Assessing the impact of increasing lung screening eligibility by relaxing the maximum years-since-quit threshold: A simulation modeling study.

BACKGROUND: In 2021, the US Preventive Services Task Force expanded its lung screening recommendation to include persons aged 50-80 years who had ever smoked and had at least 20 pack-years of exposure and less than 15 years since quitting (YSQ). However, studies have suggested that screening persons who formerly smoked with longer YSQ could be beneficial. METHODS: The authors used two validated lung cancer models to assess the benefits and harms of screening using various YSQ thresholds (10, 15, 20, 25, 30, and no YSQ) and the age at which screening was stopped. The impact of enforcing the YSQ criterion only at entry, but not at exit, also was evaluated. Outcomes included the number of screens, the percentage ever screened, screening benefits (lung cancer deaths averted, life-years gained), and harms (false-positive tests, overdiagnosed cases, radiation-induced lung cancer deaths). Sensitivity analyses were conducted to evaluate the effect of restricting screening to those who had at least 5 years of life expectancy. RESULTS: As the YSQ criterion was relaxed, the number of screens and the benefits and harms of screening increased. Raising the age at which to stop screening age resulted in additional benefits but with more overdiagnosis, as expected, because screening among those older than 80 years increased. Limiting screening to those who had at least 5 years of life expectancy would maintain most of the benefits while considerably reducing the harms. CONCLUSIONS: Expanding screening to persons who formerly smoked and have greater than 15 YSQ would result in considerable increases in deaths averted and life-years gained. Although additional harms would occur, these could be moderated by ensuring that screening is restricted to only those with reasonable life expectancy.

Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.

BACKGROUND: 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. OBJECTIVE: 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. DESIGN: Comparative modeling analysis. DATA SOURCES: National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. TARGET POPULATION: 1960 U.S. birth cohort. TIME HORIZON: 45 years. PERSPECTIVE: U.S. health care sector. INTERVENTION: 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. OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. RESULTS OF BASE-CASE ANALYSIS: 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%). RESULTS OF SENSITIVITY ANALYSES: Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. LIMITATION: Risk models were restricted to age, sex, and smoking-related risk predictors. CONCLUSION: Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. PRIMARY FUNDING SOURCE: National Cancer Institute (NCI).

Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force.

Importance: The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years. Objective: To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies. Design, Setting, and Participants: Comparative simulation modeling with 4 lung cancer natural history models for individuals from the 1950 and 1960 US birth cohorts who were followed up from aged 45 through 90 years. Exposures: Screening with varying starting ages, stopping ages, and screening frequency. Eligibility criteria based on age, cumulative pack-years, and years since quitting smoking (risk factor-based) or on age and individual lung cancer risk estimation using risk prediction models with varying eligibility thresholds (risk model-based). A total of 1092 LDCT screening strategies were modeled. Full uptake and adherence were assumed for all scenarios. Main Outcomes and Measures: Estimated lung cancer deaths averted and life-years gained (benefits) compared with no screening. Estimated lifetime number of LDCT screenings, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths (harms). Results: Efficient screening programs estimated to yield the most benefits for a given number of screenings were identified. Most of the efficient risk factor-based strategies started screening at aged 50 or 55 years and stopped at aged 80 years. The 2013 USPSTF-recommended criteria were not among the efficient strategies for the 1960 US birth cohort. Annual strategies with a minimum criterion of 20 pack-years of smoking were efficient and, compared with the 2013 USPSTF-recommended criteria, were estimated to increase screening eligibility (20.6%-23.6% vs 14.1% of the population ever eligible), lung cancer deaths averted (469-558 per 100 000 vs 381 per 100 000), and life-years gained (6018-7596 per 100 000 vs 4882 per 100 000). However, these strategies were estimated to result in more false-positive test results (1.9-2.5 per person screened vs 1.9 per person screened with the USPSTF strategy), overdiagnosed lung cancer cases (83-94 per 100 000 vs 69 per 100 000), and radiation-related lung cancer deaths (29.0-42.5 per 100 000 vs 20.6 per 100 000). Risk model-based vs risk factor-based strategies were estimated to be associated with more benefits and fewer radiation-related deaths but more overdiagnosed cases. Conclusions and Relevance: Microsimulation modeling studies suggested that LDCT screening for lung cancer compared with no screening may increase lung cancer deaths averted and life-years gained when optimally targeted and implemented. Screening individuals at aged 50 or 55 years through aged 80 years with 20 pack-years or more of smoking exposure was estimated to result in more benefits than the 2013 USPSTF-recommended criteria and less disparity in screening eligibility by sex and race/ethnicity.

Cost-Effectiveness Analysis of Lung Cancer Screening in the United States: A Comparative Modeling Study.

Background: Recommendations vary regarding the maximum age at which to stop lung cancer screening: 80 years according to the U.S. Preventive Services Task Force (USPSTF), 77 years according to the Centers for Medicare & Medicaid Services (CMS), and 74 years according to the National Lung Screening Trial (NLST). Objective: To compare the cost-effectiveness of different stopping ages for lung cancer screening. Design: By using shared inputs for smoking behavior, costs, and quality of life, 4 independently developed microsimulation models evaluated the health and cost outcomes of annual lung cancer screening with low-dose computed tomography (LDCT). Data Sources: The NLST; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; SEER (Surveillance, Epidemiology, and End Results) program; Nurses' Health Study and Health Professionals Follow-up Study; and U.S. Smoking History Generator. Target Population: Current, former, and never-smokers aged 45 years from the 1960 U.S. birth cohort. Time Horizon: 45 years. Perspective: Health care sector. Intervention: Annual LDCT according to NLST, CMS, and USPSTF criteria. Outcome Measures: Incremental cost-effectiveness ratios (ICERs) with a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY). Results of Base-Case Analysis: The 4 models showed that the NLST, CMS, and USPSTF screening strategies were cost-effective, with ICERs averaging $49 200, $68 600, and $96 700 per QALY, respectively. Increasing the age at which to stop screening resulted in a greater reduction in mortality but also led to higher costs and overdiagnosis rates. Results of Sensitivity Analysis: Probabilistic sensitivity analysis showed that the NLST and CMS strategies had higher probabilities of being cost-effective (98% and 77%, respectively) than the USPSTF strategy (52%). Limitation: Scenarios assumed 100% screening adherence, and models extrapolated beyond clinical trial data. Conclusion: All 3 sets of lung cancer screening criteria represent cost-effective programs. Despite underlying uncertainty, the NLST and CMS screening strategies have high probabilities of being cost-effective. Primary Funding Source: CISNET (Cancer Intervention and Surveillance Modeling Network) Lung Group, National Cancer Institute.

Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study.

Background: Many health systems are exploring how to implement low-dose computed tomography (LDCT) screening programs that are effective and patient-centered. Objective: To examine factors that influence when LDCT screening is preference-sensitive. Design: State-transition microsimulation model. Data Sources: Two large randomized trials, published decision analyses, and the SEER (Surveillance, Epidemiology, and End Results) cancer registry. Target Population: U.S.-representative sample of simulated patients meeting current U.S. Preventive Services Task Force criteria for screening eligibility. Time Horizon: Lifetime. Perspective: Individual. Intervention: LDCT screening annually for 3 years. Outcome Measures: Lifetime quality-adjusted life-year gains and reduction in lung cancer mortality. To examine the effect of preferences on net benefit, disutilities (the "degree of dislike") quantifying the burden of screening and follow-up were varied across a likely range. The effect of varying the rate of false-positive screening results and overdiagnosis associated with screening was also examined. Results of Base-Case Analysis: Moderate differences in preferences about the downsides of LDCT screening influenced whether screening was appropriate for eligible persons with annual lung cancer risk less than 0.3% or life expectancy less than 10.5 years. For higher-risk eligible persons with longer life expectancy (roughly 50% of the study population), the benefits of LDCT screening overcame even highly negative views about screening and its downsides. Results of Sensitivity Analysis: Rates of false-positive findings and overdiagnosed lung cancer were not highly influential. Limitation: The quantitative thresholds that were identified may vary depending on the structure of the microsimulation model. Conclusion: Identifying circumstances in which LDCT screening is more versus less preference-sensitive may help clinicians personalize their screening discussions, tailoring to both preferences and clinical benefit. Primary Funding Source: None.

Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach.

Background: Tobacco control efforts implemented in the United States since the 1960s have led to considerable reductions in smoking and smoking-related diseases, including lung cancer. Objective: To project reductions in tobacco use and lung cancer mortality from 2015 to 2065 due to existing tobacco control efforts. Design: Comparative modeling approach using 4 simulation models of the natural history of lung cancer that explicitly relate temporal smoking patterns to lung cancer rates. Setting: U.S. population, 1964 to 2065. Participants: Adults aged 30 to 84 years. Measurements: Models were developed using U.S. data on smoking (1964 to 2015) and lung cancer mortality (1969 to 2010). Each model projected lung cancer mortality by smoking status under the assumption that current decreases in smoking would continue into the future (status quo trends). Sensitivity analyses examined optimistic and pessimistic scenarios. Results: Under the assumption of continued decreases in smoking, age-adjusted lung cancer mortality was projected to decrease by 79% between 2015 and 2065. Concomitantly, and despite the expected growth, aging, and longer life expectancy of the U.S. population, the annual number of lung cancer deaths was projected to decrease from 135 000 to 50 000 (63% reduction). However, 4.4 million deaths from lung cancer are still projected to occur in the United States from 2015 to 2065, with about 20 million adults aged 30 to 84 years continuing to smoke in 2065. Limitation: Projections assumed no changes to tobacco control efforts in the future and did not explicitly consider the potential effect of lung cancer screening. Conclusion: Tobacco control efforts implemented since the 1960s will continue to reduce lung cancer rates well into the next half-century. Additional prevention and cessation efforts will be required to sustain and expand these gains to further reduce the lung cancer burden in the United States. Primary Funding Source: National Cancer Institute.

Identification of extracellular signal-regulated kinase 1 (ERK1) direct substrates using stable isotope labeled kinase assay-linked phosphoproteomics.

Kinase mediated phosphorylation signaling is extensively involved in cellular functions and human diseases, and unraveling phosphorylation networks requires the identification of substrates targeted by kinases, which has remained challenging. We report here a novel proteomic strategy to identify the specificity and direct substrates of kinases by coupling phosphoproteomics with a sensitive stable isotope labeled kinase reaction. A whole cell extract was moderately dephosphorylated and subjected to in vitro kinase reaction under the condition in which (18)O-ATP is the phosphate donor. The phosphorylated proteins are then isolated and identified by mass spectrometry, in which the heavy phosphate (+85.979 Da) labeled phosphopeptides reveal the kinase specificity. The in vitro phosphorylated proteins with heavy phosphates are further overlapped with in vivo kinase-dependent phosphoproteins for the identification of direct substrates with high confidence. The strategy allowed us to identify 46 phosphorylation sites on 38 direct substrates of extracellular signal-regulated kinase 1, including multiple known substrates and novel substrates, highlighting the ability of this high throughput method for direct kinase substrate screening.

The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.

BACKGROUND: Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. DESIGN: Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. RESULTS: Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. CONCLUSIONS: Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). IMPLICATIONS: Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. HIGHLIGHTS: Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.

Contribution of smoking, disease history, and survival to lung cancer disparities in Black individuals.

BACKGROUND: Lung cancer is the leading cause of cancer deaths and disproportionately affects self-identified Black or African American ("Black") people, especially considering their relatively low self-reported smoking intensity rates. This study aimed to determine the relative impact of smoking history and lung cancer incidence risk, histology, stage, and survival on these disparities. METHODS: We used 2 lung cancer models (MichiganLung-All Races and MichiganLung-Black) to understand why Black people have higher rates of lung cancer deaths. We studied how different factors, such as smoking behaviors, cancer development, histology, stage at diagnosis, and lung cancer survival, contribute to these differences. RESULTS: Adjusted for smoking history, approximately 90% of the difference in lung cancer deaths between the overall and Black populations (born in 1960) was the result of differences in the risk of getting lung cancer. Differences in the histology and stage of lung cancer and survival had a small impact (4% to 6% for each). Similar results were observed for the 1950 and 1970 birth cohorts, regardless of their differences in smoking patterns from the 1960 cohort. CONCLUSIONS: After taking smoking into account, the higher rate of lung cancer deaths in Black people can mostly be explained by differences in the risk of developing lung cancer. As lung cancer treatments and detection improve, however, other factors may become more important in determining differences in lung cancer mortality between the Black and overall populations. To prevent current disparities from becoming worse, it is important to make sure that these improvements are available to everyone in an equitable way.

Trends in US Adult Smoking Prevalence, 2011 to 2022.

Importance: President Biden recently prioritized the fight against smoking as key to reducing cancer mortality. Objective: To assess trends in smoking and illuminate the association between smoking and reducing deaths due to cancer. Design, Setting, and Participants: This cross-sectional study used responses to National Health Interview Surveys from January 1, 2011, to December 31, 2022, to characterize trends in current smoking for key sociodemographic groups among US adults. Exposures: Age (18-24, 25-39, 40-64, and ≥65 years), family income (<200%, 200%-399%, and ≥400% of the federal poverty level [FPL]), educational level (less than high school, high school degree or General Educational Development, some college, and college degree or above), and race and ethnicity (Black, Hispanic, White, and other). Main Outcomes and Measures: Weighted current smoking prevalence with 95% CIs by analysis group from 2011 to 2022. Average annual percentage change (AAPC) in smoking prevalence by analysis group is calculated using Joinpoint regression. Results: Data from 353 555 adults surveyed by the National Health Interview Surveys from 2011 to 2022 were included (12.6% Black, 15.0% Hispanic, 65.2% White, and 7.3% other race or ethnicity). Overall, smoking prevalence decreased among adults aged 18 to 24 years from 19.2% (95% CI, 17.5%-20.9%) in 2011 to 4.9% (95% CI, 3.7%-6.0%) in 2022 at an AAPC of -11.3% (95% CI, -13.2% to -9.4%), while it remained roughly constant among adults 65 years or older at 8.7% (95% CI, 7.9%-9.5%) in 2011 and 9.4% (95% CI, 8.7%-10.2%) in 2022 (AAPC, -0.1% [95% CI, -0.8% to 0.7%]). Among adults 65 years or older, smoking prevalence increased from 13.0% (95% CI, 11.2%-14.7%) in 2011 to 15.8% (95% CI, 14.1%-17.6%) for those with income less than 200% FPL (AAPC, 1.1% [95% CI, 0.1%-2.1%]) and remained roughly constant with no significant change for those of higher income. Similar age patterns are seen across educational level and racial and ethnic groups. Conclusions and Relevance: This cross-sectional study found that smoking prevalence decreased from 2011 to 2022 in all age groups except adults 65 years or older, with faster decreases among younger than older adults. These findings suggest that the greatest gains in terms of reducing smoking-attributable morbidity and mortality could be achieved by focusing on individuals with low socioeconomic status, as this population has the highest smoking rates and the worst health prospects.

Smoking Disparities by Level of Educational Attainment and Birth Cohort in the U.S.

INTRODUCTION: Little is known about how U.S. smoking patterns of initiation, cessation, and intensity vary by birth cohort across education levels or how these patterns may be driven by other demographic characteristics. METHODS: Smoking data for adults aged ≥25 years was obtained from the National Health Interview Surveys 1966-2018. Age-period-cohort models were developed to estimate the probabilities of smoking initiation, cessation, intensity, and prevalence by age, cohort, calendar year, and gender for education levels: ≤8th grade, 9th-11th grade, high school graduate or GED, some college, and college degree or above. Further analyses were conducted to identify the demographic factors (race/ethnicity and birthplace) that may explain the smoking patterns by education. Analyses were conducted in 2020-2021. RESULTS: Smoking disparities by education have increased by birth cohort. In recent cohorts, initiation probabilities were highest among individuals with 9th-11th-grade education and lowest among individuals with at least a college degree. Cessation probabilities were higher among those with higher education. Current smoking prevalence decreased over time across all education groups, with important differences by gender. However, it decreased more rapidly among individuals with ≤8th grade education, resulting in this group having the second lowest prevalence in recent cohorts. This may be driven by the increasing proportion of non-U.S. born Hispanics in this group. CONCLUSIONS: Although smoking is decreasing by cohort across all education groups, disparities in smoking behaviors by education have widened in recent cohorts. Demographic changes for the ≤8th-grade education group need special consideration in analyses of tobacco use by education.

Birth Cohort‒Specific Smoking Patterns by Family Income in the U.S.

INTRODUCTION: In the U.S., low-income individuals generally smoke more than high-income individuals. However, detailed information about how smoking patterns differ by income, especially differences by birth cohort, is lacking. METHODS: Using the National Health Interview Survey 1983-2018 data, individual family income was calculated as a ratio of the federal poverty level. Missing income data from 1983 to 1996 were imputed using sequential regression multivariate imputation. Age‒period‒cohort models with constrained natural splines were used to estimate annual probabilities of smoking initiation and cessation and smoking prevalence and intensity by gender and birth cohort (1900-2000) for 5 income groups: <100%, 100%-199%, 200%-299%, 300%-399%, and ≥400% of the federal poverty level. Analysis was conducted in 2020-2021. RESULTS: Across all income groups, smoking prevalence and initiation probabilities are decreasing by birth cohort, whereas cessation probabilities are increasing. However, relative differences between low- and high-income groups are increasing markedly, such that there were greater declines in prevalence among those in high-income groups in more recent cohorts. Smoking initiation probabilities are lowest in the ≥400% federal poverty level group for males across birth cohorts, whereas for females, this income group has the highest initiation probabilities in older cohorts but the lowest in recent cohorts. People living below the federal poverty level have the lowest cessation probabilities across cohorts. CONCLUSIONS: Smoking prevalence has been decreasing in all income groups; however, disparities in smoking by family income are widening in recent birth cohorts. Future studies evaluating smoking disparities should account for cohort differences. Intervention strategies should focus on reducing initiation and improving quit success among low-income groups.

Patterns of Birth Cohort‒Specific Smoking Histories by Race and Ethnicity in the U.S.

INTRODUCTION: U.S. smoking prevalence varies greatly by race/ethnicity. However, little is known about how smoking initiation, cessation, and intensity vary by birth cohort and race/ethnicity. METHODS: Adult smoking data were obtained from the 1978-2018 National Health Interview Surveys. Age‒period‒cohort models with constrained natural splines were developed to estimate historical smoking patterns among non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian and Pacific Islander, and non-Hispanic American Indian and Alaskan Native individuals. Annual smoking prevalence and probabilities of smoking initiation, cessation, and intensity by age, year, gender, and race/ethnicity were estimated for the 1900 to 2000 birth cohorts. Analysis was conducted in 2020-2021. RESULTS: Smoking initiation probabilities were highest for the American Indian and Alaskan Native population, second highest among the non-Hispanic White population, and lowest among Asian and Pacific Islander and Hispanic populations across birth cohorts. Historically, initiation probabilities among non-Hispanic Black populations were comparable with those among non-Hispanic White populations but have decreased since the 1970 birth cohort. Cessation probabilities were lowest among American Indian and Alaskan Native and non-Hispanic Black populations and highest among non-Hispanic White and Asian and Pacific Islander populations across cohorts and ages. Initiation and cessation probabilities produce observed patterns of smoking where prevalence among American Indian and Alaskan Native populations is highest across all ages and cohorts. Across cohorts, smoking prevalence among non-Hispanic Black populations, particularly males, is lower than among non-Hispanic White populations at younger ages but higher at older ages. CONCLUSIONS: There are important and persistent racial/ethnic differences in smoking prevalence, initiation, cessation, and intensity across U.S. birth cohorts. Targeted interventions should address widening smoking disparities by race/ethnicity, particularly for American Indian and Alaskan Native and non-Hispanic Black populations.

Smoking Histories by State in the U.S.

INTRODUCTION: Smoking rates across U.S. states have declined at different rates over time because some states have progressive tobacco control policies, whereas others have yet to adopt them. Therefore, each state has its own unique historical experience of smoking initiation, cessation, and prevalence. This study characterizes smoking histories for each U.S. state by birth cohort. METHODS: Using 1965-2018 National Health Interview Survey and 1992-2019 Tobacco Use Supplement to the Current Population Survey data, statistical methods applied an age‒period‒cohort modeling framework to reconstruct population-level smoking histories for each state. Smoking initiation, cessation, and intensity by age, gender, and cohort were estimated for each state. These were used to construct state-specific trends in the prevalence of current, former, and never smoking as well as the mean smoking duration and pack years. Analysis was conducted from 2017 to 2022. RESULTS: California and Kentucky, respectively, are exemplar states of more and less aggressive tobacco control. Initiation probabilities were consistently lower in California than in Kentucky, and cessation probabilities were higher. Hence, the smoking prevalence derived from these parameters is higher in Kentucky. The intensity of cigarette smoking was higher in Kentucky than in California, yielding considerably higher estimated pack years when used with the other parameters. Summaries of smoking trends are given for all states. CONCLUSIONS: Smoking initiation, cessation, and intensity trends vary substantially across states, resulting in major differences in estimated smoking prevalence, duration, and pack years. Some states show improvements in smoking metrics over time with more recent birth cohorts, but others have shown very little.

Patterns of Birth Cohort‒Specific Smoking Histories in Brazil.

INTRODUCTION: Smoking prevalence has decreased considerably in Brazil from 34.8% in 1989 to 12.6% in 2019 owing to the implementation of strong tobacco control policies. However, recent data show that the downward trend may be stagnating. Detailed analyses of historical smoking patterns by birth cohort could guide tobacco control decision making in Brazil. METHODS: Using the 2008 Global Adult Tobacco Survey and the 2013 and 2019 National Health Surveys, historical smoking patterns in Brazil were estimated, supplemented with data from the 2006‒2019 Surveillance System of Risk Factors for Chronic Diseases by Telephone Interviews. Age‒period‒cohort models with constrained natural splines were applied to estimate the annual probabilities of smoking initiation and cessation, current smoker prevalence, and mean cigarettes smoked per day by age, gender, and birth cohort. Analysis was conducted in 2021‒2022. RESULTS: Current smoker prevalence has declined considerably since the 1950 and 1955 birth cohorts for males and females, respectively, reflecting decreased smoking initiation and increased smoking-cessation probabilities over time. Among female cohorts born on or after 2000, smoking initiation may be increasing even as their smoking cessation has increased considerably. Mean cigarettes smoked per day has remained relatively constant across period and cohorts, showing only a minor decrease among males. CONCLUSIONS: These detailed cohort-specific smoking parameters can be used to inform models that evaluate the impact of tobacco use and policies on long-term health outcomes and guide public health decision making in Brazil. Stagnant mean cigarettes smoked per day, increasing female smoking initiation, and limited improvement in male cessation among recent cohorts present challenges to tobacco control.

Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study.

BACKGROUND: Although lung cancer screening (LCS) has been proven effective in reducing lung cancer mortality, it is associated with some potential harms, such as false positives and invasive follow-up procedures. Determining the time to next screen based on individual risk could reduce harms while maintaining health gains. Here, we evaluate the benefits and harms of LCS strategies with adaptive schedules, and compare these with those from non-adaptive strategies. METHODS: We extended the Lee and Zelen risk threshold method to select screening schedules based on individual's lung cancer risk and life expectancy (adaptive schedules). We compared the health benefits and harms of these adaptive schedules with regular (non-adaptive) schedules (annual, biennial and triennial) using a validated lung cancer microsimulation model. Outcomes include lung cancer deaths (LCD) averted, life years gained (LYG), discounted quality adjusted life years (QALYs) gained, and false positives per LCD averted. We also explored the impact of varying screening-related disutilities. RESULTS: In comparison to standard regular screening recommendations, risk-dependent adaptive screening reduced screening harms while maintaining a similar level of health benefits. The net gains and the balance of benefits and harms from LCS with efficient adaptive schedules were improved compared to those from regular screening, especially when the screening-related disutilities are high. CONCLUSIONS: Adaptive screening schedules can reduce the associated harms of screening while maintaining its associated lung cancer mortality reductions and years of life gained. Our study identifies individually tailored schedules that optimize the screening benefit/harm trade-offs.

Comparison of Cholangiocarcinoma and Hepatocellular Carcinoma Incidence Trends from 1993 to 2012 in Lampang, Thailand.

Liver cancer is the most common cancer in Northern Thailand, mainly due to the dietary preference for raw fish, which can lead to infection by the parasite, O. viverrini, a causal agent of cholangiocarcinoma. We conducted a temporal trend analysis of cross-sectional incidence rates of liver cancer in Lampang, Northern Thailand. Liver cancer data from 1993-2012 were extracted from Lampang Cancer Registry. The multiple imputation by chained equations method was used to impute missing histology data. Imputed data were analyzed using Joinpoint and age-period-cohort (APC) models to characterize the incidence rates by gender, region, and histology, considering hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). We observed a significant annual increase in CCA incidence and a considerable decrease in HCC incidence for both genders in Lampang. The APC analysis suggested that CCA incidence rates were higher in older ages, younger cohorts, and later years of diagnosis. In contrast, HCC incidence rates were higher in older generations and earlier years of diagnosis. Further studies of potential risk factors of CCA are needed to better understand and address the increasing burden of CCA in Lampang. Our findings may help to draw public attention to cholangiocarcinoma prevention and control in Northern Thailand.

Impact of Joint Lung Cancer Screening and Cessation Interventions Under the New Recommendations of the U.S. Preventive Services Task Force.

INTRODUCTION: In 2021, the U.S. Preventive Services Task Force (USPSTF) revised its lung cancer screening recommendations expanding its eligibility. As more smokers become eligible, cessation interventions at the point of screening could enhance the benefits. Here, we evaluate the effects of joint screening and cessation interventions under the new recommendations. METHODS: A validated lung cancer natural history model was used to estimate lifetime number of low-dose computed tomography screens, percentage ever screened, lung cancer deaths, lung cancer deaths averted, and life-years gained for the 1960 U.S. birth cohort aged 45 to 90 years (4.5 million individuals). Screening occurred according to the USPSTF 2013 and 2021 recommendations with varying uptake (0%, 30%, 100%), with or without a cessation intervention at the point of screening with varying effectiveness (15%, 100%). RESULTS: Screening 30% of the eligible population according to the 2021 criteria with no cessation intervention (USPSTF 2021, 30% uptake, without cessation intervention) was estimated to result in 6845 lung cancer deaths averted and 103,725 life-years gained. These represent 28% and 34% increases, respectively, relative to screening according to the 2013 guidelines (USPSTF 2013, 30% uptake, without cessation intervention). Adding a cessation intervention at the time of the first screen with 15% effectiveness (USPSTF 2021, 30% uptake, with cessation intervention with 15% effectiveness) was estimated to result in 2422 additional lung cancer deaths averted (9267 total, ∼73% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 322,785 life-years gained (∼318% increase). Screening 100% of the eligible according to the 2021 guidelines with no cessation intervention (USPSTF 2021, 100% uptake, without cessation intervention) was estimated to result in 23,444 lung cancer deaths averted (∼337% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 354,330 life-years gained (∼359% increase). Adding a cessation intervention with 15% effectiveness (USPSTF 2021, 100% uptake, with cessation intervention with 15% effectiveness) would result in 31,998 lung cancer deaths averted (∼497% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 1,086,840 life-years gained (∼1309% increase). CONCLUSIONS: Joint screening and cessation interventions would result in considerable lung cancer deaths averted and life-years gained. Adding a one-time cessation intervention of modest effectiveness (15%) results in comparable life-years gained as increasing screening uptake from 30% to 100% because while cessation decreases mortality from many causes, screening only reduces lung cancer mortality. This simulation indicates that incorporating cessation programs into screening practice should be a priority as it can maximize overall benefits.

Cost-Effectiveness of a Telephone-Based Smoking Cessation Randomized Trial in the Lung Cancer Screening Setting.

BACKGROUND: There are limited data on the cost-effectiveness of smoking cessation interventions in lung cancer screening settings. We conducted an economic analysis embedded in a national randomized trial of 2 telephone counseling cessation interventions. METHODS: We used a societal perspective to compare the short-term cost per 6-month bio-verified quit and long-term cost-effectiveness of the interventions. Trial data were used to micro-cost intervention delivery, and the data were extended to a lifetime horizon using an established Cancer Intervention Surveillance and Modeling Network lung cancer model. We modeled the impact of screening accompanied by 8 weeks vs 3 weeks of telephone counseling (plus nicotine replacement) vs screening alone based on 2021 screening eligibility. Lifetime downstream costs (2021 dollars) and effects (life-years gained, quality-adjusted life-years [QALYs]) saved were discounted at 3%. Sensitivity analyses tested the effects of varying quit rates and costs; all analyses assumed nonrelapse after quitting. RESULTS: The costs for delivery of the 8-week vs 3-week protocol were $380.23 vs $144.93 per person, and quit rates were 7.14% vs 5.96%, respectively. The least costly strategy was a 3-week counseling approach. An 8-week (vs 3-week) counseling approach increased costs but gained QALYs for an incremental cost-effectiveness ratio of $4029 per QALY. Screening alone cost more and saved fewer QALYs than either counseling strategy. Conclusions were robust in sensitivity analyses. CONCLUSIONS: Telephone-based cessation interventions with nicotine replacement are considered cost-effective in the lung screening setting. Integrating smoking cessation interventions with lung screening programs has the potential to maximize long-term health benefits at reasonable costs.

National Cancer Institute Smoking Cessation at Lung Examination Trials Brief Report: Baseline Characteristics and Comparison With the U.S. General Population of Lung Cancer Screening-Eligible Patients.

Introduction: The National Cancer Institute Smoking Cessation at Lung Examination (SCALE) Collaboration includes eight clinical trials testing smoking cessation interventions delivered with lung cancer screening (LCS). This investigation compared pooled participant baseline demographic and smoking characteristics of seven SCALE trials to LCS-eligible smokers in three U.S. nationally representative surveys. Methods: Baseline variables (age, sex, race, ethnicity, education, income, cigarettes per day, and time to the first cigarette) from 3614 smokers enrolled in SCALE trials as of September 2020 were compared with pooled data from the Tobacco Use Supplement-Current Population Survey (2018-2019), National Health Interview Survey (2017-2018), and Population Assessment of Tobacco and Health (wave 4, 2016-2017) using the U.S. Preventive Services Task Force 2013 (N = 4803) and 2021 (N = 8604) LCS eligibility criteria. Results: SCALE participants have similar average age as the U.S. LCS-eligible smokers using the 2013 criteria but are 2.8 years older using the 2021 criteria (p < 0.001). SCALE has a lower proportion of men, a higher proportion of Blacks, and slightly higher education and income levels than national surveys (p < 0.001). SCALE participants smoke an average of 17.9 cigarettes per day (SD 9.2) compared with 22.4 (SD 9.3) using the 2013 criteria and 19.6 (SD 9.7) using the 2021 criteria (p < 0.001). The distribution of time to the first cigarette differs between SCALE and the national surveys (p < 0.001), but both indicate high levels of nicotine dependence. Conclusions: SCALE participants smoke slightly less than the LCS-eligible smokers in the general population, perhaps related to socioeconomic status or race. Other demographic variables reveal small but statistically significant differences, likely of limited clinical relevance with respect to tobacco treatment outcomes. SCALE trial results should be applicable to LCS-eligible smokers from the U.S. population.