title: Evaluating lung cancer screening in China: Implications for eligibility criteria design from a microsimulation modeling approach.
abstract: More than half of males in China are current smokers and evidence from western countries tells us that an unprecedented number of smoking-attributable deaths will occur as the Chinese population ages. We used the China Lung Cancer Policy Model (LCPM) to simulate effects of computed tomography (CT)-based lung cancer screening in China, comparing the impact of a screening guideline published in 2015 by a Chinese expert group to a version developed for the United States by the U.S. Centers for Medicare & Medicaid Services (CMS). The China LCPM, built using an existing lung cancer microsimulation model, can project population outcomes associated with interventions for smoking-related diseases. After calibrating the model to published Chinese smoking prevalence and lung cancer mortality rates, we simulated screening from 2016 to 2050 based on eligibility criteria from the CMS and Chinese guidelines, which differ by age to begin and end screening, pack-years smoked, and years since quitting. Outcomes included number of screens, mortality reduction, and life-years saved for each strategy. We projected that in the absence of screening, 14.98 million lung cancer deaths would occur between 2016 and 2050. Screening with the CMS guideline would prevent 0.72 million deaths and 5.8 million life-years lost, resulting in 6.58% and 1.97% mortality reduction in males and females, respectively. Screening with the Chinese guideline would prevent 0.74 million deaths and 6.6 million life-years lost, resulting in 6.30% and 2.79% mortality reduction in males and females, respectively. Through 2050, 1.43 billion screens would be required using the Chinese screening strategy, compared to 988 million screens using the CMS guideline. In conclusion, CT-based lung cancer screening implemented in 2016 and based on the Chinese screening guideline would prevent about 20,000 (2.9%) more lung cancer deaths through 2050, but would require about 445 million (44.7%) more screens than the CMS guideline.
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
authors.forename: Deirdre F
authors.initials: DF
authors.lastname: Sheehan
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
authors.forename: Steven D
authors.initials: SD
authors.lastname: Criss
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.|Harvard Medical School, Boston, Massachusetts, United States of America.|Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: G Scott
authors.initials: GS
authors.lastname: Gazelle
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.|Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Pari V
authors.initials: PV
authors.lastname: Pandharipande
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.|Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Chung Yin
authors.identifier: http://orcid.org/0000-0001-6431-7830
authors.initials: CY
authors.lastname: Kong
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002681:China / Q000453:epidemiology; D003198:Computer Simulation; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology / Q000401:mortality; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D008962:Models, Theoretical; D011159:Population Surveillance; D015995:Prevalence; D012307:Risk Factors; D055815:Young Adult