title: Evaluating temporal trends from occupational lead exposure data reported in the published literature using meta-regression.
abstract: OBJECTIVE
  The published literature provides useful exposure measurements that can aid retrospective exposure assessment efforts, but the analysis of this data is challenging as it is usually reported as means, ranges, and measures of variability. We used mixed-effects meta-analysis regression models, which are commonly used to summarize health risks from multiple studies, to predict temporal trends of blood and air lead concentrations in multiple US industries from the published data while accounting for within- and between-study variability in exposure.
  
  
  METHODS
  We extracted the geometric mean (GM), geometric standard deviation (GSD), and number of measurements from journal articles reporting blood and personal air measurements from US worksites. When not reported, we derived the GM and GSD from other summary measures. Only industries with measurements in ≥2 time points and spanning ≥10 years were included in our analyses. Meta-regression models were developed separately for each 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 a combination of the between- and within-study variances. The within-study variances were calculated as 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 of 2-11% per year in 8 of the 13 industries. The air measurement models predicted a statistically significant declining trend (3% per year) in only one of the seven industries; an increasing trend (7% per year) was also observed for one industry. Of the five industries that met our inclusion criteria for both air and blood, the exposure declines per year tended to be slightly greater based on blood measurements than on air measurements.
  
  
  CONCLUSIONS
  Meta-analysis provides a useful tool for synthesizing occupational exposure data to examine exposure trends that can aid future retrospective exposure assessment. Data remained too sparse to account for other exposure predictors, such as job category or sampling strategy, but this limitation may be overcome by using additional data sources.
authors.affiliation: 1.Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA 3.National Cancer Control Institute, National Cancer Center, Goyang 410-769, Korea.
authors.forename: Dong-Hee
authors.initials: DH
authors.lastname: Koh
authors.affiliation: 1.Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Jun-Mo
authors.initials: JM
authors.lastname: Nam
authors.affiliation: 1.Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Barry I
authors.initials: BI
authors.lastname: Graubard
authors.affiliation: 2.National Environmental Health Research Center, National Health Research Institutes, Taipei 11503, Taiwan.
authors.forename: Yu-Cheng
authors.initials: YC
authors.lastname: Chen
authors.affiliation: 1.Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Locke
authors.affiliation: 1.Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA friesenmc@mail.nih.gov.
authors.forename: Melissa C
authors.initials: MC
authors.lastname: Friesen
mesh_terms: D000395:Air Pollutants, Occupational / Q000032:analysis; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D007221:Industry / Q000706:statistics & numerical data / Q000639:trends; D007854:Lead / Q000032:analysis* / Q000097:blood; D016013:Likelihood Functions; D008962:Models, Theoretical; D016273:Occupational Exposure / Q000032:analysis*; D011237:Predictive Value of Tests; D012044:Regression Analysis
chemical_list: D000395:Air Pollutants, Occupational; D007854:Lead
keywords: exposure; lead; meta-analysis; meta-regression; temporal trend