Implementing low-dose computed tomography screening for lung cancer in Canada: implications of alternative at-risk populations, screening frequency, and duration.

Authors

Evans WK, Flanagan WM, Miller AB, Goffin JR, Memon S, Fitzgerald N, Wolfson MC

Affiliations

• McMaster University, Hamilton, ON;
• Statistics Canada, Ottawa, ON;
• Dalla Lana School of Public Health, Toronto, ON;
• Canadian Partnership Against Cancer, Toronto, ON;
• University of Ottawa, Ottawa, ON.

Abstract

BACKGROUND: Low-dose computed tomography (ldct) screening has been shown to reduce mortality from lung cancer; however, the optimal screening duration and "at risk" population are not known.

METHODS: The Cancer Risk Management Model developed by Statistics Canada for the Canadian Partnership Against Cancer includes a lung screening module based on data from the U.S. National Lung Screening Trial (nlst). The base-case scenario reproduces nlst outcomes with high fidelity. The impact in Canada of annual screening on the number of incident cases and life-years gained, with a wider range of age and smoking history eligibility criteria and varied participation rates, was modelled to show the magnitude of clinical benefit nationally and by province. Life-years gained, costs (discounted and undiscounted), and resource requirements were also estimated.

RESULTS: In 2014, 1.4 million Canadians were eligible for screening according to nlst criteria. Over 10 years, screening would detect 12,500 more lung cancers than the expected 268,300 and would gain 9200 life-years. The computed tomography imaging requirement of 24,000-30,000 at program initiation would rise to between 87,000 and 113,000 by the 5th year of an annual nlst-like screening program. Costs would increase from approximately $75 million to $128 million at 10 years, and the cumulative cost nationally over 10 years would approach $1 billion, partially offset by a reduction in the costs of managing advanced lung cancer.

CONCLUSIONS: Modelling various ways in which ldct might be implemented provides decision-makers with estimates of the effect on clinical benefit and on resource needs that clinical trial results are unable to provide.