1. CT Screening for Lung Cancer vs. Smoking Cessation: A Cost-Effectiveness Analysis Pamela M. McMahon, PhD; Chung Yin Kong, PhD; Bruce E. Johnson; Milton C. Weinstein, PhD; Jane C. Weeks, MD, MS; G. Scott Gazelle, MD, MPH, PhD Department of Radiology & Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School

2. Key Question Modeling studies suggest that CT screening may decrease lung cancer mortality Several randomized trials will report mortality endpoints in the next few years  If trials show evidence of benefit from CT screening, would it be a good value relative to other cancer control interventions?

3. Methods Existing lung cancer simulation model to simulate 6 cohorts of individuals in multiple scenarios For each scenario, predict total costs and total quality-adjusted life years (QALYs) for the cohort costs in 2006 US $ costs & QALYs discounted at 3% annually Calculate incremental cost-effectiveness ratios ICERs; defined as Δcost/ΔQALY ICERs compared to benchmark of $100k/QALY

4. Lung Cancer Policy Model (LCPM) Two versions – we used single cohort LCPM population LCPM replicates US trends, 1975-2000 Model synthesizes available data smoking histories from de-identified US survey data observational studies, cancer registries (SEER) Validated (single arm screening study) Affiliate of the NCI consortium http://cisnet.cancer.gov/

5. Features of the LCPM Microsimulation of individual life-histories aggregated to cohort (population) statistics Underlying natural history model 5 lung cancer cell types and benign nodules Smokers face increased risks of death from competing causes (e.g., CVD, COPD, others) Screening biases and mortality reduction from screening are predicted based on model inputs (program characteristics)

6. Lung Cancer Policy Model Schematic General population Dead Treatment & Survival Follow-up Diagnosis & Staging

7. Lung Cancer Policy Model Schematic General population Dead Treatment & Survival Follow-up Diagnosis & Staging Screening

8. Natural History Model Risks of lung cancer depend on accumulated smoking exposure age, sex and birth cohort Cancers grow (Gompertz) and can metastasize Clinical staging, treatment modeled explicitly can be varied to evaluate management strategies Unobservable natural history parameters are estimated through extensive model calibration

9. Year Incidence per 100,000 + SEER LCPM

10. Interventions Compared No intervention Screening with helical CT Smoking cessation alone Combined CT screening/smoking cessation  Interventions modeled as one-time & annual

11. Inputs Relevant to the Analysis Cessation rates background annual cessation = 3% effectiveness (1-year abstinence) = 4% to 30% Program characteristics eligibility (age, pack-yrs, time since quitting), adherence number and frequency of screens, CT performance, cost follow-up protocol, +/- radiation risk Costs SEER-Medicare, CPT codes, wholesale prices patient and caregiver time costs

12. Projected costs and effects – base case (perfect adherence)

13. Results: white males age 50 in 1990 $300 cessation with 16% abstinence at 1 year

14. Summary: 1)Combined interventions provided most benefit to most individuals but yielded ICERs over $100,000/QALY (vs. cessation alone) 2)CT alone was dominated, regardless of cessation effectiveness *Scenario shown on previous plot; all in cohorts of males aged 50 Results ICERs (in $1,000s/QALY)

15. Additional Sensitivity Analyses Including radiation risk for new lung cancers  ICER for annual CT screening vs. no screening by 14% (70 year old men) to 85% (50 year old women) Additional influential program characteristics lower screening adherence increased ICERs ‘stricter’ eligibility for screening reduced ICERs for annual screening but none were below $100K/QALY

16. Conclusions Screening with helical CT costs more but provides fewer benefits than cessation alone Combined screening + cessation provides benefits to more individuals but costs more than $100,000/QALY (vs. cessation alone) Results are dependent on model assumptions model simulates guideline care analyses are currently limited to whites data on smoking histories and lung cancer incidence by single year, which are needed for calibration, were not available for minorities

17. Acknowledgements National Cancer Institute R01 CA97337 + Supp (Gazelle) R25 CA92203 (Gazelle) R00 CA126147 (McMahon) American Cancer Society 117494-RSGHP-09-148-01-CPHPS (Gazelle) Colleen Bouzan, Angela Tramontano CISNET lung cancer investigators