Cost-effective approach to lung cancer risk for a radiological dispersal device (RDD) scenario

Abstract

A release of radioactive material into the environment can lead to hazardous exposure of the population and serious future concerns about health issues such as an increased incidence of cancer. In this context, a practical methodology capable of providing useful basic information from the scenario can be valuable for immediate decisions and future risk assessment. For this work, the simulation of a radiological dispersal device (RDD) filled with americium-241 was considered. The radiation dose simulated by the HotSpot code was used as an input to the epidemiological equations from BEIR V producing the data used to assess the risk of lung cancer development. The methodology could be useful in providing training for responders aimed to the initial support addressed to decision-making for emergency response at the early phase of an RDD scenario. The results from the simulation allow estimating (a) the size of the potentially affected population, (b) the type of protection action considering gender and location of the individuals, (c) the absorbed doses, (d) the matrix of lung cancer incidence predictions over a period of 5 years, and (e) the cost-effectiveness in the initial decision environment.

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  • 1. Maillie, H. D., & Jacobson, A. P. (1992). A graphical method of estimating fatal radiation-induced cancers using the BEIR V method. Health Phys., 63(3), 273–280.

  • 2. Maillie, H. D., Simon, W., Watts, R. J., & Quinn, B. R. (1993). Determining person-years of life lost using the BEIR V method. Health Phys., 64(5), 461–466.

  • 3. Rother, F. C., Rebello, W. F., Healy, M. J., Silva, M. M., Cabral, P. A., Vital, H. C., & Andrade, E. R. (2016). Radiological risk assessment by convergence methodology model in RDD scenarios. Risk Anal., 36(11), 2039–2046. DOI: 10.1111/risa.12557.

  • 4. Andrade, C. P. S., Souza, C. J., Camerini, E. S. N., Alves, I. S., Vital, H. C., Healy, M. J. F., & Andrade, E. R. (2018). Support to triage and public risk perception considering long-term response to a Cs-137 radiological dispersive device scenario. Toxicol. Ind. Health, 34(6), 433–438. https://doi.org/10.1177/0748233718762920.

  • 5. Purves, M., & Parkes, D. (2016). Validation of the DIFFAL, HPAC and HotSpot dispersion models using the Full-Scale Radiological Dispersal Device (FSRDD) field trials witness plate deposition dataset. Health Phys., 110(5), 481–490.

  • 6. Thomson, W. H., & Roberts, P. J. (1986). Cost-benefit analysis in radiation protection. Nucl. Med. Commun., 7(12), 855–856.

  • 7. Weatherburn, H. (1984). A realistic approach to cost-benefit analysis in radiation protection. Br. J. Radiol., 57(681), 847–848. https://doi.org/10.1259/0007-1285-57-681-847.

  • 8. International Commission on Radiological Protection. (1983). Cost-benefit analysis in the optimization of radiation protection. Ann. ICRP, 10(2/3). (ICRP Publication 37).

  • 9. Homann, S. G. (2013). HotSpot Health Physics Codes Version 3.0 User’s Guide. Lawrence Livermore National Laboratory, CA, USA.

  • 10. Harper, F. T., Musolino, S. V., & Wente, W. B. (2007). Realistic radiological dispersal device hazard boundaries and ramifications for early consequence management decisions. Health Phys., 93(1), 1–16.

  • 11. International Atomic Energy Agency. (1996). Methods for estimating the probability of cancer from occupational radiation exposure. Vienna: IAEA. (IAEATECDOC-870).

  • 12. Preston, D. L., Ron, E., Tokuoka, S., Funamoto, S., Nishi, N., Soda, M., Mabuchi, K., & Kodama, K. (2007). Solid cancer incidence in atomic bomb survivors: 1958–1998. Radiat. Res., 168(1), 1–64. https://doi.org/10.1667/RR0763.1.

  • 13. Lee, W. C. (2014). Excess relative risk as an effect measure in case-control studies of rare diseases. PLoS One, 10(4), e0121141. https://doi.org/10.1371/journal.pone.0121141.

  • 14. Darby, S. C., Doll, R., Gill, S. K., & Smith, P. G. (1987). Long term mortality after a single treatment course with X-rays in patients treated for ankylosing spondylitis. Br. J. Cancer, 55(2), 179–190. https://doi.org/10.1038/bjc.1987.35.

  • 15. Narendran, N., Luzhna, L., & Kovalchuk, O. (2019). Sex difference of radiation response in occupational and accidental exposure. Front. Genet., 10, 260. https://doi.org/10.3389/fgene.2019.00260.

  • 16. International Commission on Radiological Protection. (2007). The 2007 Recommendations of the International Commission on Radiological Protection. Ann. ICRP, 37(2/4), 1–332. (ICRP Publication 103).

  • 17. International Commission on Radiological Protection. (1989). Optimization and decision-making in radiological protection. A report of a Task Group of Committee 4 of the International Commission on Radiological Protection. Ann. ICRP, 20(1), 1–60.

  • 18. International Commission on Radiological Protection. (1973). Implications of Commission recommendations that doses be kept as low as readily achievable. (ICRP Publication 22). Oxford: Pergamon Press.

  • 19. Dillon, M., Kane, J., Nasstrom, J., Homann, S., & Pobanz, B. (2016). Summary of building protection factor studies for external exposure to ionizing radiation. Lawrence Livermore National Laboratory, CA, USA. (LLNL-TR-684121).

  • 20. Mettler, F. A. Jr. (2005). Medical resources and requirements for responding to radiological terrorism. Health Phys., 89(5), 488–493.

  • 21. Conklin, C., & Edwards, J. (2000). Selection of protective action guides for nuclear incidents. J. Hazard. Mater., 75(2/3), 131–144. https://doi.org/10.1016/S0304-3894(00)00176-X.

  • 22. Sorensen, J. H., Shumpert, B. L., & Vogt, B. M. (2004). Planning for protective action decision making: evacuate or shelter-in-place. J. Hazard. Mater., 109(1/3), 1–11. https://doi.org/10.1016/j.jhazmat.2004.03.004.

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