OBJECTIVES: A system for retrospective occupational exposure assessment combining the efficiency of a job exposure matrix (JEM) and the precision of a subsequent individual expert exposure assessment (IEEA) was developed. All steps of the exposure assessment were performed by an interdisciplinary expert panel in the context of a case-control study on male germ cell cancer nested in the car manufacturing industries. METHODS: An industry-specific JEM was developed and automatic exposure estimation was performed based on this JEM. A subsample of exposure ratings was done by IEEA to identify determinants of disagreement between the JEM and the individual review. Possible determinants were analyzed by calculating odds ratios (ORs) of disagreement between ratings with regard to different dimensions (e.g. high versus low intensity of exposure). Disagreement in ≥20% of the sampled exposure ratings with a statistically significant OR was chosen as a threshold for inclusion of the exposure ratings into a final IEEA. RESULTS: The most important determinants of disagreement between JEM and individual review were working outside of the production line (disagreement 80%), low probability of exposure (disagreement 25%), and exposure depending on specific activities like usage of specific lacquers (disagreement 32%) for jobs within the production line. These determinants were the selection criteria of exposure ratings for the subsequent final IEEA. CONCLUSIONS: Combining a JEM and a subsequent final IEEA for a selected subset of exposure ratings is a feasible and labor-saving approach for exposure assessment in large occupational epidemiological studies.
OBJECTIVES: A system for retrospective occupational exposure assessment combining the efficiency of a job exposure matrix (JEM) and the precision of a subsequent individual expert exposure assessment (IEEA) was developed. All steps of the exposure assessment were performed by an interdisciplinary expert panel in the context of a case-control study on male germ cell cancer nested in the car manufacturing industries. METHODS: An industry-specific JEM was developed and automatic exposure estimation was performed based on this JEM. A subsample of exposure ratings was done by IEEA to identify determinants of disagreement between the JEM and the individual review. Possible determinants were analyzed by calculating odds ratios (ORs) of disagreement between ratings with regard to different dimensions (e.g. high versus low intensity of exposure). Disagreement in ≥20% of the sampled exposure ratings with a statistically significant OR was chosen as a threshold for inclusion of the exposure ratings into a final IEEA. RESULTS: The most important determinants of disagreement between JEM and individual review were working outside of the production line (disagreement 80%), low probability of exposure (disagreement 25%), and exposure depending on specific activities like usage of specific lacquers (disagreement 32%) for jobs within the production line. These determinants were the selection criteria of exposure ratings for the subsequent final IEEA. CONCLUSIONS: Combining a JEM and a subsequent final IEEA for a selected subset of exposure ratings is a feasible and labor-saving approach for exposure assessment in large occupational epidemiological studies.
Authors: Heidi J Fischer; Ximena P Vergara; Michael Yost; Michael Silva; David A Lombardi; Leeka Kheifets Journal: J Expo Sci Environ Epidemiol Date: 2015-05-13 Impact factor: 5.563