Publication Date |
2000 |
Personal Author |
Ramachandran, G. |
Page Count |
60 |
Abstract |
A variety of health effects are caused by chronic, cumulative exposure over time to pollutants. In these cases, to establish dose-response relationships for epidemiological and risk assessment purposes, it is vital to determine the exposures of individuals or cohorts as functions of time. Most existing occupational exposure databases, however, do not contain continuous records of historical exposures to airborne contaminants. These gaps in historical record may be filled by using the knowledge-base that experts and professionals in the field possess. In this project, we present a new framework, based on Bayesian probabilistic reasoning for obtaining estimates of exposure histories for airborne particulates from limited historical measurements, using subjective expert judgment. The framework has great potential applications in instances where there is sparse information or missing data on past exposures. Traditional methods, using only available historical measurements result in estimates with large uncertainties. Limited information on estimates of airborne concentrations and worker exposures to airborne nickel aerosol are used to estimate the uncertainty in historical air monitoring data. This uncertainty arises from environmental variabilities, systematic biases as well as uncertainty due to various measurement criteria used over a period of several decades. Retrospective exposure reconstruction based solely on such historical measurements leads to estimates with such large error bars as to be not useful for developing quantitative dose-response relationships for epidemiology. |
Keywords |
|
Source Agency |
|
Corporate Authors |
Minnesota Univ., Minneapolis. School of Public Health.; National Inst. for Occupational Safety and Health, Washington, DC. |
Supplemental Notes |
Sponsored by National Inst. for Occupational Safety and Health, Washington, DC. |
Document Type |
Technical Report |
Title Note |
Final rept. |
NTIS Issue Number |
200517 |