Objectives The published literature provides useful data for examining exposure differences across industries, jobs and time periods, but the analysis is challenging because the data is usually in summary form. We used mixed-effects meta-analysis regression models, which are commonly used to summarise health risks from multiple studies, to predict temporal trends of lead blood and air concentrations in multiple US industries from the published data.

Method We extracted the geometric mean (GM) and geometric standard deviation (GSD) of blood and personal air measurements from US worksites from the literature. When not reported, we derived the GM and GSD from other summary measures. Industries with measurements in ≥2 years and spanning ≥10 years were included. Models were developed separately by industry and sample type. Each model used the log-transformed GM as the dependent variable and calendar year as the independent variable. It also incorporated a random intercept that weighted each study by the inverse of the sum of the between- and within-study variances. Within-study variances consisted of the squared log-transformed GSD divided by the number of measurements. Maximum likelihood estimation was used to obtain the regression parameters and between-study variances.

Results The blood measurement models predicted statistically significant declining trends (2–11% per year) in 5 of the 13 industries. The air measurement models predicted statistically significant declining trends (1–3%) in 2 of the 10 industries; increasing trends (7–10%) were observed for 2 industries.

Conclusions Meta-analysis provides a useful tool for synthesising occupational exposure data that can aid future retrospective exposure assessment.