title: Modelling the cost-effectiveness of public awareness campaigns for the early detection of non-small-cell lung cancer.
abstract: BACKGROUND
  Survival rates in lung cancer in England are significantly lower than in many similar countries. A range of Be Clear on Cancer (BCOC) campaigns have been conducted targeting lung cancer and found to improve the proportion of diagnoses at the early stage of disease. This paper considers the cost-effectiveness of such campaigns, evaluating the effect of both the regional and national BCOC campaigns on the stage distribution of non-small-cell lung cancer (NSCLC) at diagnosis.
  
  
  METHODS
  A natural history model of NSCLC was developed using incidence data, data elicited from clinical experts and model calibration techniques. This structure is used to consider the lifetime cost and quality-adjusted survival implications of the early awareness campaigns. Incremental cost-effectiveness ratios (ICERs) in terms of additional costs per quality-adjusted life-years (QALYs) gained are presented. Two scenario analyses were conducted to investigate the role of changes in the 'worried-well' population and the route of diagnosis that might occur as a result of the campaigns.
  
  
  RESULTS
  The base-case theoretical model found the regional and national early awareness campaigns to be associated with QALY gains of 289 and 178 QALYs and ICERs of £13 660 and £18 173 per QALY gained, respectively. The scenarios found that increases in the 'worried-well' population may impact the cost-effectiveness conclusions.
  
  
  CONCLUSIONS
  Subject to the available evidence, the analysis suggests that early awareness campaigns in lung cancer have the potential to be cost-effective. However, significant additional research is required to address many of the limitations of this study. In addition, the estimated natural history model presents previously unavailable estimates of the prevalence and rate of disease progression in the undiagnosed population.
authors.affiliation: Centre for Health Economics, University of York, York YO10 5DD, UK.
authors.forename: S
authors.initials: S
authors.lastname: Hinde
authors.affiliation: Centre for Health Economics, University of York, York YO10 5DD, UK.
authors.forename: C
authors.initials: C
authors.lastname: McKenna
authors.affiliation: School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK.
authors.forename: S
authors.initials: S
authors.lastname: Whyte
authors.affiliation: 1] Glenfield Hospital, Leicester LE3 9QP, UK [2] National Cancer Intelligence Network, London, UK.
authors.forename: M D
authors.initials: MD
authors.lastname: Peake
authors.affiliation: St James's University Hospital, Leeds LS9 7TF, UK.
authors.forename: M E J
authors.initials: ME
authors.lastname: Callister
authors.affiliation: Doncaster Royal Infirmary, Doncaster DN2 5LT, UK.
authors.forename: T
authors.initials: T
authors.lastname: Rogers
authors.affiliation: Centre for Health Economics, University of York, York YO10 5DD, UK.
authors.forename: M
authors.initials: M
authors.lastname: Sculpher
mesh_terms: D000328:Adult; D000368:Aged; D001364:Awareness; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis*; D003362:Cost-Benefit Analysis; D042241:Early Diagnosis; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008875:Middle Aged