Modelling Patient Pathways Of Low-Dose Computed Tomography Screening For Lung Cancer In Hungary

Abstract

Scientific evidence confirms the efficacy of low-dose computed tomography in lung cancer screening. This study aimed at assessing the life-years gained and the disease-free life-years gained with different frequencies of low-dose computed tomography screening for lung cancer, compared to the current situation of no organized lung cancer screening in Hungary. A health economic model consisting of a decision tree and Markov sub-models was built. Closed cohort of 10,000 smokers between the age of 50 and 74 was followed over life-time horizon. The model moves the population at risk through the screening process, follows undiagnosed and diagnosed patients, and handles individuals who underwent lung cancer operation separately. Patient pathways describing lung cancer screening and disease course were validated against empirical data. Assessment of robustness of results was performed by sensitivity analyses. The model resulted in an additional 0.0833 and 0.2407 estimated life-years gain per individual and an additional 0.1329 and 0.3334 estimated disease-free life-years gain per individual with biannual and annual screening frequencies, respectively. As a result of the validation process, the 5-year estimated survival rates from the model were comparable to data published in scientific literature. The input parameters were tested in sensitivity analyses that confirmed the robustness of the model outputs. Low-dose computed tomography lung cancer screening can provide additional health benefits in life-years and disease-free life-years gained compared to the current situation of no lung cancer screening in Hungary. In the future, the model can be populated with local cost and utility data; therefore, it is suitable for assessing the cost-effectiveness of introducing low-dose computed tomography lung cancer screening as a public health intervention and supporting formal decision-making.