Estimating occupational beryllium exposure from compliance monitoring data.

This article was downloaded by: [McGill University Library] On: 02 April 2013, At: 09:07 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Archives of Environmental & Occupational Health Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/vaeh20

Estimating Occupational Beryllium Exposure from Compliance Monitoring Data a

Michele P. Hamm MSc & Igor Burstyn PhD

b

a

Community and Occupational Medicine Program, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada b

Department of Environmental and Occupational Health, School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA Version of record first published: 17 Jun 2011.

To cite this article: Michele P. Hamm MSc & Igor Burstyn PhD (2011): Estimating Occupational Beryllium Exposure from Compliance Monitoring Data, Archives of Environmental & Occupational Health, 66:2, 75-86 To link to this article: http://dx.doi.org/10.1080/19338244.2010.511309

PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

Archives of Environmental & Occupational Health, Vol. 66, No. 2, 2011 C 2011 Taylor & Francis Group, LLC Copyright

Estimating Occupational Beryllium Exposure from Compliance Monitoring Data Downloaded by [McGill University Library] at 09:07 02 April 2013

Michele P. Hamm, MSc; Igor Burstyn, PhD

ABSTRACT. Occupational exposure to beryllium is widespread and is a health risk. The objectives of this study were to develop plausible models to estimate occupational airborne beryllium exposure. Compliance monitoring data were obtained from the Occupational Safety and Health Administration for 12,148 personal measurements of beryllium exposure from 1979 to 2005. Industry codes were maintained as reported or collapsed based on the number of measurements per cell of a job-exposure matrix (JEM). Probability of exposure was predicted based on year, industry, job, and sampling duration. In these models, probability of exposure decreased over time, was highest in full-shift personal samples, and varied with industry and job. The probability of exposure was calculated using 6 JEMs, each providing similar rankings of the likelihood of non-negligible exposure to beryllium. These statistical models, with expert appraisal, are suitable for the assessment of the probability of elevated occupational exposure to beryllium. KEYWORDS: epidemiology, job-exposure matrix, statistical exposure model

D

ue to its unique combination of physicochemical properties, beryllium is currently in wide use, with applications in the nuclear, aerospace, telecommunications, electronic, metal alloy, biomedical, and semiconductor industries.1 Recent estimates from data collected by the United States Occupational Health and Safety Administration (US OSHA) and the National Institute for Occupational Safety and Health (NIOSH) place the number of potentially exposed workers in government and private industry in the United States at 134,000, the majority of which are outside of primary beryllium production.2 Although significant, this figure likely remains an underestimate, as sampling has not covered all relevant industries.1 This is alarming because of the increased risk of the development of beryllium sensitization and chronic beryllium disease among exposed workers, even at very low levels of exposure.3 According to OSHA, the permissible exposure limit for beryllium over an 8-hour

work-shift is 2.0 µg/m3.4 The exposure limit was meant to offer protection against chronic beryllium disease,5 but there is some doubt that current exposure limits offer adequate protection if enforced.6 The guidelines of the US Department of Energy are 10 times lower, urging the maintenance of beryllium exposure below a time-weighted average (TWA) of 0.2 µg/m3,7 and an even lower limit of 0.05 µg/m3 for 8-hour TWA exposure to beryllium as inhalable particulate matter was proposed by the American Conference of Governmental and Industrial Hygienists.8 Furthermore, the concerns over risk of lung cancer due to exposure to beryllium further highlight the importance of keeping exposure as low as possible due to the commonly made assumption that there is no threshold for carcinogenicity.9 The interest in occupational exposure to beryllium in the general working population is fueled by the widespread likelihood of exposure and the associated risks to health.2 We

Michele P. Hamm and Igor Burstyn are with the Community and Occupational Medicine Program, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada. Igor Burstyn is also with the Department of Environmental and Occupational Health, School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA. 2011, Vol. 66, No. 2

75

Downloaded by [McGill University Library] at 09:07 02 April 2013

wanted to investigate the utility of US OSHA monitoring data for constructing a general job-exposure matrix (JEM). Although the nonrandom nature of the OSHA strategy to ensure compliance is a known limitation to using compliance monitoring data in epidemiological applications, the data collected in the OSHA Integrated Management Information System (IMIS) has been employed previously for similar investigations of exposure to wood dust in the United States10 and to formaldehyde in Canada.11 Our objectives are to develop several plausible models for estimating beryllium exposure (in essence job-exposure matrices [JEMs]) and to compare the degree of agreement among them in order to judge whether these alternative JEMs are expected to yield different exposure estimates in a hypothetical epidemiological study. Specifically, we will focus on predicting the proportion of workers exposed to “elevated above background” concentrations (defined as detectable) of beryllium in a given year, job/task, and industry. In addition, we examine the exposure ranking among those with this detectable exposure in relation to exposure deemed to be elevated in the work of Henneberger at al2 on essentially the same data. We acknowledge the limitations of the data available to us and use it in the belief that some monitoring data, however imperfect, can be used to inform exposure assessment. The JEMs we develop are not designed for any study in particular, but are meant to serve as general research tools for epidemiologists interested in assessing the health impact of beryllium in the general working population, ie, outside of heavily exposed occupational cohorts.

METHODS Data and definition of exposure threshold US OSHA monitors exposure to ensure compliance with government-mandated occupational exposure limits, as well as in response to requests for exposure assessment. Government inspectors can elect to monitor exposures if they suspect that a workplace is not compliant, which is known as worst-case sampling strategy, the goal of which is to investigate suspicion of overexposure or exceedance. This is likely to result in the observed exposure being skewed towards higher values compared to typical exposures that would be observed in a random survey. The measurement of metals in the air includes a panel of compounds, beryllium among them. We requested from OSHA IMIS all samples in which measurement of beryllium was attempted, without regard for whether the measurements were made upon suspicion of noncompliance with exposure limits for beryllium or another metal, as information on the specific reason for monitoring exposure to metals was not recorded. The resulting data set consisted of 12,148 individual (not stationary/area) airborne beryllium exposure measurements, collected between May 1979 and July 2005. The Standard Industrial Classification codes (SICCs; US Office of Management and Budget) asso76

ciated with every measurement were extracted, as well as the year of measurement and the description of the job performed by the monitored individual (details below). One of the limitations of OSHA data is that whether a measurement is nondetectable is coded by the same variable that determines whether the measurement is the result of long- or short-term sampling. This makes it problematic to model exposure intensity because long- and short-term samples have different sensitivities such that the short-term samples will have a larger exposure measurement method limit of detection (LOD). To overcome these complications, the choice of an exposure threshold that defines the presence of exposure in such a way as to exceed the analytical limit of detection of even a short-term sample would provide a less biased estimate of the proportion of samples that had “elevated” exposure. The alternative is to perform sensitivity analyses of modeled exposure concentrations using the assumptions about censored exposure levels as conducted by Lavoué et al,11 but these are not sensible in the present context of the large proportion of nondetectable measurements that is typical of beryllium exposure surveys12 and OSHA IMIS data in particular.2 Beryllium was measured using different methods in our data set (actual method not recorded in IMIS), but flame spectroscopy was the least sensitive among them, with a limit of detection of 0.1 µg/m3.13 Henneberger et al2 also reported that in OSHA IMIS data, only exposures above 0.1 µg/m3 could be reliably inferred to indicate detectable exposure, both because of the limited sensitivity of historically used analytical methods and the tendency in the database to record nondetectable measurements as “0.1”. Therefore, we chose >0.1 µg/m3 to define the presence of “exposure” to airborne beryllium. However, we do recognize that the limit of detection does depend on volume of air sampled, resulting in different limits of detection in long- versus short-term samples. We have no information to address this problem other than choosing exposure thresholds that exceed most (if not all) limits of detection. Beryllium exposure ≥0.5 µg/m3 was also examined, because it has been used as a definition of high exposure on the grounds that it is the current recommended exposure limit promulgated by NIOSH.2 Coding job descriptions Job descriptions were provided in the database as uncoded free text descriptors recorded by individuals who made measurements at workplaces. Based on similarities between categories, all jobs with legible descriptions were grouped together when appropriate. This was completed by one author (M.H.) and checked for accuracy by the other (I.B.). For example, mold assembler, mold maker, mold clean, south moulding, central moulding, mold man, mold operator, molder/pourer, mold line hunter, molder/core loop, molding/casting operator, sand molder, molder/finisher, green sand mold, automatic molder, and auto molder were all recoded as “molder.” To our knowledge, this time-consuming task was never attempted for OSHA IMIS exposure data. The Archives of Environmental & Occupational Health

OSHA compliance monitoring data for beryllium (Be)

Maintain original SICCs at 3-4 digit codes

Collapse SICCs at 3-4 digit codes to form large groups

JEM original


Threshold >0.1 ug/m3

1

Job descripons, Year, Sampling duraon

JEM collapsed

Threshold ≥0.5 ug/m3

2


3

Poisson regression model of probability of exposure


4



5

6

Fig. 1. The flow chart of six Be job-exposure matrices (JEM) constructed to predict Pr([Be]) > threshold. (Figure is provided in color online.)

recoding was managed via a custom-built Microsoft Access 2000 application. Additionally, job categories were assembled to reflect their a priori likelihood of beryllium exposure. Using the classification scheme employed by Henneberger et al2 based on OSHA and NIOSH data from 1979 to 1996, any jobs considered to be “highly exposed” (defined as having 2 or more measurements ≥0.5 µg/m3) were maintained in their original form, as were jobs in which the number of individual measurements was ≥30 µg/m3 and the number of members with beryllium exposure >0.1 µg/m3 was greater than zero. Jobs with fewer than 30 measurements, but with the number of members with beryllium exposure >0.1 µg/m3 greater than zero were classified as “other.” Forty-four job categories were defined, with 41 jobs and classifications for “missing,” “other,” and “unknown” (see Appendix). The entire job-coding scheme is available from the corresponding author upon request. Setting the target of a sample size for each group to be at least 30 measurements in which some are detectable was somewhat arbitrary, but was guided by the desire to have stable estimates of the probability of exposure in each cell of the JEM. This notion of the desirability of a “large sample” size was retained in aggregating across industry codes (below). Job-exposure matrices that use only industry codes (SICCs) All job-exposure matrices created for the study are summarized in the flow chart in Figure 1. The SICC classification system provides a hierarchical categorization of specific industry identifiers and is used by 2011, Vol. 66, No. 2

OSHA. This system consists of major industrial sectors (divisions A to J) that are segregated into major groups denoted by 2-digit codes (4 to 19 within a division); within major groups, the 3-digit industry groups are nested that are further subdivided into 4-digit SIC codes. The codes characterize what the employer/industry does and do not reflect characteristics of occupations or tasks performed by individuals. We tested 2 systems of classifying SICCs. In the first (JEMoriginal ), SICCs were maintained as provided, in the format of 3- and 4-digit codes, and the probabilities of beryllium exposure >0.1 and ≥0.5 µg/m3 were determined for each code. In the second (JEMcollapsed ), all SICCs were initially collapsed to 2-digit codes. If at 2 digits, the frequency of beryllium exposure >0.1 µg/m3 was ≥30, the SICC was reexpanded to its 4digit form. When expanded, if the number of measurements was 0.1 µg/m3 was >0, the SICC was left as is. If, however, the number of measurements was 0.1 µg/m3, it was put into an “other” category. In the case of beryllium exposure ≥0.5 µg/m3, the same collapsing scheme was used, and any categories with no measurements at the higher exposure level were put into the “other” category. For each system of classifying SICCs, the proportion of personal beryllium measurements was calculated, yielding 2 straightforward JEMs that ignore time trends, duration of exposure monitoring, and type of work performed within industry. SICCs’ structure and definitions are in the public domain: 77

http://www.osha.gov/pls/imis/sicsearch.html (accessed August 2009).


Regression model of probability of exposure and comparing JEMs The probability of beryllium exposure >0.1 or ≥0.5 µg/m3 was then modeled using modified Poisson regression,14 incorporating year (since 1978), an indicator of whether the sample was a TWA, SICCs from JEMcollapsed (described above), and systematically recoded job descriptions as predictors. We used Poisson rather than logistic regression, as it provides more precise effect estimates and allows for ease of interpretation of relative risks. The model has the following general form: loge (Pr[y = 1]) = a + b × X, where y = 1 if beryllium exposure exceeds investigator-set threshold and y = 0 otherwise, a is the intercept reflecting overall prevalence of exposure above a given threshold in the data, b and X are matrices of regression coefficients and predictor variables, respectively. Effect coding was used to allow comparisons of the probability of exposure in a specific category to the overall mean of exposure probability; in doing so, categories for which we do not intend to make predictions, such as “unknown job,” were coded as −1 (ie, omitted). The level of statistical significance was set to have the chance of type I

error of less than 5%. The predicted probability of beryllium exposure above a given threshold was standardized to TWA samples. This predicted value was plotted against the probability of highly exposed workers calculated from each job exposure matrix; Spearman rank and Pearson linear correlations were calculated. All statistical analyses were conducted using Stata version 8.0 (StataCorp, College Station, TX). RESULTS Descriptive analysis Beryllium measurements ranged from concentrations below the level of detection (not specified) to 19,000 µg/m3, although 99% (12,076) were ≤10 µg/m3 and 84% (10,215) were < LOD. All measurements considered in our analysis were taken from personal samples. Most were short-term measurements (11,238). Overall, 9% (1,046) of beryllium measurements were above the 0.1 µg/m3 threshold and 5% (668) were ≥0.5 µg/m3. Although most measurements in the database were short-term, full-shift time-weighted average (TWA) measurements were associated with higher probability of exposure to beryllium in excess of either threshold. For example, of 910 TWA measurements, 510 (56%) had beryllium exposure >0.1 µg/m3 and 297 (33%) had

Table 1.—-Time Trends in the Probability of “Elevated” Exposure to Beryllium Beryllium concentration >0.1 µg/m3 Year 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

78

Total measurements 31 75 111 152 107 655 688 556 838 963 654 974 735 672 697 469 430 358 580 379 409 640 192 188 196 287 112

N 3 26 14 10 26 30 81 11 60 128 26 103 81 42 143 41 21 18 54 13 35 20 15 16 10 10 9

Proportion 0.097 0.347 0.126 0.066 0.243 0.046 0.118 0.020 0.072 0.133 0.040 0.106 0.110 0.063 0.205 0.087 0.049 0.050 0.093 0.034 0.086 0.031 0.078 0.085 0.051 0.035 0.080

Standard error 0.171 0.093 0.089 0.078 0.084 0.038 0.036 0.042 0.033 0.030 0.038 0.030 0.035 0.037 0.034 0.044 0.047 0.052 0.040 0.050 0.047 0.039 0.069 0.070 0.070 0.058 0.091

Beryllium concentration ≥0.5 µg/m3 N

Proportion

Standard error

3 17 8 6 11 22 64 7 40 100 22 62 39 20 95 21 3 11 36 10 31 10 6 9 6 7 2

0.097 0.227 0.072 0.039 0.103 0.034 0.093 0.013 0.048 0.104 0.034 0.064 0.053 0.030 0.136 0.045 0.007 0.031 0.062 0.026 0.076 0.016 0.031 0.048 0.031 0.024 0.018

0.171 0.102 0.091 0.079 0.092 0.038 0.036 0.042 0.034 0.031 0.038 0.031 0.036 0.038 0.035 0.045 0.048 0.052 0.040 0.051 0.048 0.039 0.071 0.071 0.070 0.058 0.094

Archives of Environmental & Occupational Health


exposure ≥0.5 µg/m3, compared to 536 (5%) and 371 (3%) of 11,238 of short-term measurements, respectively. Between 1979 and 2005, a slight trend was observed towards reduced exposure to beryllium, although this seems to be driven by a marked, but isolated, elevation in the probability of beryllium exposure in 1980. The probability of “exposure” >0.1 µg/m3 generally declined to less than 10% after 1993, but prior to that, years with 10% to 20% of samples with detectable beryllium concentrations were not uncommon; the pattern was similar for exposure defined with the criterion of ≥0.5 µg/m3 (Table 1). The job category with the highest proportion of exposed workers to >0.1 µg/m3 of beryllium was “blaster,” with 46% (92) exposed (Appendix 1). This was followed by “foundry” (25; 42%), and “sandblaster” (33; 35%). All other job categories had beryllium exposure >0.1 µg/m3 in fewer than 30% of their workers. The same pattern was observed for job categories associated with beryllium exposure ≥0.5 µg/m3: the highest proportion of exposed measurements occurred within the “blaster” (61; 30%), “foundry” (16; 27%), and “sandblaster” (24; 26%) categories. Most of the beryllium measurements were made in manufacturing industries, followed by construction (Table 2). Using the SICC Division structure, codes falling between SICCs 10 and 14 (mining) were associated with the highest proportion of beryllium exposure at 27% (n = 4) at both the 0.1 and 0.5 µg/m3 cutoffs, followed by SICCs 15 to 17 (construction), with 21% (n = 201) for the 0.1 µg/m3 and 13% (n = 122) for the 0.5 µg/m3 cutoffs. The largest number of beryllium measurements above either threshold was in the manufacturing SICC division (codes 30 to 39), which includes metals, electronic and electrical equipment, ceramics, and metal industries. The other divisions were associated with beryllium exposure in less than 10% of their workers (Table 2). Probability of exposure according to industry JEMoriginal , which was used to calculate the probability of exposure to beryllium at both the 0.1 µg/m3 and 0.5 µg/m3 thresholds, was comprised of 389 SIC classifications plus “unknown” (n = 6). JEMcollapsed , as used in the calculations associated with beryllium exposure >0.1 µg/m3, had 80, and that for exposure ≥0.5 µg/m3 had 65 industries (excluding “unknown/other,” n = 178 for exposure >0.1 µg/m3 and n = 1,564 for exposure ≥0.5 µg/m3). The industry codes associated with a probability of exposure >0.1 µg/m3 of 50% or higher in JEMoriginal and an exposure probability of 25% and higher in JEMcollapsed , as well as the corresponding probabilities of exposure ≥0.5 µg/m3, are illustrated in Table 3. These 2 job-exposure matrices are available from the authors upon request. In JEMoriginal , the probability of beryllium exposure was 100% in 8 industries (SICCs 1081, 2022, 2436, 2491, 2843, 3596, 3716, and 4151). However, in each of these, the number of work2011, Vol. 66, No. 2

ers exposed was small (0.1 µg/m3 and ≥0.5 µg/m3 in relation to the relevant predictor variables are shown in Tables 4 and 5. Data were too sparse to model job-by-industry interactions (detailed cross-tabulation not shown). The statistical models confirmed that exposure to beryllium declined since 1979 and that TWA samples were more likely to report elevated exposures. As shown in Table 4, the SIC codes associated with the largest frequency of exposure >0.1 µg/m3 compared to the overall exposure prevalence were “jewelry, precious metal” (RR 9.1; 95% confidence interval [CI] 6.4 to 12.8), followed by “nonferrous foundries, except aluminum and copper” (RR 7.0; 95% CI 5.7 to 8.5) and “primary metal products, not elsewhere classified” (RR 6.6; 95% CI 4.6 to 9.5). A number of industries had less than average frequency of beryllium exposure, including “gray and ductile foundries” (RR 0.1; 95% CI 0.03 to 0.5) and “electrical machinery, equipment, and supplies” (RR 0.1; 95% CI 0.04 to 0.5). Welders (n = 3,921) were highly represented in the database, but in general were not at an increased risk of elevated beryllium exposure (RR 0.4; 95% CI 0.3 to 0.5). The job associated with the highest risk of beryllium exposure >0.1 µg/m3 was “maintenance” (RR 2.2; 95% CI 1.1 to 4.3). The risk of beryllium exposure ≥0.5 µg/m3 (Table 5) was increased for workers in “metal ores or oil and gas field services or heavy construction” (RR 15.5; 95% CI 8.7 to 27.6), “primary metal products, not elsewhere classified” (RR 14.6; 95% CI 10.0 to 21.3), and “nonferrous foundries, except aluminum and copper” (RR 11.6; 95% CI 8.9 to 15.0). Benchers had the highest risk of beryllium exposure ≥0.5 µg/m3 (RR 2.8; 95% CI 1.4 to 5.7). We now illustrate how the model summarized in Table 4 can be used to populate the cells of the JEM with predicted exposure probabilities. To predict the probability that a worker’s full-shift exposure is >0.1 µg/m3 in 1990, while 79

Table 2.—-Beryllium Exposure by SICC Divisions ∗

SICC division (Standard Industrial Classification Code range) B: Mining (10–14) C: Construction (15–17) D: Manufacturing (20–29) D: Manufacturing (30–39) E: Transportation, Communications, Electric, Gas, and Sanitary Services (40–49) F: Wholesale Trade (50–51) G: Retail Trade (52–59) H: Finance, Insurance, and Real Estate (60–67) I: Services (70–79) I: Services (80–89) J: Public Administration (91–99) Total


∗

≥0.5 µg/m3 beryllium, N

Total

4 201 29 784 11 3 0 0 8 5 1 1, 046

4 122 19 515 3 2 0 0 2 1 0 668

15 945 523 9, 550 210 341 32 3 376 59 94 12, 148

There were no measurements from division A (Agriculture, Forestry, and Fishing).

being an operator in a factory that produces semiconductors, we perform the calculations shown below: 3 under conditions K Probability of exposure >0.1 µg/m x described by X = exp(a) × all x b Example: Probability of exposure >0.1 µg/m3 for K = “operator in SICC 3674 in 1990 who worked full shift” = 0.057 (= exposure frequency associated with the intercept) × 4.605 (= RR for TWA) × 0.971[1990–1978] (= RR for year) × 2.063 (= RR for industry [SICC 3674]) × 1.313 (= RR for job title [operator]) = 0.50 Comparison of 3 exposure assessment methods with 2 thresholds for “exposure” Plotting the TWA-standardized prediction of beryllium exposure >0.1 µg/m3 (using the model in Table 4) against JEMoriginal (SICC as is) yielded a Spearman’s rank correlation of .74. This value was increased somewhat to .78 for the comparison of JEMcollapsed (1-, 2-, 3-, or 4-digit SICCs) against the TWA-standardized predicted values. When JEMoriginal and JEMcollapsed were compared against each other, Spearman’s rank correlation was .92. Pearson (linear) correlations were weaker, but showed a similar trend in their strengths: .66 for JEMoriginal and .68 for JEMcollapsed versus predictions of the model in Table 4, respectively; the linear correlation of JEMoriginal and JEMcollapsed was .90. The prediction of beryllium exposure ≥0.5 µg/m3 using Poisson model against JEMoriginal was associated with a Spearman’s rank correlation of .86 and rose slightly to .93 against JEMcollapsed . The plot of JEMoriginal and JEMcollapsed had a correlation coefficient of .96. The corresponding Pearson correlations were .67, .72, and .91. These plots are not graphically illustrated here. The exposure matrices for exposure to beryllium with cutoffs at 0.1 µg/m3 and at ≥0.5 µg/m3 were highly cor80

>0.1 µg/m3 beryllium, N

related, with Spearman rank coefficients of .97 for both JEMoriginal and JEMcollapsed , and Pearson coefficients of .82 for JEMoriginal and .94 for JEMcollapsed . Poisson regression models from Tables 4 and 5 produced similar predictions of the probability of exposure to beryllium, with ranking being less similar (Spearman correlation .82) than Pearson correlation (.93). The p values for all correction coefficients were 0.1 µg/m3 criteria (≥0.5 µg/m3 values given for comparison) in 2 Job-Exposure Matrices (JEMoriginal and JEMcollapsed )a Be exposure >0.1 µg/m3 SICC

∗

JEMoriginal 1081 1522 1611 2022 2436 2491 2631 2834 2843


3261 3297 3369 3596 3716 3769 3843 3911 3942 4151 4231 8731 JEMcollapsed 1611 1721 1799 2819 3341 3365 3369 3399 384 3911

Description of industry/business

Metal Mining Services General Contractors—Residential Buildings, Other Than Single-Family Highway and Street Construction, Except Elevated Highways Natural, Processed, and Imitation Cheese Softwood Veneer and Plywood Wood Preserving Paperboard Mills Pharmaceutical Preparations Surface Active Agents, Finishing Agents, Sulfonated Oils, and Assistants Vitreous China Plumbing Fixtures and China and Earthenware Fittings and Bathroom Accessories Nonclay Refractories Nonferrous Foundries, Except Aluminum and Copper Scales and Balances, Except Laboratory Motor Homes Guided Missile Space Vehicle Parts and Auxiliary Equipment, NEC Dental Equipment and Supplies Jewelry, Precious Metal Dolls and Stuffed Toys School Buses Terminal and Joint Terminal Maintenance Facilities for Motor Freight Transportation Commercial Physical and Biological Research Highway and Street Construction, Except Elevated Highways Painting and Paper Hanging Special Trade Contractors, NEC Industrial Inorganic Chemicals, NEC Secondary Smelting and Refining on Nonferrous Metals Aluminum Foundries Nonferrous Foundries, Except Aluminum and Copper Primary Metal Products, NEC Electromedical and Electrotherapeutic Apparatus Jewelry, Precious Metal

Be exposure ≥0.5µg/m3

Number exposed

Proportion exposed

Number exposed

Proportion exposed

4 9 14 1 1 1 3 1 3

1 0.529 0.5 1 1 1 0.75 0.5 1

4 3 14 1 0 1 3 1 3

1 0.176 0.5 1 0 1 0.75 0.5 1

6 6 137 2 4 12 16 27 3 1 1 2 14 93 53 7 206 55 137 22 16 27

0.6 0.6 0.578 1 1 0.706 0.8 0.771 0.75 1 0.5 0.5 0.5 0.356 0.344 0.259 0.375 0.301 0.578 0.431 0.372 0.771

6

0.6

6 99 0 1 9 14 17 0 0 0

0.6 0.418 0 0.25 0.529 0.7 0.486 0 0 0

1

0.25

14 65 20 4 158 38 99 21 14 17

0.5 0.249 0.13 0.148 0.288 0.208 0.418 0.412 0.326 0.486

aSee ∗

text for definitions. Standard Industrial Classification Codes.

observed that longer-term measurements are more sensitive in detecting the presence of beryllium, but this can be an artifact of worst-case sampling within the data set: occupational hygienists chose to sample longer when they suspected high exposures to a given metal. Historical decline in the proportion of exposures above the chosen thresholds is consistent with general time trends in exposure observed in developed countries,15 and for airborne beryllium in particular.16 Our work suffers from several limitations. We only consider airborne concentrations that may give rise to both inhalational and dermal exposure, and do so to a different extent in any given workplace environment. Chemical form (eg, salt, base, or alloy), solubility, and particle size of beryllium com2011, Vol. 66, No. 2

pound were not considered, because only elemental beryllium in the air was recorded. These parameters modify the biologically effective dose and subsequent health risks,17,18 implying that ignoring them in exposure assessment is yet another source of uncertainty. This can perhaps be mediated by expert adjustment of base exposure estimates from our JEMs using relevant additional contextual information (eg, from interviews of subjects or their fellow workers/employers in the context of community-based care-referent study). If concentrations in the air are not correlated with the likelihood of dermal contact or ingestion, and physicochemical properties of beryllium emissions are not constant across industries, then exposure estimates based on air monitoring alone may 81

Table 4.—-Multivariable Poisson Regression Modela: Relative Risk of Beryllium Exposure >0.1 µg/m3

Predictor


Year (since 1978: 1979 = 1, 1980 = 2, etc.) Time-weighted average SICC: Industry Increased risk of exposure ∗ 1: Metal Ores or Oil and Gas Field Services or Heavy Construction 15: Building Construction General Contractors and Operative Builders 26: Paper And Allied Products 37: Transportation Equipment (other) 45: Transportation by Air 326: Pottery Products 329: Nonmetallic Mineral Products 384: Electromedical and Electrotherapeutic Apparatus 1611: Highway and Street Construction, Except Elevated Highways 1622: Bridge, Tunnel, and Elevated Highway Construction 1721: Painting and Paper Hanging 1799: Special Trade Contractors, NEC 2819: Industrial Inorganic Chemicals, NEC 3089: Plastics Products, NEC 3341: Secondary Smelting and Refining of Nonferrous Metals 3351: Rolling, Drawing, and Extruding of Copper 3365: Aluminum Foundries 3366: Copper Foundries 3369: Nonferrous Foundries, Except Aluminum and Copper 3399: Primary Metal Products, NEC 3479: Coating, Engraving, and Allied Services, NEC 3499: Fabricated Metal Products, NEC 3536: Overhead Traveling Cranes, Hoists, and Monorail Systems 3544: Special Dies and Tools, Die Sets, Jigs and Fixtures, and Industrial Molds 3599: Industrial and Commercial Machinery and Equipment, NEC 3674: Semiconductors and Related Devices 3731: Ship Building and Repairing 3911: Jewelry, Precious Metal Decreased risk of exposure 33: Primary Metal Industries (other) 34: Fabricated Metal Products, Except Machinery and Transportation Equipment (other) 35: Industrial and Commercial Machinery and Computer Equipment (other) 36: Electronic and Other Electrical Equipment and Components, Except Computer Equipment (other) 3321: Gray and Ductile Iron Foundries 3471: Electroplating, Plating, Polishing, Anodizing, and Coloring 5093: Scrap and Waste Materials Job description Increased risk of exposure Furnace and incinerator Operator Casting Mixer Finisher Sandblaster Foundry Blaster Maintenance Decreased risk of exposure Welder

No. of exposed

No. of measurements

Relative risk

95%

CI

— 510

— 12,148

0.97 4.61

0.96 4.08

0.98 5.20

5 10 4 20 6 6 17 16 14 5 93 53 7 13 206 11 55 47 137 22 9 14 5 13

25 41 30 301 32 37 76 43 28 34 261 154 27 58 549 52 183 569 237 51 57 223 41 73

4.78 4.69 3.00 1.47 3.97 2.76 3.33 4.63 5.18 2.33 3.69 4.75 4.20 3.40 4.36 2.39 3.42 1.62 6.97 6.62 2.62 2.25 3.27 2.73

2.74 2.92 1.49 1.00 2.01 1.40 2.34 3.17 3.44 1.35 2.87 3.69 2.46 2.09 3.66 1.62 2.75 1.24 5.71 4.62 1.69 1.42 1.57 1.66

8.34 7.54 6.03 2.14 7.84 5.46 4.73 6.77 7.78 4.01 4.73 6.11 7.17 5.52 5.19 3.51 4.25 2.12 8.51 9.48 4.06 3.57 6.84 4.48

19 8 23 27

185 39 153 35

1.98 2.06 2.56 9.07

1.36 1.36 1.71 6.43

2.88 3.13 3.81 12.78

3 13

302 830

0.24 0.47

0.08 0.28

0.73 0.78

6 2

1,040 373

0.20 0.14

0.09 0.04

0.44 0.54

2 2 2

430 265 219

0.12 0.20 0.19

0.03 0.05 0.05

0.45 0.84 0.78

127 109 32 14 12 33 25 92 5

500 587 130 63 76 93 59 201 30

1.31 1.31 1.36 1.63 1.76 1.80 1.96 2.00 2.19

1.09 1.08 1.04 1.11 1.07 1.31 1.30 1.56 1.12

1.58 1.59 1.77 2.40 2.90 2.47 2.95 2.56 4.26

65

3, 921

0.42

0.31

0.56

statistically significant predictors are shown; model has intercept of −2.858. SICC 1 includes 1081 (Metal Mining Services), 1381 (Drilling Oil and Gas Wells), 1389 (Oil and Gas Services), 161 (Highway and Street Construction, Except Elevated Highways), and 1623 (Water, Sewer, Pipeline, and Communications and Power Line Construction). NEC = Not Elsewhere Classified; (other) = more specific codes with this 2-digit code are present in the model.

aOnly ∗

82


Table 5.—-Multivariable Poisson Regression Modela: Relative Risk of Beryllium Exposure ≥0.5 µg/m3


Predictor Year (since 1978: 1979 = 1, 1980 = 2, etc.) Time weighted average SICC: Industry Increased risk of exposure ∗ 1: Metal Ores or Oil and Gas Field Services or Heavy Construction 15: Building Construction General Contractors and Operative Builders 26: Paper And Allied Products 28: Chemicals and Chemical Preparations, NEC 326: Pottery Products 329: Nonmetallic Mineral Products 384: Electromedical and Electrotherapeutic Apparatus 1541: General Contractors—Industrial Buildings and Warehouses 1611: Highway and Street Construction, Except Elevated Highways 1629: Heavy Construction, NEC 1721: Painting and Paper Hanging 1795: Wrecking and Demolition Work 1799: Special Trade Contractors, NEC 2819: Industrial Inorganic Chemicals, NEC 3089: Plastics Products, NEC 3341: Secondary Smelting and Refining of Nonferrous Metals 3351: Rolling, Drawing, and Extruding of Copper 3365: Aluminum Foundries 3366: Copper Foundries 3369: Nonferrous Foundries, Except Aluminum and Copper 3399: Primary Metal Products, NEC 3479: Coating, Engraving, and Allied Services, NEC 3499: Fabricated Metal Products, NEC 3544: Special Dies and Tools, Die Sets, Jigs and Fixtures, and Industrial Molds 3569: General Industrial Machinery and Equipment, NEC 3599: Industrial and Commercial Machinery and Equipment, NEC 3674: Semiconductors and Related Devices 3731: Ship Building and Repairing 3911: Jewelry, Precious Metal Decreased risk of exposure 35: Industrial and Commercial Machinery and Computer Equipment (other) 3321: Gray and Ductile Iron Foundries Job description Increased risk of exposure Furnace and incinerator Casting Saw Mixer Sandblaster Finisher Blaster Bencher Decreased risk of exposure Welder Pour Grinder Other

No. of exposed

No. of measurements

Relative risk

95%

CI

— 297

— 12, 148

0.96 3.27

0.94 2.81

0.97 3.82

5 3 3 9 6 16 14 3 14 4 65 6 20 4 8 158 7 38 26 99 21 5 8 5

25 41 30 145 37 76 43 38 28 90 261 123 154 27 58 549 52 183 569 237 51 57 223 73

15.51 3.67 5.33 2.28 5.09 6.80 8.58 3.51 9.70 2.53 4.82 2.30 3.84 4.89 4.21 6.86 3.12 4.99 1.78 11.55 14.58 2.89 2.97 1.81

8.71 1.28 1.90 1.23 2.60 4.61 5.46 1.57 6.16 1.15 3.37 1.11 2.42 2.20 2.35 5.50 1.69 3.65 1.21 8.89 9.97 1.30 1.57 1.01

27.61 10.48 14.96 4.22 9.94 10.02 13.48 7.86 15.27 5.58 6.88 4.76 6.07 10.91 7.56 8.55 5.75 6.84 2.62 15.01 21.32 6.39 5.60 3.22

3 11 5 10 17

67 185 39 153 35

4.70 2.77 2.83 2.03 10.03

1.54 1.61 1.37 1.22 6.32

14.33 4.78 5.86 3.39 15.92

1 1

1, 040 430

0.07 0.11

0.01 0.02

0.50 0.75

106 27 9 11 24 11 61 4

500 130 37 63 93 76 201 30

1.39 1.54 1.57 1.64 2.18 2.19 2.40 2.84

1.11 1.10 1.07 1.02 1.40 1.21 1.68 1.41

1.73 2.14 2.31 2.64 3.39 3.96 3.42 5.71

23 3 14 70

3, 921 282 513 1, 832

0.24 0.26 0.56 0.60

0.16 0.08 0.35 0.47

0.38 0.81 0.90 0.78

statistically significant predictors are shown; model has intercept of −3.154. SICC 1 includes 1081 (Metal Mining Services), 1381 (Drilling Oil and Gas Wells), 1389 (Oil and Gas Services), 161 (Highway and Street Construction, Except Elevated Highways), and 1623 (Water, Sewer, Pipeline, and Communications and Power Line Construction). NEC = Not Elsewhere Classified; (other) = specific codes with this 2-digit code are present in the model. aOnly ∗

2011, Vol. 66, No. 2

83


produce an estimate poorly correlated with biologically effective doses. Such errors in exposure, if nondifferential, would attenuate epidemiological associations.19 However, even with just the dichotomization of an imperfectly measured continuous exposure metric, we created the potential for differential exposure misclassification that may bias epidemiological results in any direction.20 This emphasizes the need for formal incorporation if measurement error correction into any epidemiological study that uses our JEM (or any other JEM for that matter). Categorization of actual jobs performed by monitored workers is limited to tasks that can be inferred from unsystematically recorded descriptions of tasks and job titles. This may introduce random errors that would bias apparent associations in the statistical model, but not necessarily towards the null.21 The bias in our exposure models would certainly be away from the null if more accurate information was recorded for workers who were suspected to be (and were) more highly exposed, resulting in differential misclassification of determinants of exposure with respect to the probability of exposure to beryllium. Therefore, predictions based on SICCs alone may be deemed more reliable, although they do suffer from within-industry heterogeneity when information on tasks is ignored altogether. Note that industry and job were seen as independent predictors of the probability of exposure in the Poisson models. As noted by Henneberger et al,2 in the US OSHA IMIS data many categories associated with a clearly elevated probability of exposure compared to other groups were not precisely described in the database (eg, listed as SICC with NEC, “not elsewhere classified,” designation). This makes it rather challenging to appropriately code job histories without expert appraisal of the likelihood of beryllium exposure in a given “special” trade for work, say, with electrical components NEC. It also complicates the application of our JEM in gaining insights into plausible intervention to control exposures. This reflects the specialized use of beryllium in industry, despite the widening of the application of the metal. The high proportion of nondetectable measurements in our data presented a formidable challenge. Our JEM only considered the probability of exposure, yet it is plausible that some persons will have rare, yet intense exposures that may well be more hazardous, in a given context, than a chronic low level of exposure.6 Consequently, even if our estimates of exposure probability were perfect, the application of the JEM would still result in misclassification of exposure with respect to the biologically effective dose. We could not model continuous exposure in the current context due to the very large proportion of missing data. Our attempts to use Tobit regression (details not shown) or imputing nondetects yielded unstable results that were determined primarily about assumptions we made about the distribution of nondetects, rather than observed data. Furthermore, we deliberately did not obtain data on the state and employer from which measurements were made, because the high proportion of nondetects in the context of beryllium exposure precludes meaningful modeling of 84

within-state and within-employer random effects. Nonetheless, it is conceivable that latent correlation structures among measurements biased the exposure estimates that our JEMs can provide. Conclusions We developed statistical models that are suitable for the assessment of the probability of non-negligible occupational exposure to beryllium in a sample from the general population. Although the resulting job-exposure matrices are based on monitoring data, due to the numerous limitations listed above, they are expected to yield error-prone estimates that will have to be treated with caution in epidemiological analyses and most certainly modified by experts equipped with relevant additional data. Nonetheless, our results are useful in the sense that they do provide rational, even if imperfect, guidance for the design of epidemiological protocols when the objective is to study the risks of occupational exposure to beryllium in a sample drawn from the general working population. ********** Igor Burstyn was supported by the New Investigator Award from the Canadian Institutes for Health Research and the Population Health Investigator Award from the Alberta Heritage Foundation for Medical Research. Drs Nicola M. Cherry and Jeremy Beach provided valuable comments on the early draft of the manuscript. Mr Bruce Beveridge of US OSHA extracted the data and was most helpful in processing the data request from the investigators under the Freedom of Information Act. Dr Paul K. Henneberger of NISOH provided invaluable advice on the use of OSHA IMIS beryllium data. Dr Gregory Day commented on the draft of the manuscript. The conclusions reached in this report are those of the authors alone and do not reflect the opinions of either US OSHA or NIOSH, or any of the funding agencies. For comments and further information, address correspondence to Igor Burstyn, Department of Environmental and Occupational Health, School of Public Health, Drexel University, 1505 Race Street, Room 1332, Philadelphia, PA, 19102, USA. E-mail: [email protected]

********** References 1. Kreiss K, Day GA, Schuler CR. Beryllium: a modern industrial hazard. Annu Rev Public Health. 2007;28:259–277. 2. Henneberger PK, Goe SK, Miller WE, et al. Industries in the United States with airborne beryllium exposure and estimates of the number of current workers potentially exposed. J Occup Environ Hyg. 2004;1:648–659. 3. Day GA, Stefaniak AB, Weston A, et al. Beryllium exposure: dermal and immunological considerations. Int Arch Occup Environ Health. 2006;79:161–164. 4. Occupational Health and Safety (OSHA). Toxic and Hazardous Substances: 29CFR1910.1000, Table Z-2. Available at: http://www.osha.gov/pls/oshaweb/owadisp.show document?p table= STANDARDS&p id=9993.AccessedSeptember25,2009. 5. Eisenbud M. The standard for control of chronic beryllium disease. Appl Occup Environ Hyg. 1998;131:25–31. 6. Borak J. The beryllium occupational exposure limit: historical origin and current inadequacy. J Occup Environ Med. 2006;482:109–116. 7. Chronic beryllium disease prevention program: final rule. Federal Register 1999;64:68853–68914. 8. American Conference of Governmental and Industrial Hygienists. Annual Reports of the Committees on TLVs and BEIs for Year 2004. Archives of Environmental & Occupational Health

9. 10. 11. 12.

13. 14.

16. Creely KS, Cowie H, Van Tongeren M, et al. Trends in inhalation exposure—a review of the data in the published scientific literature. Ann Occup Hyg. 2007;518:665–678. 17. Stefaniak AB, Hoover MD, Day GA, et al. Characterization of physicochemical properties of beryllium aerosols associated with prevalence of chronic beryllium disease. J Environ Monit. 2004;66:523– 532. 18. Stefaniak AB, Chipera SJ, Day GA, et al. Physicochemical characteristics of aerosol particles generated during the milling of beryllium silicate ores: implications for risk assessment. J Toxicol Environ Health A. 2008:71:1468–1481. 19. Armstrong BG. Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med. 1998;55(10):651–656. 20. Gustafson P. Measurement Error and Misclassification in Statistics and Epidemiology: Impact and Bayesian Adjustment. Boca Raton, FL: Chapman & Hall/CRC; 2004. 21. Burstyn I. Measurement error and model specification in determining how duration of tasks affects level of occupational exposure. Ann Occup Hyg. 2009;533:265–270.


15.

Cincinnati, OH: American Conference of Governmental and Industrial Hygienists; 2005. Brüske-Hohlfeld I. Environmental and occupational risk factors for lung cancer. Methods Mol Biol. 2009;472:3–23. Teschke K, Marion SA, Vaughan TL, et al. Exposures to wood dust in U.S. industries and occupations, 1979 to 1997. Am J Ind Med. 1999;356:581–589. Lavoué J, Vincent R, Gerin M. Formaldehyde exposure in U.S. industries from OSHA air sampling data. J Occup Environ Hyg. 2008;59:575–587. Kolanz ME, Madl AK, Kelsh MA, et al. A comparison and critique of historical and current exposure assessment methods for beryllium: implications for evaluating risk of chronic beryllium disease. Appl Occup Environ Hyg. 2001;165:593–614. Kolanz M. Evaluating beryllium exposure data. Environ Health Perspect. 2006;1144:A213; author reply A213–A215. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–706. Kromhout H, Vermeulen R. Long-term trends in occupational exposure: are they real? What causes them? What shall we do with them? Ann Occup Hyg. 2000;445:343–354.

2011, Vol. 66, No. 2

85

Appendix.—-Classification Scheme for Coding Job Descriptions >0.1 µg/m3 Be Job description


Assembler ∗ Ball mill ∗ Bencher ∗ Blaster Boilermaker Brazer ∗ Burner ∗ Casting Crane ∗ Cutter Driver Fabricator ∗ Finisher ∗ Foundry ∗ Furnace and incinerator ∗ Grinder ∗ Laborer ∗ Leadman ∗ Machinist ∗ Maintenance Mechanic ∗ Melter ∗ Mill Missing/Unknown Mixer ∗ Molder ∗ Operator Other ∗ Painter ∗ Plater ∗ Polisher ∗ Pour Press Production ∗ Sandblaster Sander ∗ Saw Solder ∗ Sprayer ∗ Supervisor Technician Torcher ∗ Welder Total ∗

86

≥0.5 µg/m3 Be

N

N

%

143 33 30 201 52 93 178 130 86 159 32 38 76 59 500 513 406 34 156 30 105 181 78 1, 018 63 121 587 1, 465 364 103 147 282 54 54 93 41 37 125 104 140 50 66 3, 921 12, 148

17 8 5 92 1 2 8 32 2 15 2 2 12 25 127 32 51 4 21 5 7 24 10 79 14 7 109 136 20 2 5 9 3 14 33 4 10 3 2 18 4 5 65 1, 046

12 24 17 46 1.9 2.2 4.5 25 2.3 9.4 6.3 5.3 16 42 25 6.2 13 12 13 17 6.7 13 13 7.8 22 5.8 19 9.3 5.5 1.9 3.4 3.2 5.6 26 35 9.8 27 2.4 1.9 13 8.0 7.6 1.7 8.6

N 12 2 4 61 0 0 6 27 0 8 0 1 11 16 106 14 27 4 9 4 5 20 10 59 11 6 69 70 12 2 4 3 2 8 24 4 9 1 0 12 1 1 23 668

% 8.4 6.1 13 30 0 0 3.4 21 0 5.0 0 2.6 14 27 21 2.7 6.7 12 5.8 13 4.8 11 13 5.8 17 5.0 12 4.8 3.3 1.9 2.7 1.1 3.7 15 26 9.8 24 0.8 0 8.6 2.0 1.5 0.6 5.5

Reported beryllium exposure ≥0.5 µg/m3 in at least 1 industry according to Henneberger et al., 2004.2


Occupational and non-occupational allergic contact dermatitis from beryllium.

Cross-classified occupational exposure data.

Biological monitoring for occupational exposure to toluene.

Biological monitoring of occupational exposure to tetrahydrofuran.

Occupational carcinogenesis. Statistical extrapolation methods for estimating risks from animal data.

Biological monitoring for occupational cadmium exposure: the urinary metallothionein.

Biological and chemical monitoring of occupational exposure to ethylene oxide.

Assessment of occupational health risk from multiple exposure: review of industrial solvent interaction and implication for biological monitoring of exposure.

Comparison of methods for analyzing left-censored occupational exposure data.

Estimating effective dose from 3D imaging with interventional fluoroscopy systems using limited exposure data.

Occupational dermatitis from exposure to polyurethane chemicals.

Occupational chronic exposure to metals. II. Nickel exposure of stainless steel welders--biological monitoring.

Exposure factors associated with chronic beryllium disease development in Beryllium BioBank participants.

Evaluating temporal trends from occupational lead exposure data reported in the published literature using meta-regression.

Estimating fat body mass from antrhopometric data.

Estimating gallstone incidence from prevalence data.

Estimating linkage disequilibrium from conditional data.

Occupational exposure to blood and compliance with standard precautions among health care workers in Beijing, China.

Compliance: A barrier to occupational rehabilitation?

Analysing health data from rare occupational cohorts.

Monitoring compliance in clinical trials.

A novel alternative to environmental monitoring to detect workers at risk for beryllium exposure-related health effects.

Occupational exposure to manganese.

Monitoring compliance with transfusion guidelines in hospital departments by electronic data capture.