Monte Carlo simulation to reconstruct formaldehyde exposure levels from summary parameters reported in the literature

This study presents a procedure allowing the numerical synthesis of exposure data reported in different ways in the literature, including summary parameters and single measurements. The procedure was applied to literature regarding formaldehyde exposure in the reconstituted wood panels industry, including oriented-strand board (OSB), medium density fibre board (MDF) and particle board (PB).

For each publication providing summary parameters we estimated geometric means (GM) and geometric standard deviations (GSD) by assuming lognormality of exposure levels. Monte Carlo simulation was performed to re-create datasets from the sample sizes and estimated GMs and GSDs, allowing their subsequent formatting together with the single measurements. The precision and bias of the methods used to estimate GMs and GSDs were evaluated.

Altogether, the 13 articles included in our study yielded a final database of 874 data, of which 732 were simulated. For both area and personal data, exposures corresponding to MDF and PB were similar while OSB levels were lower. The most recent available personal levels (1985-1994) were highest in PB for jobs performed in the vicinity of the press (GM=0.63 mg m-3). Corresponding area levels were highest for PB in the main production zone (GM=0.43 mg m-3). Mixed-effects models fitted to area PB data explained 38% of the total variability. A 6-fold decrease in exposures from 1965 to 1995 was estimated. Replication of the simulation process yielded relative standard deviations of the calculated GMs and GSDs between 10 and 20%. The relative biases of the methods used to estimate GMs and GSDs varied across methods and decreased with higher sample sizes (from approximately 15% for n=5 to less than 5% for n=30, in absolute value). The precision also varied across methods and improved with higher sample sizes (from approximately 30% for n=5 to approximately 10% for n=30).

This methodology constitutes a new meta-analysis tool that should improve the interpretation of industrial hygiene literature data, but needs to be further validated.