Predictors of lead absorption in children of lead workers.
OBJECTIVE
This study was conducted to determine whether children of lead workers are at a higher risk of lead absorption and if so, to identify risk factors for absorption, including lifestyle, household, environmental and parental work practices with lead.


METHODS
This cross-sectional study recruited 17 'lead worker' and 13 comparison 'non-lead worker' households. Companies and eligible employees were contacted using mail-out packs. Children were aged between 12 and 72 months. Data collection involved administration of a questionnaire and collection of dust, soil, water, paint-scraping samples, and blood from the children for the determination of lead and ferritin levels. Statistical analysis was performed using SPSS.


RESULTS
Participants included seven of 29 eligible employees from 14 'scheduled' lead workplaces and 12 of 19 eligible employees from 19 'leadlighter' workplaces identified. The average lead level for workers was 13.9 micrograms/dl, children of lead workers 6.93 micrograms/dl (n = 22) and comparison group 3.93 micrograms/dl (n = 16). Water lead levels were all < 2.5 ppm. The lead content of three soil samples and nine dust samples was above suggested guideline levels. Lead levels were significantly higher in children of lead workers but all were within recommended levels. Inadequate practice of lead work hygiene measures, such as inadequate use of protective equipment and taking work clothes home, was a common factor in lead worker households that were found to have elevated soil and dust lead levels. The differences in lead levels between groups did not change significantly when adjustment was made for the clustering effect of more than one child per household.


CONCLUSIONS
Children of lead workers are at higher risk of lead absorption. Poor work hygiene practices of lead workers suggest an association with elevated lead levels in their children. A number of other predictors were suggested by this study but the small numbers of participants made it difficult to detect statistically significant differences between subgroups.
Chan
J
J
Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Sim
M
M
Golec
R
R
Forbes
A
A
D000328:Adult; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D007854:Lead / Q000032:analysis* / Q000097:blood / Q000493:pharmacokinetics; D008297:Male; D018811:Maternal Exposure / Q000009:adverse effects*; D016273:Occupational Exposure / Q000009:adverse effects*; D018812:Paternal Exposure / Q000009:adverse effects*; D012044:Regression Analysis; D012307:Risk Factors; D012959:Socioeconomic Factors
D007854:Lead