An investigation of the dependence of exposure variability on the interval between measurements.
Investigations have suggested that owing to the effects of autocorrelation and/or non-stationary behaviour exposure variability increases with the number of days between measurements. This study confirmed such increasing variability with the interval between observations in a collection of occupational data sets after controlling for factors likely to contribute to variability and for sample size. Consecutive shift-long exposure measurements for 53 workers from five different data sets in 123 time series were analysed. When the data were combined a clear increasing trend in the variance was observed with interval, but a breakdown by data set revealed that this trend was present in only two of the five data sets. The effect was further isolated in 30% of the workers who contributed data and in 29% of the total number of time series analysed. Amongst the data where the trend was evident the combination of autocorrelation and non-stationary behavior explained the increase in 64% of the time series. Significant autocorrelation was detected for a small group of workers in only one of the data sets and for a minority of cases amongst workers who contributed more than one time series to the analysis. Thus, autocorrelation of shift-long exposures does not appear to be pervasive and is unlikely to present significant problems when implementing statistically-based sampling strategies. On the other hand, the issue of non-stationarity remains equivocal. Although only a small proportion of time series was found to be non-stationary, the period investigated was short (around 30 days) and it remains to be seen whether the problem is more pronounced over longer time scales.
School of Public Health, University of North Carolina, Chapel Hill 27599-7400.
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Symanski
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Rappaport
D000704:Analysis of Variance; D002617:Chemical Industry; D004784:Environmental Monitoring; D016273:Occupational Exposure / Q000032:analysis; D013997:Time Factors