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

Area of Science:

• Occupational Health
• Environmental Science
• Biostatistics

Background:

• Published exposure data often lacks detailed analytical methods, posing challenges for retrospective exposure assessment.
• Mean, range, and variability measures are common but difficult to analyze for temporal trends.
• Mixed-effects meta-analysis regression models offer a robust approach to synthesize diverse exposure data.

Purpose of the Study:

• To predict temporal trends of blood and air lead concentrations in US industries using published data.
• To apply mixed-effects meta-analysis regression models to account for within- and between-study variability.
• To assess the utility of meta-analysis for retrospective occupational exposure assessment.

Main Methods:

• Extracted geometric mean (GM) and geometric standard deviation (GSD) from journal articles on US worksite blood and air lead measurements.
• Included industries with measurements from ≥2 time points spanning ≥10 years.
• Developed industry- and sample-type-specific meta-regression models with calendar year as the independent variable and log-transformed GM as the dependent variable, incorporating random intercepts to account for study variances.

Main Results:

• Blood lead levels showed statistically significant annual declines of 2-11% in 8 out of 13 industries.
• Air lead levels exhibited a statistically significant decline in only one of seven industries, with one industry showing an increase.
• In industries with both air and blood data, blood lead declines were generally greater than air lead declines.

Conclusions:

• Meta-analysis is a valuable tool for synthesizing occupational exposure data and examining exposure trends.
• This method aids in retrospective exposure assessment by identifying temporal patterns.
• Limitations include sparse data for predictors like job category, suggesting the need for additional data sources.