OBJECTIVES: To estimate the fiscal benefits and societal net surplus following implemention of a lung cancer screening programme in the UK.

METHODS: A population cohort fiscal analysis was modelled alongside a cost-effectiveness analysis of annual screening rounds of low dose volume computed tomography for lung cancer versus no screening. The lifetime risk of progression and death, and treatment costs, were sourced from the literature and projected by cancer stage at diagnosis for each cohort. Quality adjusted life-years were estimated using health state utilities from the CanCORS study. Fiscal states were based on the labour market status of adults treated for lung cancer and transitions were sourced from a multicentre cross-sectional study, depending on the stage at diagnosis and disease status. State benefits, tax revenue and healthcare unit costs were based on UK official data. Societal gains from screening were estimated as the sum of the social surplus (added earnings from employment and monetized incremental QALYs) and the fiscal balance for the government.

RESULTS: The fiscal balance was estimated as a cost of £931 million, and the tax revenue exceeded the total healthcare costs from the screening programme (recruitment, screening, diagnostic and treatment costs). Earnings from employment are estimated to increase by £1,388 million and QALY gains are valued at £7,900 million, following screening implementation. The societal net-benefit was valued at £8,357 million (£6,394 per screening participant).

CONCLUSIONS: Earlier diagnosis and longevity associated with screening keep patients economically active for longer and improves lifetime tax revenue for government. Furthermore, improvement in morbidity and mortality associated with earlier diagnosis from lung cancer screening programs generate sufficient tax revenue to pay for screening programs. Fiscal analysis of health technologies can be used to inform government cross-sectorial impact of healthcare investments and should be included in economic evaluations.