Dataset Information

Clinical impact and cost-effectiveness of integrating smoking cessation into lung cancer screening: a microsimulation model.

ABSTRACT:

Background

Low-dose computed tomography (CT) screening can reduce lung cancer mortality in people at high risk; adding a smoking cessation intervention to screening could further improve screening program outcomes. This study aimed to assess the impact of adding a smoking cessation intervention to lung cancer screening on clinical outcomes, costs and cost-effectiveness.

Methods

Using the OncoSim-Lung mathematical microsimulation model, we compared the projected lifetime impact of a smoking cessation intervention (nicotine replacement therapy, varenicline and 12 wk of counselling) in the context of annual low-dose CT screening for lung cancer in people at high risk to lung cancer screening without a cessation intervention in Canada. The simulated population consisted of Canadians born in 1940-1974; lung cancer screening was offered to eligible people in 2020. In the base-case scenario, we assumed that the intervention would be offered to smokers up to 10 times; each intervention would achieve a 2.5% permanent quit rate. Sensitivity analyses varied key model inputs. We calculated incremental cost-effectiveness ratios with a lifetime horizon from the health system's perspective, discounted at 1.5% per year. Costs are in 2019 Canadian dollars.

Results

Offering a smoking cessation intervention in the context of lung cancer screening could lead to an additional 13% of smokers quitting smoking. It could potentially prevent 12 more lung cancers and save 200 more life-years for every 1000 smokers screened, at a cost of $22 000 per quality-adjusted life-year (QALY) gained. The results were most sensitive to quit rate. The intervention would cost over $50 000 per QALY gained with a permanent quit rate of less than 1.25% per attempt.

Interpretation

Adding a smoking cessation intervention to lung cancer screening is likely cost-effective. To optimize the benefits of lung cancer screening, health care providers should encourage participants who still smoke to quit smoking.

SUBMITTER: Evans WK

PROVIDER: S-EPMC7641238 | biostudies-literature | 2020 Jul-Sep

REPOSITORIES: biostudies-literature

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Publications

Clinical impact and cost-effectiveness of integrating smoking cessation into lung cancer screening: a microsimulation model.

Evans William K WK Gauvreau Cindy L CL Flanagan William M WM Memon Saima S Yong Jean Hai Ein JHE Goffin John R JR Fitzgerald Natalie R NR Wolfson Michael M Miller Anthony B AB

CMAJ open 20200701 3

<h4>Background</h4>Low-dose computed tomography (CT) screening can reduce lung cancer mortality in people at high risk; adding a smoking cessation intervention to screening could further improve screening program outcomes. This study aimed to assess the impact of adding a smoking cessation intervention to lung cancer screening on clinical outcomes, costs and cost-effectiveness.<h4>Methods</h4>Using the OncoSim-Lung mathematical microsimulation model, we compared the projected lifetime impact of ...[more]

PMID: 32963023

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Project description:BACKGROUND:Previous simulation studies estimating the impacts of lung cancer screening have ignored the changes in smoking prevalence over time in the United States. Our primary rationale was to perform, to our knowledge, the first simulation study that estimates the health outcomes of lung cancer screening with explicit modeling of smoking trends for the whole US population. METHODS/FINDINGS:Utilizing a well-validated microsimulation model, we estimated the benefits and harms of an annual low-dose computed tomography screening scenario with a realistic screening adherence rate versus a no-screening scenario for the US population from 2016-2030. The Centers for Medicare and Medicaid Services (CMS) eligibility criteria were applied: age 55-77 years at time of screening, history of at least 30 pack-years of smoking, and current smoker or former smoker with fewer than 15 years since quitting. In the screened population, cumulative mortality reduction was projected to reach 16.98% (95% CI 16.90%-17.07%). Cumulative mortality reduction was estimated to be 3.52% (95% CI 3.50%-3.53%) for the overall study population, with annual mortality reduction peaking at 4.38% (95% CI 4.36%-4.41%) in 2021 and falling to 3.53% (95% CI 3.50%-3.56%) by 2030. Lung cancer screening would save a projected 148,484 life-years (95% CI 147,429-149,540) across the total population through 2030. There were estimated to be 9,054 (95% CI 9,011-9,098) overdiagnosed cases among the 252,429 (95% CI 251,208-253,649) screen-detected lung cancer diagnoses, yielding an overdiagnosis rate of 3.59%. The limitations of our study are that we do not explicitly model race or socioeconomic status and our model was calibrated to data from studies performed in academic centers, both of which may impact the generalizability of our results. We also exclusively model the effects of the CMS guidelines for lung cancer screening and not any other screening strategies. CONCLUSIONS:The mortality reduction and life-years gained estimated by this study are lower than those of single birth cohort studies. Single cohort studies neglect the changing dynamics of smoking behavior across generations, whereas this study reflects the trend of decreasing smoking prevalence since the 1960s. Maximum benefit could be derived from lung cancer screening through 2021; in later years, mortality reduction due to screening will decline. If a comprehensive screening program is not implemented in the near future, the opportunity to achieve these benefits will have passed.

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Dataset Information

Clinical impact and cost-effectiveness of integrating smoking cessation into lung cancer screening: a microsimulation model.

Background

Methods

Results

Interpretation

Publications

Clinical impact and cost-effectiveness of integrating smoking cessation into lung cancer screening: a microsimulation model.

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