title: Public health impact of changes in smoking behavior: results from the Tobacco Policy Model.
abstract: OBJECTIVE
  The relative magnitude of the public health gains from preventing smoking initiation versus encouraging cessation or avoiding relapse in different ages and genders is estimated and compared.
  
  
  METHODS
  Health gains are defined as the predicted increase in Quality-Adjusted Life-Years (QALYs) to the US population during a century. To estimate QALYs, we developed the Tobacco Policy Model. The model simulates a 10% reduction in the annual probability of initiation versus a 10% increase in cessation versus a 10% reduction in relapse in males and females in six age groups: 10 to 19, 20 to 29, 30 to 39, 40 to 49, 50 to 59 and 60 to 69.
  
  
  RESULTS
  Among youth and young adults, reducing initiation yields far more QALYs than encouraging cessation or averting relapse. In middle-aged adults, cessation yields the most QALYs, followed by averting relapse and reducing initiation. In the oldest age group, averting relapse yields the most QALYs followed by cessation and reducing initiation. In general, increasing cessation and reducing relapse is more beneficial in males than in females whereas reducing initiation is more beneficial in females.
  
  
  CONCLUSIONS
  The relative value of preventing initiation, encouraging cessation, and averting relapse differs by age and gender. Reducing initiation in youth is likely to offer the largest public health impact during the next century.
authors.affiliation: School of Social Ecology, University of California, Irvine, CA 92697-7075, USA. tengs@uci.edu
authors.forename: T O
authors.initials: TO
authors.lastname: Tengs
authors.forename: N D
authors.initials: ND
authors.lastname: Osgood
authors.forename: T H
authors.initials: TH
authors.lastname: Lin
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D003198:Computer Simulation; D005260:Female; D015438:Health Behavior; D006291:Health Policy; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011634:Public Health / Q000639:trends*; D019057:Quality-Adjusted Life Years; D012680:Sensitivity and Specificity; D016540:Smoking Cessation / Q000706:statistics & numerical data*; D012984:Software; D014481:United States / Q000453:epidemiology