Abstract PO-264: Examining factors of adherence to low-dose computed tomography lung cancer screenings among high-risk patients

Author(s):  
Cherie P. Erkmen ◽  
Mark Lee ◽  
Lin Zhu ◽  
Ra'Ann Merceir ◽  
Timmy R. Lin ◽  
...  
2017 ◽  
Vol 53 (10) ◽  
pp. 568-573
Author(s):  
Pilar Garrido ◽  
Marcelo Sánchez ◽  
José Belda Sanchis ◽  
Nicolás Moreno Mata ◽  
Ángel Artal ◽  
...  

2021 ◽  
pp. 096914132110182
Author(s):  
Mansur Haji Esmaeili ◽  
Farshad Seyednejad ◽  
Alireza Mahboub-Ahari ◽  
Hossein Ameri ◽  
Hadi Abdollahzad ◽  
...  

Objective The results of recent studies have shown that using low-dose computed tomography (LDCT) for screening of lung cancer (LC) improves cancer outcomes. The objective of the current study was to evaluate the cost-effectiveness of LDCT in an Iranian high-risk population. Methods A Markov cohort simulation model with four health states was used to evaluate the cost-effectiveness of LDCT from a healthcare system perspective in the people aged 55–74 who smoked 25 or more cigarettes per day for 10–30 years. Cost data were collected, reviewing 324 medical records of patients with LC, and utilities and transition probabilities were extracted from the literature. The Monte Carlo simulation method was applied to run the model. Probabilistic sensitivity analysis and one-way analysis were also performed. Results LC screening in comparison to a no-screening strategy was costly and effective. The incremental cost-effectiveness ratio of screening versus no-screening was IRR (Iranian rials) 98,515,014.04 which falls below the Iranian threshold of three times GDP (gross domestic product) per capita. One-way and probabilistic sensitivity analyses demonstrated that the results of the economic analysis were robust to variations in the key inputs for both. Conclusions Using LDCT for screening of LC patients in a high-risk population is a cost-effective strategy.


Medicine ◽  
2018 ◽  
Vol 97 (27) ◽  
pp. e11233 ◽  
Author(s):  
Xiaojing Wang ◽  
Hongli Liu ◽  
Yuanbing Shen ◽  
Wei Li ◽  
Yuqing Chen ◽  
...  

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