Estimating central tendency from a single spot measure: A closed-form solution for lognormally distributed biomarker data for risk assessment at the individual level

ABSTRACT

Exposure-based risk assessment employs large cross-sectional data sets of environmental and biomarker measurements to predict population statistics for adverse health outcomes. The underlying assumption is that long-term (many years) latency health problems including cancer, autoimmune and cardiovascular disease, diabetes, and asthma are triggered by lifetime exposures to environmental stressors that interact with the genome. The aim of this study was to develop a specific predictive method that provides the statistical parameters for chronic exposure at the individual level based upon a single spot measurement and knowledge of global summary statistics as derived from large data sets. This is a profound shift in exposure and health statistics in that it begins to answer the question “How large is my personal risk?” rather than just providing an overall population-based estimate. This approach also holds value for interpreting exposure-based risks for small groups of individuals within a community in comparison to random individuals from the general population.

Acknowledgments

The authors are indebted to Paul Price of U..S EPA for posing this interesting question and to Myriam Medina-Vera of U.S. EPA for suggesting the communities-based application for exposure assessment at the individual level. The U.S. Environmental Protection Agency through its Office of Research and Development has subjected this article to agency administrative review and approved it for publication.

Funding

The authors are employees of the U.S. Environmental Protection Agency, which funded and managed the research described.

Additional information

Funding

The authors are employees of the U.S. Environmental Protection Agency, which funded and managed the research described.