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.

Cost-utility analysis of a potential lung cancer screening program for a high-risk population in Germany: A modelling approach

“…Capturing this in a model requires simulating the period of disease progression before diagnosis (i.e. the natural history of disease) and, separately, the treatment of lung cancer and aftercare [28]. To do so, we combined two Markov models covering both periods.…”

“…We used a cohort-based Markov model because lung cancer is a noncommunicable disease and there was no need to account for interaction between individuals [34]. The structure of the model was informed by recent studies evaluating the cost-effectiveness of different cancer interventions and on feedback from radiologists and experts in modelling [23,28,35]. As noted above, our model consisted of two separate components to distinguish between (a) the natural history of disease and (b) treatment paths and aftercare depending on patients' lung cancer stage at diagnosis (see Fig.…”

“…We applied pooled quality of life scores taken from a meta- analysis by Sturza [48]. These had been used in the same manner in previous cost-effectiveness analyses of lung cancer prevention initiatives [22,28]. Following that approach, quality of life scores for latestage lung cancer patients in our model were comparable to those reported for German patients [50].…”

Lung cancer

“…A trial of a cancer symptom awareness campaign, including lung cancer, in rural Western Australia did not reduce time to diagnosis, although this may have been due to the absence of television advertising 11. There is recent evidence suggesting that early awareness campaigns have the potential to be cost-effective 12. Social media may also have a role in raising awareness.…”

What is the impact of rerouting a cancer diagnosis from emergency presentation to GP referral on resource use and survival? Evidence from a population-based study

“…A number of early studies investigated the effect of increasing the number of early diagnoses on costs using simulation modelling approaches [ 11 , 19 – 22 ]. Our evidence is in line with Hinde et al (2015) and Frontier Economics’ findings that increasing the number of early diagnoses is associated with a substantial increment in costs due to non-conversions and a small reduction in costs due to less intensive treatments for patients with a positive diagnosis.…”