Sage Journals: Discover world-class research

Abstract

This study evaluates changes over time in occupational exposure to n-hexane by longitudinal repeated measurements analysis of data from the Biological Monitoring Registry from 1991 to 1998. The main sources of variability in n-hexane exposure among manufacturing workers in Florence province (Italy) are inspected. The 2,5-hexanedione concentrations in urine of industrial workers are explained by structural, individual and factory information. Here we analyse the effectiveness of a 1994 law on workplace conditions based on variability decomposition of measured 2,5-hexanedione concentrations.

We propose a hierarchical Bayesian model which takes into account the different levels of aggregation of data. The results show that for leather and shoe factories, the within-subject and within-factory variance components remain the most important over the time of study, whereas the between-factory components decreased in accordance with the expected effect of the new legislation.

Keywords

hierarchical Bayesian model measurements variability occupational exposure

Get full access to this article

View all access options for this article.

References

Arlien-Soborg P (1992) Solvent neurotoxicity. CRC Press, Inc .

Baldasseroni A , Bavazzano P , Buiatti E , Lanciotti E , Lorini C , Toti S and Biggeri A (2003) Occupational exposure to n-hexane in Italy - analysis of a registry of biological monitoring . International Archives of Occupational and Environmental Health 76 (4), 260-266 .

Berger J (1985) Statistical decision theory and bayesian analysis. Springer-Verlag , 2nd edition.

Breslow NE and Clayton DG (1993) Approximate inference in generalized linear mixed models . Journal of the American Statistical Association 88, 9-25 .

Diggle P , Liang K and Zeger S (1994) Longitudinal data analysis. Oxford University Press .

Gelfand A , Sahu S and Carlin B (1995) Efficient parametrization for normal linear mixed effects models . Biometrika 82, 479-488 .

Gelman A (2006) Prior distributions for variance parameters in hierarchical models . Bayesian Analysis, in press, available online at: http://www.stat.columbia.edu/∼gelman/research/published.

Gilks W , Richardson S and Spiegelhalter D (1996) Markov Chain Monte Carlo Methods in Practice, Chapman & Hall .

Kromhout H , Symanski E and Rappaport S (1993) A comprehensive evaluation of within-and between-worker components of occupational exposure to chemical agents . Annals of Occupational Hygiene 37 (3), 253-270 .

10.

Perbellini L , Brugnone F and Pavan I (1980) Identification of the metabolites of n-hexane, cyclohexane, and their isomers in men’s urine . Toxicology and Applied Pharmacology 53, 220-9 .

11.

Ramachandran G (2001) Retrospective exposure assessment using Bayesian methods . The Annals of Occupational Hygiene 45 (8), 651-667 .

12.

Spiegelhalter D , Thomas A , Best N and Gilks W (1994) BUGS: Bayesian inference using Gibbs sampling, Version 0.5 (version ii). MRC Biostatistics Unit Cambridge .

13.

Symanski E , Bergamaschi E and Mutti A (2001a) Inter-and intra-individual sources of variation in levels of urinanary styrene metabolites . International Archives of Occupational and Environmental Health 74, 336-344 .

14.

Symanski E , Chan W and Chang C (2001b) Mixed-effects models for the evaluation of long-term trends in exposure levels with an example from the nickel industry . The Annals of Occupational Hygiene 45 (1), 71-81 .

A hierarchical Bayesian model for a variability analysis of measurements of occupational n-hexane exposure in Italy

Abstract

Keywords

Get full access to this article

References