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Premature Deaths, Statistical Lives, and Years of Life Lost: Identification, Quantification, and Valuation of Mortality Risks

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  • James K. Hammitt
  • Peter Morfeld
  • Jouni T. Tuomisto
  • Thomas C. Erren

Abstract

Mortality effects of exposure to air pollution and other environmental hazards are often described by the estimated number of “premature” or “attributable” deaths and the economic value of a reduction in exposure as the product of an estimate of “statistical lives saved” and a “value per statistical life.” These terms can be misleading because the number of deaths advanced by exposure cannot be determined from mortality data alone, whether from epidemiology or randomized trials (it is not statistically identified). The fraction of deaths “attributed” to exposure is conventionally derived as the hazard fraction (R – 1)/R, where R is the relative risk of mortality between high and low exposure levels. The fraction of deaths advanced by exposure (the “etiologic” fraction) can be substantially larger or smaller: it can be as large as one and as small as 1/e (≈0.37) times the hazard fraction (if the association is causal and zero otherwise). Recent literature reveals misunderstanding about these concepts. Total life years lost in a population due to exposure can be estimated but cannot be disaggregated by age or cause of death. Economic valuation of a change in exposure‐related mortality risk to a population is not affected by inability to know the fraction of deaths that are etiologic. When individuals facing larger or smaller changes in mortality risk cannot be identified, the mean change in population hazard is sufficient for valuation; otherwise, the economic value can depend on the distribution of risk reductions.

Suggested Citation

  • James K. Hammitt & Peter Morfeld & Jouni T. Tuomisto & Thomas C. Erren, 2020. "Premature Deaths, Statistical Lives, and Years of Life Lost: Identification, Quantification, and Valuation of Mortality Risks," Risk Analysis, John Wiley & Sons, vol. 40(4), pages 674-695, April.
  • Handle: RePEc:wly:riskan:v:40:y:2020:i:4:p:674-695
    DOI: 10.1111/risa.13427
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    References listed on IDEAS

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    5. James K. Hammitt & Fangli Geng & Xiaoqi Guo & Chris P. Nielsen, 2019. "Valuing mortality risk in China: Comparing stated-preference estimates from 2005 and 2016," Journal of Risk and Uncertainty, Springer, vol. 58(2), pages 167-186, June.
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    Cited by:

    1. Cropper, Maureen L. & Joiner, Emily & Krupnick, Alan, 2023. "Revisiting the Environmental Protection Agency's Value of a Statistical Life," RFF Working Paper Series 23-30, Resources for the Future.
    2. Adler, Matthew, 2020. "What should we spend to save lives in a pandemic? A critique of the value of statistical life," LSE Research Online Documents on Economics 105283, London School of Economics and Political Science, LSE Library.
    3. James K. Hammitt & Tuba Tunçel, 2023. "Monetary values of increasing life expectancy: Sensitivity to shifts of the survival curve," Journal of Risk and Uncertainty, Springer, vol. 67(3), pages 239-269, December.
    4. Hammitt, James K., 2020. "Accounting for the distribution of benefits & costs in benefit-cost analysis," TSE Working Papers 20-1116, Toulouse School of Economics (TSE).
    5. Lucie Adélaïde & Olivier Chanel & Mathilde Pascal, 2022. "Health effects from heat waves in France: an economic evaluation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(1), pages 119-131, February.

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