title: Evaluating the impacts of screening and smoking cessation programmes on lung cancer in a high-burden region of the USA: a simulation modelling study.
abstract: OBJECTIVE
  While the US Preventive Services Task Force has issued recommendations for lung cancer screening, its effectiveness at reducing lung cancer burden may vary at local levels due to regional variations in smoking behaviour. Our objective was to use an existing model to determine the impacts of lung cancer screening alone or in addition to increased smoking cessation in a US region with a relatively high smoking prevalence and lung cancer incidence.
  
  
  METHODS
  Computer-based simulation model.
  
  
  METHODS
  Simulated population of individuals 55 and older based on smoking prevalence and census data from Northeast Pennsylvania.
  
  
  METHODS
  Hypothetical lung cancer control from 2014 to 2050 through (1) screening with CT, (2) intensified smoking cessation or (3) a combination strategy.
  
  
  METHODS
  Primary outcomes were lung cancer mortality rates. Secondary outcomes included number of people eligible for screening and number of radiation-induced lung cancers.
  
  
  RESULTS
  Combining lung cancer screening with increased smoking cessation would yield an estimated 8.1% reduction in cumulative lung cancer mortality by 2050. Our model estimated that the number of screening-eligible individuals would progressively decrease over time, indicating declining benefit of a screening-only programme. Lung cancer screening achieved a greater mortality reduction in earlier years, but was later surpassed by smoking cessation.
  
  
  CONCLUSIONS
  Combining smoking cessation programmes with lung cancer screening would provide the most benefit to a population, especially considering the growing proportion of patients ineligible for screening based on current recommendations.
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Angela C
authors.initials: AC
authors.lastname: Tramontano
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Deirdre F
authors.initials: DF
authors.lastname: Sheehan
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Pamela M
authors.initials: PM
authors.lastname: McMahon
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Emily C
authors.initials: EC
authors.lastname: Dowling
authors.affiliation: Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
authors.forename: Theodore R
authors.initials: TR
authors.lastname: Holford
authors.affiliation: Northeast Regional Cancer Institute, Scranton, Pennsylvania, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Ryczak
authors.affiliation: Northeast Regional Cancer Institute, Scranton, Pennsylvania, USA.
authors.forename: Samuel M
authors.initials: SM
authors.lastname: Lesko
authors.affiliation: Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, USA.
authors.forename: David T
authors.initials: DT
authors.lastname: Levy
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Chung Yin
authors.initials: CY
authors.lastname: Kong
mesh_terms: D003198:Computer Simulation; D005260:Female; D006801:Humans; D015994:Incidence; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D010414:Pennsylvania / Q000453:epidemiology; D015397:Program Evaluation / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D012907:Smoking / Q000453:epidemiology*; D016540:Smoking Cessation / Q000706:statistics & numerical data*
keywords: lung cancer; screening; simulation modeling; smoking cessation