Radiation detriment calculation methodology: Summary of ICRP Publication 152

Nobuhiko Ban*, Enora Cléro, Ludovic Vaillant, Wei Zhang, Nobuyuki Hamada, Dale Preston, Dominique Laurier

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Radiation detriment is a concept to quantify the burden of stochastic effects from exposure of the human population to low-dose and/or low-dose-rate ionising radiation. As part of a thorough review of the system of radiological protection, the International Commission on Radiological Protection (ICRP) has compiled a report on radiation detriment calculation methodology as Publication 152. It provides a historical review of the detriment calculation with details of the procedure used in ICRP Publication 103. A selected sensitivity analysis was conducted to identify the parameters and calculation conditions that can be major sources of variation and uncertainty. It has demonstrated that sex, age at exposure, dose and dose-rate effectiveness factor, dose assumption in the lifetime risk calculation, and lethality fraction have a substantial impact on the calculated values of radiation detriment. Discussions are also made on the issues to be addressed and possible ways for improvement toward the revision of general recommendations. These include update of the reference population data and cancer severity parameters, revision of cancer risk models, and better handling of the variation with sex and age. Finally, emphasis is placed on transparency and traceability of the calculation, along with the need to improve the way of expressing and communicating the detriment.

Original languageEnglish
Article number023001
JournalJournal of Radiological Protection
Issue number2
Publication statusPublished - Jun 2022

Bibliographical note

Publisher Copyright:
© 2022 Society for Radiological Protection. Published on behalf of SRP by IOP Publishing Limited. All rights reserved.


  • ICRP
  • nominal risk
  • radiation detriment
  • sensitivity analysis
  • stochastic effects


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