A Caution for Monte Carlo Risk Assessment of Long Term Exposures based on Short-Term Exposure Study Data

Abstract

Monte Carlo risk assessments commonly take as input empirical or parametric exposure distributions from specially designed exposure studies. The exposure studies typically have limited duration, since their design is based on statistical and practical factors (such as cost and respondent burden). For these reasons, the exposure period studied rarely corresponds to the biologic exposure period, which we define as the time at risk that is relevant for quantifying exposure that may result in health effects. Both the exposure period studied and the biologic exposure period will often differ from the exposure interval used in a Monte Carlo analysis. Such time period differences, which are often not accounted for, can have dramatic effects on the ultimate risk assessment. When exposure distributions are right skewed and/or follow a lognormal distribution, exposure will usually be overestimated for percentiles above the median by direct use of exposure study empirical data, since biologic exposure periods are generally longer than the exposure periods in exposure assessment studies. We illustrate the effect that biologic exposure time period and response error can have on exposure distributions, using soil ingestion as an example. Beginning with variance components from lognormally distributed soil ingestion estimates, we illustrate the effect of different modeling assumptions, and the sensitivity of the resulting analyses to these assumptions. We develop a strategy for determining appropriate exposure input distributions for soil ingestion, and illustrate this using data on soil ingestion in children.

Keywords:

• soil ingestion
• children
• exposure period
• lognormal distributions