Sonya Cressman1, Stuart J Peacock2, Martin C Tammemägi3, William K Evans4, Natasha B Leighl5, John R Goffin6, Alain Tremblay7, Geoffrey Liu5, Daria Manos8, Paul MacEachern9, Rick Bhatia10, Serge Puksa6, Garth Nicholas11, Annette McWilliams12, John R Mayo13, John Yee13, John C English13, Reka Pataky14, Emily McPherson15, Sukhinder Atkar-Khattra16, Michael R Johnston17, Heidi Schmidt18, Frances A Shepherd5, Kam Soghrati19, Kayvan Amjadi11, Paul Burrowes20, Christian Couture21, Harmanjatinder S Sekhon11, Kazuhiro Yasufuku22, Glenwood Goss11, Diana N Ionescu16, David M Hwang23, Simon Martel21, Don D Sin24, Wan C Tan24, Stefan Urbanski20, Zhaolin Xu8, Ming-Sound Tsao5, Stephen Lam25. 1. The Canadian Centre for Applied Research in Cancer Control, Vancouver, British Columbia, Canada; The British Columbia Cancer Agency, Vancouver, British Columbia, Canada. Electronic address: scressman@bccrc.ca. 2. The Canadian Centre for Applied Research in Cancer Control, Vancouver, British Columbia, Canada; The British Columbia Cancer Agency, Vancouver, British Columbia, Canada; Simon Fraser University, Vancouver, British Columbia, Canada. 3. Brock University, St. Catharines, Ontario, Canada. 4. Cancer Care Ontario, Toronto, Ontario, Canada; McMaster University, Hamilton, Ontario, Canada. 5. University Health Network, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, Toronto, Ontario, Canada. 6. McMaster University, Hamilton, Ontario, Canada; The Juravinski Cancer Centre and McMaster University, Hamilton, Ontario, Canada. 7. Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Alberta, Canada. 8. Queen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada. 9. Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Alberta, Canada; Foothills Medical Centre, Calgary, Alberta, Canada. 10. Memorial University, St. John's, Newfoundland, Canada. 11. Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. 12. Fiona Stanley Hospital, Perth, Western Australia, Australia; University of Western Australia, Perth, Western Australia, Australia. 13. The University of British Columbia, Vancouver, British Columbia, Canada; The Vancouver General Hospital, Vancouver, British Columbia, Canada. 14. The Canadian Centre for Applied Research in Cancer Control, Vancouver, British Columbia, Canada; The British Columbia Cancer Agency, Vancouver, British Columbia, Canada; The University of British Columbia, Vancouver, British Columbia, Canada. 15. Mathematica Policy Research, Washington, DC, USA. 16. The British Columbia Cancer Agency, Vancouver, British Columbia, Canada. 17. Beatrice Hunter Cancer Research Institute, Halifax, Nova Scotia, Canada; Dalhousie University, Halifax, Nova Scotia, Canada. 18. Joint Department of Medical Imaging (University Health Network, Sinai Health Systems, Women's College Hospital) Toronto, Ontario, Canada. 19. Trillium Health Partners, Mississauga, Ontario, Canada. 20. Foothills Medical Centre, Calgary, Alberta, Canada. 21. Université Laval, Québec, Québec. 22. Princess Margaret Cancer Centre, Toronto, Ontario, Canada. 23. University Health Network, Toronto, Ontario, Canada. 24. Centre for Heart Lung Innovation, Institute for Heart and Lung Health, St. Paul's Hospital, Vancouver, British Columbia, Canada. 25. The British Columbia Cancer Agency, Vancouver, British Columbia, Canada; The University of British Columbia, Vancouver, British Columbia, Canada.
Abstract
INTRODUCTION: Lung cancer risk prediction models have the potential to make programs more affordable; however, the economic evidence is limited. METHODS: Participants in the National Lung Cancer Screening Trial (NLST) were retrospectively identified with the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The high-risk subgroup was assessed for lung cancer incidence and demographic characteristics compared with those in the low-risk subgroup and the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), which is an observational study that was high-risk-selected in Canada. A comparison of high-risk screening versus standard care was made with a decision-analytic model using data from the NLST with Canadian cost data from screening and treatment in the PanCan study. Probabilistic and deterministic sensitivity analyses were undertaken to assess uncertainty and identify drivers of program efficiency. RESULTS: Use of the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial with a threshold set at 2% over 6 years would have reduced the number of individuals who needed to be screened in the NLST by 81%. High-risk screening participants in the NLST had more adverse demographic characteristics than their counterparts in the PanCan study. High-risk screening would cost $20,724 (in 2015 Canadian dollars) per quality-adjusted life-year gained and would be considered cost-effective at a willingness-to-pay threshold of $100,000 in Canadian dollars per quality-adjusted life-year gained with a probability of 0.62. Cost-effectiveness was driven primarily by non-lung cancer outcomes. Higher noncurative drug costs or current costs for immunotherapy and targeted therapies in the United States would render lung cancer screening a cost-saving intervention. CONCLUSIONS: Non-lung cancer outcomes drive screening efficiency in diverse, tobacco-exposed populations. Use of risk selection can reduce the budget impact, and screening may even offer cost savings if noncurative treatment costs continue to rise. Crown
INTRODUCTION:Lung cancer risk prediction models have the potential to make programs more affordable; however, the economic evidence is limited. METHODS:Participants in the National Lung Cancer Screening Trial (NLST) were retrospectively identified with the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The high-risk subgroup was assessed for lung cancer incidence and demographic characteristics compared with those in the low-risk subgroup and the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), which is an observational study that was high-risk-selected in Canada. A comparison of high-risk screening versus standard care was made with a decision-analytic model using data from the NLST with Canadian cost data from screening and treatment in the PanCan study. Probabilistic and deterministic sensitivity analyses were undertaken to assess uncertainty and identify drivers of program efficiency. RESULTS: Use of the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial with a threshold set at 2% over 6 years would have reduced the number of individuals who needed to be screened in the NLST by 81%. High-risk screening participants in the NLST had more adverse demographic characteristics than their counterparts in the PanCan study. High-risk screening would cost $20,724 (in 2015 Canadian dollars) per quality-adjusted life-year gained and would be considered cost-effective at a willingness-to-pay threshold of $100,000 in Canadian dollars per quality-adjusted life-year gained with a probability of 0.62. Cost-effectiveness was driven primarily by non-lung cancer outcomes. Higher noncurative drug costs or current costs for immunotherapy and targeted therapies in the United States would render lung cancer screening a cost-saving intervention. CONCLUSIONS:Non-lung cancer outcomes drive screening efficiency in diverse, tobacco-exposed populations. Use of risk selection can reduce the budget impact, and screening may even offer cost savings if noncurative treatment costs continue to rise. Crown
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