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.

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 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.

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.

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.