Abstract
Objectives
To examine the relation between occupation and cumulative lead exposure—assessed by measuring bone lead—in a community-dwelling population
Method
We measured bone lead concentration with K-shell X-Ray Fluorescence in 1,320 men in the Normative Aging Study. We categorized job titles into 14 broad US Census Bureau categories. We used ordinary least squares regression to estimate bone lead by job categories adjusted for other predictors.
Results
Service Workers, Construction and Extractive Craft Workers, and Installation, Maintenance and Repair Craft Workers had the highest bone lead concentrations. Including occupations significantly improved the overall model (p<0.001) and reduced by −15% to −81% the association between bone lead and education categories.
Conclusion
Occupation significantly predicts cumulative lead exposure in a community-dwelling population, and accounts for a large proportion of the association between education and bone lead.
Keywords: biomarker, bone lead, job exposure matrix, job title, occupation
INTRODUCTION
Negative health effects of lead exposure have been well-documented in the past decades. Even at low exposure levels, lead has been shown to adversely affect the nervous system, cardiovascular health, renal system and mortality.(1) Beginning in the 1970s, legislative efforts to reduce environmental lead exposure through the banning of lead in gasoline and paint were largely successful. The National Health and Nutrition Examination Survey (NHANES) documented the prevalence of adults with blood lead levels of 10 μg/dL (0.48 μmol/L) or higher declined from 3.3% in 1988–1994 to 0.7% in 1999–2002.(2)
Despite the reduction of environmental lead exposure, occupational exposures remain a significant source of lead burden in the working population. Research studies have documented high lead exposure and poisoning in specific professions such as automobile mechanics,(3) smelter, battery,(4) iron,(5) lead,(6) and construction workers.(7) These types of studies, however, are usually based on monitoring workers in specific occupations. Other studies have examined determinants of lead exposure among community-exposed populations, but these have generally only considered blood lead—a short-term biomarker, not considered occupation, or only considered occupation crudely, such as blue collar vs. white collar.(8) No study of which we are aware has considered the association between occupation and cumulative lead exposure in a general community sample, likely in large part because of the difficulty of estimating cumulative exposure in such a group. Analysis of lead in bone can provide such an index of cumulative lead exposure. Understanding how occupation affects cumulative lead exposure—in particular using a biomarker that can assess cumulative exposure after that exposure has occurred as bone lead can do—would have important implications for assessing occupational lead exposure contributions to workers health in the absence of data from occupational lead monitoring programs. Furthermore, a complete understanding of non-occupational determinants of lead exposure requires considering specific past occupations as associations with other demographic factors may be accounted for by occupational differences.
In this study, we conducted an analysis of bone lead concentrations in relation to job titles in a population of community-dwelling men in the Boston area, and examined whether occupation in such a group provides additional information about cumulative lead exposure not explained by known demographic predictors of such exposure.
METHODS
Study Population
Our study population is a subgroup of the Normative Aging Study (NAS), a longitudinal study of men established by the Veterans Administration in 1963 when 2,280 men from the Greater Boston area between the ages of 21 and 80 years were enrolled.(9) Men with a history of treatment for hypertension, systolic blood pressure >140 mm Hg, diastolic blood pressure >90 mm Hg, or other chronic conditions, including heart disease, diabetes mellitus, and cancer, were not admitted into the NAS. The attrition rate has been less than 1% annually. NAS subjects have reported for medical examinations every three to five years. During these visits, participants responded to questionnaires on smoking history, education level, food intake and other variables that may influence health.
NAS participants came in for bone lead measurements between 1991 and 2002. After exclusions for measurement uncertainty (see Bone Lead Assessment) there were 863 participants with a valid patella lead measurement and 868 with a valid tibia lead measurement out of 1,320 active participants. NAS participants with bone lead measurements were similar to those without bone lead measurements with respect to several demographic variables.(10) The human research committees of the VA and Harvard School of Public Health approved this research and written consent was obtained from all participants.
Occupation Assessment
NAS participants were asked about their occupation on their regular NAS questionnaires between 1962 and 1980. A total of 40 individual job titles were reported, and we categorized these into the 14 broad job categories of the US Bureau of the Census 2000 classification of private industry employees (Table 1).(11)
Table 1.
Reported Normative Aging Study Occupational Titles by US Census Bureau Occupational Groups.
| Occupational Group Title for Census 2000 | Normative Aging Study Occupational Titles | ||||
|---|---|---|---|---|---|
| Management, Business and Financial Workers | lawyer | accountant | executive | ||
| Science, Engineering and Computer Professionals | engineer | scientist | |||
| Healthcare Practitioner Professionals | doctor | ||||
| Other Professional Workers | teacher | professor | other professional | other manager | |
| Technicians | technician | ||||
| Sales Workers | banker | retail | salesman | ||
| Administrative Support Workers | administrative | personnel | clerk | ||
| Construction and Extractive Craft Workers | foreman | carpenter | electrician | painter | plumber |
| Installation, Maintenance and Repair Craft Workers | repair | mechanic | skilled craft | other craft | |
| Production Operative Workers | machine operator | factory | other operative | ||
| Transportation and Material Moving Operative Workers | vehicle driver | deliveryman | |||
| Laborers and Helpers | janitor | manual labor | farm | ||
| Protective Service Workers | policeman | firefighter | guard | ||
| Service Workers, except Protective | letter carrier | attendant | other service | ||
Bone Lead Assessment
Bone lead measurements were taken with a K-XRF instrument (ABIOMED, Danvers, MA) at both the mid-tibia shaft (midpoint between the tibial plateau and the medial malleolus) and patella. Mid-tibia (shin bone) is primarily composed of cortical bone with a lead half-life of many decades. Patella (knee cap bone) is primarily trabecular bone and has a lead half-life of a few years.(12) Thirty minute measurements were taken at each site, after each region had been washed with a 50% solution of isopropyl alcohol. The K-XRF beam collimator was sited perpendicular to the flat bony surface of the tibia and at 30° in the lateral direction for the patella. We excluded individuals with patella measurement uncertainty greater than 15 μg/g bone mineral (n=3) and tibia measurement uncertainty greater than 10 μg/g (n=7) because these measurements usually reflect excessive subject movement during the measurement.
Statistical Methods
We used ordinary least squares regression to examine the association between occupation and bone lead concentration. We utilized two approaches in using reports of occupation. ‘Ever report’ considered a participant to be in a given occupational category if the subject ever reported working in that occupation between 1960 and 1982. Therefore, in this analysis, a participant could be considered to have been in more than one occupational group. The ‘only occupation’ approach was restricted to those participants who only reported working in one occupational category between 1960 and 1982. Therefore, in this analysis, each participant was assigned to only one occupational group. We adjusted analyses for other predictors of lead exposure(10) (assessed at the time of bone lead measurement) including, age (linear continuous), race (white, non-white), education (less than high school, high school graduate, some college or technical school, college graduate, graduate/professional school), smoking (never, former, current; and cumulative years smoked as a continuous variable), and alcohol consumption (<2 drinks per day, 2+ drinks per day; and grams/day categorized into: none, 0.1–10, 10.1–50, >50). Indicator variables were created for any missing data. The significance of adding the occupational groups to the model was tested with a likelihood ratio test (LRT) with 13 degrees of freedom. Tests for trend of the education variable were done by including a single ordinal term for the different education categories (less than high school; high school graduate; some college or technical school; college graduate; graduate and professional school) and assessing its significance. SAS (Gary, IN) version 9.2 was used for all data analysis.
RESULTS
The birth year of the study subjects ranged from 1899 to 1943. At the time of bone lead assessment, the mean (SD) age was 67.3 (7.3) years. Demographic characteristics of the study sample are shown in table 2. Table 3 shows the crude mean patella and tibia lead concentrations for subjects ever reporting an occupation in a given group and the mean concentrations adjusted for known predictors of bone lead concentration. The adjusted mean shown can be interpreted as the mean for a 67 year old, white male non-alcohol drinker, never smoker, with a high school education. Because these characteristics are associated with lower bone lead concentrations, the adjusted means are consistently lower than the unadjusted means. In this analysis, the highest patella lead concentrations are among Installation, Maintenance and Repair Craft Workers (repair, mechanic, skilled craft, other craft), and Protective Service Workers (policeman, firefighter, guard). The highest tibia lead concentrations are among Protective Service Workers (policeman, firefighter, guard), and Construction and Extractive Workers (foreman, carpenter, electrician, painter, plumber). The relative ranking of lead concentrations are similar between the crude and adjusted means. The supplemental table shows the crude bone lead concentration by individual job titles.
Table 2.
Demographic Characteristics of Study Population
| Variables | N (total=869) | % |
|---|---|---|
|
| ||
| Age (at K-XRF assessment) | ||
| 48–59 | 137 | 15.8% |
| 60–64 | 195 | 22.4% |
| 65–70 | 241 | 27.7% |
| Over 70 | 296 | 34.1% |
|
| ||
| Race | ||
| White | 844 | 97.1% |
| Non-white | 25 | 2.9% |
|
| ||
| Education | ||
| Less than high school | 86 | 9.9% |
| High school graduate | 293 | 33.7% |
| Some college/technical school | 211 | 24.3% |
| College graduate | 145 | 16.7% |
| Graduate/professional school | 98 | 11.3% |
| No Information | 36 | 4.1% |
|
| ||
| Smoking status | ||
| Never smoker | 257 | 29.6% |
| Former smoker | 543 | 62.5% |
| Current smoker | 67 | 7.7% |
| No information | 2 | 0.2% |
|
| ||
| Cumulative Smoking (pack-years) | ||
| 0 | 257 | 29.6% |
| 1–20 | 238 | 27.4% |
| More than 20 | 352 | 40.5% |
| No Information | 22 | 2.5% |
|
| ||
| Currently consuming more than 2 alcoholic drinks per day | ||
| More or equal to 2 drinks per day | 172 | 19.8% |
| Less than 2 drinks per day | 649 | 74.7% |
| No Information | 48 | 5.5% |
|
| ||
| Alcohol consumed (grams per day) | ||
| None | 49 | 5.6% |
| 0.1–10 | 214 | 24.6% |
| 10.1–50 | 274 | 31.5% |
| >50 | 288 | 33.1% |
| No Information | 44 | 5.1% |
Table 3.
Unadjusted and adjusted* mean lead levels by ever report of different occupation groups.
| Occupation Groups | Patella Lead | Tibia Lead | ||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| N | Mean (μg/g) | SD | Adjusted Mean (μg/g) | N | Mean (μg/g) | SD | Adjusted Mean (μg/g) | |
|
| ||||||||
| Management, Business and Financial Workers | 190 | 27.7 | 15.1 | 30.3 | 190 | 19.1 | 10.7 | 19.1 |
| Science, Engineering and Computer Professionals | 97 | 24.5 | 15.4 | 27.9 | 98 | 17.1 | 10.7 | 18.5 |
| Healthcare Practitioner Professionals | 5 | 26.8 | 5.8 | 27.5 | 5 | 20.0 | 8.8 | 20.6 |
| Other Professional Workers | 274 | 27.5 | 15.7 | 29.2 | 274 | 18.4 | 9.3 | 17.7 |
| Technicians | 37 | 25.1 | 15.0 | 26.2 | 38 | 16.9 | 9.5 | 16.1 |
| Sales Workers | 89 | 28.6 | 17.5 | 29.3 | 91 | 19.8 | 12.7 | 19.3 |
| Administrative Support Workers | 112 | 29.1 | 14.4 | 28.0 | 113 | 21.0 | 11.5 | 18.5 |
| Construction and Extractive Craft Workers | 76 | 33.4 | 26.7 | 30.0 | 76 | 25.9 | 20.0 | 22.6 |
| Installation, Maintenance and Repair Craft Workers | 150 | 35.6 | 24.3 | 35.1 | 149 | 24.2 | 17.5 | 21.8 |
| Production Operative Workers | 30 | 34.4 | 26.5 | 31.4 | 30 | 24.3 | 16.8 | 20.9 |
| Transportation and Material Moving Operative Workers | 22 | 33.4 | 18.4 | 29.4 | 22 | 23.1 | 17.0 | 18.1 |
| Laborers and Helpers | 27 | 34.9 | 22.6 | 28.1 | 27 | 26.6 | 13.2 | 19.9 |
| Protective Service Workers | 198 | 36.1 | 21.1 | 34.6 | 200 | 25.8 | 14.5 | 23.7 |
| Service Workers, except Protective | 51 | 32.0 | 16.1 | 29.6 | 51 | 23.8 | 12.7 | 19.8 |
Adjusted for age, race, alcohol consumption, smoking, and education. Value shown is that predicted for a 67 year old, white male non-alcohol drinker, never smoker, with a high school graduate education.
Table 4 shows the crude and adjusted mean lead concentrations by occupation only for those NAS participants who reported only one occupation group. The highest patella lead concentrations were found among Installation, Maintenance and Repair Craft Workers (repair, mechanic, skilled craft, other craft), and Construction and Extractive Craft Workers (foreman, carpenter, electrician, painter, plumber). The highest tibia lead concentrations were found among Construction and Extractive Craft Workers (foreman, carpenter, electrician, painter, plumber) and Service Workers (both protective [policeman, firefighter, guard] and others [letter carrier, attendant, other service). In this analysis, several occupation groups had small numbers of NAS men and the relative ranking of these occupations tended to vary more substantially from the analysis based on ever reporting of an occupation. However, the relative ranking of occupations with larger numbers of NAS men remained similar in the two analyses.
Table 4.
Unadjusted and adjusted* mean lead levels by occupation group among men reporting having remained in the same occupation.
| Occupation Groups | Patella Lead | Tibia Lead | ||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| N | Mean (μg/g) | SD | Adjusted Mean (μg/g) | N | Mean (μg/g) | SD | Adjusted Mean (μg/g) | |
|
| ||||||||
| Management, Business and Financial Workers | 51 | 29 | 16 | 27.8 | 51 | 18.8 | 9.7 | 16.7 |
| Science, Engineering and Computer Professionals | 39 | 22.4 | 14.3 | 22.6 | 39 | 15.3 | 11.1 | 14.1 |
| Healthcare Practitioner Professionals | 3 | 24 | 6.1 | 20.8 | 3 | 19.7 | 12.3 | 18.0 |
| Other Professional Workers | 63 | 27.4 | 18.1 | 28.8 | 63 | 21.8 | 8.9 | 17.3 |
| Technicians | 6 | 32.8 | 14.9 | 32.7 | 6 | 19.4 | 13.6 | 17.6 |
| Sales Workers | 21 | 31 | 17.6 | 30.3 | 21 | 21.8 | 9.2 | 19.7 |
| Administrative Support Workers | 24 | 29 | 14.9 | 22.0 | 24 | 19.4 | 10 | 12.5 |
| Construction and Extractive Craft Workers | 16 | 43.4 | 22.3 | 37.7 | 16 | 31.7 | 18.5 | 26.6 |
| Installation, Maintenance and Repair Craft Workers | 48 | 39.6 | 21.7 | 38.5 | 48 | 23.9 | 16.9 | 22.3 |
| Production Operative Workers | 4 | 16 | 3.8 | 24.7 | 4 | 9.8 | 6.4 | 15.4 |
| Transportation and Material Moving Operative Workers | 4 | 21.8 | 9.9 | 24.8 | 4 | 9.4 | 4.8 | 11.5 |
| Laborers and Helpers | 4 | 34.8 | 24.4 | 24.9 | 4 | 27 | 9.9 | 17.3 |
| Protective Service Workers | 181 | 36.9 | 21.6 | 35.8 | 181 | 26.4 | 14.6 | 23.8 |
| Service Workers, except Protective | 13 | 35.8 | 19.2 | 33.8 | 13 | 28.2 | 18.2 | 24.5 |
Adjusted for age, race, alcohol consumption, smoking, and education. Value shown is that predicted for a 67 year old, white male non-alcohol drinker, never smoker, with a high school graduate education.
Adding ever report of an occupation to the model with only the demographic predictors significantly improved the model prediction (patella and tibia: p<0.001). The same was seen in the analysis of those only reporting one occupation (patella and tibia: p<0.001). In both analyses, the associations with other demographic variables did not change substantially except for education. Table 5 shows the association between education level and bone lead with and without occupation (ever report of a given occupation) also in the model. Including occupation as a predictor substantially reduces the association with education, particular for higher levels of education. The association between having a graduate education compared with a high school degree was reduced by 72.6% for patella bone lead and 69.9% for tibia lead. The reduction of the association with education was more pronounced for the analysis of only men reporting one occupation.
Table 5.
Association* between education and bone lead concentrations with and without adjustment for occupation (n=477).
| Educational Attainment | Patella Lead (95% CI), μg/g | Tibia Lead (95% CI), μg/g | ||||
|---|---|---|---|---|---|---|
|
| ||||||
| Without Occupation | With Occupation | % Change | Without Occupation | With Occupation | % Change | |
|
| ||||||
| Less than high school | 4.8 (−0.6, 10.3) | 4.1 (−1.3, 9.5) | −14.6% | 6.4 (2.7, 10.1) | 5.4 (1.7, 9.1) | −15.6% |
| High school graduate | Reference | Reference | -- | Reference | Reference | -- |
| Less than college | −3.9 (−8.5, 0.7) | −2.3 (−7.61, 2.6) | −41.0% | −2.1 (−5.2, 1.1) | −0.6 (−3.8, 2.7) | −71.4% |
| College graduate | −9.8 (−14.9, −4.8) | −2.8 (−9.5, 3.9) | −71.4% | −6.8 (−10.3, −3.3) | −1.3 (−5.9, 3.2) | −80.9% |
| Graduate and professional school | −10.6 (−16.1, −5.2) | −2.9 (−10.2, 4.4) | −72.6% | −8.3 (−12.0, −4.6) | −2.5 (−7.5, 2.4) | −69.9% |
|
| ||||||
| P-for trend | <0.0001 | 0.04 | <0.0001 | 0.01 | ||
Adjusted for age, race, alcohol consumption and smoking.
DISCUSSION
In our sample of community-dwelling men, we found that occupation was a significant predictor of cumulative lead exposure—as measured by lead in bone—above and beyond what is predicted by age, race, education, smoking and alcohol consumption, previously described predictors in this cohort.(10) Considering occupation in the prediction of cumulative lead exposure did not change the associations with other demographic predictors, except for education. The contribution of education level to predicting cumulative lead exposure was reduced, in some cases substantially, when occupation was included in models. This suggests that, although there is still an independent contribution of education to cumulative lead exposure, education is to a large degree acting as a surrogate for occupation in predicting cumulative lead exposure. On the other hand, the other demographic factors are not. Thus, specific occupations rather than cruder groupings (or none at all) should be taken into account when considering likelihood of past lead exposure. These data also suggest that bone lead measurements can capture past occupational lead exposure in community-based populations in which occupational lead monitoring programs may be rare.
In our analysis, we found Protective Service Workers to have consistently high bone lead concentrations across bone type and whether we considered multiple occupations or not. This is consistent with reports suggesting that firemen and policeman are prone to have higher lead exposures.(13, 14) Construction and Extractive Craft Workers also tended to have high bone lead measurements. This job category contains foreman, carpenter, electrician, plumber, and painters. Lead-based paint was commonly used in buildings constructed before 1978, and lead in plumbing solder was also frequently used. In addition to residential buildings, commercial buildings, along with structures such as bridges, also contain lead paint.(15) Due to the large reservoir of lead in older construction, workers can be exposed through projects involving demolition, renovation, repair or painting of new building,(16) especially when lead particles are aerosolized—for example, from sanding.(17) Installation, Maintenance and Repair Craft Workers also had high bone lead measurements possibly related to similar exposure scenarios as for Construction workers.
A handful of previous studies have analyzed occupational determinants of lead levels in more general community populations, although few have utilized bone lead measurements. One study in India found that traffic police, bus drivers, and auto-shop workers have higher blood lead levels than office workers. The author suggested that exposure to leaded gasoline played a major role in determining the blood lead levels.(18) In a Swiss population, a study on environmental factors of blood lead levels found blood lead for blue-collar workers to be higher than nonindustrial employees, suggesting pathways of occupational exposure to lead.(19) In a study in the Beirut area, men working in white-collar jobs including offices and retail shops had a mean (sd) blood lead level of 12.7 (3.7) μg/dl, which is statistically significantly lower than blue-collar workers who had a mean of 18.4 (9.8) μg/dl.(20) Two previous studies in the Boston area have examined bone lead measurements and occupation. In the NAS, the simple dichotomy of occupation into “blue collar” or “white collar” found blue collar workers to have higher bone lead levels than white collar workers when adjusted for demographic variables.(21) A study of a separate, predominantly minority community in the Boston area, found the same relationship with blue collar and white collar occupations.(22) Our current findings generally reflect this distinction, with blue collar occupations tending to have higher bone lead concentrations than white collar occupations, although we expand the detail on this observation by considering differences with a more detailed distribution of occupations. Job exposure matrices for lead exposure have not been based on bone lead analyses as estimates of cumulative lead exposure, but our results suggest this could potentially be an approach to pursue in general community settings. Bone lead measurements can be used as a supplement to job exposure matrix methodology, such as professional judgments of industrial hygienists(23–25) and area exposure samplings.(26)
Finally, our study elucidates one pathway in which socioeconomic status can affect health. There is a well-documented relation between socioeconomic disparities and health outcomes; however, the mechanism of the association is not well understood. Socioeconomic status (SES), usually assessed by variables such as income and education, is linked to a wide range of health problems, including cardiovascular diseases, arthritis, cancer, and higher mortality.(27, 28)
Education is often considered a fundamental SES component because it shapes future occupational opportunities and earning potential. It also is linked to access to information and resources in promoting health-seeking behavior. Previous research has shown that education is predictive of cardiovascular diseases.(29) Lead burden in the body has been shown to be associated with hypertension,(30, 31) cardiovascular diseases,(32–34) diabetes, renal function,(35) lower cognitive functions(36) and mortality.(37) Although education predicts lead exposure, our results suggest that some of that is mediated by occupation. Thus, some of the association between education and cardiovascular disease may be explained by occupational lead exposure. Hence, lead exposure from occupational sources, is perhaps one mechanism in which SES ultimately affects health.
Our study is the first to offer a detailed analysis of occupational determinants of lead burden in the community by utilizing bone lead measurements. A limitation of our study was that only men were studied, thus, these results can only be generalizable to women with precautions as lead levels in women may be influenced by differences in bone kinetics such as menopause, estrogen and pregnancy.(38) Finally, our data does not distinguish between occupational exposure and those of environmental sources, such as leaded paint, drinking water, residence location and dietary ingestions. In conclusion, we found occupations in Construction and Extractive Craft Workers and Installation, Maintenance and Repair Craft Workers, and Protective Services to have the highest predicted bone lead levels. Occupation may account for an important portion of the association between education and bone lead.
Supplementary Material
What This Paper Adds.
Lead exposure can be an occupational hazard, but no study has examined the relation between occupation and cumulative lead exposure in a community-dwelling population.
We found that in a sample of community-dwelling men occupation was a significant predictor of bone lead concentration—a biomarker of cumulative lead exposure.
Occupation accounted for a large part of the well-known association between education and cumulative lead exposure.
Our study suggests that some associations between education and health outcomes could be mediated by occupational lead exposures.
Acknowledgments
Sources of funding
John S. Ji was supported by the Education and Research Center for Occupational Safety and Health CDC/NIOSH grant award T42/OH008416 at the Harvard School of Public Health. Dr. David Sparrow is supported by a VA Research Career Scientist award.
The authors gratefully acknowledge Rachel Grashow and Joe Braun for their insightful comments, and Hongshu Guan for technical support.
Footnotes
Conflicts of interest
The authors declare no conflicts of interest.
References
- 1.Needleman HL, Bellinger D. The health effects of low level exposure to lead. Annu Rev Public Health. 1991;12:111–140. doi: 10.1146/annurev.pu.12.050191.000551. [DOI] [PubMed] [Google Scholar]
- 2.Muntner P, Menke A, DeSalvo KB, Rabito FA, Batuman V. Continued decline in blood lead levels among adults in the United States: the National Health and Nutrition Examination Surveys. Arch Intern Med. 2005;165:2155–2161. doi: 10.1001/archinte.165.18.2155. [DOI] [PubMed] [Google Scholar]
- 3.Goldman RH, Baker EL, Hannan M, Kamerow DB. Lead poisoning in automobile radiator mechanics. N Engl J Med. 1987;317:214–218. doi: 10.1056/NEJM198707233170406. [DOI] [PubMed] [Google Scholar]
- 4.Baker EL, Jr, Landrigan PJ, Barbour AG, et al. Occupational lead poisoning in the United States: clinical and biochemical findings related to blood lead levels. Br J Ind Med. 1979;36:314–322. doi: 10.1136/oem.36.4.314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Levin SM, Goldberg M, Doucette JT. The effect of the OSHA lead exposure in construction standard on blood lead levels among iron workers employed in bridge rehabilitation. Am J Ind Med. 1997;31:303–309. doi: 10.1002/(sici)1097-0274(199703)31:3<303::aid-ajim6>3.0.co;2-y. [DOI] [PubMed] [Google Scholar]
- 6.Todd AC, Lee BK, Lee GS, Ahn KD, Moshier EL, Schwartz BS. Predictors of DMSA chelatable lead, tibial lead, and blood lead in 802 Korean lead workers. Occupational and environmental medicine. 2001;58:73–80. doi: 10.1136/oem.58.2.73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Phillips B. Lead exposure in road construction. Occup Health Saf. 2011;80:28, 30–21. [PubMed] [Google Scholar]
- 8.Theppeang K, Glass TA, Bandeen-Roche K, Todd AC, Rohde CA, Schwartz BS. Gender and race/ethnicity differences in lead dose biomarkers. Am J Public Health. 2008;98:1248–1255. doi: 10.2105/AJPH.2007.118505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bell B, Rose CL, Damon A. The Veterans Administration longitudinal study of healthy aging. Gerontologist. 1966;6:179–184. doi: 10.1093/geront/6.4.179. [DOI] [PubMed] [Google Scholar]
- 10.Hu H, Payton M, Korrick S, et al. Determinants of bone and blood lead levels among community-exposed middle-aged to elderly men. The normative aging study. Am J Epidemiol. 1996;144:749–759. doi: 10.1093/oxfordjournals.aje.a008999. [DOI] [PubMed] [Google Scholar]
- 11.Classification of Private Industry Employees By EEO-1 Job Categories. [accessed 2013 February 20th]; Available from: http://www.eeoc.gov/employers/eeo1survey/jobclassguide.cfm.
- 12.Rabinowitz MB. Toxicokinetics of bone lead. Environ Health Perspect. 1991;91:33–37. doi: 10.1289/ehp.919133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lorber M, Gibb H, Grant L, Pinto J, Pleil J, Cleverly D. Assessment of inhalation exposures and potential health risks to the general population that resulted from the collapse of the World Trade Center towers. Risk Anal. 2007;27:1203–1221. doi: 10.1111/j.1539-6924.2007.00956.x. [DOI] [PubMed] [Google Scholar]
- 14.Kokayi K, Altman CH, Callely RW, Harrison A. Findings of and treatment for high levels of mercury and lead toxicity in ground zero rescue and recovery workers and lower Manhattan residents. Explore (NY) 2006;2:400–407. doi: 10.1016/j.explore.2006.06.008. [DOI] [PubMed] [Google Scholar]
- 15.Levin SM, Goldberg M. Clinical evaluation and management of lead-exposed construction workers. Am J Ind Med. 2000;37:23–43. doi: 10.1002/(sici)1097-0274(200001)37:1<23::aid-ajim4>3.0.co;2-u. [DOI] [PubMed] [Google Scholar]
- 16.Waller K, Osorio AM, Maizlish N, Royce S. Lead exposure in the construction industry: results from the California Occupational Lead Registry, 1987 through 1989. Am J Public Health. 1992;82:1669–1671. doi: 10.2105/ajph.82.12.1669. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Choe KT, Trunov M, Menrath W, Succop P, Grinshpun SA. Relationship between lead levels on painted surfaces and percent lead in the particles aerosolized during lead abatement. Appl Occup Environ Hyg. 2002;17:573–579. doi: 10.1080/10473220290095844. [DOI] [PubMed] [Google Scholar]
- 18.Potula VV, Hu H. Occupational and Lifestyle Determinants of Blood Lead Levels among Men in Madras, India. Int J Occup Environ Health. 1996;2:1–4. doi: 10.1179/oeh.1996.2.1.1. [DOI] [PubMed] [Google Scholar]
- 19.Berode M, Wietlisbach V, Rickenbach M, Guillemin MP. Lifestyle and environmental factors as determinants of blood lead levels in a Swiss population. Environ Res. 1991;55:1–17. doi: 10.1016/s0013-9351(05)80136-7. [DOI] [PubMed] [Google Scholar]
- 20.Nuwayhid I, McPhaul K, Bu-Khuzam R, Duh SH, Christenson RH, Keogh JP. Determinants of elevated blood lead levels among working men in Greater Beirut. J Med Liban. 2001;49:132–139. [PubMed] [Google Scholar]
- 21.Elmarsafawy SF, Tsaih SW, Korrick S, et al. Occupational determinants of bone and blood lead levels in middle aged and elderly men from the general community: the Normative Aging Study. Am J Ind Med. 2002;42:38–49. doi: 10.1002/ajim.10078. [DOI] [PubMed] [Google Scholar]
- 22.Lin C, Kim R, Tsaih SW, Sparrow D, Hu H. Determinants of bone and blood lead levels among minorities living in the Boston area. Environ Health Perspect. 2004;112:1147–1151. doi: 10.1289/ehp.6705. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Friesen MC, Coble JB, Lu W, et al. Combining a job-exposure matrix with exposure measurements to assess occupational exposure to benzene in a population cohort in shanghai, china. Ann Occup Hyg. 2012;56:80–91. doi: 10.1093/annhyg/mer080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Cocco P, Dosemeci M, Heineman EF. Brain cancer and occupational exposure to lead. J Occup Environ Med. 1998;40:937–942. doi: 10.1097/00043764-199811000-00001. [DOI] [PubMed] [Google Scholar]
- 25.van Wijngaarden E, Dosemeci M. Brain cancer mortality and potential occupational exposure to lead: findings from the National Longitudinal Mortality Study, 1979–1989. Int J Cancer. 2006;119:1136–1144. doi: 10.1002/ijc.21947. [DOI] [PubMed] [Google Scholar]
- 26.Koh DH, Bhatti P, Coble JB, et al. Calibrating a population-based job-exposure matrix using inspection measurements to estimate historical occupational exposure to lead for a population-based cohort in Shanghai, China. J Expo Sci Environ Epidemiol. 2012 doi: 10.1038/jes.2012.86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Pappas G, Queen S, Hadden W, Fisher G. The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986. N Engl J Med. 1993;329:103–109. doi: 10.1056/NEJM199307083290207. [DOI] [PubMed] [Google Scholar]
- 28.Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff (Millwood) 2002;21:60–76. doi: 10.1377/hlthaff.21.2.60. [DOI] [PubMed] [Google Scholar]
- 29.Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992;82:816–820. doi: 10.2105/ajph.82.6.816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Cheng Y, Schwartz J, Sparrow D, Aro A, Weiss ST, Hu H. Bone lead and blood lead levels in relation to baseline blood pressure and the prospective development of hypertension: the Normative Aging Study. Am J Epidemiol. 2001;153:164–171. doi: 10.1093/aje/153.2.164. [DOI] [PubMed] [Google Scholar]
- 31.Hu H, Aro A, Payton M, et al. The relationship of bone and blood lead to hypertension. The Normative Aging Study. JAMA. 1996;275:1171–1176. [PubMed] [Google Scholar]
- 32.Eum KD, Nie LH, Schwartz J, et al. Prospective cohort study of lead exposure and electrocardiographic conduction disturbances in the Department of Veterans Affairs Normative Aging Study. Environ Health Perspect. 2011;119:940–944. doi: 10.1289/ehp.1003279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Jain NB, Potula V, Schwartz J, et al. Lead levels and ischemic heart disease in a prospective study of middle-aged and elderly men: the VA Normative Aging Study. Environ Health Perspect. 2007;115:871–875. doi: 10.1289/ehp.9629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Peters JL, Kubzansky LD, Ikeda A, et al. Lead concentrations in relation to multiple biomarkers of cardiovascular disease: the Normative Aging Study. Environ Health Perspect. 2012;120:361–366. doi: 10.1289/ehp.1103467. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Tsaih SW, Korrick S, Schwartz J, et al. Lead, diabetes, hypertension, and renal function: the normative aging study. Environ Health Perspect. 2004;112:1178–1182. doi: 10.1289/ehp.7024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Weisskopf MG, Wright RO, Schwartz J, et al. Cumulative lead exposure and prospective change in cognition among elderly men: the VA Normative Aging Study. Am J Epidemiol. 2004;160:1184–1193. doi: 10.1093/aje/kwh333. [DOI] [PubMed] [Google Scholar]
- 37.Weisskopf MG, Jain N, Nie H, et al. A prospective study of bone lead concentration and death from all causes, cardiovascular diseases, and cancer in the Department of Veterans Affairs Normative Aging Study. Circulation. 2009;120:1056–1064. doi: 10.1161/CIRCULATIONAHA.108.827121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Vahter M, Akesson A, Liden C, Ceccatelli S, Berglund M. Gender differences in the disposition and toxicity of metals. Environ Res. 2007;104:85–95. doi: 10.1016/j.envres.2006.08.003. [DOI] [PubMed] [Google Scholar]
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