Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis
In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk mod...
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Published in: | Annals of internal medicine Vol. 176; no. 3; pp. 320 - 332 |
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01-03-2023
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Abstract | In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening.
To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds.
Comparative modeling analysis.
National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator.
1960 U.S. birth cohort.
45 years.
U.S. health care sector.
Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model.
Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost.
Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%).
Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions.
Risk models were restricted to age, sex, and smoking-related risk predictors.
Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration.
National Cancer Institute (NCI). |
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AbstractList | In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening.
To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds.
Comparative modeling analysis.
National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator.
1960 U.S. birth cohort.
45 years.
U.S. health care sector.
Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model.
Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost.
Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%).
Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions.
Risk models were restricted to age, sex, and smoking-related risk predictors.
Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration.
National Cancer Institute (NCI). BACKGROUNDIn their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening.OBJECTIVETo evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds.DESIGNComparative modeling analysis.DATA SOURCESNational Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator.TARGET POPULATION1960 U.S. birth cohort.TIME HORIZON45 years.PERSPECTIVEU.S. health care sector.INTERVENTIONAnnual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model.OUTCOME MEASURESIncremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost.RESULTS OF BASE-CASE ANALYSISRisk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%).RESULTS OF SENSITIVITY ANALYSESRisk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions.LIMITATIONRisk models were restricted to age, sex, and smoking-related risk predictors.CONCLUSIONRisk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration.PRIMARY FUNDING SOURCENational Cancer Institute (NCI). |
Author | Meza, Rafael Munshi, Vidit Ten Haaf, Kevin Hemmati, Mehdi Jeon, Jihyoun de Koning, Harry J Bastani, Mehrad Tammemägi, Martin Toumazis, Iakovos Plevritis, Sylvia K Han, Summer S Gazelle, G Scott Kong, Chung Yin de Nijs, Koen Cao, Pianpian Feuer, Eric J |
AuthorAffiliation | 8 Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland 2 Department of Epidemiology, University of Michigan, Ann Arbor 4 Feinstein Institute for Medical Research, Northwell Health, New York 7 Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada 5 Department of Radiology, Massachusetts General Hospital, Boston 6 Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California 10 Department of Biomedical Data Sciences, Stanford University, Stanford, California 3 Erasmus MC-University Medical Center, Rotterdam, the Netherlands 9 Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York 1 Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas |
AuthorAffiliation_xml | – name: 1 Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas – name: 4 Feinstein Institute for Medical Research, Northwell Health, New York – name: 8 Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland – name: 3 Erasmus MC-University Medical Center, Rotterdam, the Netherlands – name: 9 Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York – name: 7 Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada – name: 6 Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California – name: 2 Department of Epidemiology, University of Michigan, Ann Arbor – name: 5 Department of Radiology, Massachusetts General Hospital, Boston – name: 10 Department of Biomedical Data Sciences, Stanford University, Stanford, California |
Author_xml | – sequence: 1 givenname: Iakovos orcidid: 0000-0002-3462-2137 surname: Toumazis fullname: Toumazis, Iakovos organization: Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.) – sequence: 2 givenname: Pianpian orcidid: 0000-0001-8886-9672 surname: Cao fullname: Cao, Pianpian organization: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.) – sequence: 3 givenname: Koen orcidid: 0000-0003-1451-0557 surname: de Nijs fullname: de Nijs, Koen organization: Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.) – sequence: 4 givenname: Mehrad surname: Bastani fullname: Bastani, Mehrad organization: Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York (M.B.) – sequence: 5 givenname: Vidit surname: Munshi fullname: Munshi, Vidit organization: Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.) – sequence: 6 givenname: Mehdi orcidid: 0000-0002-0560-3893 surname: Hemmati fullname: Hemmati, Mehdi organization: Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.) – sequence: 7 givenname: Kevin orcidid: 0000-0001-5006-6938 surname: Ten Haaf fullname: Ten Haaf, Kevin organization: Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.) – sequence: 8 givenname: Jihyoun orcidid: 0000-0001-7003-3412 surname: Jeon fullname: Jeon, Jihyoun organization: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.) – sequence: 9 givenname: Martin orcidid: 0000-0002-4989-5058 surname: Tammemägi fullname: Tammemägi, Martin organization: Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada (M.T.) – sequence: 10 givenname: G Scott surname: Gazelle fullname: Gazelle, G Scott organization: Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.) – sequence: 11 givenname: Eric J orcidid: 0000-0003-4842-7751 surname: Feuer fullname: Feuer, Eric J organization: Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland (E.J.F.) – sequence: 12 givenname: Chung Yin orcidid: 0000-0001-6431-7830 surname: Kong fullname: Kong, Chung Yin organization: Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York (C.Y.K.) – sequence: 13 givenname: Rafael orcidid: 0000-0002-1076-5037 surname: Meza fullname: Meza, Rafael organization: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, and Department of Integrative Oncology, BC Cancer Research Institute, British Columbia, Canada (R.M.) – sequence: 14 givenname: Harry J orcidid: 0000-0003-4682-3646 surname: de Koning fullname: de Koning, Harry J organization: Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.) – sequence: 15 givenname: Sylvia K surname: Plevritis fullname: Plevritis, Sylvia K organization: Department of Biomedical Data Sciences, Stanford University, Stanford, California (S.K.P.) – sequence: 16 givenname: Summer S orcidid: 0000-0002-2013-6883 surname: Han fullname: Han, Summer S organization: Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California (S.S.H.) |
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Cites_doi | 10.1001/jama.2016.6255 10.1038/sj.bjc.6605459 10.7326/M20-1392 10.1002/cncr.32026 10.1097/JTO.0b013e31822e59b3 10.1177/0272989X06290497 10.1002/cncr.33835 10.1371/journal.pmed.1001764 10.7326/M18-1250 10.7326/M19-0322 10.7326/M17-2701 10.21037/tlcr-20-753 10.1177/0272989X15570364 10.1016/j.lungcan.2018.05.008 10.1002/cncr.28623 10.1002/cam4.1896 10.1002/cncr.33839 10.1093/jnci/djz165 10.1513/AnnalsATS.202111-1253OC 10.1177/0962280221995972 10.7326/M13-2316 10.1093/jncics/pkz035 10.1016/j.jtho.2020.02.008 10.1016/j.amepre.2013.10.022 10.1001/jama.2013.285112 10.1158/1055-9965.EPI-14-0745 10.7326/M17-2561 10.1007/s10552-017-0907-x 10.1016/j.lungcan.2020.07.007 10.1016/j.jtho.2018.08.1819 10.1258/jms.2011.010127 10.21037/tlcr-20-985 10.1002/ijc.33578 10.1513/AnnalsATS.201810-690RL 10.1001/jamaoncol.2015.2472 10.1001/jama.2021.1117 10.1001/jamanetworkopen.2019.0204 10.1093/jncics/pkab081 10.1016/j.jtho.2017.04.021 10.1016/j.chest.2021.06.066 10.1093/jnci/djaa211 10.1111/j.1539-6924.2011.01652.x 10.1016/j.athoracsur.2020.07.051 10.7326/M17-1401 10.1016/j.lungcan.2018.10.029 10.1371/journal.pmed.1002277 10.7326/M19-1263 10.1007/s10552-011-9866-9 10.1016/j.chest.2020.04.063 10.1093/jnci/djz164 10.1001/jama.2021.1077 10.1001/jamaoncol.2021.4942 10.1056/NEJMoa1211776 10.1093/jnci/djaa013 10.1111/j.1539-6924.2011.01681.x 10.1513/AnnalsATS.201902-102OC 10.1016/j.jtho.2020.08.006 10.1056/NEJMp1405158 10.1093/jnci/djab002 10.1371/journal.pone.0099978 10.1093/jnci/djz041 10.1016/S1470-2045(21)00590-8 10.1002/cncr.28833 10.1136/thoraxjnl-2017-211377 |
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Snippet | In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on... BACKGROUNDIn their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people... |
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SubjectTerms | Aged, 80 and over Cost-Benefit Analysis Cost-Effectiveness Analysis Early Detection of Cancer - methods Humans Lung Lung Neoplasms - diagnostic imaging Mass Screening - methods Middle Aged Quality-Adjusted Life Years |
Title | Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis |
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