Bias and random errors in historical data of exposure to organic solvents.

The biases and random errors in data from industrial hygiene measurements have been evaluated. Such data are often used for exposure assessment in epidemiologic studies by people who are not professional industrial hygienists. The quality of such data is, therefore, of paramount importance. Data sets from two investigations of the exposure to organic solvents in the same industry have been analyzed. The study shows that the errors and analysis of field samples are larger than errors determined from laboratory data but smaller than errors from other sources. One exception, in which laboratory errors are large, is when data originate from different ways of determining recovery. Very large biases may arise for certain combinations of analytes and extraction liquid when the "equilibrium method" is used. The data sets from the two investigations had a bias, which was as large as a factor of 5 to 10. This bias is believed to be caused by use of two different sampling strategies. In all industrial hygiene data, large random errors are embedded because of day-to-day variations in exposure levels; therefore, the data are mostly used grouped into three classes of low, medium, and high exposure. To do so is meaningful, as a large amount of data will compensate for misclassifications. But a large amount of data cannot compensate for the bias introduced when merging data generated under different sampling strategies. Data describing the same object, but originating from different sampling strategies, should not be pooled without proper tests, which show that data are comparable.(ABSTRACT TRUNCATED AT 250 WORDS)