Selecting human health metrics for environmental decision-support tools

Risk Anal. 2002 Oct;22(5):965-83. doi: 10.1111/1539-6924.00264.


Environmental decision-support tools often predict a multitude of different human health effects due to environmental stressors. The accounting and aggregating of these morbidity and mortality outcomes is key to support decision making and can be accomplished by different methods that we call human health metrics. This article attempts to answer two questions: Does it matter which metric is chosen? and What are the relevant characteristics of these metrics in environmental applications? Three metrics (quality adjusted life years (QALYs), disability adjusted life years (DALYs), and willingness to pay (WTP)) have been applied to the same diverse set of health effects due to environmental impacts. In this example, the choice of metric mattered for the ranking of these environmental impacts and it was found for this example that WTP was dominated by mortality outcomes. Further, QALYs and DALYs are sensitive to mild illnesses that affect large numbers of people and the severity of these mild illnesses are difficult to assess. Eight guiding questions are provided in order to help select human health metrics for environmental decision-support tools. Since health metrics tend to follow the paradigm of utility maximization, these metrics may be supplemented with a semi-quantitative discussion of distributional and ethical aspects. Finally, the magnitude of age-dependent disutility due to mortality for both monetary and nonmonetary metrics may bear the largest practical relevance for future research.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Biometry
  • Decision Support Techniques*
  • Environment*
  • Environmental Health* / economics
  • Humans
  • Life Tables
  • Morbidity
  • Mortality
  • Quality-Adjusted Life Years
  • Risk Assessment / economics
  • Risk Assessment / ethics
  • Risk Assessment / methods*
  • Risk Assessment / statistics & numerical data