Retrospective Exposure Assessment for Occupational Disease of an Individual Worker Using an Exposure Database and Trend Analysis
- Additional Document Info
- View All
This article outlines a hierarchy of data required for retrospective exposure assessment for occupational disease of an individual worker. It then outlines in a step-wise manner how trend analysis using a relatively large exposure database can be used to estimate such exposure. The process of how a large database containing exposure measurements can be prepared for estimating historic occupational exposures of individual workers in relation to their illnesses is described. The asbestos subset from a large government collected air monitoring database called Medical Surveillance (MESU) was selected to illustrate the cleaning and analysis processes. After unidentifiable values were removed, the cleaned dataset was examined for possible sources of variability such as changes to sampling protocol. Limit of detection (LOD) values were substituted for all non-detectable values prior to the calculation of descriptive statistic using left censored analysis methods (i.e., maximum likelihood estimation (MLE), Kaplan Meier (KM), and simple substitution). The JoinPoint Regression Program was used to perform trend analysis and calculate an annual percentage change (APC) value for the available sampling period. An asbestos case study is presented to illustrate how the APC can then be combined with more recent job and/or process specific exposure data to estimate historic levels. The MESU asbestos dataset contained 1,610 samples from 1984-1995. An average of 17% of this data was left censored. The asbestos air sampling methods in Ontario changed around 1990. LOD values of 0.06 f/cc and 0.02 f/cc were substituted for LOD values pre- and post-1990, respectively. The annual mean fiber levels for the MLE method were an average of 44% lower than KM and substitution methods. The corresponding APC for MLE method was -6.5% and -7.7% for KM and simple substitution. The findings of this paper illustrate how the temporal trend of an exposure databases can be used to efficiently estimate historic contaminant levels in the presence of limited historical information.
has subject area