Lung cancer control in the immunotherapy era: meeting the need for data

Abstract

Two revolutions promise to improve outcomes for people with lung cancer. Novel cancer medications deliver unprecedented survival outcomes for individuals with incurable disease. Meanwhile, lung imaging in high risk populations reduces lung cancer mortality through early diagnosis, and is a live policy issue in Australia. With the changes underway, it has become difficult for decision-makers to obtain relevant data to inform cost-effectiveness analyses of lung cancer control. Uncertainty in survival, costs, and utilities (a measure of quality-of-life) reduces confidence in economic evaluations, highlighting a need for better data. The challenge of meeting this need is the central problem addressed in this thesis. To anticipate the impact of new interventions, it is necessary to know the proportion of individuals who receive treatment. Analysis of 45 and Up Study data linked to health databases found that half of all lung cancer patients received systemic therapy with substantial variations by clinical and socio-demographic factors. These treatment rates were applied in a discrete event simulation to estimate per-patient costs and survival time. The model incorporated clinical trial data, reimbursement costs, and a contemporary treatment algorithm. Costs for individuals with stage IV disease diagnosed in 2021 were predicted to have increased by $40,000 relative to individuals diagnosed 2006-2013, though outcomes were sensitive to assumptions relating to drug prices and overall utilisation. The topic of utility weights was also addressed. Utilities in a subset of the 45 and Up cohort were estimated for a range of conditions based on linked data and self-report and found that disability and lung cancer were associated with major declines in quality-of-life. A literature review was conducted to examine the use of utilities in published cost-effectiveness analyses of lung screening to document current practices and to highlight areas of improvement. Finally, an analysis was then performed to estimate utilities by eligibility for lung screening. It found that quality-of-life was lower for eligible adults due to their history of heavy smoking. These updated and improved modelling inputs on survival, costs, and quality-of-life can be used to provide robust estimates of cost-effective lung cancer control interventions.