The use of Monte Carlo simulation techniques to predict population blood lead levels

Abstract

Effective management of the risks posed by lead depends on an understanding of the relationship between exposure (the presence and accessibility of lead in the environment) and dose (blood lead levels). Our paper begins by outlining the type of information most valuable to a decision maker addressing the lead problem. A useful exposure-dose characterisation must address multiple contamination sources simultaneously, provide estimates of the number of people with blood lead levels exceeding critical thresholds, and assess the influence of "modifying factors" (e.g. the soil and dust ingestion rate) on population blood lead variability. We describe a pilot effort to develop an "urban setting" lead exposure-dose model, and use this model to compare three approaches for generating model input quantities: (1) worst-case estimates, (2) central estimates and (3) Monte Carlo simulation. Using the criteria outlined above, we find that the Monte Carlo technique provides the most useful model output. We describe the population blood lead level distribution generated by the model, as well as the relative influence of environmental and behavioural factors on the variability of the population distribution. Finally, we assess the impact of parameter uncertainty on the model output, and contend this type of information can help identify areas in which further empirical study would be most valuable.