title: Cost-effectiveness of cervical cancer screening: comparison of screening policies.
abstract: BACKGROUND
  Recommended screening policies for cervical cancer differ widely among countries with respect to targeted age range, screening interval, and total number of scheduled screening examinations (i.e., Pap smears). We compared the efficiency of cervical cancer-screening programs by performing a cost-effectiveness analysis of cervical cancer-screening policies from high-income countries.
  
  
  METHODS
  We used the microsimulation screening analysis (MISCAN) program to model and determine the costs and effects of almost 500 screening policies, some fictitious and some actual (i.e., recommended by national guidelines). The costs (in U.S. dollars) and effects (in years of life gained) were compared for each policy to identify the most efficient policies.
  
  
  RESULTS
  There were 15 efficient screening policies (i.e., no alternative policy exists that results in more life-years gained for lower costs). For these policies, which considered two to 40 total scheduled examinations, the age range expanded gradually from 40-52 years to 20-80 years as the screening interval decreased from 12 to 1.5 years. For the efficient policies, the predicted gain in life expectancy ranged from 11.6 to 32.4 days, compared with a gain of 46 days if cervical cancer mortality were eliminated entirely. The average cost-effectiveness ratios increased from $6700 (for the longest screening interval) to $23 900 per life-year gained. For some countries, the recommended screening policies were close to efficient, but the cost-effectiveness could be improved by reducing the number of scheduled examinations, starting them at later ages, or lengthening the screening interval.
  
  
  CONCLUSIONS
  The basis for the diversity in the screening policies among high-income countries does not appear to relate to the screening policies' cost-effectiveness ratios, which are highly sensitive to the number of Pap smears offered during a lifetime.
authors.affiliation: Department of Public Health, Faculty of Medicine and Health Sciences, Erasmus University Rotterdam, The Netherlands. vanmarle@mgz.fgg.eur.nl
authors.forename: M Elske
authors.initials: ME
authors.lastname: van den Akker-van Marle
authors.forename: Marjolein
authors.initials: M
authors.lastname: van Ballegooijen
authors.forename: Gerrit J
authors.initials: GJ
authors.lastname: van Oortmarssen
authors.forename: Rob
authors.initials: R
authors.lastname: Boer
authors.forename: J Dik F
authors.initials: JD
authors.lastname: Habbema
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D005260:Female; D005843:Geography; D006801:Humans; D008403:Mass Screening / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data; D008875:Middle Aged; D065006:Papanicolaou Test; D012680:Sensitivity and Specificity; D012984:Software; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000191:economics* / Q000401:mortality / Q000628:therapy; D014626:Vaginal Smears / Q000191:economics / Q000706:statistics & numerical data