terms included the JEM intensity ratings for job and industry (both ordinal, 0-3) and a time trend that we incorporated as a b-spline. The random-effects terms included job (n = 33) and industry nested within job (n = 399). We predicted the benzene concentration in two ways: (i) a calibrated JEM estimate was calculated using the fixed-effects model parameters for calendar year and JEM intensity ratings; (ii) a job-/industry-specific estimate was calculated using the fixed-effects model parameters and the best linear unbiased predictors from the random effects for job and industry using an empirical Bayes estimation procedure. Finally, we applied the predicted benzene exposures to a prospective population-based cohort of women in Shanghai, China (n = 74 942).
Exposure levels were 13 times higher in 1965 than in 2000 and declined at a rate that varied from 4 to 15% per year from 1965 to 1985, followed by a small peak in the mid-1990s. The job-/industry-specific estimates had greater differences between exposure levels than the calibrated JEM estimates (97.5th percentile/2.5th percentile exposure level, (B)(G)R(95)(B): 20.4 versus 3.0, respectively). The calibrated JEM and job-/industry-specific estimates were moderately correlated in any given year (Pearson correlation, r(p) = 0.58). We classified only those jobs and industries with a job or industry JEM exposure probability rating of 3 (>50% of workers exposed) as exposed. As a result, 14.8% of the subjects and 8.7% of the employed person-years in the study population were classified as benzene exposed. The cumulative exposure metrics based on the calibrated JEM and job-/industry-specific estimates were highly correlated (r(p) = 0.88).
We provide a useful framework for combining quantitative exposure data with expert-based exposure ratings in population-based studies that maximized the information from both sources. Our framework calibrated the ratings to a concentration scale between ratings and across time and provided a mechanism to estimate exposure when a job/industry group reported by a subject was not represented in the exposure database. It also allowed the job/industry groups' exposure levels to deviate from the pooled average for their respective JEM intensity ratings.
An inaccurate evaluation of exposure is considered a possible cause for the inadequate conclusiveness of epidemiological research on adverse effects of extremely low frequency-magnetic fields (ELF-MF). The objective of this study is to provide an evaluation of current ELF-MF exposure in workers, the specific contribution of occupational exposure to overall 24-h exposure, and the representativeness of a job exposure matrix (JEM). ELF-MF exposure was monitored in 543 workers for 2 days using personal meters. Time-weighted average (TWA) levels at work, at home and outside the home were calculated. A JEM based on the 1988 International Standard Classification of Occupations (ISCO 88) was created. Median exposure at work, at home and outside the home were 0.14, 0.03 and 0.05 μT, respectively. Occupational exposure accounted for about 60% of 24-h exposure. In the JEM, about 50% of the classified occupations included significantly different individual TWAs. Occupational exposure to ELF-MF appeared low. Median exposure levels at home and outside were 20-28% of the occupational level, giving a minor contribution to overall day-to-day exposure. The frequent occurrence of workers with different TWA included under the same job title highlights the risk of misclassification in epidemiological studies on ELF-MF effects based on JEM.
Objectives:
Occupational exposure assessment for population-based case-control studies is challenging due to the wide variety of industries and occupations encountered by study participants. We developed and evaluated statistical models to estimate the intensity of exposure to three chlorinated solvents-methylene chloride, 1,1,1-trichloroethane, and trichloroethylene-using a database of air measurement data and associated exposure determinants.
Methods:
A measurement database was developed after an extensive review of the published industrial hygiene literature. The database of nearly 3000 measurements or summary measurements included sample size, measurement characteristics (year, duration, and type), and several potential exposure determinants associated with the measurements: mechanism of release (e.g. evaporation), process condition, temperature, usage rate, type of ventilation, location, presence of a confined space, and proximity to the source. The natural log-transformed measurement levels in the exposure database were modeled as a function of the measurement characteristics and exposure determinants using maximum likelihood methods. Assuming a single lognormal distribution of the measurements, an arithmetic mean exposure intensity level was estimated for each unique combination of exposure determinants and decade.
Results:
The proportions of variability in the measurement data explained by the modeled measurement characteristics and exposure determinants were 36, 38, and 54% for methylene chloride, 1,1,1-trichloroethane, and trichloroethylene, respectively. Model parameter estimates for the exposure determinants were in the anticipated direction. Exposure intensity estimates were plausible and exhibited internal consistency, but the ability to evaluate validity was limited.
Conclusions:
These prediction models can be used to estimate chlorinated solvent exposure intensity for jobs reported by population-based case-control study participants that have sufficiently detailed information regarding the exposure determinants.
Pesticides have been associated with increased risks for a range of conditions including Parkinson's disease, but identifying the agents responsible has proven challenging. Improved pesticide exposure estimates would increase the power of epidemiological studies to detect such an association if one exists.
Categories of pesticide use were identified from the tasks reported in a previous community-based case-control study in Scotland. Typical pesticides used in each task in each decade were identified from published scientific and grey literature and from expert interviews, with the number of potential agents collapsed into 10 groups of pesticides. A pesticide usage database was then created, using the task list and the typical pesticide groups employed in those tasks across seven decades spanning the period 1945-2005. Information about the method of application and concentration of pesticides used in these tasks was then incorporated into the database.
A list was generated of 81 tasks involving pesticide exposure in Scotland covering seven decades producing a total of 846 task per pesticide per decade combinations. A Task-Exposure Matrix for PESTicides (TEMPEST) was produced by two occupational hygienists who quantified the likely probability and intensity of inhalation and dermal exposures for each pesticide group for a given use during each decade.
TEMPEST provides a basis for assessing exposures to specific pesticide groups in Scotland covering the period 1945-2005. The methods used to develop TEMPEST could be used in a retrospective assessment of occupational exposure to pesticides for Scottish epidemiological studies or adapted for use in other countries.
Environmental exposures to ambient air particulate matter (PM), ozone (O(3)), environmental tobacco smoke (ETS), and to dioxin and related compounds are of considerable public health concern, and risk assessments for them have generally been based on linear, non-threshold models derived from epidemiological study data. While the epidemiological databases for PM, O(3), and ETS have been sufficient to show that adverse health effects are occurring, the relative risks have been quite low, and it has not been possible, to date, to identify thresholds or non-linear relationships for them. For dioxin and related compounds, the evidence for excess cancer risks has been inadequate to establish causality, and there is suggestive evidence that hormesis may have occurred.
An extensive literature review of published metalworking fluid (MWF) aerosol measurement data was conducted to identify the major determinants that may affect exposure to aerosol fractions (total or inhalable, thoracic and respirable) and mass median diameters (MMDs). The identification of determinants was conducted through published studies and analysis of published measurement levels. For the latter, weighted arithmetic means (WAMs) by number of measurements were calculated and compared using analysis of variance and t-tests. The literature review found that the major factors affecting aerosol exposure levels were, primarily, decade, type of industry, operation and fluid and engineering control measures. Our analysis of total aerosol levels found a significant decline in measured levels from an average of 5.36 mg m(-3) prior to the 1970s and 2.52 mg m(-3) in the 1970s to 1.21 mg m(-3) in the 1980s, 0.50 mg m(-3) in the 1990s and 0.55 mg m(-3) in the 2000s. Significant declines from the 1990s to the 2000s also were found in thoracic fraction levels (0.48 versus 0.40 mg m(-3)), but not for the respirable fraction. The WAMs for the auto (1.47 mg m(-3)) and auto parts manufacturing industry (1.83 mg m(-3)) were significantly higher than that for small-job machine shops (0.68 mg m(-3)). In addition, a significant difference in the thoracic WAM was found between the automotive industry (0.46 mg m(-3)) and small-job machine shops (0.32 mg m(-3)). Operation type, in particular, grinding, was a significant factor affecting the total aerosol fraction [grinding operations (1.75 mg m(-3)) versus other machining (0.95 mg m(-3))], but the levels associated with these operations were not statistically different for either the thoracic or the respirable fractions. Across all decades, the total aerosol fraction for straight oils (1.49 mg m(-3)) was higher than for other fluid types (soluble = 1.08 mg m(-3), synthetic = 0.52 mg m(-3) and semisynthetic = 0.50 mg m(-3)). Fluid type was also found to be partly associated with differences in the respirable fraction level. We found that the total aerosols were measured by a variety of sampling media, devices and analytical methods. This diversity of approaches makes interpretation of the study results difficult. In conclusion, both the literature review and the measurement data analyzed found that decade and type of industry, operation and fluid were important determinants of total aerosol exposure. Industry type and fluid type were associated with differences in exposure to the thoracic and respirable fraction levels, respectively.
We investigated the association between occupational exposure to extremely low-frequency magnetic fields (MFs) and the risk of glioma and meningioma. Occupational exposure to MF was assessed for 489 glioma cases, 197 meningioma cases, and 799 controls enrolled in a hospital-based case-control study. Lifetime occupational history questionnaires were administered to all subjects; for 24% of jobs, these were supplemented with job-specific questionnaires, or "job modules," to obtain information on the use of electrically powered tools or equipment at work. Job-specific quantitative estimates for exposure to MF in milligauss were assigned using a previously published job exposure matrix (JEM) with modification based on the job modules. Jobs were categorized as < or =1.5 mG, >1.5 to <3.0 mG, and > or =3.0 mG. Four exposure metrics were evaluated: (1) maximum exposed job; (2) total years of exposure >1.5 mG; (3) cumulative lifetime exposure; and (4) average lifetime exposure. Odds ratios (ORs) were calculated using unconditional logistic regression with adjustment for the age, gender, and hospital site. The job modules increased the number of jobs with exposure > or =3.0 mG from 4% to 7% relative to the JEM. No statistically significant elevation in ORs or trends in ORs across exposure categories was observed using four different exposure metrics for the three tumor types analyzed. Occupational exposure to MFs assessed using job modules was not associated with an increase in the risk for glioma, glioblastoma, or meningioma among the subjects evaluated in this study.
Random error (misclassification) in exposure measurements usually biases a relative risk, regression coefficient, or other effect measure towards the null value (no association). The most important exception is Berkson type error, which causes little or no bias. Berkson type error arises, in particular, due to use of group average exposure in place of individual values. Random error in exposure measurements, Berkson or otherwise, reduces the power of a study, making it more likely that real associations are not detected. Random error in confounding variables compromises the control of their effect, leaving residual confounding. Random error in a variable that modifies the effect of exposure on health--for example, an indicator of susceptibility--tends to diminish the observed modification of effect, but error in the exposure can create a supurious appearance of modification. Methods are available to correct for bias (but not generally power loss) due to measurement error, if information on the magnitude and type of error is available. These methods can be complicated to use, however, and should be used cautiously as "correction" can magnify confounding if it is present.
Exposure-response trends in occupational studies of chronic disease are often modeled via log-linear models with cumulative exposure as the metric of interest. Exposure levels for most subjects are often unknown, but can be estimated by assigning known job-specific mean exposure levels from a sample of workers to all workers. Such assignment results in (nondifferential) measurement error of the Berkson type, which does not bias the estimate of exposure effect in linear models but can result in substantial bias in log-linear models with dichotomous outcomes. This bias was explored in estimated exposure-response trends using cumulative exposure.
Simulations were conducted under the assumptions that (i) exposure level is assigned to all workers based on the job-specific means from a sample of workers, (ii) exposure level and duration are log-normal, (iii) the true exposure-response model is log-linear for cumulative exposure, (iv) the disease is rare, and (v) the variance of job-specific exposure level increases with its job-specific mean. Results Assignment of job-specific mean exposure levels from a sample of workers causes an upward bias in the estimated exposure-response trend when there is little variance in the duration of exposure but causes a downward bias when duration has a large variance. This bias can be substantial (eg, 30-50%).
Berkson errors in exposure result in little bias in estimating exposure-response trends when the standard deviation of duration is approximately equal to its mean, which is common in many occupational studies. No bias occurs when the variance of exposure level is constant across jobs, but such conditions are probably uncommon.
One important source of error in study results is error in measuring exposures. When interpreting study results, one should consider the impact that exposure-measurement error (EME) might have had on study results.
To assess how often this consideration is made and the form it takes, journal articles were randomly sampled from original articles appearing in the American Journal of Epidemiology and Epidemiology in 2001, and the International Journal of Epidemiology between December 2000 and October 2001.
Twenty-two (39%) of the 57 articles surveyed mentioned nothing about EME. Of the 35 articles that mentioned something about EME, 16 articles described qualitatively the effect EME could have had on study results. Only one study quantified the impact of EME on study results; the investigators used a sensitivity analysis. Few authors discussed the measurement error in their study in any detail.
Overall, the potential impact of EME on error in epidemiologic study results appears to be ignored frequently in practice.
A population-based job exposure matrix (JEM) was developed to assess personal exposures to power-frequency magnetic fields (MF) for epidemiologic studies. The JEM compiled 2,317 MF measurements taken on or near workers by 10 studies in the United States, Sweden, New Zealand, Finland, and Italy. A database was assembled from the original data for six studies plus summary statistics grouped by occupation from four other published studies. The job descriptions were coded into the 1980 Standard Occupational Classification system (SOC) and then translated to the 1980 job categories of the U.S. Bureau of the Census (BOC). For each job category, the JEM database calculated the arithmetic mean, standard deviation, geometric mean, and geometric standard deviation of the workday-average MF magnitude from the combined data. Analysis of variance demonstrated that the combining of MF data from the different sources was justified, and that the homogeneity of MF exposures in the SOC occupations was comparable to JEMs for solvents and particulates. BOC occupation accounted for 30% of the MF variance (p < 10(-6)), and the contrast (ratio of the between-job variance to the total of within- and between-job variances) was 88%. Jobs lacking data had their exposures inferred from measurements on similar occupations. The JEM provided MF exposures for 97% of the person-months in a population-based case-control study and 95% of the jobs on death certificates in a registry study covering 22 states. Therefore, we expect this JEM to be useful in other population-based epidemiologic studies.
Associations between oligomeric isocyanate exposure, sensitization, and respiratory disease have received little attention, despite the extensive use of isocyanate oligomers.
To investigate exposure-response relationships of respiratory symptoms and sensitization in a large population occupationally exposed to isocyanate oligomers during spray painting.
The prevalence of respiratory symptoms and sensitization was assessed in 581 workers in the spray-painting industry. Personal exposure was estimated by combining personal task-based inhalatory exposure measurements and time activity information. Specific IgE and IgG to hexamethylene diisocyanate (HDI) were assessed in serum by ImmunoCAP assay and enzyme immunoassays using vapor and liquid phase HDI-human serum albumin (HDI-HSA) and HSA conjugates prepared with oligomeric HDI.
Respiratory symptoms were more prevalent in exposed workers than among comparison office workers. Log-linear exposure-response associations were found for asthmalike symptoms, chronic obstructive pulmonary disease-like symptoms, and work-related chest tightness (prevalence ratios for an interquartile range increase in exposure of 1.2, 1.3 and 2.0, respectively; P </= 0.05). The prevalence of specific IgE sensitization was low (up to 4.2% in spray painters). Nevertheless, IgE to N100 (oligomeric HDI)-HSA was associated with exposure and work-related chest tightness. The prevalence of specific IgG was higher (2-50.4%) and strongly associated with exposure.
The results provide evidence of exposure-response relationships for both work-related and non-work-related respiratory symptoms and specific sensitization in a population exposed to oligomers of HDI. Specific IgE was found in only a minority of symptomatic individuals. Specific IgG seems to be merely an indicator of exposure.
A database of benzene, toluene, and xylene measurements was compiled from an extensive literature review that contained information on several exposure determinants, including job type, operation, mechanism of release, process type, ventilation, temperature, distance from the source, quantity, and location. The database was used to develop statistical models for benzene, toluene, and xylene exposure as a function of operation and other workplace determinants. These models can be used to predict exposure levels for subjects enrolled in community-based case-control studies. This article presents the derived parameter estimates for specific operations and additional workplace exposure determinants and describes a number of statistical and data limitation issues that are inherent in determinants modeling of historical published data. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource(s): a PDF file of QQ plots and a Word file with references used in the benzene/toluene/xylene exposure database].
Premature rupture of membranes (PROM) is a major factor that predisposes women to preterm delivery. Results from previous studies have suggested that there are associations between exposure to air pollution and preterm birth, but evidence of a relationship with PROM is sparse. Modified Community Multiscale Air Quality models were used to estimate mean exposures to particulate matter less than 10 µm or less than 2.5 µm in aerodynamic diameter, nitrogen oxides, carbon monoxide, sulfur dioxide, and ozone among 223,375 singleton deliveries in the Air Quality and Reproductive Health Study (2002-2008). We used log-linear models with generalized estimating equations to estimate adjusted relative risks and 95% confidence intervals for PROM per each interquartile-range increase in pollutants across the whole pregnancy, on the day of delivery, and 5 hours before delivery. Whole-pregnancy exposures to carbon monoxide and sulfur dioxide were associated with an increased risk of PROM (for carbon monoxide, relative risk (RR) = 1.09, 95% confidence interval (CI): 1.04, 1.14; for sulfur dioxide, RR = 1.15, 95% CI: 1.06, 1.25) but not preterm PROM. Ozone exposure increased the risk of PROM on the day of delivery (RR = 1.06, 95% CI: 1.02, 1.09) and 1 day prior (RR = 1.04, 95% CI: 1.01, 1.07). In the 5 hours preceding delivery, there were 3%-7% increases in risk associated with exposure to ozone and particulate matter less than 2.5 µm in aerodynamic diameter and inverse associations with exposure to carbon monoxide and nitrogen oxides. Acute and long-term air pollutant exposures merit further study in relation to PROM.
This article presents a meta-analysis of experiments testing the hypothesis that consciousness (in particular, mental intention) can cause tossed dice to land with specified targets face up. Seventy-three English language reports, published from 1935 to 1987, were retrieved. This litera- ture describes 148 studies reported by a total of 52 investigators, involving more than 2 million dice throws contributed by 2,569 subjects. The full database indicates the presence of a physical bias that artifactually inflated hit rates when higher dice faces (e.g., the "6" face) were used as targets. Analysis of a subset of 59 homogeneous studies employing experimental protocols that controlled for these biases suggests that the experimental effect size is independently replicable, significantly positive, and not explain- ~ able as an artifact of selective reporting or differences in methodological quality. The estimated effect size for the full database lies more than 19 standard deviations from chance while the effect size for the subset of bal- l anced, homogeneous studies lies 2.6 standard deviations from chance. We conclude that this database provides weak cumulative evidence for a genu- ine relationship between mental intention and the fall of dice.
Job exposure matrices (JEMs) are used to measure exposures based on information about particular jobs and tasks. JEMs are especially useful when individual exposure data cannot be obtained. Nonetheless, there may be other workplace exposures associated with the study disease that are not measured in available JEMs. When these exposures are also associated with the exposures measured in the JEM, biases due to uncontrolled confounding will be introduced. Furthermore, individual exposures differ from JEM measurements due to differences in job conditions and worker practices. Uncertainty may also be present at the assessor level since exposure information for each job may be imprecise or incomplete. Assigning individuals a fixed exposure determined by the JEM ignores these uncertainty sources. We examine the uncertainty displayed by bias analyses in a study of occupational electric shocks, occupational magnetic fields, and amyotrophic lateral sclerosis.
© 2015 Society for Risk Analysis.
Objectives: The published literature provides useful exposure measurements that can aid retrospective exposure assessment efforts, but
the analysis of this data is challenging as it is usually reported as means, ranges, and measures of variability. We used
mixed-effects meta-analysis regression models, which are commonly used to summarize health risks from multiple studies, to
predict temporal trends of blood and air lead concentrations in multiple US industries from the published data while accounting
for within- and between-study variability in exposure.
M y brother—the editor of this magazine—and I have spent more than one long afternoon challenging each other to estimate various things, such as the number of bacteria on Earth. The only requirement for this "game" is that the challenger must have an idea of the answer based on some reference source, which presumably is based on the estimates of someone more knowledgeable than either of us. Out of this game arose the idea that someone, maybe us, should write a book on estimating as a kind of useful art. Like most ideas for projects, we never got around to it, but happily, someone else did. That book is Guesstimation —Solving the World's Problems on the Back of a Cocktail Napkin, by Lawrence Weinstein and John A. Adams, both of Old Do-minion University. In teaching a course on semiconductor materials, I usu-ally start off the semester by asking the students to compare the number of grains of sand on all the beaches on Earth to the number of atoms in a single grain of sand. To do this, several assumptions must be made. For example, we might ask what constitutes a beach? How much land mass should we account for? Do we include lakes? How can we know the precise length of the land or ocean interfaces on all of the continents, and, if we could, how much of that is beaches? How deep do we go down into the beach, and how far inland does the beach extend? How large is a grain of sand? What are the atomic size scales in a hy-pothetical grain of sand? To get started, I somewhat arbitrarily choose a "beach" to be 1-m deep and 20-m wide. Also, I roughly suppose that North and South America combined have 50,000 km of beaches, and I multiply that by five to account for the rest of the world. I keep my estimates to multiples of 1, 2, 5, and 10 to make the mental math easier. Decimal points and the numbers 3, 4, 6, 7, 8, and 9 are forbidden, although I keep track of exponents, which can be any integer. I also imagine, without experimentation, that a grain of sand is about half of a millimeter wide, is cubical, and, as a rough guess for a generalized mineral, has atoms at about 2-angstrom centers. Taking all of this estimation into ac-count, I obtain the value of about 10 19 for both the grains of sand on the beaches and atoms in a grain of sand. Of course, alternative assumptions lead to different results, but for my purposes this set of assumptions, or any similar set, is suf-ficient to show that atoms are quite small. This example also helps make the point that semiconductor devices on the nanoscale are impressively small. Through this estima-tion, I hope that the students gain some appreciation for the scales in which they will be working for the semester. This pastime also helps to put numbers in perspective in other ways. For example, it is variously estimated that the number of subatomic particles in the universe is about 10 80 , which can be arrived at by knowing a few basic cosmological facts, including the estimated size of the universe, the num-ber of particles floating around in deep space, the number of stars in a galaxy, and the number of galaxies in the universe. It turns out that either using the material between the stars or using the stars themselves for this estimate gives about the same answer, but a factor of two or so does not change the answer in a meaningful way. The important lesson here is to gain a sense of large numbers as the exponent increases gradually and to note that small changes to exponents make a big difference to the answer. Students without a good feel for the effect of exponents typically guess that the number of grains of sand on the Earth's beaches is 10 100 or 10 1000 . These guesses point out a lack of feel for large numbers, which play an important role in a basic semiconductors class.
Many construction activities can put workers at risk of breathing silica containing dusts, and there is an important body of literature documenting exposure levels using a task-based strategy. In this study, statistical modeling was used to analyze a data set containing 1466 task-based, personal respirable crystalline silica (RCS) measurements gathered from 46 sources to estimate exposure levels during construction tasks and the effects of determinants of exposure. Monte-Carlo simulation was used to recreate individual exposures from summary parameters, and the statistical modeling involved multimodel inference with Tobit models containing combinations of the following exposure variables: sampling year, sampling duration, construction sector, project type, workspace, ventilation, and controls. Exposure levels by task were predicted based on the median reported duration by activity, the year 1998, absence of source control methods, and an equal distribution of the other determinants of exposure. The model containing all the variables explained 60% of the variability and was identified as the best approximating model. Of the 27 tasks contained in the data set, abrasive blasting, masonry chipping, scabbling concrete, tuck pointing, and tunnel boring had estimated geometric means above 0.1mg m(-3) based on the exposure scenario developed. Water-fed tools and local exhaust ventilation were associated with a reduction of 71 and 69% in exposure levels compared with no controls, respectively. The predictive model developed can be used to estimate RCS concentrations for many construction activities in a wide range of circumstances.
Objectives:
Electric shocks have been suggested as a potential risk factor for neurological disease, in particular for amyotrophic lateral sclerosis. While actual exposure to shocks is difficult to measure, occurrence and variation of electric injuries could serve as an exposure proxy. We assessed risk of electric injury, using occupational accident registries across Europe to develop an electric shock job-exposure-matrix (JEM).
Materials and methods:
Injury data were obtained from five European countries, and the number of workers per occupation and country from EUROSTAT was compiled at a 3-digit International Standard Classification of Occupations 1988 level. We pooled accident rates across countries with a random effects model and categorised jobs into low, medium and high risk based on the 75th and 90th percentile. We next compared our JEM to a JEM that classified extremely low frequency magnetic field exposure of jobs into low, medium and high.
Results:
Of 116 job codes, occupations with high potential for electric injury exposure were electrical and electronic equipment mechanics and fitters, building frame workers and finishers, machinery mechanics and fitters, metal moulders and welders, assemblers, mining and construction labourers, metal-products machine operators, ships' decks crews and power production and related plant operators. Agreement between the electrical injury and magnetic field JEM was 67.2%.
Conclusions:
Our JEM classifies occupational titles according to risk of electric injury as a proxy for occurrence of electric shocks. In addition to assessing risk potentially arising from electric shocks, this JEM might contribute to disentangling risks from electric injury from those of extremely low frequency magnetic field exposure.
Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's (1954) book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable (e.g., graduate GPA) is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit (i.e., equal) weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (50 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
In constructing quantitative measures of exposure for the study of chronic occupational disease, researchers have generally used a cumulative exposure calculated as the sum of the products of period-specific exposure concentrations and the time each individual spent in each exposure category. There has been some disagreement and lack of clarity about the use of the geometric or arithmetic mean of exposure for this calculation. This paper explores the difference in the use of the two measures and defines a relative bias introduced with the geometric vs. the arithmetic mean. The magnitude of the bias is calculated in two linear models of possible exposure-response relationships. The theoretical basis for the choice of one mean over the other is then explored. It is suggested that when adopting a linear exposure response model, the arithmetic mean is the more appropriate measure. In other models, such as a linear-log (outcome is proportional to the logarithm of exposure) model, the geometric mean would be more appropriate.
To describe mortality in workers exposed to chrysotile asbestos, and determine exposure-response relationships between asbestos exposure and mortality from lung cancer and asbestosis by fitting and comparing different models.
A prospective cohort consisting of 586 workers in an asbestos textile factory was followed from 1 January 1972 to 31 December 2006. A structured questionnaire was administered to collect personal information and exposure data. Paired concentration samples measured in the workshops were used to convert dust concentrations to fibre concentrations. Individual cumulative asbestos exposure was estimated as the product of fibre concentrations and duration of employment in each job and expressed as fibre-years/ml. The vital status of cohort members was followed annually. Poisson regression analysis was applied to fit log-linear, log-quadratic, power, additive relative risk and categorical models to estimate exposure-response relationships between cumulative fibre exposure and mortality from lung cancer and asbestosis.
Of the 226 deaths (14.6 per 1000 person-years) over the 35-year follow-up, 51 were from lung cancer (3.29 per 1000 person-years) and 37 from asbestosis (2.39 per 1000 person-years). A significant exposure-response relationships with either lung cancer or asbestosis (p<0.001) was observed in the final model. The power model with lagged 10 years was found to be the best model of those evaluated for both lung cancer (β coefficient=0.53) and asbestosis (β coefficient=0.63).
The study confirmed strong associations between exposure to chrysotile asbestos and lung cancer and asbestosis, in which clear exposure-response relationships were observed.
Environmental epidemiologic studies are often hierarchical in nature if they estimate individuals' personal exposures using ambient metrics. Local samples are indirect surrogate measures of true local pollutant concentrations which estimate true personal exposures. These ambient metrics include classical-type nondifferential measurement error. The authors simulated subjects' true exposures and their corresponding surrogate exposures as the mean of local samples and assessed the amount of bias attributable to classical and Berkson measurement error on odds ratios, assuming that the logit of risk depends on true individual-level exposure. The authors calibrated surrogate exposures using scalar transformation functions based on observed within- and between-locality variances and compared regression-calibrated results with naive results using surrogate exposures. The authors further assessed the performance of regression calibration in the presence of Berkson-type error. Following calibration, bias due to classical-type measurement error, resulting in as much as 50% attenuation in naive regression estimates, was eliminated. Berkson-type error appeared to attenuate logistic regression results less than 1%. This regression calibration method reduces effects of classical measurement error that are typical of epidemiologic studies using multiple local surrogate exposures as indirect surrogate exposures for unobserved individual exposures. Berkson-type error did not alter the performance of regression calibration. This regression calibration method does not require a supplemental validation study to compute an attenuation factor.
Epidemiologic studies of mobile phone users have relied on self reporting or billing records to assess exposure. Herein, we report quantitative measurements of mobile-phone power output as a function of phone technology, environmental terrain, and handset design. Radiofrequency (RF) output data were collected using software-modified phones that recorded power control settings, coupled with a mobile system that recorded and analyzed RF fields measured in a phantom head placed in a vehicle. Data collected from three distinct routes (urban, suburban, and rural) were summarized as averages of peak levels and overall averages of RF power output, and were analyzed using analysis of variance methods. Technology was the strongest predictor of RF power output. The older analog technology produced the highest RF levels, whereas CDMA had the lowest, with GSM and TDMA showing similar intermediate levels. We observed generally higher RF power output in rural areas. There was good correlation between average power control settings in the software-modified phones and power measurements in the phantoms. Our findings suggest that phone technology, and to a lesser extent, degree of urbanization, are the two stronger influences on RF power output. Software-modified phones should be useful for improving epidemiologic exposure assessment.
To design and construct a standardised tool to provide exposure information associated with commonly used asbestos products and their related tasks in New South Wales (NSW), Australia.
Asbestos dust exposure measurements taken during workplace inspections in the 1970s and 1980s were collected and stored in an exposure database. Measurements were assigned to specific asbestos product and task groups and divided into two sampling periods 1970-1979 and 1980-1989.
A total of 1578 asbestos air measurements collected from WorkCover and Dust Diseases Board company records were entered into a custom built exposure database. An asbestos-specific exposure matrix (ASTEM) was constructed in Microsoft Excel 2000, consisting of 3 axes incorporating 12 tasks, 8 asbestos products and the 2 time periods based on 872 individual measurements extracted from the exposure database. Each matrix cell contains the mean asbestos exposure levels measured in fibres/ml, 5th and 95th percentiles and number of data points in the set.
An ASTEM has been developed which provides exposure levels for different task/product combinations. When used in conjunction with a detailed occupational history, it will improve exposure estimates of a worker's cumulative asbestos exposure.
The occupational epidemiological literature on extremely low frequency electric and magnetic fields (EMF) and health encompasses a large number of studies of varying design and quality that have addressed many health outcomes, including various cancers, cardiovascular disease, depression and suicide, and neurodegenerative diseases, such as Alzheimer disease and amyotrophic lateral sclerosis (ALS). At a 2006 workshop we reviewed studies of occupational EMF exposure with an emphasis on methodological weaknesses, and proposed analytical ways to address some of these. We also developed research priorities that we hope will address remaining uncertainties. Broadly speaking, extensive epidemiological research conducted during the past 20 years on occupational EMF exposure does not indicate strong or consistent associations with cancer or any other health outcomes. Inconsistent results for many of the outcomes may be attributable to numerous shortcomings in the studies, most notably in exposure assessment. There is, however, no obvious correlation between exposure assessment quality and observed associations. Nevertheless, for future research, the highest priorities emerge in both the areas of exposure assessment and investigation of ALS. To better assess exposure, we call for the development of a more complete job-exposure matrix that combines job title, work environment and task, and an index of exposure to electric fields, magnetic fields, spark discharge, contact current, and other chemical and physical agents. For ALS, we propose an international collaborative study capable of illuminating a reported association with electrical occupations by disentangling the potential roles of electric shocks, magnetic fields and bias. Such a study will potentially lead to evidence-based measures to protect public health.
Methods for deriving quantitative estimates of asbestos-associated health risks are reviewed and their numerous assumptions and uncertainties described. These methods involve extrapolation of risks observed at past relatively high asbestos concentration levels down to usually much lower concentration levels of interest today--in some cases, orders of magnitude lower. These models are used to calculate estimates of the potential risk to workers manufacturing asbestos products and to students enrolled in schools containing asbestos products. The potential risk to workers exposed for 40 yr to 0.5 fibers per milliliter (f/ml) of mixed asbestos fiber type (a permissible workplace exposure limit under consideration by the Occupational Safety and Health Administration (OSHA) ) are estimated as 82 lifetime excess cancers per 10,000 exposed. The risk to students exposed to an average asbestos concentration of 0.001 f/ml of mixed asbestos fiber types for an average enrollment period of 6 school years is estimated as 5 lifetime excess cancers per one million exposed. If the school exposure is to chrysotile asbestos only, then the estimated risk is 1.5 lifetime excess cancers per million. Risks from other causes are presented for comparison; e.g., annual rates (per million) of 10 deaths from high school football, 14 from bicycling (10-14 yr of age), 5 to 20 for whooping cough vaccination. Decisions concerning asbestos products require participation of all parties involved and should only be made after a scientifically defensible estimate of the associated risk has been obtained. In many cases to date, such decisions have been made without adequate consideration of the level of risk or the cost-effectiveness of attempts to lower the potential risk. 73 references.
A method is presented which links on-site electromagnetic field monitoring data with pre-existing work history data. The linkage is used to estimate cumulative and average annualized magnetic field exposure for a case-control study. On-site electromagnetic field monitoring data for 1,966 volunteer utility employees, at 59 sites in the United States and three other countries, were obtained from a large project (the EMDEX project) designed to collect, analyze, and document 60-Hz electric and magnetic field exposures for a diverse population. These data represent 9 primary work environments, and 16 job classification categories, amounting to 144 unique job categories which were consolidated using the job-exposure matrix presented into 282 three-digit Dictionary of Occupational Title (DOT) codes. The DOT code categories were then linked to lifetime occupational histories from a case-control study of leukemia. The method may be extended to link additional job titles with monitoring information. Job titles linked with electromagnetic field monitoring information provide more specific estimates of exposure intensity than previous ordinal estimates of exposure. Therefore, estimates of cumulative electromagnetic field exposure are achievable, as well as high and low level exposure estimates.
Since substantial bias can result from assigning some type of mean exposure to a group, risk assessments based on epidemiological data should avoid the grouping of data whenever possible. However, ungrouped data are frequently unavailable, and the question arises as to whether an arithmetic or geometric mean is the most appropriate summary measure of exposure. It is argued in this paper that one should use the type of mean for which the total risk that would result if every member of the population was exposed to the mean level is as close as possible to the actual total population risk. Using this criterion an arithmetic mean is always preferred over a geometric mean whenever the dose response is convex. In each of several data sets examined in this paper for which the dose response was not convex, an arithmetic mean was still preferred based on this criterion.
Epidemiological studies and laboratory research suggest that exposure to extremely low frequency (<300 Hz) magnetic fields is associated with an increase in risk of developing a number of rare diseases including Ieukaemia. Overall the risks to health appear to be small but a more accurate exposure assessment technique would help evaluate the true extent of any health effects. In this pilot study we aimed to identify and evaluate personal, work and environmental factors and their influence on measured exposure levels with a view to developing a method of reconstructing exposure.
Office workers and power utility workers were studied using personal dosimeters to measure magnetic field exposure, along with frequent observation or measurement of factors related to exposure. Factors such as average and closest distance to source, time at position and current flow were combined in a series of metrics to investigate simple models of personal exposure.
The results indicate that mean and peak magnetic field exposure levels are linked to current flow and the average distance of a worker from the source of the magnetic field. These factors are more accurate predictors of high exposures than they are of lower and average levels. It may be possible, with further work, to produce a model of exposure to magnetic fields for use in epidemlo logical studies. © 1998 British Occupational Hygiene Society. Published by Elsevler Science Ltd.
Most recent epidemiologic studies investigating the potential health effects of occupational magnetic field (MF) exposure have relied on MF measurement data linked to job titles. These measurements are summarized by occupational categories, which represent similar groups of job titles. However, job titles alone explain only a small proportion of exposure variability. A comprehensive MF occupational exposure database was used to (1) develop summary job-specific estimates of magnetic field exposure, (2) evaluate the impact of incorporating work environment data to improve electric and magnetic field exposure assessment, and (3) evaluate the use of random versus nonrandom sampling when estimating mean MF exposure levels by occupational categories. Uniform classification systems were developed for occupational and work environment data. A factorial design was used to summarize and calculate arithmetic means and 95% confidence intervals for occupational MF data, assuming that the total variation in MF exposure resulted from variation in occupation, work environment, utility, worker, and day. Occupation-specific means varied across different work environments, particularly for craft workers. Although within-worker and between-worker variability account for a large proportion (over 50%) of exposure variation, work environment (24%) accounted for more exposure variability than occupation (4.9%) or utility (15%). Some differences were observed when results were compared from surveys that used random and nonrandom sampling; however, these differences were not consistent or systematic. It was concluded that MF exposure assessment should consider work environment in addition to job title to reduce exposure misclassification.
The job exposure matrix (JEM) has been employed to assign cumulative exposure to workers in many epidemiological studies. In these studies, where quantitative data are available, all workers with the same job title and duration are usually assigned similar cumulative exposures, expressed in mgm(-3)xyears. However, if the job is composed of multiple tasks, each with its own specific exposure profile, then assigning all workers within a job the same mean exposure can lead to misclassification of exposure. This variability of exposure within job titles is one of the major weaknesses of JEMs. A method is presented for reducing the variability in the JEM methodology, which has been called the task exposure matrix (TEM). By summing the cumulative exposures of a worker over all the tasks worked within a job title, it is possible to address the variability of exposure within the job title, and reduce possible exposure misclassification. The construction of a TEM is outlined and its application in the context of a study in the primary aluminium industry is described. The TEM was found to assign significantly different cumulative exposures to the majority of workers in the study, compared with the JEM and the degree of difference in cumulative exposure between the JEM and the TEM varied greatly between contaminants.
A task exposure database (TED) was developed to facilitate data collation for construction of a task exposure matrix (TEM) for Healthwise, a series of studies on cancer and respiratory morbidity in the alumina and primary aluminium industry. Following the construction of job classifications for the eight study sites, the site hygienists identified all historical air monitoring time-weighted average (TWA) data, from their respective sites. The earliest data were sampled in the late 1970s, and over 17,000 personal samples were recorded over the eight sites over a twenty-year period. TED, a Microsoft Access database, was developed for use by site occupational hygienists to collate these exposure data across the mines, refineries and smelters. All data conforming to strict criteria for use were recorded using TED and provided to the study group. Following the individual data point entry, a calculator program in TED systematically calculated the geometric means, arithmetic means, and maximum and minimum results at the task level. Other features of TED included fields for flagging "significant changes" and "stepwise changes" in exposure. TED established a standardised means of data collation that later formed the basis for the construction of a TEM for the study. A TEM is similar to a job exposure matrix (JEM) except that the basic unit of categorization is at the task level instead of at the job level. Both a TEM and JEM have been constructed independently for Healthwise. The possible reduction of exposure misclassification and improvement in validity of exposure characterization with the use of the TEM, is currently under investigation. The Healthwise TEM consists of annual TWA and peak data results for each site for various airborne contaminants, including fluorides, coal tar pitch volatiles, sulfur dioxide, inspirable dust, alumina dust, bauxite dust, and oil mist. Construction of the TEM for the Healthwise study was completed in late 1998 and consists of over 33,700 TWA years of task exposure data.
There is a growing need for transparency concerning ways in which existing exposure data are weighted for their relative value and quality. Currently, this evaluation is largely subjective and is dependent on the quality of the judgement of the individual assessor or expert group. In this paper some general guidelines are presented for a quality assessment procedure. Such a predetermined procedure potentially enhances the consistency among different assessors and assessments and facilitates harmonization of assessment procedures. The guidelines are presented in the context of a decision tree with four decision rules for data quality, i.e. 'availability of occupational hygiene information', 'variability and precision issues', 'internal validity' and 'external validity'. These methodological issues are considered to be the most important aspects of data quality and will be discussed in this paper. The decision tree eventually results in three quality classes, i.e. exposure data providing sufficient information, supplementary information and data which should be excluded from the exposure assessment process. The guidelines should not be used in a rigid manner but have to be interpreted in the light of the particular circumstances and purposes of the assessment.
Most epidemiological studies on adverse health effects among women in relation to occupational magnetic field exposure have been based on information about men's exposure.
To create a job-exposure matrix for occupational exposure to extremely low frequency magnetic fields among women.
Measurements were performed using personal magnetic field meters (Emdex Lite) carried by the subjects for 24 hours on a normal workday. Subjects were volunteer women working in the occupations identified as common among women in Stockholm County based on the 1980 census. A total of 471 measurements were made in 49 different occupations, with a minimum of 5 and a maximum of 24 measurements in each occupation. The included occupations cover about 85% of the female population gainfully employed in 1980. Parameters representing average and peak magnetic field exposures, temporal change in the exposure, and proportion of time spent above certain exposure levels were calculated both for the workday and for the total 24 hour period grouped by occupational titles. The occupations with higher than average exposure were cashiers, working proprietors in retail trade, air stewardesses, dental nurses, cooks, post-office clerks and kitchen maids.
This new job-exposure matrix substantially increases the knowledge about magnetic field exposure among women and can be used for exposure assessment in future studies.
This study presents a procedure allowing the numerical synthesis of exposure data reported in different ways in the literature, including summary parameters and single measurements. The procedure was applied to literature regarding formaldehyde exposure in the reconstituted wood panels industry, including oriented-strand board (OSB), medium density fibre board (MDF) and particle board (PB).
For each publication providing summary parameters we estimated geometric means (GM) and geometric standard deviations (GSD) by assuming lognormality of exposure levels. Monte Carlo simulation was performed to re-create datasets from the sample sizes and estimated GMs and GSDs, allowing their subsequent formatting together with the single measurements. The precision and bias of the methods used to estimate GMs and GSDs were evaluated.
Altogether, the 13 articles included in our study yielded a final database of 874 data, of which 732 were simulated. For both area and personal data, exposures corresponding to MDF and PB were similar while OSB levels were lower. The most recent available personal levels (1985-1994) were highest in PB for jobs performed in the vicinity of the press (GM=0.63 mg m-3). Corresponding area levels were highest for PB in the main production zone (GM=0.43 mg m-3). Mixed-effects models fitted to area PB data explained 38% of the total variability. A 6-fold decrease in exposures from 1965 to 1995 was estimated. Replication of the simulation process yielded relative standard deviations of the calculated GMs and GSDs between 10 and 20%. The relative biases of the methods used to estimate GMs and GSDs varied across methods and decreased with higher sample sizes (from approximately 15% for n=5 to less than 5% for n=30, in absolute value). The precision also varied across methods and improved with higher sample sizes (from approximately 30% for n=5 to approximately 10% for n=30).
This methodology constitutes a new meta-analysis tool that should improve the interpretation of industrial hygiene literature data, but needs to be further validated.
Manual for Measuring Occupational Electric and Magnetic Field Exposures. DHHS, CDC, National Institute for Occupational Safety and Health (NIOSH)
- Jd Bowman
- Ma Kelsh
- Wt Kaune
Bowman JD, Kelsh MA, Kaune WT. Manual for Measuring Occupational Electric and
Magnetic Field Exposures. DHHS, CDC, National Institute for Occupational Safety
and Health (NIOSH): Cincinnati, OH, USA, 1998: http://www.cdc.gov/niosh/docs/
98-154/pdfs/98-154.pdf.
Harrell Miscellaneous. R package version 3.17-0 2015: http://CRAN.R-project.org/package
- Fe Harrell
- Jr
- Hmisc
Harrell FE Jr, Hmisc: Harrell Miscellaneous. R package version 3.17-0 2015:
http://CRAN.R-project.org/package; http://www.inside-r.org/packages/cran/hmisc/docs/
wtd.stats = Hmisc.